\\n\\n
More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:{caption:"IntechOpen Maintains",originalUrl:"/media/original/113"}},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"10211",leadTitle:null,fullTitle:"The Science of Emotional Intelligence",title:"The Science of Emotional Intelligence",subtitle:null,reviewType:"peer-reviewed",abstract:"Emotional intelligence (EI) is the best instrument to build stronger relationships, communicate effectively, relieve stress, overcome challenges, and achieve career and personal goals. As such, this book covers a variety of topics related to the science of EI. Chapters address the science and philosophy behind EI, using EI to cope with consequences, strategies to develop EI in early childhood education, neuromarketing, emotional responding and adversity, brain networks of emotional prosody processing, humor events and wellbeing, and much more.",isbn:"978-1-83968-636-8",printIsbn:"978-1-83968-635-1",pdfIsbn:"978-1-83968-637-5",doi:"10.5772/intechopen.90978",price:119,priceEur:129,priceUsd:155,slug:"the-science-of-emotional-intelligence",numberOfPages:222,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"447fc7884303a10093bc189f4c82dd47",bookSignature:"Simon George Taukeni",publishedDate:"September 15th 2021",coverURL:"https://cdn.intechopen.com/books/images_new/10211.jpg",numberOfDownloads:4552,numberOfWosCitations:0,numberOfCrossrefCitations:2,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:2,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:4,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"August 31st 2020",dateEndSecondStepPublish:"September 28th 2020",dateEndThirdStepPublish:"November 27th 2020",dateEndFourthStepPublish:"February 15th 2021",dateEndFifthStepPublish:"April 16th 2021",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"202046",title:"Dr.",name:"Simon George",middleName:null,surname:"Taukeni",slug:"simon-george-taukeni",fullName:"Simon George Taukeni",profilePictureURL:"https://mts.intechopen.com/storage/users/202046/images/system/202046.jpg",biography:"Simon George Taukeni is an author, editor, and academic. He has been working at the University of Namibia since 2011. He is also a part-time tutor at Namibia University of Science and Technology (NUST). He is a former post-doctoral research fellow at the University of Fort Hare, South Africa.\n\nDr. Taukeni has a Ph.D., MPH, MEd, and BEd, as well as a specialized postgraduate diploma in Behavioral and Emotional Disorders. \n\nHe has collaborated with many local and international researchers and scholars in his capacity as an editor, internal and external examiner, and principal project investigator.",institutionString:"University of Namibia",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"University of Namibia",institutionURL:null,country:{name:"Namibia"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"21",title:"Psychology",slug:"psychology"}],chapters:[{id:"76719",title:"The Science and Philosophy of Emotional Intelligence: A Pragmatic Perspective",doi:"10.5772/intechopen.97837",slug:"the-science-and-philosophy-of-emotional-intelligence-a-pragmatic-perspective",totalDownloads:403,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This article is aimed at exploring the relevance of the concept of emotional intelligence (EI) from a pragmatic perspective. Although the empirical and conceptual articles are written and published on EI, however it does not suffice the purpose for a practitioner of EI who is naïve to the field of EI, either s/he does not understand the psychological literature or does not have time to study EI in great details. Hence, this article is written from a naïve perspective to make the concept useful and that could be used in our daily life. Drawn from psychological literature, this article is simplifying a complex relationship between human intelligence and emotions and clarifies our understanding about the cognitive and affective spheres of human personality. Further, the article also explains the evolutionary or biological basis of EI and also suggests a managerial use of EI for the field of leadership and decision making. The chapter concludes with a developmental focus of EI. The article is mainly using observations and anecdotes based on the author’s personal experience from his training programs with more than ten thousand managers in India and taught students in Denmark, Italy and South Africa.",signatures:"Ajay K. Jain",downloadPdfUrl:"/chapter/pdf-download/76719",previewPdfUrl:"/chapter/pdf-preview/76719",authors:[{id:"234707",title:"Dr.",name:"Ajay K.",surname:"Jain",slug:"ajay-k.-jain",fullName:"Ajay K. Jain"}],corrections:null},{id:"76879",title:"The Process of Emotional Regulation",doi:"10.5772/intechopen.96195",slug:"the-process-of-emotional-regulation",totalDownloads:291,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Emotional regulation is a developmental skill that everyone must learn. It is the ability for you to tune into, make sense of and control your own strong feelings. It affectively influences how well you can adapt to situations and events in your life, as well as how you navigate and adapt to the world. A lack of healthy emotional regulation can lead one to become dysregulated. Two important phenomenon underpin our ability to regulate: emotional complexity and splitting. The more acknowledgment and understanding that we give to our emotions (not just the thoughts in our mind but the feelings in our better), the more control and problem solving skills we can harness to sustain a good standard of personal well-being.",signatures:"Lorraine OB Madden and Rebecca Reynolds",downloadPdfUrl:"/chapter/pdf-download/76879",previewPdfUrl:"/chapter/pdf-preview/76879",authors:[{id:"332288",title:"Mrs.",name:"Lorraine OB",surname:"Madden",slug:"lorraine-ob-madden",fullName:"Lorraine OB Madden"},{id:"332289",title:"Ms.",name:"Rebecca",surname:"Reynolds",slug:"rebecca-reynolds",fullName:"Rebecca Reynolds"}],corrections:null},{id:"76928",title:"Emotional Intelligence, Identification, and Self-Awareness According to the Sphere Model of Consciousness",doi:"10.5772/intechopen.98209",slug:"emotional-intelligence-identification-and-self-awareness-according-to-the-sphere-model-of-consciousn",totalDownloads:348,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:1,abstract:"While emotion and cognition were previously considered separate concepts, current research demonstrates an interplay between them. In the current chapter, we discuss the importance of the body in relation to emotional intelligence (EI) and executive functioning. In particular, we address a specific movement meditation called Quadrato Motor Training (QMT), which has been shown to enhance emotion regulation and neurocognitive functions. We then examine the importance of emotion regulation in the context of the Sphere Model of Consciousness (SMC) and related neurocognitive studies. The SMC is a neuro-phenomenal model of consciousness based on three main axes: Emotion, Time, and Self-Determination. It presents all phenomenal experiences in a sphere-shaped matrix, aiming to account for different interactions among the axes. Through this model, the processes leading to improved EI can be framed in a general theory of consciousness and described in relation to the three axes. We discuss three key concepts in relation to the SMC: (1) EI; (2) identification, namely excessive self-involvement or feeling caught up by experience (3) self-awareness, or awareness and management of ongoing inner processes.",signatures:"Patrizio Paoletti and Tal Dotan Ben-Soussan",downloadPdfUrl:"/chapter/pdf-download/76928",previewPdfUrl:"/chapter/pdf-preview/76928",authors:[{id:"191040",title:"Dr.",name:"Tal",surname:"Dotan Ben-Soussan",slug:"tal-dotan-ben-soussan",fullName:"Tal Dotan Ben-Soussan"},{id:"320894",title:"Dr.",name:"Patrizio",surname:"Paoletti",slug:"patrizio-paoletti",fullName:"Patrizio Paoletti"}],corrections:null},{id:"76847",title:"Emotional Intelligence for Coping with the Consequences of Childhood Trauma",doi:"10.5772/intechopen.97838",slug:"emotional-intelligence-for-coping-with-the-consequences-of-childhood-trauma",totalDownloads:304,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Childhood trauma has been a serious public health problem and its long-term repercussions are widely studied. Childhood trauma can deregulate the stress-related biological pathways, incapacitating the individual to process these experiences and, consequently, producing a lasting impact in later stages of life. Exposure to adverse childhood experiences has been associated with poorer quality of life and a higher risk for harmful behaviors and illness. The emotional consequences of childhood trauma are inevitable, and the development of strategies for their coping and manage become decisive and urgent. In this chapter we will cover the most current perspectives on childhood trauma, its impact on later life stages and the resulting emotional process. Finally, it will be discussed how emotional intelligence can be a useful resource for coping with stressful situations resulting from traumatic experiences in childhood.",signatures:"Bruna Amélia Moreira Sarafim-Silva and Daniel Galera Bernabé",downloadPdfUrl:"/chapter/pdf-download/76847",previewPdfUrl:"/chapter/pdf-preview/76847",authors:[{id:"332282",title:"Assistant Prof.",name:"Daniel",surname:"Bernabé",slug:"daniel-bernabe",fullName:"Daniel Bernabé"},{id:"341229",title:"MSc.",name:"Bruna",surname:"Sarafim-Silva",slug:"bruna-sarafim-silva",fullName:"Bruna Sarafim-Silva"}],corrections:null},{id:"76892",title:"Strategies to Develop Emotional Intelligence in Early Childhood",doi:"10.5772/intechopen.98229",slug:"strategies-to-develop-emotional-intelligence-in-early-childhood",totalDownloads:363,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Children have many opportunities in early childhood education that support their emotions. These opportunities need to be transformed into learning situations appropriate to their development and developed. Learnings cannot happen independently of emotional intelligence. Social–emotional skills must be developed in education to achieve both academic success and success in life. It is important to support emotional intelligence in early childhood education to enable children to be emotionally healthy, to cope with difficulties, to respect differences, and to gain a social perspective by working in collaboration with others. Emotional intelligence training helps not only children but everyone in the classroom setting, especially educators who are unsure of how to work with a child with an emotional or behavioral problem. Since emotional intelligence can be developed and strengthened by training at all ages, it can be a way of teaching for educators as they regularly include methods and techniques in the program. Based on this, in this section, the emotional intelligence of children, programs methods and strategies will be discussed in terms of supporting emotional intelligence in the early years.",signatures:"İlkay Ulutaş, Kübra Engin and Emine Bozkurt Polat",downloadPdfUrl:"/chapter/pdf-download/76892",previewPdfUrl:"/chapter/pdf-preview/76892",authors:[{id:"93247",title:"Dr.",name:"İlkay",surname:"Ulutaş",slug:"ilkay-ulutas",fullName:"İlkay Ulutaş"},{id:"417128",title:"Ms.",name:"Kübra",surname:"Engin",slug:"kubra-engin",fullName:"Kübra Engin"},{id:"417129",title:"Ms.",name:"Emine",surname:"Bozkurt Polat",slug:"emine-bozkurt-polat",fullName:"Emine Bozkurt Polat"}],corrections:null},{id:"75970",title:"Machine Learning and EEG for Emotional State Estimation",doi:"10.5772/intechopen.97133",slug:"machine-learning-and-eeg-for-emotional-state-estimation",totalDownloads:342,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Defining “emotion” and its accurate measuring is a notorious problem in the psychology domain. It is usually addressed with subjective self-assessment forms filled manually by participants. Machine learning methods and EEG correlates of emotions enable to construction of automatic systems for objective emotion recognition. Such systems could help to assess emotional states and could be used to improve emotional perception. In this chapter, we present a computer system that can automatically recognize an emotional state of a human, based on EEG signals induced by a standardized affective picture database. Based on the EEG signal, trained deep neural networks are then used together with mappings between emotion models to predict the emotions perceived by the participant. This, in turn, can be used for example in validation of affective picture databases standardization.",signatures:"Krzysztof Kotowski and Katarzyna Stapor",downloadPdfUrl:"/chapter/pdf-download/75970",previewPdfUrl:"/chapter/pdf-preview/75970",authors:[{id:"332435",title:"Prof.",name:"Katarzyna",surname:"Stapor",slug:"katarzyna-stapor",fullName:"Katarzyna Stapor"},{id:"332714",title:"MSc.",name:"Krzysztof",surname:"Kotowski",slug:"krzysztof-kotowski",fullName:"Krzysztof Kotowski"}],corrections:null},{id:"75414",title:"Cognitive Load Measurement Based on EEG Signals",doi:"10.5772/intechopen.96388",slug:"cognitive-load-measurement-based-on-eeg-signals",totalDownloads:451,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Measurement of cognitive load should be advantageous in designing an intelligent navigation system for the visually impaired people (VIPs) when navigating unfamiliar indoor environments. Electroencephalogram (EEG) can offer neurophysiological indicators of perceptive process indicated by changes in brain rhythmic activity. To support the cognitive load measurement by means of EEG signals, the complexity of the tasks of the VIPs during navigating unfamiliar indoor environments is quantified considering diverse factors of well-established signal processing and machine learning methods. This chapter describes the measurement of cognitive load based on EEG signals analysis with its existing literatures, background, scopes, features, and machine learning techniques.",signatures:"Tasmi Tamanna and Mohammad Zavid Parvez",downloadPdfUrl:"/chapter/pdf-download/75414",previewPdfUrl:"/chapter/pdf-preview/75414",authors:[{id:"330821",title:"Assistant Prof.",name:"Mohammad Zavid",surname:"Parvez",slug:"mohammad-zavid-parvez",fullName:"Mohammad Zavid Parvez"},{id:"330823",title:"Dr.",name:"Tasmi",surname:"Tamanna",slug:"tasmi-tamanna",fullName:"Tasmi Tamanna"}],corrections:null},{id:"77045",title:"Neuromarketing: The New Dawn and Disruption in Marketing Research",doi:"10.5772/intechopen.98299",slug:"neuromarketing-the-new-dawn-and-disruption-in-marketing-research",totalDownloads:463,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:1,abstract:"The 21st Century is termed as the ‘Century of the Brain’ and the ‘Human Brain’ has 100 billion neurons and each neuron is connected to 10000 other neurons. ‘Meme’ is a unit of information that is stored in the brain and these units are effective at influencing a person who is making choices and decisions within 2.6 seconds. The ‘Neuromarketing’ is emerged from the realms of ‘Neuro Science’ as a new dawn and digi-driven disruption in the arena of ‘Marketing Research’ that studies the cognitive and emotional brain responses to marketing stimuli. The ‘Consumer Neuro Science’ by instigating brain wave tools figuring out what customers’ thoughts and brain responses are towards a product, service, advertisement, or even packaging. Neuromarketing mapping the activities in specific regional strata of the brain, capturing and predicting the psychological as well as physiological behavioral changes of buyers and/or consumers at a point-of-sale. The chapter intends to present the conceptual focus of ‘Buying Brain’ viz., Neuro Marketing, the role of ‘Memes’ in buying decisions, besides the tools and techniques adopted in persuading Buyers as Consumers and/or Prosumers that remain unearthed and unexplored.",signatures:"Kovvali Bhanu Prakash and Appidi Adi Sesha Reddy",downloadPdfUrl:"/chapter/pdf-download/77045",previewPdfUrl:"/chapter/pdf-preview/77045",authors:[{id:"339941",title:"Prof.",name:"Kovvali Bhanu",surname:"Prakash",slug:"kovvali-bhanu-prakash",fullName:"Kovvali Bhanu Prakash"},{id:"356969",title:"Prof.",name:"Appidi Adi Sesha",surname:"Reddy",slug:"appidi-adi-sesha-reddy",fullName:"Appidi Adi Sesha Reddy"}],corrections:null},{id:"76857",title:"Emotional Responding and Adversity",doi:"10.5772/intechopen.97932",slug:"emotional-responding-and-adversity",totalDownloads:176,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The experience of emotions is a ubiquitous human experience, as is the experience of adversity. In the aftermath of an adverse life event, a variety of emotional experiences can occur. This chapter reviews the relationship between emotional responding and adversity within the science of emotion and resilience. Current literature on possible emotional responses to adversity are reviewed, including literature on both resilience and psychopathology. Multiple trajectories following the experience of various types of potentially traumatic events are outlined, including predictors for each of these trajectories. In addition, forms of psychopathology in emotional responding after adversity are discussed, including posttraumatic stress disorder, prolonged grief disorder, adjustment disorders, and other mental health conditions. Information regarding risk and resilience factors for each disorder are discussed, and evidence regarding treatment is briefly summarized.",signatures:"Tom Buqo",downloadPdfUrl:"/chapter/pdf-download/76857",previewPdfUrl:"/chapter/pdf-preview/76857",authors:[{id:"331419",title:"Ph.D. Student",name:"Tom",surname:"Buqo",slug:"tom-buqo",fullName:"Tom Buqo"}],corrections:null},{id:"75806",title:"Brain Networks of Emotional Prosody Processing in a Foreign Language Versus Mother Tongue",doi:"10.5772/intechopen.97016",slug:"brain-networks-of-emotional-prosody-processing-in-a-foreign-language-versus-mother-tongue",totalDownloads:294,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Increased interest in the relationships between the brain and behavior over the past several decades has made brain network process of emotional prosody a topic of study in disciplines like neurology, psychiatry, neurolinguistics and neuroscience. Because emotional prosody has a key role to implication of timbre component, mood sense, and prosodic content. Also, it serves a highly important function for sense, the meaning to be reflected, and ability to provide effective communication. Therefore, the knowledge of how the emotional prosody sequence works in the brain will contribute to both language development and foreign language teaching as well as clinical evaluation of individuals with verbal communication difficulty. In the literature, neuroimaging and neurophysiological studies about investigating emotional prosody have produced controversial results in specifying similarities versus differences mother tongue acquisition and foreign learning neural networks. For this reason, this review study takes an interdisciplinary perspective to identify the neural networks of emotional prosody in mother tongue and foreign language learning process with different imaging modalities.",signatures:"Zeynep Z. Sonkaya and A. Riza Sonkaya",downloadPdfUrl:"/chapter/pdf-download/75806",previewPdfUrl:"/chapter/pdf-preview/75806",authors:[{id:"331783",title:"Associate Prof.",name:"A. Rıza",surname:"Sonkaya",slug:"a.-riza-sonkaya",fullName:"A. Rıza Sonkaya"},{id:"331784",title:"Dr.",name:"Zeynep Z.",surname:"Sonkaya",slug:"zeynep-z.-sonkaya",fullName:"Zeynep Z. Sonkaya"}],corrections:null},{id:"76419",title:"Humor Daily Events and Well-Being: The Role of Gelotophobia and Psychological Work Climate",doi:"10.5772/intechopen.96631",slug:"humor-daily-events-and-well-being-the-role-of-gelotophobia-and-psychological-work-climate",totalDownloads:277,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This study aims to: (1) analyze the relationship between humor-daily events and well-being; (2) test the mediating role of positive affect in this relationship; (3) analyze the moderating role of gelotophobia between humor-daily events and positive affect, and; (4) explore the moderating role of psychological climate between positive affect and well-being. To test these goals, we conducted a quasi-experimental study with 93 participants. We used regressions and bootstrapping analyses to test the moderated mediation model. The relationship between the humor-daily events and well-being was mediated by positive affect and this relation was moderated by psychological work, such that this relationship was stronger when a positive psychological work climate was identified. Gelotophobia did not moderate the relationship between humor daily-events and positive affect, however, it significantly and negatively predicted positive affect. This paper adds considerable evidence of the relationship between humor-related daily events and its impact on well-being. Psychological work climate strengthens the association between positive affect and well-being, after humor daily events.",signatures:"Ana Junça Silva, António Caetano and Rita Rueff Lopes",downloadPdfUrl:"/chapter/pdf-download/76419",previewPdfUrl:"/chapter/pdf-preview/76419",authors:[{id:"332444",title:"Ph.D.",name:"Ana",surname:"Junça Silva",slug:"ana-junca-silva",fullName:"Ana Junça Silva"}],corrections:null},{id:"78269",title:"Emotional Intelligence as a Haruspex of Societal Aftermath in Adults with Asperger Syndrome",doi:"10.5772/intechopen.99163",slug:"emotional-intelligence-as-a-haruspex-of-societal-aftermath-in-adults-with-asperger-syndrome",totalDownloads:141,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Asperger’s syndrome is not a learning disability and it has features of autism spectrum disorder, nonverbal learning disabilities and ADHD. Asperger syndrome in the context of the domains within which some of those characteristics occur. To begin with social interaction and then move to language and then move to other “in the social interaction domain. Individuals with Asperger’s syndrome tend to have limited and sometime inappropriate kinds of behaviours in the social interaction domain. They have difficulties with nonverbal communication For example they have hard time reading gestures or facial expressions and sometimes their gesture and facial expressions, don’t communicate what it is that they’re thinking and feeling they often have difficulties understanding emotional cues so they miscue when listening to someone or watching somebody they may then say something inappropriate or they may repeat something that isn’t appropriate to the situation. Because they’re miss reading the social aspect of the situation or the emotional aspect of the situation. Individuals with Asperger’s are often seen to have low eye contact either not making good eye contact or sustaining good eye contact and in this social interaction domain. Those with Asperger’s tend also to be at risk for not having many friends, they tend to be socially isolated in the language area. Those with Asperger’s often are extremely verbal, highly verbal and their language can be very sophisticated at times what they do, when they’re talking is that they tend to discuss themselves and their interests and not focus on the interests of others. It can be a one-sided conversation and some of that prosody the flow the reciprocal flow between one person another can be awkward they tend to be literal in the ways that they understand language”.",signatures:"Anjali Daisy",downloadPdfUrl:"/chapter/pdf-download/78269",previewPdfUrl:"/chapter/pdf-preview/78269",authors:[{id:"295943",title:"Dr.",name:"Anjali",surname:"Daisy",slug:"anjali-daisy",fullName:"Anjali Daisy"}],corrections:null},{id:"77065",title:"Critical Dimensions of EQ among Malay Women Entrepreneur in Malaysia",doi:"10.5772/intechopen.97883",slug:"critical-dimensions-of-eq-among-malay-women-entrepreneur-in-malaysia",totalDownloads:160,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This paper highlights the dimensions of emotional quotients (EQ) of Malay women entrepreneurs who own either micro enterprises or small and medium size enterprises in Klang Valley, Malaysia. EQ comprise of five dimensions which are social skills, self-awareness, self-regulation, self-motivation, and empathy. About 1000 questionnaires were distributed around Klang Valley, Malaysia, with a 20% response rate. Out of 200, only 169 questionnaires were able to be used for the analysis of this study. The findings show that the most important dimension of EQ during economic crisis was self-regulation followed by self-motivation, empathy, social skills, and self-awareness. After the economic crisis, the most important dimension was self-awareness followed by social skills, self-motivation, self-regulation, and empathy. Hence, emotional quotients are important for Malay women entrepreneurs during the economic crisis to remain positive and endure business challenges in managing their business operations for business sustainability. The findings also highlighted that after the economic crisis, the focus was more towards facing business challenges from employees, customers, and other stakeholders due to increasing demand in products/services and business activities. To conclude, all the elements of each dimension were considered important for Malay women entrepreneurs during and after the economic crisis.",signatures:"Nazatul Shima Abdul Rani, K. Sarojani Devi Krishnan, Zurinah Suradi and Nurita Juhdi",downloadPdfUrl:"/chapter/pdf-download/77065",previewPdfUrl:"/chapter/pdf-preview/77065",authors:[{id:"332273",title:"Dr.",name:"Nazatul Shima Abdul",surname:"Rani",slug:"nazatul-shima-abdul-rani",fullName:"Nazatul Shima Abdul Rani"},{id:"332277",title:"Dr.",name:"K. Sarojani Devi",surname:"Krishnan",slug:"k.-sarojani-devi-krishnan",fullName:"K. Sarojani Devi Krishnan"},{id:"332278",title:"Dr.",name:"Zurinah",surname:"Suradi",slug:"zurinah-suradi",fullName:"Zurinah Suradi"},{id:"332296",title:"Dr.",name:"Nurita",surname:"Juhdi",slug:"nurita-juhdi",fullName:"Nurita Juhdi"}],corrections:null},{id:"76788",title:"Emotional Competence of Women Administrators",doi:"10.5772/intechopen.97884",slug:"emotional-competence-of-women-administrators",totalDownloads:184,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Leadership is a social mechanism where action toward a common goal is affected by any person or community. The organization is in trouble without effective leadership, it has been said. Any educational institution’s success hinges greatly on how competent the leaders are. The research’s main objective is to assess the emotional competencies of women school administrators at public and private schools in Siquijor, Central Visayas, Philippines. The study focuses on the five (5) dimensions of emotional competence which are self-awareness, self-regulation, motivation, empathy and social skills. The study was conducted to fifty-seven (57) school heads or administrators during School Year 2016–2017. The study revealed that the women administrators are all experienced and possess a high degree of emotional competence relative to their performance as leaders and administrators.",signatures:"Mary Grace B. Lubguban",downloadPdfUrl:"/chapter/pdf-download/76788",previewPdfUrl:"/chapter/pdf-preview/76788",authors:[{id:"331431",title:"Associate Prof.",name:"Mary Grace B.",surname:"Lubguban",slug:"mary-grace-b.-lubguban",fullName:"Mary Grace B. 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The study lasted for six months in 2017. The sampling was done by using the cluster method and stratified random sampling, which was based on hospital type and the level of health officers. The data analysis approach used in this study was the partial least square (PLS), using WarpPLS software. The results show that emotional intelligence significantly and positively affected team performance with a path coefficient value of 0.138 and a p-value of 0.050. 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Eleven experts examined the didactic tools in which the scene of the painting had been depicted, through the use of white plastic figures modeled using a 3D printer. The models had been positioned to accurately correspond with the reference painting, with an explanatory narration supplied as an audio recording. Each of the experts involved were asked the same open questions in interviews that were audio-recorded and later transcribed. This feedback was analyzed and eleven concerns for consideration were determined: 1. How the figures felt to touch 2. Modeling and placement of the figure, 3. Position of the character, 4. Size, 5. The accurate 3D depiction of the 2D image, 6. Perspectives or visual points of view of the scene, 7. Enough representation of the painting in the model, 8. Distribution of visual components within the scene, 9. Perceptual appraisals, 10. Size of the model, 11. Touch of the whole model. The results indicated that the size of the model and the figurines was appropriate for their function. The figurines felt pleasant to handle and adequately described the postures and placement. 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From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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MS is characterized by the formation of multiple lesions along the nerve fibers in the brain, spinal cord and optic nerves (Bradl and Lassmann, 2009; Bruck, 2005; Bruck and Stadelmann, 2005; Chitnis et al., 2009; Hafler, 2004; Holland, 2009; Mah and Thannhauser, 2010; Pohl et al., 2007). The precise triggers of autoreactive T cell development remain to be fully understood, however, it is clear that myelin antigens are the major target (Grau-Lopez et al., 2009). T cell activation results in cytokine release and recruitment of other immune cells that results in tissue damage not only to the myelin sheath but, over time and with repeated attacks, to the underlying axons as well. Demyelination and axonal damage impairs or interrupts nerve transmission, giving rise to clinical signs and symptoms.
Clinically, neurological symptoms in patients with MS vary from mild to severe and typically include one or more of the following: sensory symptoms (numbness, tingling, other abnormal sensations, visual disturbances, dizziness), motor symptoms (weakness, difficulty walking, tremor, bowel/bladder problems, poor coordination, and stiffness), and other symptoms such as heat sensitivity, fatigue, emotional changes, cognitive changes and sexual symptoms (Bronner et al., 2010). While some persons have a limited number of “attacks” or “relapses” and remain fairly healthy for decades, others may deteriorate rapidly from the time of diagnosis, with poor quality of life and shortened lifespan. There is no way of knowing at the clinical onset what course the disease will take (Andersen, 2010; Bradl and Lassmann, 2009; Bruck, 2005).
In this chapter how the autoimmune process is triggered as well as current clinical options to try and reduce disease symptoms are addressed. While the induction of long-term durable antigen-specific T cell tolerance is the desired treatment option, such a therapy remains to be clinically developed. Instead, once a diagnosis of MS is made, immune based treatment is generally begun, with numerous therapies aimed primarily at inactivating T cells and other immune functions.
The ability for the immune system to differentiate between self and non-self is critical for host preservation. Deficits in self-non-self discrimination can result in opportunistic infections or immunological over-reactivity resulting in immunopathology and autoimmunity. It is therefore, not surprisingthat multiple genetic factors that influence the sensitivity of the immune system are known to trigger autoimmune mediated diseases. However it is hypothesized that clinical symptom development may only manifest after exposure to certain environmental factors, including viral infection. The interplay of genetics and the environment in regards to the development of MS, and other autoimmune diseases, has not been completely elucidated. No matter what the potential switch that causes MS initiation the activation, proliferation and effector functions of auto-reactive CD4+ T cells appears to be critical for disease development and progression (Goverman, 2009; Miller and Eagar, 2001; Miller et al., 2001).
i. Predisposing genetic factors
The significantly higher concordance rates of MS in monozygotic twins compared to dizygotic twins (Hansen et al., 2005; Islam et al., 2006; Willer et al., 2003), the 2-fold increased risk of disease development in siblings of affected individuals (Ebers et al., 2004) as well as the observed increased susceptibility in offspring from two affected parents, compared to those with only one affected parent (Ebers et al., 2000; Robertson et al., 1997) all point to a strong genetic component in the pathogenesis of MS. However, like many other complex autoimmune diseases, MS is not transferred from parent to offspring via classic Mendelian genetics and the disease trait involves a large number of genes (Hoffjan and Akkad, 2010). Until recently, most gene variations associated with increased or decreased susceptibility were thought to be within the human leukocyte antigen (HLA) loci (Ramagopalan et al., 2009). However, recent studies have also identified risk-conferring alleles within several non-HLA genes (Nischwitz et al., 2011). Importantly, most of these genes are known to play important roles in T cell activation and function, which further supports the concept that a dysfunctional immune process is involved in the initiation and progression of MS (Nischwitz et al., 2011).
ii. HLA genes
Allelic variations within the major histocompatibility complex (MHC) exert the greatest individual effect on the risk of MS (Ramagopalan et al., 2009). Initial studies published in 1972 identified the HLA Class I antigens
Other HLA-DR2 alleles that confer susceptibility in some populations include
iii. Non-HLA genes
Early gene linkage studies failed to validate associations between non-HLA genes and the development of MS, potentially due to the small individual contribution of each gene to disease (Nischwitz et al., 2011). However, in recent years, several GWAS have identified polymorphisms within a number of non-HLA genes that play an important role in the development of MS (Pravica et al., 2012). These include genes that are involved in cytokine pathways, such as those encoding the IL-2, IL-7, IL-12 and TNF receptors, which are important for T cell development, homeostasis, proliferation and differentiation (2009; Baranzini et al., 2009; Sawcer
Also, variations within genes coding for co-stimulatory molecules, such as CD40, CD58, CD80 and CD86, which promote the activation of T cells, were also implicated in susceptibility to MS (2009; Baranzini et al., 2009; Sawcer
Variations within other genes that can affect T cell functioning, including CD6, CLEC16A, and the vitamin D alpha hydroxylase gene CYP27B1 are also implicated in the pathogenesis of MS (2009; Baranzini et al., 2009; Sawcer
There is clearly further research to be performed to better understand the role of genetics and MS development. However the data clearly show that genes associated with T cell activation and other immune functions certainly highlight the importance of targeting immune factors when treating disease.
Although it is clear that genetics play a key role in determining susceptibility to MS, concordance rates between monozygotic twins (
i. Infectious factors
For many years, underlying infections have been implicated in the induction of the autoreactive CD4+ T cell response that leads to MS (Kakalacheva and Lunemann, 2011). Roles for several pathogens, including Epstein Barr Virus (EBV), Human Herpes Virus-6 (HHV-6) and Varicella Zoster Virus (VZV) have been investigated. There is considerable evidence that links EBV with the initiation and progression of MS (Ascherio and Munger, 2007a, b; Dyment et al., 2004). EBV infects over 90% of the world population and causes infectious mononucleosis (IM) in a large proportion of individuals, which is characterized by glandular fever and the massive expansion of virus-specific T cells (Vetsika and Callan, 2004). Pooled data from 18 clinical studies revealed a significant link between IM and an elevated risk of MS (Kakalacheva et al., 2011).
Furthermore, in individuals that concurrently tested positive for IM and the HLA allele
ii. Non-infectious factors
Smoking and Vitamin Dd have been identified as the two primary non-infectious environmental factors that can contribute to MS susceptibility. Although the elevated risk of MS development in individuals who smoke was originally defined identified in a study in the 1960’s (reviewed in (Wingerchuk, 2012)), it has become more prominent in recentl yearsy. Smoking is argued to increase the chance of MS development by a factor of 1.5 (Wingerchuk, 2012). In addition, patients that smoke increase the potential for rapid MS development. In a recent Belgium study, patients that smoked were more likely to develop a score of 6 on the Extended Disability Status Scale. This represents an increased potential to develop intermittent or unilateral constant assistance (cane, crutch or brace) required to walk 100 meters without resting (D\'Hooghe M
Vitamin D is a potent immunomodulatory molecule that has been shown to affect numbers and activity of regulatory T cells. Several epidemiological studies have identified a significant link between the incidence of MS and distance from the equator (Kurtzke et al., 1979; Miller et al., 1990; Vukusic et al., 2007). Although MS occurred more frequently at high latitudes, this effect was negated in populations that consumed a vitamin D-rich diet (Agranoff and Goldberg, 1974; Swank et al., 1952; Westlund, 1970). These findings are supported by a large study in which high serum levels of the vitamin D metabolite 25(OH)D were shown to correspond with a significantly decreased risk of MS (Munger et al., 2006). In a separate study, low serum levels of 25(OH)D were associated with relapse and the degree of disability in MS patients (Smolders et al., 2008a).
A possible explanation for these findings is the indirect immunomodulatory functions of vitamin D on T cells (Bartels et al., 2010; Smolders et al., 2008b). Also, T cells express vitamin D receptors (VDR), suggesting a direct vitamin D- T cell interaction resulting in T cell regulation (Cantorna, 2011). Indeed, a recent study using the EAE mouse model demonstrated that vitamin D could inhibit auto-reactive T cells, which express high levels of VDR, but did not affect numbers of regulatory T cells, which express low levels of VDR (Mayne et al., 2011). An earlier study also showed that survival of EAE-induced mice could be prolonged with vitamin D injection (Hayes, 2000).
Multiple sclerosis is initiated by the activation of auto-reactive CD4+ T cells specific for a single or few myelin epitopes in the CNS (Vanderlugt and Miller, 2002). Inflammation caused by this initial response recruits and activates other CD4+ T cell clones specific for a range of other self-epitopes, a process which is referred to as “epitope spreading” (Lehmann et al., 1992). This process occurs, within experimental settings, in a hierarchical fashion, likely the result of differential antigen liberation, processing and presentation by various antigen-presenting cell (APC) populations. In addition the availability of self-reactive CD4+ T cell clones throughout the course of disease is also important. Epitope spreading was originally described and characterized in the Experimental Autoimmune Encephalomyelitis (EAE) model of MS, but also occurs in Theiler’s murine encephalomyelitis virus induced demeylinating disease (TMEV-IDD) (Lehmann et al., 1992; Miller et al., 2001; Miller
1. Epitope spreading in EAE
Experimental autoimmune encephalomyelitis is induced in susceptible murine strains by immunization with myelin peptides in conjunction with adjuvant (Miller et al., 2010). This disease initiation method, with a single and defined myelin peptide allows for the observation and measurement of changing T cell specificities over time (Vanderlugt and Miller, 2002). Using this model epitope spreading has been described as a hierarchical event, with a defined path through which T cells specific for certain epitopes emerge. Epitope spreading is a critical phenomenon in the SJL model of EAE, as it is responsible for the relapsing remitting pattern of disease (Vanderlugt and Miller, 2002).
The first study to demonstrate epitope spreading was reported in 1992 by Lehmann and colleagues (Lehmann
Studies in our laboratory have also characterized epitope spreading in EAE induced by immunization of SJL mice with the immunodominant PLP epitope PLP139-151(Vanderlugt et al., 2000). In this model, T cell responses are initially specific for PLP139-151. However, the first relapse, which occurs within 30-40 days after immunization, coincides with T cell responses against PLP178-191. During the second relapse, which occurs between 50-70 days after immunization, T cells are also shown to respond to MBP84-104. Understanding of the epitope spreading hierarchy has allowed for epitope specific therapeutic targeting in EAE. The induction of tolerance against relapse-associated peptides blocks the progression of disease, even though PLP139-151 responses remain intact (Vanderlugt et al., 2000). These observations highlight the role of changing T cell specificities in mediating chronic disease as well as the need for therapeutic strategies that address these specific T cells populations (Vanderlugt and Miller, 2002).
2. Epitope spreading in TMEV-IDD
Theiler’s murine encephalomyelitis virus- (TMEV)-induced demyelinating disease (TMEV-IDD) is induced by intracranial inoculation of SJL/J mice with TMEV, resulting in low-level chronic CNS infection that progresses into myelin-specific autoimmune disease (Getts et al., 2010).The initial CD4+ T cell-mediated immune response against chronic TMEV infection of the CNS causes significant damage to myelin, which in turn results in the activation of myelin-specific T cell clones (Karpus et al., 1995; Miller et al., 1997a). Similar to EAE, this occurs in a hierarchical order, beginning with the immunodominant PLP139-151 epitope (Miller et al., 1997b). Subsequent T cell reactivity against other peptides, including PLP178-191, PLP56-70 and MOG92-106 has been demonstrated as disease progresses (Miller et al., 2001).
These findings correspond with antigen presentation by CNS APC. These cells present viral peptides but not myelin peptides up to day 40 post-immunization, at which time point there are still no clinical signs of disease and no evidence of myelin destruction (Katz-Levy et al., 1999; Katz-Levy
In further support of epitope spreading after TMEV inoculation, tolerance induction to multiple myelin epitopes using MP-4 during ongoing TMEV-IDD in SJL mice was shown to significantly attenuate disease progression, reduce demyelination and decrease CNS leukocyte infiltration (Neville et al., 2002).
3. Epitope spreading in MS
Evidence of epitope spreading in human MS patients is growing, with a number of small studies at least supporting a potential for epitope spreading in human disease. A study by Tuohy and colleagues conducted over several years followed peripheral T cell responses to myelin epitopes in three patients with isolated monosymptomatic demyelinating syndrome (IMDS) (Tuohy et al., 1997; Tuohy
The pathologic role of T cells in driving MS has resulted in numerous therapies aimed at inactivating T cells and/or the induction of T cell tolerance. Tolerance induction in autoimmune disease refers to a reinstatement of sustained, specific non-responsiveness of the native immune system to self-antigen. Manipulation of T cell activation and differentiation pathways has been at the center of current tolerance induction theory, and the basis of tolerance induction utilizing current immunosuppressive agents. Over recent years, experimental models have shown that it is possible to exploit the mechanisms that normally maintain immune homeostasis and tolerance to self-antigens, as well as to reintroduce tolerance to self-antigen in an autoimmune setting (Getts et al., 2011; Kohm et al., 2005; Podojil et al., 2008; Turley and Miller, 2007). However, in the clinical setting the utilization of co-stimulatory blockade, soluble peptide, altered peptide ligands among others have yielded disappointing results. As such while the induction of tolerance remains the optimal future treatment for MS current therapies are focused on agents that are disease modifying.
Over the last three decades a number of broad acting immune modifying therapeutic options have been developed and introduced to treat MS-patients. None of these therapeutic options is a cure, currently available therapies aim instead to prevent or at least reduce the frequency of relapsing inflammatory events, with the idea of reducing impact of disease on overall quality of life over time (Miller and Rhoades, 2012; Rio et al., 2011). In addition to the clear efficacy requirement long-term safety is also paramount for any MS therapy, with typical MS patients requiring treatment for many decades. The available MS therapies may be divided based on function into “immune modulatory” or “disease modifying” drugs (DMFs) as well as classic immune suppressive substances. In addition, a third group has recently emerged, which includes monoclonal antibodies (biologics). These drugs act by direct interference with specific immune system functions or by broad immune subset depletion. DMFs are typically used early in the course of the disease, whereas immune suppressive drugs and biologics are mostly viewed as treatment options in those patients with abnormally high disease activity, a high risk of sustained disability and/or show poor response to the front line therapeutics (Table 1).
The most widely used disease modifying drugs are Interferon- (IFN) and glatiramer acetate (GLAT) (Johnson, 2012). Both drugs were approved after large phase III studies, which were conducted in the 1990s. These studies proved the efficacy of these drugs in relapsing remitting MS. IFN- and GLAT reduce the relapse rate in relapsing remitting MS patients by up to 50% (Boster et al., 2011; Johnson, 2012; Limmroth et al., 2011). Furthermore, both agents significantly slowed the progression of disease and have an excellent safety profile allowing for long-term utilization. However, there remain a number of administration and efficacy issues with these drugs. Administration is required weekly at a minimum via subcutaneous or intramuscular injection, resulting in significant discomfort to patients. In addition, while IFN- and GLAT have relatively comparable efficacy, there is some patient to patient variability. For example a patient that is not responsive to IFN- may be responsive to GLAT and vicesa versa. Unfortunately no marker exists that may predict those populations that should be prescribed IFN- over GLAT or GLAT over IFN-. Currently trial and error serve as the best strategy for physicians to use when determining the optimal treatment regimen.
The exact mechanism(s) through which GLAT or IFN- modify disease progression in MS patients are not completely defined, with multiple mechanisms likely to be involved. There is evidence suggesting IFN- can inhibit T-cell co-stimulation and activation (Chen et al., 2012). In an experimental setting, IFN- inhibits immune-cell migration by increasing soluble Intercellular Adhesion Molecule 1 (ICAM-1) and Vascular Cell Adhesion Molecule-1 (VCAM-1), as well as by decreasing very late antigen-4 (VL4-4) on the cell surface of T cells. It has also been shown that IFN- can stabilize the blood brain barrier by reducing matrix metalloproteinase-9, an important tissue degradation enzyme.
GLAT is a randomized mixture of synthetic polypeptides consisting of the amino acids l-alanine, l-lysine, l-glutamic acid and l-tyrosine. GLAT was originally designed to induce CNS inflammation in animals by stimulating the myelin auto-antigen MBP, however, subsequent studies showed that the product appeared to be a protective immunomodulator. The ability for this drug to prevent relapses and disease progression is supported by large clinical studies. Mechanistically, GLAT may compete with myelin peptides for access to peptide binding cleft in MHC complex (Racke and Lovett-Racke, 2011). In addition to MHC binding, GLAT may stimulate a TH2 environment through its ability to modulate antigen presenting cellsAPC such as dendritic cells and monocytes (Miller et al., 1998). Evidence for the ability of GLAT to induce a TH2 biased immune response includes the finding that GLAT promotes the expression of anti-inflammatory cytokines such as IL-10 and TGF-b in the CNS of MS patients (Neuhaus et al., 2001). More recent studies revealed that GLAT elevates the levels of T-regulatory (Tregs) cells and reduces the levels of potentiallyharmful Th-17 cells (Lalive et al., 2011).
It is difficult to establish the long-term efficacy of drugs in MS because the disease can be highly variable and unpredictable. Still, the available long-term observational data point toward a significant prevention and delay of disability in most MS-pPatients treated with either GLAT or IFN- over a long time. Furthermore, there is sparse evidence that the early treatment reduces the long-term mortality of MS-patients (Goodin et al., 2012).
More recently, new disease-modifying drugs have become or are expected to soon be available (Buck and Hemmer, 2011; Fox and Rhoades, 2012) (Table 1). These drugs include more convenient agents that can be applied orally and may have enhanced efficacy in regards to reducing patient disease activity relative to GLAT or IFN-(Killestein et al., 2011)(Hartung and Aktas, 2011). However, the long-term safety profiles of these substances remains questionable, with more time needed to adequately address the safety profile of these agents.
If front line disease modifying therapies fail to provide sufficient relief, therapeutic escalation to include more effective therapies has to be considered (Repovic and Lublin, 2011). The most effective currently available therapy for escalation is the monoclonal antibody Natalizumab (Tysabri®). Natalizumab acts via the blockade of the VLA-4 receptor, which plays a significant role in leukocyte migration into the brain parenchyma (Rudick and Sandrock, 2004). Clinical studies with Natalizumab have shown this drug to have high efficacy in terms of its ability to prevent disease relapses and progression (Chaudhuri and Behan, 2003; O\'Connor
RR-MS, CIS | Flu-like symptoms | good safety profile, inconvenient administr., moderate efficacy (Sanford and Lyseng-Williamson, 2011) | |
RR-MS, CIS | Local irritation, | good safety profile, inconvenient administr., moderate efficacy (Lalive | |
RR-MS or escalation in RR-MS1 | Lymphopenia, arrhythmia, macular edema | Increased relapse reduction compared to IFN-(Singh | |
Escalation in RR-MS | Infections, hepatopathy, allergic response, PML | Excellent efficacy, severe viral encephalitis as a dangerous side-effect (Keegan, 2011; Pucci | |
Escalation in RR-MS, PP-MS, SP-MS, with fast progression | Leukopenia, infections, cardiomyopathy, leukemia | Immunosupressive escalation option. Option in progressive MS courses(Rizvi | |
Escalation in RR-MS, PP-MS, SP-MS, with fast progression | Leukopenia, infections | Therapeutic option if other escalation therapies including mitoxantrone fail(Rinaldi | |
RR-MS? (phase-III trial ongoing) | lymphopenia, hepathopathy | (Warnke | |
RR-MS? (phase-III trial ongoing) | gastrointestinal complaints | (Kappos | |
RR-MS? (phase-III trial ongoing) | Hepatopathy, thrombosis? | (Thone and Gold, 2011) | |
Escalation therapy? (trials ongoing) | Severe infections and sepsis possible, allergic response | (Chaudhuri, 2012; Kappos | |
RR-MS, escalatation? (trials ongoing) | Cutaneous rash, infections | Increased relapse reduction compared to IFN- likely(Stuve and Greenberg, 2010) | |
Escalation therapy? (trials ongoing) | Induction of autoimmune diseases, infections (Cossburn | Increased relapse reduction compared to IFN-(Coles |
The need for safer therapies, combined with animal data showing the ability for short course immune induction therapy (SCIIT) to induce long term disease remission, has supported a new approach to treating MS. SCIIT is a therapeutic strategy employing rapid, specific, short-term modulation of the immune system usually using a biologic therapeutic to induce long term T cell non-responsiveness. Alemtuzumab clinical studies are leading the way in employing this therapeutic concept. In this example, a one week dosing regimen with Alemtuzumab has been in phase 2 and 3 studies shown to have a long term dramatic impact on disease, reducing disease relapses for over a year (Coles
In situations where all other avenues have been exhausted and disease continues to progress at an unusually rapid rate, physicians may prescribe the chemotherapy drugs mitoxantrone or cyclophosphamide (Neuhaus et al., 2006; Perini et al., 2006; Rinaldi et al., 2009; Stuve et al., 2004; Theys et al., 1981). These drugs are often considered as final options due to their potent immunosuppressive and other serious effects. These drugs can suppress both cell-mediated and humoral immunity and often result in lymphopenia, increasing malignancy and infection risk. Results from smaller clinical studies suggest, that treating with these immunosuppressive drugs at the very beginning of the disease and in addition to immune modulating drugs might have a beneficial impact on the course of the disease. However, the harmful side effects associated with thesedrugs means their use is usually restricted to patients that have failed other treatment options, such as Natalizumab.
Multiple Sclerosis (MS) is a chronic, progressive, immune mediated central nervous system disorder that affects both adults and children. The precise triggers of autoreactive T cell development remain to be fully understood, however, it appears that a host of genetic and environmental factors contribute to disease development. Disease initiation may be the result of a single myelin specific T cell clone being activated, however, animal models and preliminary human data suggest that epitope spreading which results in the activation of numerous myelin specific T cells is important for disease progression. Therapies capable of inducing T cell tolerance, thereby rendering these myelin specific T cells inactive remain to be developed for human use. Instead a number of disease modifying agents are available, with GLAT and IFN- being the primary front line MS treatments. In those patients refractory to these therapies or who show a rapid disease progression, escalation to more broad acting therapies, such as Natalizumab may be considered. Unfortunately, while escalating therapies may have enhanced efficacy this comes with increases in safety concerns. In progressive MS patients whereby all other therapies have failed or no longer show efficacy more toxic chemotherapeutic agents are usually the last resort.
Currently within the field of MS treatment, reduction of relapse rates by around 50% is considered to be a success. As such even patients who are considered treatment successes suffer relapses. During these relapses CNS damage and epitope spreading continue to occur with further neurological impairment the result. Future therapies need to have a higher objective and bring the relapse rate down by 75-100%. This goal may not be out of reach with short course Alemtuzumab therapy shown to induce disease remission for an extended period of time. While the safety profile of this drug remains highly questionable, the observed efficacy certainly generates promise that safer more efficacious therapeutic options for MS treatment may soon be available.
Uncertainty shocks call the the market’s knowledge-gathering role into question. The equity market as an oracle works well when it provides rapid price discovery that reflects the underlying fundamentals of an economy. But when facing uncertainty shocks the equity market’s function as a consensus mechanism that reveals economic reality appears at first glance, poorly suited for the environment it faces. An oracle needs a reliable channel for obtaining information. In the face of uncertainty, the equity market turns into a network of pipes where funds flows in ways that leave many skilled observers of market moves caught off guard. The shock filters through to the inter-temporal trade-offs of investors and makes forecasting more of a bet on imagined scenarios than the result of astute modeling that is carefully tested with historic data.
The relevance of wavelet methodology for examining whether the uncertainty measures are correlated at different scales and frequencies with market and sector returns may be more easily imagined with a metaphor.1 The uncertainty shock operates as a push from behind that a person strolling down the street experiences. The push may be hard and throw the person completely off his path. He may end up face down and in a panic imagining the worse outcome. The push may also be soft from which the person experiences a quick feeling of panic but quickly recovers and continues his walk. It is not that the person is caught completely off guard. The push comes after a signal such as the sound of feet running from behind or the quiet sound of someone walking at a slightly faster rate to catch-up. The relationship between the signal and push is based on short-lived features of the environment. However, very different outcomes are possible. Wavelet methodology is particularly well-suited for capturing these different outcomes because it is designed to capture short-lived features of the environment. Wavelet methodology provides a snapshot of the outcomes in the form of market and sector returns that result from various shocks.
A defining feature of wavelet methodology that makes it particularly well-suited for capturing the economic effects of uncertainty shocks is that at a given point in time the same signal can be analyzed by different wavelets. Most importantly, it is capable of capturing an uncertainty signal that only lasts for a finite period of time. It can also handle non-stationarity which often characterizes uncertainty shocks. Wavelet coherence plots help us discover whether the measures of the shock provide new information that is not reflected in market and sector returns at various scales.
In this chapter, we investigate the statistical relationship between three well-accepted measures of uncertainty and both market and sector returns. The three measures are Macroeconomic and Financial Uncertainty of Jurado, Ludvigson, Ng (JLN) [2] and Economic Policy Uncertainty by Baker, Bloom and Davis (BBD) [3]. We explore the extent to which the impact of uncertainty is sector specific. Employing the wavelet equivalent of correlation, we observe that in the presence of significant coherence, market returns are anti-phase with all three measures of uncertainty. Between market volatility and financial uncertainty, we also observe very high in-phase coherence at low frequencies for prolonged periods of time. However, this is not the case when considering the volatilty of Economic Policy Uncertainty or Macro Uncertainty. For those measures, while there are periods of high coherence, these periods are not as extensive as found with financial uncertainty. One conclusion is that the prolongness of the coherence differs depending on the measure of uncertainty.
Looking at the coherence plots with sector returns and the three measures of uncertainty, we find prolonged high coherence at low frequencies and intermittent coherence at high frequencies. For each coherence plot, we also consider the conditional coherence, after partialing out the effects of the market. By and large, most of the coherence disappears pointing to the question of whether there is any sector-specific uncertainty. Our focus is on six sectors, Telecom, Bus. Equip, Shops. Manufacturing, Energy and Money where each had at least one period of high conditional coherence. For each sector, based on our observation of the conditional coherence plot, we sampled the scales using a Discrete Wavelet Transform (DWT). A DWT is used to run a regression of sector returns against both an uncertainty measure and market returns. A rolling regression is used from which we find the time-pattern of the uncertainty coefficients. It was often the case that the uncertainty had a significant negative impact on sector returns. These snapshots that the wavelet coefficients provide point to the general result that there are significant differences in how uncertainty filters through the sectors that are different from what the market reaction alone tells us.
The remainder of our chapter proceeds as follows: Section 2 highlights research based on wavelet analysis in applied financial economics of particular relevance for our analysis. The important concepts used in wavelet analysis that are applied in our analysis are introduced in Section 3. The data and uncertainty measures are discussed in Section 4. The analysis and results are presented in Section 5. The conclusions follow in Section 6.
The modern strain of literature relating to uncertainty, and its effects on the economy, grew out concerns in the post credit crisis era that firms were holding off on investments due to uncertainty about the future. Bloom [4] shows that a number of cross-sectional measures of uncertainty are correlated with time series measures of volatility. The cross-sectional measures of uncertainty he considers are the standard deviation of pre-tax profit growth, a stock return measure and the standard deviation of total factor productivity. His time series measure of volatility is stock market volatility. In addition, he evaluates the impact of uncertainty on the real economy using a VAR. He finds that a shock to stock market volatility causes a 1 percent drop in industrial production over a 4 month period. He also reports a similar effect on employment. Bloom identifies 17 major instances of uncertainty based on the stock market volatility measure. Baker, Bloom and Davis (2013) develop a measure of policy uncertainty based on newspaper coverage frequency. They find that their index proxies for movements in policy-related economic uncertainty. Specifically, tight presidential elections, Gulf Wars I and II, the 9/11 attacks, the failure of Lehman Brothers, and the 2011 debt-ceiling dispute are associated with spikes in the index.
Jurado, Ludvigson and Ng [2] develop a measure of uncertainty based on the h-period ahead forecasting error, where h = 1, 3, and 12 months. Using a comprehensive data set of 132 macroeconomic series they aggregate the forecast errors for each series to create a macroeconomic uncertainty index. In contrast to Bloom [4], their analysis finds that there are three major episodes of uncertainty in the 1960–2016 period: 1973–1974, and 1981–1982 recessions, and the Great recession of 2007–2009. Bali, Brown and Tang [5] create an index of macroeconomic uncertainty based on ex-ante measures of cross-sectional dispersion in economic forecasts by the Survey of Professional Forecasters. After controlling for a number of factors, they find a statistically significant negative relationship between their measure of uncertainty and future stock returns. Ludvigson, Ma and Ng [6] examine the question of whether uncertainty is a source of business cycle fluctuations, or an endogenous response. Their analysis distinguishes macroeconomic uncertainty and uncertainty about real economic activity from financial uncertainty. They find that financial uncertainty is primarily an exogenous shock. In addition they find that higher uncertainty about real economic activity is likely to be endogenous, in response to business cycle fluctuations.
Prior to the work of Ramsey2, the use of wavelets in economic and financial analysis was largely non-existent. Today, however, wavelet analysis is a well known, and widely applied tool for any economist who studies time series data.3 The reason for the rapid increase in wavelet based applications is that the wavelet transform yields a localized decomposition in both time and frequency domain. This stands in sharp contrast to the traditional Fourier transform often used by economists that is global in the sense that there is no time component after the Fourier transform is applied.4 Since the wavelet transform yields a decomposition that is in localized frequency and time, it has proven to be particularly useful. A clear application where wavelet methodology benefits the analysis is when applied to investment decisions over different time horizons.5
The wavelet transform consists of a father wavelet and a set of mother wavelets. Given a function
The mother wavelets, also in discrete form, are defined as:
Where J is the number of scales or levels,
Wavelet functions transform a time series, f(t), into a series of wavelet coefficients,
and,
Where
Applying the transforms results in a time series of length k of smooth coefficients at the maximal scale J, and J time series of detailed coefficients each of length k. If there are 6 scales, the frequency of the first scale is associated with the interval [1/4, 1/2], and the frequency of scale 6 is associated with the interval [1/128, 1/64].
The number of coefficients differs by scale. If the length of the data series is n, and divisible by
A time series f(t) can be represented in decomposed form, known as the multi-resolution analysis of f(t), as follows:
Using a more convenient summary notation,
The discrete wavelet transform decomposes a time series into orthogonal signal components at different scales.
In the case of monthly data, as we use in our analysis, decomposing the series into six scales (D1-D6) corresponds to 2–4, 4–8, 8–16, 16–32, 32–64, and 64–128 months. D1 is the shortest scale (highest frequency) component and D6 is the longest scale (lowest frequency) component. The smooth component (S6) captures the trend of the original series.
The continuous wavelet transform (CWT) is also a useful approach for gaining insight into the localized time-scale decomposition of a time series. One advantage that the CWT has over the DWT is that it produces a powerful visual for detecting time-scale patterns. The CWT, which is based on continuous variations in the scale
where,
The DWT can be viewed as a critical sampling of the CWT with
The wavelet power spectrum, or squared amplitude, measures the local variance of a time series at different scales. It is defined as
In addition to the wavelet power spectrum, we also employ wavelet coherence to measure the co-movement of two time series across time and scale. To define coherence we need to define of two other measures, the cross wavelet transform (XWT), and the cross wavelet power (XWP). The XWT is defined as
The XWP is the absolute value of the XWT,
The wavelet coherence, is defined as:
Where S is a smoothing operator in time and scale, and
The applicability of wavelet methodology to investigate uncertainty shocks is rooted in the fact that market returns reflect an aggregation of investors’ decisions. Investors do not all share the same time horizon. Wavelet methodology is used so that localized information that affects returns is not lost.
The equity return data used for our analysis is from the Kenneth French Data Library (Table 1) [13]. The market portfolio (MKT) is a composite portfolio of all stocks traded on the NYSE, AMEX, and NASDAQ. The market is divided into 12 industry groups or sectors defined below.
1 NoDur Consumer NonDurables – Food, Tobacco, Textiles, Apparel, Leather, Toys |
2 Durbl Consumer Durables – Cars, TV’s, Furniture, Household Appliances |
3 Manuf Manufacturing – Machinery, Trucks, Planes, Off Furn, Paper, Com Printing |
4 Enrgy Oil, Gas, and Coal Extraction and Products |
5 Chems Chemicals and Allied Products |
6 BusEq Business Equipment – Computers, Software, and Electronic Equipment |
7 Telcm Telephone and Television Transmission |
8 Utils Utilities |
9 Shops Wholesale, Retail, and Some Services (Laundries, Repair Shops) |
10 Hlth Healthcare, Medical Equipment, and Drugs |
11 Money Finance |
12 Other Other – Mines, Constr, BldMt, Trans, Hotels, Bus Serv, Entertainment |
Kenneth French 12 Industry Data Set.
All returns are reported in excess of the risk free rate. The risk-free rate is measured by the yield on the 1-month T-bill.10 The sample frequency is monthly, and the sample period is July 1960 to Dec. 2019.11 The sample period includes eight recessions. These are illustrated in Figure 1. All but three were less than a year in duration. The 1974–1975 recession was 16 months, this was the time of the first OPEC price shock, when oil prices quadrupled. The recession starting in July 1981 lasted 16 months. This coincided with Fed interest rate tightening which was implemented to reduce inflation. Finally the Great Recession of 2008–2009 had a duration of 18 months. An examination of the cumulative returns of each sector indicates a high degree of variability across sectors and over time for a given sector. Figure 2 shows the sectors with cumulative growth that exceeds the market for the sample period. Figure 3 shows sectors with cumulative growth near or below cumulative market growth. The sector with the highest cumulative growth over the sample period is Consumer Non-durables (NoDur) with growth of almost 6400%, compared with the market as a whole which increased 2378%.The sector with the lowest cumulative returns is Durable Goods (900%). The effect of the technology bubble burst (2000–2001), on Telecom, and BusEq returns is salient. The drop is so precipitous that by the onset of the Great Recession (Dec. 2007), cumulative returns for these sectors was still below peak (March 2000). They did not reach the March 2000 peak until 2016. As a whole, Figures 2 and 3 indicate a change occurring with the 2001 recession in that when it comes to cumulative returns, sector returns appear to part ways. One result is that some sectors recovered quickly from the 2001 and 2007 recessions and some recovered very slowly.
US Economic Recessions, 1960–2020 - NBER Dating.
Cumulative Returns - High Growth Sectors.
Cumulative Returns - Low Growth Sectors.
Figure 4 contains the wavelet power spectrum for market returns. Wavelet power is a measure of variance local to time and scale. The most striking feature of this chart is that most of the power occurs intermittently at high frequencies. The wavelet power spectrum for the Durables sector (the lowest growth sector) is shown in Figure 5, and the power spectrum for the Consumer Non-durables sector (the highest growth sector) is shown in Figure 6. Both sectors look similar to the market at high frequencies. At intermediate frequencies (16–32 months) the Durable goods sector shows high power during the Great Recession, but the the Non-durables goods sector does not. This is outlined in white for expository purposes. Consumer non-durables have relatively high power at the 32–64 month frequency during the 1970s, while the Durable Goods sector has less variability associated with this frequency band.12
Wavelet Power Spectrum - U.S. Equity Market.
Wavelet Power Spectrum -Durable Goods.
Wavelet Power Spectrum - Consumer Non-durables.
A set of descriptive statistics for the monthly excess returns (%) is reported in Table 2. Monthly returns range from a high of 42.6% for Durable goods (Apr. 2009) to a low of minus 32.7% also for Durable goods (Oct. 2008). Skewness is negative for most sectors, the exceptions being except Durable goods (0.13%); Excess kurtosis is positive (leptokurtic) for all of the sectors, suggesting that the distribution of returns has fatter tails than a Normal distribution. It ranges from 1.0 for Utilities to 4.8 for Durables.
Sector | Mean | Std Dev | Median | Minimum | Maximum | Skewness | Kurtosis |
---|---|---|---|---|---|---|---|
NoDur | 0.68 | 4.24 | 0.76 | −21.63 | 18.3 | −0.33 | 2.03 |
Durbl | 0.51 | 6.15 | 0.46 | −32.71 | 42.62 | 0.13 | 4.77 |
Manuf | 0.59 | 5.22 | 0.98 | −29.18 | 21.07 | −0.49 | 2.52 |
Enrgy | 0.63 | 5.4 | 0.66 | −19.01 | 23.6 | −0.03 | 1.23 |
Chems | 0.53 | 4.55 | 0.74 | −25.19 | 19.71 | −0.26 | 2.11 |
BusEq | 0.63 | 6.36 | 0.66 | −26.41 | 20.32 | −0.24 | 1.3 |
Telcm | 0.51 | 4.59 | 0.62 | −16.43 | 21.22 | −0.18 | 1.14 |
Utils | 0.49 | 3.95 | 0.64 | −12.94 | 18.26 | −0.15 | 1.01 |
Shops | 0.67 | 5.08 | 0.75 | −28.83 | 25.28 | −0.31 | 2.4 |
Hlth | 0.67 | 4.87 | 0.77 | −21.06 | 29.01 | −0.03 | 2.29 |
Money | 0.64 | 5.38 | 0.91 | −22.53 | 20.59 | −0.38 | 1.59 |
Other | 0.49 | 5.34 | 0.82 | −29.81 | 18.85 | −0.5 | 2.16 |
Note: n = 714 |
Summary Statistics - Sector Returns.
We use three measures of uncertainty in our analysis, macroeconomic and financial uncertainty from Jurado, Ludvigson, and Ng [2], and economic policy uncertainty from and Baker, Bloom and Davis [3]. These are arguably the most common measures of uncertainty, and they are both updated on a regular basis and available online.13 JLN and BBD are different measures of uncertainty constructed using very different methodologies. We summarize both approaches in this section.
JLN define uncertainty for variable
where,
where
JLN used a total of 132 economic series to estimate macroeconomic uncertainty. The series span the following categories: real output and income, employment and hours, real retail, manufacturing and trade sales, consumer spending, housing starts, inventories and inventory sales ratios, orders and unfilled orders, compensation and labor costs, capacity utilization measures, price indexes, bond and stock market indexes, and foreign exchange measures.
The financial uncertainty series is comprised of uncertainty measures for 147 financial series. These series include valuation ratios such as the dividend-price ratio and earnings-price ratio, growth rates of aggregate dividends and prices, default and term spreads, yields on corporate bonds of different ratings grades, yields on Treasuries and yield spreads, and a broad cross-section of industry equity returns. In addition, returns on 100 portfolios of equities sorted into 10 size and 10 book-market categories are included. The data set also includes excess return on the market, small-minus-big and high-minus-low portfolio returns, a momentum factor, a measure of the bond risk premium, and also a small stock value spread.
JLN provide measures of financial and macroeconomic uncertainty based on 1, 3, and 12 month forecast horizons. Our analysis focuses on the one month horizon series, denoted as h = 1. Their macroeconomic uncertainty series is shown in Figure 7. In general the peaks of the series align with the NBER recession dates (shaded areas). The three highest peaks are in the mid-1970’s during the first OPEC oil shock, the early 1980’s when there were back to back recessions, and the recession of 2008–2009. Figure 8 shows the wavelet power spectrum for this series. The power is highest for periods of 32 to 128 months. Unlike the time plot of uncertainty which peaks at each recession the power spectrum has two basic clusters of uncertainty. It does not distinguish among the first four recessionary periods, and instead shows one extended period of uncertainty from the early 1970’s to the late 1980’s. The second period of uncertainty is the Great Recession which is notable for the range of scale which is from 8 to 256 months. The Great Moderation in the 1990’s (outlined in white) is also apparent as a break in the low frequency power. In contrast power spectrum for the market returns 4 macroeconomic uncertainty tends to have low power at high frequencies.
Macroeconomic Uncertainty, Jurado, Ludvigson and Ng, (Ph1), horizon = 1 month.
Wavelet Power Spectrum - Macroeconomic Uncertainty (EP1).
Figure 9 displays the financial uncertainty series for h = 1. The peaks of this series do not align as closely with recessions as does the macroeconomic uncertainty. For instance there is a noteworthy spike in Oct. 1987 (outlined in red) when the equity market dropped, and there was no recession. Events such as the 1997 Asian financial crisis, the 1998 Russian financial crisis and the 2000 Tech. bubble bust are all apparent prior to the 2001 recession. Also, the magnitude of the peaks in the financial uncertainty index are greater than those of the macroeconomic uncertainty index. Alignment of the peaks of the macroeconomic index with recessions, and to a lesser extent the financial uncertainty index, is intuitive as forecasting a turning point is difficult if not impossible. One way useful to think about this is with regard to asset returns which can be written as,
Financial Uncertainty, Jurado, Ludvigson and Ng, (Fh1), (bottom), horizon = 1 month.
It is generally possible to forecast the absolute value of returns but not returns themselves. The reason is that one cannot forecast the sign.
The wavelet power spectrum or variance (Figure 10) for financial uncertainty is generally highest at low frequencies. The 1987 stock market crash (outlined in white) is one of several instances where high variance can be also be observed at medium (8 to 32 month) frequencies.14 The scale of uncertainty is lower from 1960 to 1990 (32–64 months) than it is from 1990 to 2020 (up to 128 months). In effect low frequency financial uncertainty exists through the sample period.
Wavelet Power Spectrum - Financial Uncertainty (Fh1).
Baker, Bloom and Davis (BBD) construct a measure of economic policy uncertainty using three major components. For the first component, they search ten major newspapers and create an index based on the volume of news relating to economic policy uncertainty. The second component, which is designed to capture uncertainty in the federal tax code, is derived from Congressional Budget Office reports on temporary tax code due to expire over the next ten years. The third component uses the dispersion of opinions among professional forecasters regarding the future of the Consumer Price Index, Federal expenditures and, State and Local Government Expenditures. The forecasts are from the Philadelphia Federal Reserves Survey of Professional Forecasters.15 These three components are combined to create the Index of Economic Policy Uncertainty. The index is shown in Figure 11. Unfortunately the series starts in 1985, so we are unable to study this series over the same sample period as the JLN uncertainty indices. The BBD index shown in Figure 11, appears to have a local maxima during each of the 3 recessions, but it tends to be volatile with peaks at other important dates such as the two Gulf wars (1991, and 2003), the debt ceiling debates (2011–2012), the fiscal cliff (2013), the government shutdowns (1995,2013,2018), and the election of Trump (2016).16 The wavelet power spectrum for the BBD series is shown in Figure 12. High power is found at the 128 month scale from the mid-1990’s to the present (outlined in white). There are also a series of high frequency (2–16 months) spikes in the wavelet power which are not present in the other two uncertainty indices.
Economic Policy Uncertainty Index - Baker, Bloom and Davis (EC1).
Wavelet Power Spectrum - Economic Policy Uncertainty.
We begin our analysis by examining the relationship between the three measures of uncertainty, and the market portfolio. Figure 13 shows the coherence between macroeconomic uncertainty and excess market returns. Red indicates high coherence and blue indicate no coherence. Coherence is a measure of co-movement between the two series, similar to a correlation. Note that high coherence does imply high power. The heavy black lines around the outside of the red areas indicates statistical significance at the 95% level of confidence. The frequency is inverted in the coherence charts compared with the power spectrum charts. The coherency charts also contain phase arrows which are explained in Table 3. There are two basic categories of coherence in Figure 13. Sporadic high coherence at the 8 to 64 month scale, and prolonged coherence at the 64–128 month scale. The high coherence at the 64–128 month scales lasts from 1960 until the mid-1980s, breaks for about 15 years (outlined in white), and reoccurs from 2000 to 2019. The period of the break in coherence is shorter than the typical time frame known as the Great Moderation (mid-1980’s to 2007). The phase arrows are pointing left indicating that the two series are out of phase. The sporadic high coherence at the 16 to 32 month scales occurs in the middle 1970s, and to a much greater extent during the Great Recession. The phase arrows indicate that the two series are in anti-phase.
Wavelet Coherence - Macroeconomic Uncertainty and U.S. Equity Market Returns.
Left arrow: anti-phase |
Right arrow: in-phase |
Down arrow: X leading Y by 90deg |
Up arrow: Y leading X by 90deg |
Phase arrow definitions.
In addition to the monthly returns, we also examine coherence of uncertainty with the absolute value of market returns that we use as a measure of market volatility. Figure 14 shows the coherence plot for the absolute value of market returns with macroeconomic uncertainty. As was the case in Figure 13 there as two periods of high coherence at a low frequency, but in this instance the scale is lower (32–64 month), the break (outlined in white) begins in the early 1990s and lasts for about 5 years. During the Great Recession, uncertainty and market volatility are in phase.17
Wavelet Coherence - Macroeconomic Uncertainty and Absolute Value of U.S. Equity Market Returns.
Figure 15 shows the coherence of the market returns with the financial uncertainty. Coherence occurs at lower scales than the coherence of the market with the macroeconomic uncertainty. There is a high degree of coherence in the 1960s and 1970s at the 16–32 month scale, then there is a break (outlined in white) of 20 years when coherence at these scales is non-existent. Beginning in 2000, coherence is high once again at the 16–32 month scales. The two series are out of phase during these periods of high coherence.
Wavelet Coherence - Financial Uncertainty and U.S. Equity Market Returns.
The coherence of financial uncertainty and market volatility is shown in Figure 16. The coherence is high and in-phase throughout the entire sample period for scales above 32 months. There also are numerous low scale periods when the two series are in phase and have high coherence.
Wavelet Coherence - Financial Uncertainty and Absolute Value of U.S. Equity Market Returns).
As shown in Figure 17 the coherence of economic policy uncertainty and market returns generally occurs at a lower scale then coherence of the two BLN indices. Statistically signficiant coherence never exceeds the 64 month scale, but it is high and nearly continuous at the 8 to 16 month scale from 1993 to 2003 (outlined in white). These periods of high coherence at lower scales appear to coincide with financial crises. During the Great Recession there is a very clear distinction in coherence between the 8–16 month scales (2008–2012), and the coherence at the 32 months scale from 2003 to 2018. The phase arrows generally point upwards indicating a lead–lag relationship.
Wavelet Coherence -Economic Policy Uncertainty and U.S. Equity Market Returns.
Figure 18 shows the coherence between economic policy uncertainty and the absolute value of market returns. Coherence is generally quite low, except for the 128 month scale beginning in the mid -1990s and going to 2019.
Wavelet Coherence -Economic Policy Uncertainty and absolute Value of U.S. Equity Market Returns).
In the previous section, we showed that regardless of the uncertainty measure employed, there is evidence of high coherence between market returns and uncertainty, and also market volatility and uncertainty. In this section, we examine the coherence between sector returns and the three measures of uncertainty. Our goal is to characterize the extent to which uncertainty has impacted individual sectors. To do this we examine the coherence of each measure of uncertainty with sector returns. However, uncertainty affects the market and we want to find the relationship between uncertainty and sector returns after removing the relationship of uncertainty with the market. In order to accomplish this, there is a “before and after.” We refer to the “after” as the conditional coherence. Conditional coherence is the coherence of sector returns with uncertainty conditional on the market returns.18 In addition, to further illustrate the extent to which uncertainty impacts a sector independently of the impact it has vis-a-vis the market portfolio, we estimate a rolling regression. The rolling regression is of sector returns against the market return and the uncertainty index for one or more scales using the discrete wavelet transform.19 Selection of the scales was based on high conditional coherence of sector returns and uncertainty measures. The functional form of the regression is,
where
As there are three measures of uncertainty and 12 sectors, it would be cumbersome to show all of the coherence plots for the 12 sectors. Instead, we have chosen to discuss a subset consisting of six sectors where each sector has at least one period of high coherence with one measure of uncertainty. While the other sectors may also have periods of high coherence, including them would add little to our analysis.20
The first sector examined is the financial sector. Figures 19–21 show the coherence of the financial sector with all three types of uncertainty both before and after the market has been partialed out. Most of the coherence with all three types of uncertainty is subsumed by the market. However, financial uncertainty is one measure of uncertainty that does matter. As shown, in Figure 20 the right hand side chart shows several areas of significant conditional coherence. Conditional coherence exists for the 32 month scale in the 1970s (circled in white). There is also a small area of conditional coherence around 1990 at the 16 month scale and it is also present at the 64–128 month scale beginning around in the mid 1990s. It is interesting to note that there is no apparent conditional coherence with Economic Policy Uncertainty in 2010 when Dodd-Frank was passed.
Wavelet Coherence -Macroeconomic Uncertainty and the Money Sector.
Wavelet Coherence - Financial Uncertainty and the Money Sector.
Wavelet Coherence -Economic Policy Uncertainty and the Money Sector.
Figure 22 shows the financial uncertainty coefficient estimates (top) and corresponding t-statistics (bottom) for scales 4 and 5 using the rolling window regression given in Eq. (16). The scales were chosen based on the conditional coherence charts. Scales 4 and 5 for financial uncertainty suggest by the presence of red hot spots that there would be significance for the uncertainty measure. The dashed lines in the t-stat chart indicate +/− 2. Although both scales are presented, scale 5 stands out. Scale 5 (32 months) is negative and statistically significant for three segments of time beginning in the late 1970s. The longest of these three periods begins in the late 1990s and ends in 2010. Given the large number of shocks all of which effected equity values, it is interesting to find that the financial uncertainty index had significance in a rolling window regression that includes both the market and the uncertainty index. Some of the important financial events include the Asian financial crisis (1997), the Russian financial crisis (1998), the collapse of Long Term Capital Management (1998), the repeal of the Glass-Steagall Act (1999), the 2001 recession, and the 2008 recession. The significant negative coefficients for this time period are an indication that there was sector specific uncertainty over and above that which impacted the market as a whole.
Rolling Regression Coefficients -Financial Uncertainty and the Money Sector.
Figures 23–25 contain the coherence of the Energy sector returns with all three type of uncertainty before (left) and after (right) the market returns are partialed out. Each of the three right hand side charts display several short periods of high conditional coherence indicating uncertainty specific to the energy sector. The longest period of conditional coherence is at scale 6 (64 months) for the Macroeconomic Uncertainty Index (circled in white). There are two small period of high conditional coherence at the 16 month scale with financial uncertainty. There is moderate conditional coherence with policy uncertainty from the mid-1980s until the mid-1990s, but the scale (128 months) is almost completely outside the cone of influence (COI) and is not statistically significant. The 2014 oil glut that led to a steep decline in oil prices has a very small area of significance in the conditional coherence charts. Note also that there is little or no sign of the boom in hydraulic fracking which began in the mid 1990s, nor the environmental backlash that began in 2013–2014 in the conditional coherence charts.
Wavelet Coherence - Macroeconomic Uncertainty and the Energy Sector.
Wavelet Coherence - Financial Uncertainty and the Energy Sector.
Wavelet Coherence -Economic Policy Uncertainty and the Energy Sector.
Figure 26 shows rolling window regression coefficients at scales 5 (32 months) and 6 (64 months) for macroeconomic uncertainty. Again, the scales were chosen based on observations from the conditional coherence charts. Scale 6 is negative and statistically significant from the mid-1960s until the mid-1970s and clearly shows the impact of the 1973 OPEC embargo. The uncertainty coefficients for scale 6 are also negative and statistically significant from 2010 to 2015 and may reflect the environmental backlash to fracking.
Rolling Regression Coefficients -Macroeconomic Uncertainty and the Energy Sector.
In summary, although the conditional coherence charts show surprisingly little sector specific uncertainty relating to the OPEC II (1979) oil shocks or the 2014 oil glut, the DWT regression shows a strong negative sector specific impact.
Over the course of the sample period, the Telecommunications Industry evolved from a heavily regulated monopoly to a more competitive industry with at least six large firms. In addition, technological changes in communications broadened the scope of services offered by the industry. This resulted in redefining communications providers as content providers and making cell phone usage an imperative for the vast majority of adults. An examination of the coherence charts (Figures 27–29) does show some periods of sector specific uncertainty. The conditional coherence chart for financial uncertainty shows high coherence at the 32 months scale throughout the 1980s (highlighted with white) which is coincident with the restructuring of AT&T. The conditional economic policy chart shows four high periods of coherence. Two are at the 32 month scale, one in the early 1990s and the second starting in 2010. There is also high coherence during the Great Recession at the 8 to 16 month scale. The fourth period of high coherence is at the 64 to 128 month scale beginning in 2005 and ending in 2010.
Wavelet Coherence -Macroeconomic Uncertainty and the Telecom. Sector.
Wavelet Coherence - Financial Uncertainty and the Telecom. Sector.
Wavelet Coherence -Economic Policy Uncertainty and the Telecom. Sector.
Based on the conditional coherence charts, rolling regressions were run for both Economic Policy Uncertainty and Financial Uncertainty indices. Significant coefficients were found for both indices. Figure 30 shows the uncertainty coefficient estimates and t-statistics for the regression of sector returns against market returns and Economic Policy Uncertainty for scales 16 and 32 months. Policy Uncertainty at scale 32 is negative and statistically significant from 1995 to 2000, and from from 2007 to 2015. The Telecommunications Act of 1996 may be partially responsible for the large drop in the 32 month coefficient in 1996–1997.
Rolling Regression Coefficients-Economic Policy Uncertainty and the Telecom. Sector.
Coefficient estimates for Financial Uncertainty at 16 and 32 month scales are shown in Figure 31. This is a longer time series than the policy uncertainty index and it shows a modest negative impact in the 1980s at the 32 month scale. There is a much larger negative impact at the 32 month scale starting in the late 1990s and extending until 2018. The significance coefficients are consistent with the explanation that the Telecommunications Sector exhibits sector specific uncertainty.
Rolling Regression Coefficients-Financial Uncertainty and the Telecom. Sector.
Coherence plots for the business equipment sector, which is comprised of computer, electronic and software firms are shown in Figures 32–34. There appears to be very little conditional coherence for Macroeconomic Uncertainty. The 32 month scale for Financial Uncertainty shows some coherence in the early 1990’s and from 2000 to 2010. Conditional coherence with Economic Policy Uncertainty is low except in the 2018–2919 period at the 8 month scale (highlighted in white). This may be the result of the policy change in favor of repealing net neutrality on the part of the Trump Administration.
Wavelet Coherence -Macroeconomic Uncertainty and the Business Equipment Sector.
Wavelet Coherence - Financial Uncertainty and the Business Equipment Sector.
Wavelet Coherence -Economic Policy Uncertainty and the Business Equipment Sector.
Figure 35 shows the coefficient estimates for Economic Policy Uncertainty at the 8 and 16 month scales. The coefficients for the 16 month scale are negative and significant from the late 1990s until 2008. This is a time of rapid growth for the internet. The tech bubble burst after partialing out the market effect clearly has a stand alone component. The significant coefficients are consistent with difficulties encountered when introducing a new technology.
Rolling Regression Coefficients - Economic Policy Uncertainty and the Business Equipment Sector.
There are several instances of significant conditional coherence for the shops sector (Figures 36–38). Notable is the coherence with Macroeconomic Uncertainty, and to a lesser extent Financial Uncertainty, at the 64 month scale from the mid 1970s to the late 1980s. The Shops sector, which consists of Wholesale, Retail, and Some Services, shows very little conditional coherence with the JLN indices after the late 1980s. This is consistent with the rise and expansion of big box retailers such as Walmart and Target and the demise of small Mom & Pop stores. Big Guns are better able to weather uncertainty storms. The rise of internet retail captured by Amazon’s IPO in 1997 does show up in the conditional coherence for Economic Policy (highlighted in white). This is suggestive that policy treatment regarding internet retail mattered, especially considering taxes.
Wavelet Coherence -Macroeconomic Uncertainty and the Shops Sector.
Wavelet Coherence - Financial Uncertainty and the Shops Sector.
Wavelet Coherence -Economic Policy Uncertainty and the Shops Sector.
The rolling regression coefficients for the shops sector with economic policy uncertainty are shown in Figure 39 for 8 and 16 month scales. The uncertainty coefficients for the 16 month scale are negative starting in 1996 and remain negative until 2015. This seems consistent with the war between internet shopping and brick and mortar retail businesses.
Rolling Regression Coefficients - Economic Policy Uncertainty and the shops Sector.
Coherence plots for the manufacturing sector are shown in Figures 40–42. There is high conditional coherence with Macroeconomic Uncertainty from 1990 to 2019 at the 128 month scale (highlighted in white). However, at least half of this coherence is outside the cone of influence (COI). The onset of this uncertainty coincides with the signing of NAFTA in 1994. There appears to be less conditional coherence with Financial Uncertainty, although there is a significant patch at the 64 month scale during the Great Recession. Conditional coherence with Economic Policy Uncertainty occurs in the mid to late 1990s at the 8 to 16 month scale (highlighted in white). These findings suggest that the market bears the risk to manufacturing captured by the indices of Macroeconomic and Financial Uncertainty. However, since Economic Policy Uncertainty is showing some hot spots of conditional coherence this suggests that the market responded to news and information regarding this sector, especially the effects of trade on manufacturing. The stand alone uncertainty that is captured may be associated with policy uncertainty regarding trade.
Wavelet Coherence -Macroeconomic Uncertainty and the Manufacturing Sector.
Wavelet Coherence - Financial Uncertainty and the Manufacturing Sector.
Wavelet Coherence -Economic Policy Uncertainty and the Manufacturing Sector.
Figure 43 shows the rolling regression coefficients for Macroeconomic Uncertainty at the 64 and 128 month scale. The 128 month scale coefficients are negative and significant throughout the 1970s and 1980s, and from 2000 to 2010. Figure 44 shows the coefficients for Economic Policy Uncertainty at the 8 and 16 month scales. The impact of concerns about NAFTA is apparent at the 16 month scale with negative coefficients in 1996 and ending in 1998.
Rolling Regression Coefficients - Macroeconomic Uncertainty and the Mfg Sector.
Rolling Regression Coefficients - Economic Policy Uncertainty and the Mfg Sector.
Wavelet methodology, by allowing local features of the environment to be captured took the lead in our exploration of uncertainty shocks. We examine changes in coherence between each uncertainty measure and the returns of each sector both before and after partialing out the coherence of uncertainty with the market portfolio. Rolling regressions were used to identify sector-specific uncertainty that is not captured by the overall market. Such uncertainty was found for the Money Sector, Energy sector, Telecommunications sector, and Manufacturing sector. These findings suggest that there are periods when the market reaction to shocks is not reflecting all the information captured by the uncertainty indices. One interpretation of our results is that an industry like Telecommunications, Money, Energy, and Manufacturing undergoing significant technological or regulatory changes will have a great reaction to shocks than the overall market response captures. These sectors have a greater sensitivity to uncertainty shocks when the design of the uncertainty metric is largely macro in orientation.
Our finding that there are episodes of uncertainty when there is increased comovements across frequency and over time for specific sectors helps paint a more complete picture of how uncertainty affects the economy through its transmission across sectors. When local features of the return environment are considered, we conclude that in the face of uncertainty shocks the market’s knowledge-gathering role could be improved by introducing uncertainty measures that in terms of information-gathering are less global and more local. Localized or micro measures of uncertainty shocks should be of direct relevance to traders and portfolio managers who must respond to such shocks in a ways that are wealth-preserving for their clients.
“The authors declare no conflict of interest.”
The material presented in thsi Chapter gives the views of the author, and not necessarily Queens College or the Bank of America.
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