Potential therapeutic interventions targeting indoxyl sulphate (IS) and p-cresyl sulphate (PCS).
\r\n\t
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He has more than 96 SCI publications, he acted as an academic editor, reviewer, and he holds several registered patents.",coeditorOneBiosketch:"Researcher in enteric health, most notably probiotics and their relationship to nutrition and disease protection in poultry as well as the design of avian enteric inflammation models for the study of the impact of diet and microbiome on growth and development.",coeditorTwoBiosketch:"My research focuses mainly on apicomplexan parasites, such as Toxoplasma Cryptosporidium, Eimeria, and minor on nematodes. Prof.Alali has more than 30 publications and he acts as a reviewer in many journals.",coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"209746",title:"Dr.",name:"Saeed",middleName:null,surname:"El-Ashram",slug:"saeed-el-ashram",fullName:"Saeed El-Ashram",profilePictureURL:"https://mts.intechopen.com/storage/users/209746/images/system/209746.jpg",biography:"Dr. Saeed El-Ashram is a professor at Foshan University, China, and Kafrelsheikh University, Egypt, and a research professor at Zhaoqing Dahuanong Biology Medicine Co., Ltd., China. Dr. El-Ashram\\'s research focuses on parasitic diseases. He has more than 100 journal publications to his credit. He is currently an academic editor and reviewer and holds several registered patents. The primary focus of his research is to understand how the animal immune system recognizes and responds to parasitic infections with and/or without a microbial community. Some are the causative agents of significant diseases in humans, such as toxoplasmosis, cryptosporidiosis, alveolar echinococcosis, and fascioliasis. Others are a substantial financial burden to food producers because of the effects these parasites have on domestic animals, for example, coccidiosis and cryptosporidiosis (livestock and poultry).",institutionString:"Foshan University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Foshan University",institutionURL:null,country:{name:"China"}}}],coeditorOne:{id:"73465",title:"Dr.",name:"Guillermo",middleName:null,surname:"Téllez",slug:"guillermo-tellez",fullName:"Guillermo Téllez",profilePictureURL:"https://mts.intechopen.com/storage/users/73465/images/system/73465.jpg",biography:"Guillermo Tellez-Isaias received his DVM and MS in Veterinary Sciences from the National Autonomous University of Mexico (UNAM), and his Ph.D. from Texas A&M University. 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Patients with CKD, including those with end-stage kidney disease (ESKD), often experience impaired uraemic toxin clearance, salt and water retention, dietary restrictions, anorexia, dysgeusia and malnutrition, which in turn leads to quantitative and qualitative alterations in gut microbiome composition (gut dysbiosis). Further effects include gut wall oedema, intestinal barrier impairment, translocation of bacteria and endotoxins across the intestinal wall and resultant systemic inflammation [1–3]. Gut dysbiosis may in turn lead to the production of various toxins and metabolites that contribute to uraemic toxicity, cardiovascular disease and progressive kidney scarring and failure [4–6]. The central role of the gut microbiome in kidney health therefore makes it an appealing therapeutic target in patients with CKD [7, 8].
Two key nephrovascular toxins produced by proteolytic bacterial fermentation in the gut are indoxyl sulphate (IS) and p-cresyl sulphate (PCS). IS is produced by tryptophan metabolism facilitated by
Although IS and PCS levels can be lowered with various therapeutic modalities, how this impacts on the risks of mortality and cardiovascular outcomes remains unclear. This review will discuss the roles of IS and PCS as therapeutic targets and examine the clinical evidence for different treatment options and their effects on CKD and cardiovascular disease risk.
Serum IS and PCS levels have been demonstrated to be elevated in patients with CKD, where IS levels may be more than 50 times and PCS levels more than 15 times the levels of those found in healthy people [12, 14]. Our group has demonstrated that IS and PCS levels are significantly elevated in patients with early-stage CKD compared with control subjects. These levels were seen to be progressively more elevated with advancing severity of CKD [13]. Increased circulating levels of IS and PCS have also been observed in living kidney donors, which were sustained at 2 years post-surgery [16]. Levels of IS and PCS appear to be most elevated in ESKD and are not effectively removed by haemodialysis [14]. In a sample of 45 haemodialysis patients, Itoh et al. observed IS and PCS levels were markedly elevated (2.99 ± 0.18 mg/dL and 3.71 ± 0.28 mg/dL, respectively) compared with the healthy subjects (0.05 ± 0.01 mg/dL and 0.22 ± 0.99 mg/dL, respectively), and these levels were only lowered by approximately 30% post-dialysis (2.02 ± 0.12 mg/dL and 2.60 ± 0.21 mg/dL, respectively). This degree of elevation and inefficient removal warrants exploration of the potential impact of these toxins in CKD.
Elevation of serum IS and PCS levels in patients with CKD is associated with CKD progression [17]. The mechanisms underpinning the adverse renal effects of IS and PCS are thought to be at least partly mediated by the production of reactive oxygen species, which in turn activate the nuclear factor kappa B pathway (NFκB) (Figure 1) [18].
Mechanisms and potential effects of indoxyl sulphate (IS) and p-cresyl sulphate (PCS) on renal, metabolic and cardiovascular outcomes.
In a prospective, observational study of 268 patients with varying stages of CKD, Wu and colleagues demonstrated a significant association between higher IS (hazard ratio [HR] 1.06, 95% confidence interval [CI] 1.04–1.09, p < 0.001) and PCS levels (HR 1.09, 95% CI 1.06–1.13, p < 0.001) and CKD progression, defined as greater than 50% reduction in estimated glomerular filtration rate (eGFR) or progression to ESKD [17]. Serum PCS and IS remained independently associated with CKD progression after adjustment for patient demographic characteristics (age, gender, diabetes mellitus, p < 0.001) or baseline renal function (p < 0.001). Additionally, IS and PCS levels at baseline were significantly higher in those patients who died during follow-up (serum PCS 12.07 [<1–42.06] mg/L vs. 4.1 (<1–36.24) mg/L in survivors, p = 0.002; serum IS 4.78 [0.7–12.54] mg/L vs. 2.07 [<0.225–53.58] mg/dL, p = 0.05). Elevated serum total PCS was also found to be significantly associated with all-cause mortality on univariable analysis (HR 1.10, 95% CI 1.05–1.15, p < 0.001) and remained a predictor of mortality independent of other risk factors on multivariable analysis adjusted for patient demographic characteristics, baseline renal function and biomarkers including highly sensitive C-reactive protein [17].
Elevated PCS has been associated with insulin resistance and may therefore predispose to the metabolic syndrome and its complications. In mouse models, the administration of PCS for 4 weeks has been observed to induce hyperglycaemia, insulin resistance, hypercholesterolaemia and fat redistribution to muscle and liver, similar to the metabolic derangements observed in CKD [22] (Figure 1). These metabolic effects appeared to be ameliorated by uraemic toxin-reducing therapy, as the use of the prebiotic agent, arabino-xylo-oligosaccharide, reduced serum PCS concentration and improved glucose tolerance, insulin resistance, dyslipidaemia and ectopic fat distribution in uraemic, subtotal nephrectomised mice [22].
IS has been demonstrated to cause concentration-dependent vascular smooth muscle cell proliferation [23] and aortic calcification with aortic wall thickening in rats [6]. This appears to apply similarly to humans, such that elevated serum IS levels have been shown to be associated with aortic calcification measured by multislice spiral computed tomography [24]. Likewise, total and free PCS levels have been linked with vascular disease [25]. Not surprisingly, elevated serum levels of both toxins have been reported to be predictors of cardiovascular events and mortality. Higher serum IS levels independently predicted overall mortality (HR 2.47, 95% CI 1.62–3.77), but not CV mortality, in 139 patients with stage 2–5 CKD participating in a study performed by the European Uraemic Toxin Work Group (EUTox) [24]. Similar results were reported in a prospective, observational cohort study of 521 US incident haemodialysis patients whereby serum IS concentrations above the median value of 1.6 mg/dL were independently associated with all-cause mortality (HR 1.30, 95% CI 1.01–1.69) after adjustment for age, sex, race, comorbidity score, baseline serum albumin, obesity and serum creatinine [26]. Elevated free PCS concentration has also been demonstrated to be an independent predictor of cardiovascular events [27, 28] and overall cardiovascular mortality [25] in CKD patients, including those ESKD receiving dialysis.
A meta-analysis by Lin and colleagues of 11 observational studies involving 1572 patients with stages 1–5 CKD followed for 0.83 to 5 years found that all-cause mortality was significantly associated with both free PCS (pooled odds ratio [OR] 1.16, 95% CI 1.03–1.30, p = 0.013) and free IS levels (pooled OR 1.10, 95% CI 1.03–1.17, p = 0.03) [15]. However, there was a moderate level of heterogeneity with I2 values of 71.5% (p = 0.004), and 74.2% (p = 0.004) for PCS and IS, respectively. Furthermore, there was a concern about publication bias based on an asymmetrical funnel plot and significant Egger’s test (p = 0.005). Following subsequent adjustment for the effect of publication bias, the adjusted point estimate of the OR reduced from 1.16 to 1.03 (95% CI 0.93–1.16), thereby raising concern about exaggeration of the observed effect size in the primary analysis. The study also reported a significantly increased risk of cardiovascular events with elevated levels of free PCS (pooled OR 1.28, 95% CI 1.10–1.50, p = 0.002), although this result was again limited by a high level of heterogeneity (I2 = 80.7%, p < 0.001). Furthermore, there was evidence of publication bias, such that when analysis was repeated using Duval and Tweedie’s trim-and-fill method, the estimate was no longer statistically significant with an adjusted OR of 1.10 (95% CI 0.93–1.27).
Given the numerous deleterious, multi-system effects that have been associated with elevated serum IS and PCS concentrations, much interest has been generated in developing therapeutic options to reduce the levels of these nephrovascular toxins with the aim of improving clinical outcomes in patients with CKD. Potential therapeutic strategies to reduce IS and PCS levels in patients with CKD may involve reducing gut synthesis, gastrointestinal sequestration, reduced proximal tubular retention and increased dialytic clearance (Table 1).
Strategy | Intervention | Outcome |
---|---|---|
Reduced gut synthesis | Very low-protein diet [29] | Reduced serum IS levels |
Dietary fibre [10, 33, 34] | Reduced serum IS and PCS levels | |
Pre-, pro- and synbiotics [35–41] | Reduced IS and PCS levels | |
Gastrointestinal sequestration | AST-120 (Kremezin) [51–56] | Reduced renal disease progression |
Ai Xi Te [54] | Reduced renal disease progression | |
Niaoduqing granules [54] | Reduced renal disease progression | |
Reduced proximal tubular retention | OAT^ modulators [11, 58, 61] | Reduced proximal tubular uptake of IS |
Increased dialytic clearance | Extended dialysis (long dialysis, short daily dialysis) [65, 66] | No clear benefit |
Haemodiafiltration [67, 68] | Reduced serum IS and PCS levels | |
Super-flux cellulose triacetate membranes [69] | Reduced serum IS levels | |
Nanoporous monolith dialysis [70] | Reduced serum IS and PCS levels |
Potential therapeutic interventions targeting indoxyl sulphate (IS) and p-cresyl sulphate (PCS).
^OAT: organic anion transporters
Since increased dietary protein load can result in heightened generation of uraemic toxins by the gut microbiota, prescription of very low-protein diets has experienced a resurgence of interest. Marzocco and colleagues performed a post-hoc analysis of a very low vs. low-protein diet cross-over study [29]. Thirty-two patients with a creatinine clearance between 20 and 55 ml/min were included and randomized to receive either a very low-protein diet (VLPD; 0.3 g/kg/day) or a low-protein diet (LPD; 0.6 g/kg/day) in the first week, then switched to the other in the second week. There was no wash-out period. The authors found that patients treated with a VLPD experienced a significant 36% reduction in serum IS levels compared with those treated with a LPD (7.12 ± 3.89 μM during VLPD vs. 11.1 ± 6.6 μM during LPD, p < 0.0001). Although a meta-analysis has identified reduction in the occurrence of renal death with a low-protein intake in CKD patients, the overall value of these diets remains a subject of debate, given that the risks of malnutrition may present a greater danger [30–32]. Furthermore, poor compliance is also likely to be an issue, as participants often did not meet dietary targets even with the intensive support provided within a trial setting.
There is newer evidence to suggest that dietary fibre may in fact be more important than dietary protein intake in terms of managing uraemic toxin levels. A single-centre, cross-sectional study of 40 patients with CKD measured baseline total and free serum IS and PCS levels and correlated this with dietary factors including dietary fibre, protein and protein-fibre index [10]. In this study, dietary fibre was found to be inversely associated with free and total serum PCS (r = −0.42 and r = −0.44, both p < 0.01) whereas dietary protein was not (r= −0.14, p = 0.38). Protein-fibre index was significantly associated with both total PCS (r = 0.43, p = 0.005) and total IS (r = 0.40, p = 0.012) levels. Increased dietary fibre as an intervention has been shown to result in significantly reduced free plasma IS in haemodialysis patients [33]. Moreover, a prospective cohort study of 390 Swedish men between the age of 70 and 71 years found an association between protein-fibre intake ratio and cardiovascular events (adjusted HR 1.33, 95% CI 1.08–1.64). These findings suggest that dietary intervention focusing on protein-fibre ratio has the potential to influence clinical outcomes [34], mediated via uraemic toxin production.
Probiotics and prebiotics represent another strategy for reducing uraemic toxin synthesis. Preparations of lactic-acid bacteria, simulating a probiotic, have been shown to decrease serum IS concentrations by 30% and also reverse aerobic bacterial overgrowth [35]. Their use has been demonstrated to result in a significant decrease in urinary PCS and an increase in faecal bifidobacteria [36]. Synbiotics, which represent a combination of pre- and probiotics, have similarly been demonstrated to reduce serum IS and PCS levels in CKD and haemodialysis patients [37–39]. More recently, the use of synbiotics for reducing uraemic toxin levels has been evaluated in the SYNbiotics Easing Renal failure by improving Gut microbiologY (SYNERGY) trial [40, 41]. In this single-centre, double-blind, placebo-controlled, cross-over trial, 37 pre-dialysis patients with stage 4 or 5 CKD were randomized to receive either synbiotic supplements or placebo for 6 weeks, followed by a 4-week wash-out period, followed by treatment with the alternative therapy for a further 6 weeks. Thirty-one participants completed both treatments. Although the study failed to demonstrate a significant change in total serum IS levels (−2 mmol/L, 95% CI −5 to 1 mmol/L, p = 0.12), the change in serum PCS levels did reach a level of statistical significance, with a 13% reduction in the treatment group (−14 mmol/L, 95% CI −27 to −2 mmol/L, p = 0.03). Furthermore, after excluding the 10 participants who had received antibiotic therapy during the trial, which is known to affect the balance of bacterial species in the gut [8, 42], the changes in serum levels with synbiotic therapy for both total IS (−5 mmol/L, 95% CI −8 to −1 mmol/L, p = 0.03) and PCS (−25 mmol/L, 95% CI −38 to −12 mmol/L, p = 0.001) were significant. The changes in free IS and PCS levels were also significant amongst antibiotic-free completers. Synbiotic therapy additionally had an effect on the stool microbiome, with significantly increased abundance of
Lastly, the use of acarbose for lowering serum levels of gut-derived uraemic toxins has been investigated. Acarbose, an alpha-glucosidase inhibitor, causes increased delivery of undigested carbohydrate to the colon, which may drive gut bacterial fermentation towards a saccharolytic pathway and away from proteolytic fermentation and toxin production. In a pilot pre-test/post-test study involving nine healthy volunteers, Evanepoel et al. demonstrated that treatment with oral acarbose 300 mg per day for 3 weeks resulted in significant reductions in both serum p-cresol concentration (1.14–1.11 mg/L, p = 0.047) and urinary excretion of p-cresol (29.93–10.54 mg/day, p = 0.03), suggesting reduced colonic generation of p-cresol, the precursor of PCS [45]. Further studies confirming this finding are required.
IS and PCS absorption from the gut may also be prevented by the use of oral intestinal adsorbents, such as AST-120 (Kremezin), which bind uraemic toxins and their precursors thereby sequestering them in the gut and allowing them to be excreted via the faeces. Oral administration of AST-120 has been shown to result in a dose-dependent decrease in serum IS and PCS in both human [46–48] and animal studies [19, 49], and its use is associated with slower progression of renal dysfunction [44, 50] and reduction of proteinuria [43, 44] in animal models of CKD. It has also been demonstrated to slow progression of renal dysfunction in early non-randomized and randomized studies in pre-dialysis patients [51–53]. In a subsequent Cochrane systematic review and meta-analysis of eight randomized controlled trials (RCTs) of AST-120 plus routine care compared with routine care alone in patients with stages 1–5 (non-dialysis) CKD, Wu et al. [54] reported that AST-120 treatment resulted in a significant reduction in the rate of decline in creatinine clearance (2 studies, 486 participants; standardized mean difference [SMD] 0.39, 95% CI 0.21–0.57; I2 = 0%), but did not significantly affect reciprocal serum creatinine slope over time (2 studies, 76 participants; mean difference [MD] 0.07 dL/mg/month, 95% CI −0.12 to 0.26; I2 = 69%), doubling of serum creatinine concentration (1 study, 460 participants; relative risk [RR] 0.55, 95% CI 0.19 to 1.62), ESKD incidence (3 studies, 504 participants; RR 0.70, 95% CI 0.15–3.35; I2 = 11%) or all-cause mortality (1 study, 460 participants; RR 0.70, 95% CI 0.19–1.62). In three separate placebo-controlled RCTs, AST-120 treatment did not significantly affect changes in serum creatinine, slope of reciprocal serum creatinine over time or creatinine clearance [54].
In the following year, the Evaluating Prevention of Progression in CKD (EPPIC)-1 and EPPIC-2 trials [55] reported on the effects of AST-120 (9 g/day) or placebo on CKD progression in 2035 patients with non-dialysis-dependent CKD treated at 239 sites in 13 countries. No significant difference was observed in time to primary end point (composite of doubling of serum creatinine, dialysis initiation and kidney transplantation) between treatment arms (pooled analysis HR 0.97, 95% CI 0.83–1.12, p = 0.64). Furthermore, the treatment group did not experience any difference in proteinuria or quality of life compared with the placebo group. Similarly, a subsequent prospective, open-label, randomized controlled trial of 579 patients with stage 3 or 4 CKD from 11 Korean centres reported that oral administration of AST (6 g/day of AST-120 in 3 divided daily doses) did not significantly affect time to the primary composite outcome of doubling of serum creatinine, eGFR decrease >50%, or initiation of renal replacement therapy (HR 1.12, 95% CI 0.85–1.48) [56]. There was no significant difference in change in serum IS levels over time between the intervention and control group (p = 0.29). The treatment also did not result in a significant difference in mortality, health-related quality of life or serious adverse effects.
A Cochrane systematic review of alternative oral adsorbents, Ai Xi Te and Niaoduqing granules, reported positive effects on CKD progression, but were limited by small samples sizes and poor methodologic quality with unclear or high risks of bias [54].
Renal proximal tubular cells contain multiple transporters that perform basolateral uptake or luminal excretion of various substances, including uraemic toxins. Such transporters include the organic anion transporters (OAT)1, OAT3 and OATP4C1, as well as the organic cation transporter (OCT)2, the multidrug and toxin extrusion proteins (MATEs), the breast cancer resistance protein (BCRP) and the adenosine triphosphate (ATP)-binding cassette transporter family [57]. Anionic substances, such as IS, enter renal proximal tubule cells via basolateral OAT, particularly OAT1 and OAT3, and are excreted into the tubular lumen by luminal OATs [58, 59]. Using cultured kidney tubule cells (LLC-PK1) and rat kidney slices, Deguchi et al. demonstrated that p-aminohippurate (OAT1 inhibitor), pravastatin (OAT3 inhibitor) and benzylpenicillin (OAT3 inhibitor) inhibited the renal tubular uptake of indoxyl sulphate to comparable extents [60]. In a 5/6-nephrectomized rat model of CKD, Enomoto et al. demonstrated that administration of IS resulted in IS accumulation in proximal tubule cells expressing OAT1 and OAT3, and was associated with more rapid CKD progression, as measured by creatinine clearance [58]. Furthermore, addition of IS to cultured rat proximal tubule (S2) cells reduced their viability, although this nephrotoxicity was abrogated by administration of the OAT1 inhibitor, probenecid [58]. Thus, OAT inhibitors, such as probenecid and statins, might be a potential strategy for preventing proximal tubule cell accumulation of IS and ensuing nephrotoxicity and CKD progression. In addition, as OATs are expressed widely throughout the body, these transporters may play a role in uraemic toxin-induced pathology in various organs. For example, Liu and colleagues demonstrated that administration of 10 µM IS to cultured Sprague-Dawley cardiac myocytes and fibroblasts stimulated myocyte hypertrophy and collagen synthesis, which was abrogated by probenecid (OAT1 antagonist) and cilastatin (OAT3 antagonist) [61].
Therapeutic manipulation of efflux transporters, such as OAT polypeptide 4C1 (SCLO4C1), may also lead to enhanced excretion of uraemic retention solutes into the urine [62]. For example, Toyohara et al. demonstrated that overexpression of SLCO4C1 in rat kidney decreased plasma levels of uraemic toxins and reduced inflammation, hypertension and cardiomegaly [11]. Moreover, renal clearance of uraemic toxins was also increased by pravastatin, which is known to upregulate proximal tubular SLCO4C1 [11].
The activities of multidrug resistance protein (MRP) 4 and BRCP efflux transporters have also been demonstrated to be downregulated by PCS
IS and PCS are highly (>90%) protein bound and are therefore not easily removed with conventional haemodialysis and peritoneal dialysis [14, 59, 64]. Long dialysis, short daily dialysis and high-flux haemodialysis have been investigated as potential methods of improving clearance of protein-bound molecules, but have failed to show clear benefit [59, 65, 66].
In contrast to conventional haemodialysis, which mainly depends on diffusion to clear solutes, haemodiafiltration combines convection and diffusion, which is potentially very useful in facilitating removal of larger molecules, such as protein-bound solutes. Haemodiafiltration has been shown in prospective cross-over studies to be superior to high-flux haemodialysis in removing IS and PCS [67, 68]. In this respect, the effectiveness of pre- and post-dilution haemodiafiltration was comparable [67, 68]. The mechanism for the improved clearance of protein-bound solutes is not well understood but seemed to be dependent on a combination of both diffusion and convection since haemofiltration (which does not involve diffusion) reduced the serum levels of protein-bound solutes but not to the same extent as haemodiafiltration [67].
The use of super-flux cellulose triacetate membranes has also been evaluated and found to be superior to low-flux haemodialysis with respect to removing IS and most protein-bound compounds, although this might be at least partly explained by an increase in removal of albumin [69]. Similarly, dialysis with the use of a nanoporous carbon monolith (pores 2–100 nm) was able to almost completely remove IS and PCS, whereas the use of a microporous monolith (<2 mm) resulted in only partial removal, and standard high-flux haemodialysis resulted in insignificant removal [70]. A potential issue with enhanced dialysis of toxins is the rebound release of further toxins from tissues, which is observed with water-based solutes. However, Martinez and colleagues demonstrated that the rebound movement of PCS and protein solutes in the first 30 minutes post-dialysis appeared to be negligible [71].
Eloot and colleagues utilised kinetic modelling to try to determine optimal dialysis parameters to facilitate protein-bound solute removal, and found that regardless of longer or more frequent dialysis, increased volume of blood processing per week was required to increase clearance [72].
In a cross-over study of 14 patients, high-clearance dialysis (high dialysate flow rate and large dialyzer) resulted in significantly greater PCS and IS clearance compared with low-clearance dialysis (PCS 23 ± 4 ml/min vs. 12 ± 3 ml/min, p < 0.001; IS 30 ± 5 ml/min vs. 17 ± 4 ml/min, p < 0.001). However, there was no significant change in serum PCS levels with high-clearance dialysis although there was a significant decrease in IS levels [73]. The authors suggested that this lack of reduction in serum PCS levels may be due to concurrent PCS generation, and thus treatment to suppress PCS production would be required in order to achieve significant reductions in serum PCS.
In summary, IS and PCS are products of bacterial metabolism within the gut. Serum IS and PCS levels are increased in patients with CKD and have been associated with CKD progression, vascular disease acceleration, adverse metabolic profile and poorer cardiovascular and overall mortality. There are several methods of lowering serum IS and PCS levels, including reduced intestinal bacterial production through dietary modification of protein and/or fibre intake or pre-, pro- and synbiotic use, gastrointestinal sequestration through oral adsorbent use, reduced cellular uptake of IS through OAT inhibition, and increased clearance through enhanced dialysis. Though these treatments have been shown in some studies to successfully reduce IS and PCS levels in sera and/or cells, it is less clear whether this translates into meaningful and sustained improvements in clinical outcomes. The studies conducted to date have been limited by small patient numbers, relatively short follow-up duration and poor methodologic quality. Given the biological plausibility and clinical importance of the adverse health outcomes thought to be mediated by these toxins, further high-quality studies are needed to evaluate the short- and long-term effects of IS and PCS lowering treatments on patient-level clinical outcomes.
Easley et al. [1] designed a tool, nicknamed volume-synchronized probability of informed trading (VPIN), with the aim to predict flash crashes. It appeared it could predict the “flash crash” of May 6, 2010, a few hours before it happened [2]. A lot of papers were published [3, 4, 5], and it was proposed to use it for regulation through a VPIN contract [2, 6]. However, critics pointed out some flaws, questioning its reliability [7, 8, 9, 10, 11] but without providing a quantitative evaluation of the prediction quality (e.g., in terms of precision and recall rates). In this study, we design a framework to detect flash crashes and thereby assess the behavior of the VPIN tool enabling as well as comparing and benchmarking with other predictive algorithms.
\nThe amount of trading data has exploded in finance thanks to the continuing progress of high-frequency techniques. It constrains practitioners to use more and more state-of-the-art algorithms to deal with this overwhelming amount of information. Computers and algorithms are more and more efficient, but still decision-making is highly dependent on both the quantity and the quality of information. Thus, errors and speculations that can make the financial market toxic, i.e., conducive to crashes, are possible. Examples in the past, such as the “flash crash” of May 6, 2010, have shown that this new paradigm in finance has made it possible to introduce a new kind of crashes characterized by their suddenness. Such quick crashes seem dangerous because of a kind of inherent unpredictability. However, predictive models to model this new framework do exist.
\nEasley et al. [12] designed a model of the high-frequency financial market based on flows of informed and uninformed traders. They showed that information is a key parameter of the spread between ask and bid of prices. The model works as follows. Each day, general conditions and circumstances may or may not result in events that can help predict the evolution of the price of a future. More precisely, for each day, nature decides whether or not there is an event that can help predict the evolution of the price of a future. This is modeled with a Bernoulli law of parameter
A tree summarizing the trading process.
The whole trading process studied is thus a mixture of Poisson processes. It enabled authors to compute ask and bid and then the spread. They showed that for reasonable cases the spread is linearly linked with the following probability they named probability of informed trading (PIN) [12]:
\nLater, Easley et al. [2] designed a new framework to easily compute this probability. Indeed PIN numbers come from a parametrized framework, and one does not have access to all these parameters. They showed however that PIN can be well approximated through a volume-clock paradigm [14], thanks to data of futures with a new formula. The approximated version of PIN was then called the volume-synchronized probability of informed trading (VPIN). It appeared that this new tool could predict the “flash crash” of May 6, 2010, a few hours before it happened [2].
\nNevertheless, the model has received a lot of critics. For example, Andersen and Bondarenko have shown [7] that VPIN is quite sensitive to the starting point of when one starts computing VPIN on a data set. It indeed questions VPIN prediction quality. Moreover, they have also shown that VPIN is sensitive to other parameters, such as the trade classification rule used [8] or how one defines the average daily volume of trades [9]. Changing the classification rule may drastically change VPIN behavior [9]. Pöppe et al. have reached the same conclusions with a different approach. Using a different classification rule can change VPIN prediction power toward a crash (in their paper a German blue-chip stock [11]). Besides, controlling ex ante parameters seems to give poorer prediction quality [8, 9]. This point has also been checked by Abad et al. [10]. Controlling ex ante realized volatility, and trading intensity, as did Andersen and Bondarenko [9], prediction quality seems to vanish. More deeply, they have also underlined that it is not obvious how one should define a VPIN prediction, analyzing more precisely toxic and nontoxic halts, as well as toxic events. Furthermore, Torben G. Andersen and Oleg Bondarenko interpret VPIN as being too sensitive to trading intensity. They have also explained that VPIN metric is sometimes unexpectedly correlated with other usual ones (such as VIX or RV) [7, 8]. More recently, it has been shown theoretically that the volume-clock paradigm of VPIN framework does not enable to really approximate fully the PIN value, although the proposed formula is close [15, 16].
\nMore generally, all these critics have pointed out that:
First, it is not obvious how one should use VPIN.
Second, prediction quality has not been studied sufficiently to assess it as being reliable.
Third, the study lacks objective benchmark.
The purpose of this chapter is to quantify the prediction quality of VPIN in order to enable practitioners to assess whether or not it can be used in the real world (e.g., for trading or regulation). That’s why:
First, we want to design a proper framework to compute precision and recall rates as well as prediction length of VPIN. This will be possible by providing a formal definition of flash crashes. To be more precise, we will use the maximum of intermediate return (MIR) [5] to define it.
Second, we want to study through this framework how sensitive VPIN is to the starting point of the data set.
In the following, we first recall VPIN model and propose a definition for flash crashes (Section 2). Second, we assess within this framework VPIN prediction quality (Section 3). Finally, we assess VPIN sensitivity to the starting point of the data set (Section 4).
\nIn this section, we first recall the VPIN model. Second, we propose a definition of flash crashes used to compute precision and recall rates. Finally, we present the data used in our tests.
\nEasley et al. [12] designed a model of the high-frequency financial market based on informed and uninformed traders. It is then possible to compute a probability of informed trading (PIN). Easley et al. [1] use these results and define an easy way to compute PIN only through the data of trades. We describe briefly VPIN model used in previous literature. The theoretic study of the model is treated in another research study.
\nFollowing Easley et al. [1], a bar is a fixed volume of trades that are successive in time. With such a definition, one can associate the following quantities with each bar:
A nominal price, computed according to a given technique (mean price, median price, closing price, opening price, etc.)
A nominal time (first trade time, last trade time)
Local maximum and minimum values of trades
In practice, the last few trades that do not fill up a bar are dropped to the next bar.
\nThe computation of VPIN requires to determine directions of trades, i.e., classifying each trade as a buy or a sell. The method used here is the following: bulk volume classification (BVC) [1, 5]. Let us note \n
where \n
A bucket is defined to be a fixed number of successive trades. Here to simplify, as bars are defined also as a fixed number of trades, a bucket will be m successive bars. Let us note \n
VPIN formula is computed on n successive buckets, where n is VPIN support. A buffer is defined as n successive buckets. Here is VPIN formula, approximating (1) upon bucket number j (\n
For a given bucket i:
\n\n
\n\n
In order to distribute all VPIN values between 0 and 1, in practice, VPIN is normalized through a normal law. We thus consider \n
A VPIN event is declared when the following occurs:
\nwhere \n
Let \n
A flash crash will depend on two things here:
The amplitude of the crash, which means extreme MIR values (e.g., 10%)
The shortness of the fall, which means the shortness of the time window within \n
We reported in this data set only one flash crash, i.e., on May 6, 2010, which lasted approximately 10 minutes according to media and financial institutions. Our definition of flash crash will obviously take into account this event.
\nIn this work, we use a comprehensive set of liquid futures trading data to illustrate the techniques to be introduced. More specifically, we will use 67 months’ worth of tick data of the five most liquid futures traded on all asset classes. The data comes to us in the form of 5 CSV files, one for each futures contract traded. The source of our data is TickWrite, a data vendor that normalizes the data into a common structure after acquiring it directly from the relevant exchanges. The total size of the comma-separated value (CSV) files is about 45.1 GB. They contain about millions of trades spanning from the beginning of January 2007 to the end of July 2012. The data set contains five of the most heavily traded futures contracts. Each has more than 100 million trades during this 67-month period. The most heavily traded futures, the file containing E-mini SP500 futures, symbol ES, has about 500 million trades involving a total number of about 3 billion contracts. The second most heavily traded futures is Euro exchange rates, symbol EC, which is 188 million trades. The next three are Nasdaq 100 (NQ), 173 million trades; light crude oil (CL), 165 million trades; and E-mini Dow Jones (YM), 110 million trades. In Figure 2, one can see an evolution of prices with time (here each tick corresponds to a bucket).
\nBucket S&P 500 values with time.
We want to define empirically a flash crash using the tools of VPIN framework, namely, bars and buckets. As volume-clock paradigm does not allow to control filling times of fixed volume of trades, here below is a summary of the steps we have followed to manage to detect flash crashes using MIR. As it is quite long and the main purpose of study is the prediction of results of the following section, we present principles and do not go into technical details:
To be sure not to miss a flash crash because of being too long in time bar or bucket, we have chosen a reasonable granularity level as in [5] (buckets per day, 200, and bars per bucket, 30).
For each financial instrument, we have recorded the number of bars necessary to capture the local 10 minutes of maximum fall of May 6, 2010, known as the “flash crash”; we refer to these numbers as “window lengths” below.
As the window lengths defined above do not have a stable distribution in time (because of the volume-clock paradigm), we have arbitrarily filtered out all events in which the time difference between minimum and maximum within a window length is longer than 20 minutes, in order to capture only quick events. Indeed, one given window length may be too big and thus allow at some date to measure a time difference between local minimum and maximum which is longer than 10 minutes whereas it would be a true flash crash with a smaller window length.1
For each instrument we recorded the amplitude of the “flash crash” and their respective MIR values.
The results made it possible to classify the five financial instruments into two groups:
Data sets where the “flash crash” and other flash crashes are significantly present: ES, NQ, and YM.
Data sets where the “flash crash” and other flash crashes are not really present. More precisely, the “flash crash” is not a rare event in the data set, and generally magnitude levels of flash crashes are low compared to other instruments.
In this section, first we present our methodology to find VPIN optimal prediction quality (for which recall and precision rates are maximal and more useful for practice). Second, we present all the results: best parameters, associated remarks, and prediction lengths.
\nHere are the parameters we will test:
Bar price: mean, median, last price, first price
MIR decision threshold
VPIN support n
VPIN classifier (student, normal)
Prediction window
VPIN decision threshold
Here we describe how we define true positive, false-negative, and false-positive events. For a given prediction window length
From a MIR flash crash detection (i.e.,
From a VPIN event at a bucket j (i.e.,
To make a useful deep search, we have computed the distribution of time difference between different amounts
Time difference distribution between 2500 S&P 500 buckets.
In Table 1 one can see the medians of the different distributions.
\nFutures | \nDays | \nNumber of bucket chosen | \n
---|---|---|
ES | \n14.8 | \n2500 | \n
EC | \n13.8 | \n2500 | \n
CL | \n15.0 | \n2500 | \n
YM | \n14.3 | \n2500 | \n
NQ | \n15.2 | \n2500 | \n
Median of time difference between 2500 buckets for the different instruments.
For the next step,
Here we describe how we intend to make a first deep search of VPIN prediction quality of events close to the “Flash Crash” of May 2010. In this algorithm described below
For each VPIN classifier (student or Gaussian), for each bar price structure (last, first, median, average) do:
For each \n
For each VPIN support \n
For \n
test prediction
store current parameters, precision and recall if and only if \n
store prediction length (distance between VPIN event and MIR event).
Remark: we first try to maximize precision+recall rate. If the local maximum found is interesting for practice (at least superior or equal to 1.2) and more powerful than a “naive” algorithm, then it sounds worth making a more serious search of precision and recall rates separately to find a good trade-off between them (e.g., thanks to a ROC curve).
\nIn Tables 2–5 one case see the best parameters that maximize precision+recall for each financial instrument and bar price structure studied.
\nFutures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \nBar price | \n||
---|---|---|---|---|---|---|---|---|
ES | \n0.9737 | \n0.2171 | \n1.1908 | \n0.062 | \n60 | \n2400 | \nGaussian | \nLast | \n
EC | \n0.9080 | \n0.9644 | \n1.8724 | \n0.006 | \n30 | \n2500 | \nGaussian | \nLast | \n
CL | \n0.9406 | \n0.9045 | \n1.8451 | \n0.022 | \n60 | \n2500 | \nStudent | \nLast | \n
NQ | \n1 | \n0.0034 | \n1.0034 | \n0.08 | \n30 | \n400 | \nGaussian | \nLast | \n
YM | \n0.8421 | \n0.1512 | \n0.9933 | \n0.064 | \n60 | \n2500 | \nGaussian | \nLast | \n
Best parameters maximizing precision+recall rate for different futures and last bar price structure in the first deep search.
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \nBar price | \n||
---|---|---|---|---|---|---|---|---|
ES | \n0.9737 | \n0.2024 | \n1.1761 | \n0.062 | \n60 | \n2400 | \nGaussian | \nFirst | \n
EC | \n0.9127 | \n0.9681 | \n1.8808 | \n0.006 | \n30 | \n2500 | \nStudent | \nFirst | \n
CL | \n0.9534 | \n0.9012 | \n1.8546 | \n0.022 | \n60 | \n2500 | \nStudent | \nFirst | \n
NQ | \n1 | \n0.0038 | \n1.0038 | \n0.08 | \n30 | \n400 | \nGaussian | \nFirst | \n
YM | \n0.8421 | \n0.1449 | \n0.9870 | \n0.064 | \n60 | \n2500 | \nGaussian | \nFirst | \n
Best parameters maximizing precision+recall rate for different futures and first bar price structure in the first deep search.
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \nBar price | \n||
---|---|---|---|---|---|---|---|---|
ES | \n0.9737 | \n0.1996 | \n1.1733 | \n0.062 | \n60 | \n2400 | \nGaussian | \nMedian | \n
EC | \n0.9037 | \n0.9718 | \n1.8755 | \n0.006 | \n30 | \n2500 | \nStudent | \nMedian | \n
CL | \n0.9447 | \n0.8951 | \n1.8398 | \n0.022 | \n60 | \n2500 | \nStudent | \nMedian | \n
NQ | \n1 | \n0.0036 | \n1.0036 | \n0.08 | \n30 | \n400 | \nGaussian | \nMedian | \n
YM | \n1 | \n0.1911 | \n1.1911 | \n0.054 | \n30 | \n2500 | \nStudent | \nMedian | \n
Best parameters maximizing precision+recall rate for different futures and median bar price structure in the first deep search.
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \nBar price | \n||
---|---|---|---|---|---|---|---|---|
ES | \n0.9737 | \n0.1950 | \n1.1687 | \n0.062 | \n60 | \n2400 | \nStudent | \nMean | \n
EC | \n0.9058 | \n0.9691 | \n1.8749 | \n0.006 | \n30 | \n2500 | \nStudent | \nMean | \n
CL | \n0.9789 | \n0.8654 | \n1.8443 | \n0.022 | \n40 | \n2500 | \nStudent | \nMean | \n
NQ | \n1 | \n0.0036 | \n1.0036 | \n0.08 | \n30 | \n400 | \nGaussian | \nMean | \n
YM | \n1 | \n0.1921 | \n1.1921 | \n0.055 | \n30 | \n2500 | \nStudent | \nMean | \n
Best parameters maximizing precision+recall rate for different futures and mean bar price structure in the first deep search.
We remark overall the following:
The choice of bar structure does not really affect the optimal choice of other parameters; nevertheless mean and median bar price structures have best precision+recall rate on average.
Recall rates are very close to 1.
Since ES, NQ, and YM precision rates are “low”, thus precision + recall rates are “low.”
Since EC and CL precision rates are “high,” thus precision + recall rates are “high” since recall is already “high.”
CL and EC had on May 6, 2010, a very low flash crash threshold, which increases a lot the number of crash of same magnitude detected in the data set.
CL and EC obtain their maximum value to the minimum bound of the deep search (respectively, a 2.2% fall and 0.6% fall). It is not the case for other instruments (in NQ cases, precision+recall optimal rate is constant from 0.8 to 0.9).
The results give two first findings:
When the flash crash is significantly present for the instrument, i.e., of high magnitude and rare in the data set (ES, YM, and NQ cases), then recall is high, which means that VPIN makes a prediction before this happens, but precision is low: VPIN detects other events that are not flash crashes.
When the flash crash is not significantly present for the instrument, i.e., of low magnitude and not rare (there are a lot of events of 10–20-minute length of same magnitude), then recall and precision are high.
This may suggest one of the following hypotheses:
VPIN seems to be a poor indicator of flash crash prediction with the usual recommended threshold 0.99.
VPIN can be a better indicator of another type of event (crashes of less important amplitude).
We will compare the results of the same deep search with the one of a naive classifier, to see whether or not the good prediction results in CL and ES cases are relevant.
\nWe made a comparison of VPIN prediction quality result with a “naive classifier,” which randomly chooses whether or not there will be a crash from each bucket of the data set. In Table 6 one can see the results of the naive classifier for the first deep search set of parameters.6 As it is a naive classifier, results do not depend on direction of prices (bar price classifier) and bar price structure.
\nFutures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \n||
---|---|---|---|---|---|---|
ES | \n1 | \n0.0355 | \n1.0355 | \n0.052 | \n50 | \n2500 | \n
EC | \n1 | \n0.9948 | \n1.9948 | \n0.004 | \n50 | \n2500 | \n
CL | \n1 | \n0.3413 | \n1.3413 | \n0.022 | \n50 | \n2500 | \n
NQ | \n1 | \n0.0076 | \n1.0076 | \n0.084 | \n60 | \n2500 | \n
YM | \n1 | \n0.0174 | \n1.0174 | \n0.055 | \n50 | \n2500 | \n
Best parameters maximizing precision+recall rate for different futures for the naive classifier.
We remark the following:
“Naive classifier” has poor results comparable to those of VPIN for ES, NQ, and YM instruments; although poor, VPIN predictions are better than “naive algorithm” on ES cases.
“Naive classifier” has better results than VPIN on EC instrument.
“Naive classifier” has worse results than VPIN on CL instrument.
We can interpret it as follows:
EC flash crash definition is barely inconsistent, with a MIR threshold of 0.006%; it is obvious that a naive algorithm does better results as the constraint is very small to detect a “flash crash” of such a magnitude.
On CL and ES cases though, VPIN predictions are better, and these results are obtained when
Anyway, previous results may conclude that for “flash crash” prediction, VPIN has overall equivalent poor power prediction with the traditional threshold
That’s why in the next paragraph, we benchmark predictive power of “naive” and VPIN algorithms:
First on higher
Second on lower bounds of crash amplitude
Third on higher
Indeed, the first hypothesis is that there are too many false VPIN predictions, i.e., false-positive events, as precision rate is too low and recall rate is too high. That’s why one may hope that making
In the following we have looked to higher bounds for
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \n|||
---|---|---|---|---|---|---|---|---|
ES | \n0.9737 | \n0.4677 | \n1.4414 | \n0.062 | \n60 | \n1600 | \nGaussian | \n0.99999 | \n
EC | \n0.9080 | \n0.9644 | \n1.8724 | \n0.006 | \n30 | \n2500 | \nGaussian | \n0.99 | \n
CL | \n0.9406 | \n0.9045 | \n1.8451 | \n0.022 | \n60 | \n2500 | \nStudent | \n0.99 | \n
NQ | \n1 | \n0.0034 | \n1.0034 | \n0.08 | \n30 | \n400 | \nGaussian | \n0.99 | \n
YM | \n0.7091 | \n0.3160 | \n1.0251 | \n0.054 | \n60 | \n2500 | \nStudent | \n0.9999 | \n
Best parameters maximizing precision+recall rate for different futures and last bar price structure allowing higher bounds for θVPIN.
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \n|||
---|---|---|---|---|---|---|---|---|
ES | \n0.9737 | \n0.3412 | \n1.3149 | \n0.062 | \n60 | \n1200 | \nGaussian | \n0.99999 | \n
EC | \n0.9127 | \n0.9681 | \n1.8808 | \n0.006 | \n30 | \n2500 | \nStudent | \n0.99 | \n
CL | \n0.9534 | \n0.9012 | \n1.8546 | \n0.022 | \n60 | \n2500 | \nStudent | \n0.99 | \n
NQ | \n1 | \n0.0038 | \n1.0038 | \n0.08 | \n30 | \n2500 | \nGaussian | \n0.99 | \n
YM | \n0.7091 | \n0.3545 | \n1.0636 | \n0.054 | \n60 | \n2500 | \nStudent | \n0.9999 | \n
Best parameters maximizing precision+recall rate for different futures and first bar price structure allowing higher bounds for θVPIN.
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \n|||
---|---|---|---|---|---|---|---|---|
ES | \n0.9737 | \n0.3306 | \n1.3043 | \n0.062 | \n30 | \n1700 | \nGaussian | \n0.99999 | \n
EC | \n0.9037 | \n0.9718 | \n1.8755 | \n0.006 | \n30 | \n2500 | \nStudent | \n0.99 | \n
CL | \n0.9447 | \n0.8951 | \n1.8398 | \n0.022 | \n60 | \n2500 | \nStudent | \n0.99 | \n
NQ | \n1 | \n0.0036 | \n1.0036 | \n0.08 | \n30 | \n400 | \nGaussian | \n0.99 | \n
YM | \n1 | \n0.1911 | \n1.1911 | \n0.054 | \n30 | \n2500 | \nStudent | \n0.99 | \n
Best parameters maximizing precision+recall rate for different futures and median bar price structure allowing higher bounds for θVPIN.
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \n|||
---|---|---|---|---|---|---|---|---|
ES | \n0.9737 | \n0.3786 | \n1.3523 | \n0.062 | \n60 | \n1600 | \nGaussian | \n0.99999 | \n
EC | \n0.9058 | \n0.9691 | \n1.8749 | \n0.006 | \n30 | \n2500 | \nStudent | \n0.99 | \n
CL | \n0.9789 | \n0.8653 | \n1.8442 | \n0.022 | \n40 | \n2500 | \nStudent | \n0.99 | \n
NQ | \n1 | \n0.0036 | \n1.0036 | \n0.08 | \n30 | \n400 | \nGaussian | \n0.99 | \n
YM | \n1 | \n0.1921 | \n1.1921 | \n0.055 | \n30 | \n2500 | \nStudent | \n0.99 | \n
Best parameters maximizing precision+recall rate for different futures and mean bar price structure allowing higher bounds for θVPIN.
We remark the following:
Precision rate has increased for each bar price structure for ES instrument, maintaining recall rate constant to
Precision + recall rate has increased for YM instrument only with a last or first bar price structure, but recall decreased a bit compared to
Compared to the “naive” algorithm, VPIN results are effectively better in ES case. In YM case we still find comparable results.
On average, mean and median bar price structures have the best precision+recall rate.
To verify whether or not we can get at least better results than a naive algorithm in data sets with a real flash crash, we study in the following first the results allowing lower bounds on
We remark the following in Tables 11–14:
Results have changed for every instrument except the ES one which has kept the same local maximum as in the first deep search.
Precision is far higher than before, while recall is still high. Therefore, overall precision + recall rates are “high.”
Optimal
On average, median bar price structure has the best precision+recall rate.
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \nBar price | \n||
---|---|---|---|---|---|---|---|---|
ES | \n0.9421 | \n0.9541 | \n1.8962 | \n0.015 | \n30 | \n2500 | \nStudent | \nLast | \n
EC | \n0.9080 | \n0.9644 | \n1.8724 | \n0.006 | \n30 | \n2500 | \nGaussian | \nLast | \n
CL | \n0.9297 | \n0.9806 | \n1.9103 | \n0.016 | \n30 | \n2500 | \nStudent | \nLast | \n
NQ | \n0.9179 | \n0.9019 | \n1.8198 | \n0.015 | \n30 | \n2500 | \nGaussian | \nLast | \n
YM | \n0.9460 | \n0.9696 | \n1.9156 | \n0.015 | \n50 | \n2500 | \nGaussian | \nLast | \n
Best parameters maximizing precision+recall rate for different futures and last bar price structure allowing higher bounds for θMIR.
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \nBar price | \n||
---|---|---|---|---|---|---|---|---|
ES | \n0.9404 | \n0.9402 | \n1.8806 | \n0.015 | \n30 | \n2500 | \nGaussian | \nFirst | \n
EC | \n0.9127 | \n0.9681 | \n1.8808 | \n0.006 | \n30 | \n2500 | \nGaussian | \nFirst | \n
CL | \n0.9233 | \n0.9728 | \n1.8961 | \n0.016 | \n30 | \n2500 | \nStudent | \nFirst | \n
NQ | \n0.8291 | \n0.9833 | \n1.8124 | \n0.01 | \n30 | \n2500 | \nStudent | \nFirst | \n
YM | \n0.9517 | \n0.9673 | \n1.9190 | \n0.015 | \n50 | \n2500 | \nStudent | \nFirst | \n
Best parameters maximizing precision+recall rate for different futures and first bar price structure allowing higher bounds for θMIR.
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \nBar price | \n||
---|---|---|---|---|---|---|---|---|
ES | \n0.9499 | \n0.9498 | \n1.8997 | \n0.015 | \n30 | \n2500 | \nStudent | \nMedian | \n
EC | \n0.9037 | \n0.9717 | \n1.8754 | \n0.006 | \n30 | \n2500 | \nStudent | \nMedian | \n
CL | \n0.9265 | \n0.9718 | \n1.8983 | \n0.016 | \n30 | \n2500 | \nStudent | \nMedian | \n
NQ | \n0.9243 | \n0.9017 | \n1.8260 | \n0.015 | \n30 | \n2500 | \nGaussian | \nMedian | \n
YM | \n0.9829 | \n0.9427 | \n1.9256 | \n0.015 | \n30 | \n2500 | \nGaussian | \nMedian | \n
Best parameters maximizing precision+recall rate for different futures and median bar price structure allowing higher bounds for θMIR.
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \nBar price | \n||
---|---|---|---|---|---|---|---|---|
ES | \n0.9526 | \n0.9454 | \n1.8979 | \n0.015 | \n30 | \n2500 | \nStudent | \nMean | \n
EC | \n0.9058 | \n0.9691 | \n1.8749 | \n0.006 | \n30 | \n2500 | \nStudent | \nMean | \n
CL | \n0.9302 | \n0.9670 | \n1.8972 | \n0.016 | \n30 | \n2500 | \nGaussian | \nMean | \n
NQ | \n0.9407 | \n0.8796 | \n1.8203 | \n0.02 | \n60 | \n2500 | \nGaussian | \nMean | \n
YM | \n0.9446 | \n0.9779 | \n1.9225 | \n0.015 | \n60 | \n2500 | \nStudent | \nMean | \n
Best parameters maximizing precision+recall rate for different futures and mean bar price structure allowing higher bounds for θMIR.
In the following, we will first compare the results to the case where we allow higher bound on
We remark in Tables 15–18 that compared to previous deep search:
There are changes only for NQ and YM instruments in, respectively, last, median, and mean bar price structures and first bar price structure, where
There is no general trend for precision or recall rates with the increase of
On average median bar price structure has the best precision+recall rate.
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \n|||
---|---|---|---|---|---|---|---|---|
ES | \n0.9421 | \n0.9541 | \n1.8962 | \n0.015 | \n30 | \n2500 | \nStudent | \n0.99 | \n
EC | \n0.9080 | \n0.9644 | \n1.8724 | \n0.006 | \n30 | \n2500 | \nGaussian | \n0.99 | \n
CL | \n0.9297 | \n0.9806 | \n1.9103 | \n0.016 | \n30 | \n2500 | \nStudent | \n0.99 | \n
NQ | \n0.9076 | \n0.9217 | \n1.8293 | \n0.02 | \n50 | \n2500 | \nStudent | \n0.999 | \n
YM | \n0.9460 | \n0.9696 | \n1.9156 | \n0.015 | \n50 | \n2500 | \nGaussian | \n0.99 | \n
Best parameters maximizing precision+recall rate for different futures and last bar price structure allowing lower bounds for θMIR and higher bounds for θVPIN.
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \n|||
---|---|---|---|---|---|---|---|---|
ES | \n0.9404 | \n0.9402 | \n1.8806 | \n0.015 | \n30 | \n2500 | \nGaussian | \n0.99 | \n
EC | \n0.9127 | \n0.9681 | \n1.8808 | \n0.006 | \n30 | \n2500 | \nStudent | \n0.99 | \n
CL | \n0.9232 | \n0.9728 | \n1.8960 | \n0.016 | \n30 | \n2500 | \nStudent | \n0.99 | \n
NQ | \n0.8291 | \n0.9833 | \n1.8124 | \n0.01 | \n30 | \n2500 | \nStudent | \n0.99 | \n
YM | \n0.9341 | \n0.9872 | \n1.9213 | \n0.015 | \n50 | \n2500 | \nGaussian | \n0.999 | \n
Best parameters maximizing precision+recall rate for different futures and first bar price structure allowing lower bounds for θMIR and higher bounds for θVPIN.
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \n|||
---|---|---|---|---|---|---|---|---|
ES | \n0.9499 | \n0.9498 | \n1.8997 | \n0.015 | \n30 | \n2500 | \nStudent | \n0.99 | \n
EC | \n0.9037 | \n0.9717 | \n1.8754 | \n0.006 | \n30 | \n2500 | \nStudent | \n0.99 | \n
CL | \n0.9265 | \n0.9718 | \n1.8983 | \n0.016 | \n30 | \n2500 | \nStudent | \n0.99 | \n
NQ | \n0.8881 | \n0.9525 | \n1.8406 | \n0.02 | \n30 | \n2500 | \nStudent | \n0.999 | \n
YM | \n0.9829 | \n0.9427 | \n1.9256 | \n0.015 | \n30 | \n2500 | \nGaussian | \n0.99 | \n
Best parameters maximizing precision+recall rate for different futures and median bar price structure allowing lower bounds for θMIR and higher bounds for θVPIN.
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \nClassifier | \n|||
---|---|---|---|---|---|---|---|---|
ES | \n0.9526 | \n0.9454 | \n1.8980 | \n0.015 | \n30 | \n2500 | \nStudent | \n0.99 | \n
EC | \n0.9058 | \n0.9691 | \n1.8749 | \n0.006 | \n30 | \n2500 | \nStudent | \n0.99 | \n
CL | \n0.9302 | \n0.9670 | \n1.8972 | \n0.016 | \n30 | \n2500 | \nGaussian | \n0.99 | \n
NQ | \n0.9188 | \n0.9150 | \n1.8338 | \n0.02 | \n40 | \n2500 | \nGaussian | \n0.999 | \n
YM | \n0.9446 | \n0.9779 | \n1.9225 | \n0.015 | \n60 | \n2500 | \nGaussian | \n0.99 | \n
Best parameters maximizing precision+recall rate for different futures and mean bar price structure allowing lower bounds for θMIR and higher bounds for θVPIN.
We remark the following for the “naive” classifier (Table 19):
It has worse results than VPIN on ES and YM cases.
It has comparable results than VPIN on NQ case.
It has better results than VPIN on EC and CL cases, where the flash crash is not really effective.
It reaches obviously best local results on lowest MIR bound of the deep search.
We may partially conclude that:
VPIN has an interesting predictive behavior on flash events of magnitude far lower (around 1.5%) than what would be considered as a crash for specific financial instrument (relatively liquid such as NQ, YM, or ES).
But VPIN has poor results comparable to those of a “naive” classifier (precision+recall rate inferior to 1.2) on flash crash events for these financial instruments.
For other instruments such as CL or EC, VPIN behaves worse than a naive classifier for these flash events. On flash events of higher amplitude (at least 1.5%), VPIN behaves better than a “naive” classifier for CL instrument.
Futures | \nRecall | \nPrecision | \nPrecision+recall | \nn | \n||
---|---|---|---|---|---|---|
ES | \n1 | \n0.7483 | \n1.7483 | \n0.01 | \n40 | \n2500 | \n
EC | \n1 | \n0.9999 | \n1.9999 | \n0.001 | \n60 | \n2500 | \n
CL | \n1 | \n0.9995 | \n1.9995 | \n0.01 | \n40 | \n2500 | \n
NQ | \n1 | \n0.8465 | \n1.8465 | \n0.01 | \n30 | \n2500 | \n
YM | \n1 | \n0.6892 | \n1.6892 | \n0.01 | \n40 | \n2500 | \n
Best parameters maximising precision+recall rate for different futures for the naive classifier allowing lower bounds for θMIR.
In this section, first we present the problem of VPIN’s sensitivity to the starting point of the bucketing process. Second, we present different calibrations to test its sensitivity. Third we make a summary of our results.
\nVPIN received among critics one which is important to precisely assess. Indeed, Bodarenko and Anderson [7] pointed out in their work that VPIN is sensitive to the starting point of the bucketing process. More precisely, if one removes the first buckets of the data set, results change. It is indeed right. We would like to know to which extent one can or cannot mitigate this effect. One idea is to test the different price bar structures. Indeed a bar structure influences trade imbalance and thus influences the appearance of VPIN events.
\nThere are at least two interesting ways of analyzing the sensitivity to the starting point of a data set:
Study the sensitivity of best precision+recall rate to the number of trades erased and to the bar price option.
Given one set of local optimal parameters, study the sensitivity of precision and recall rates to bar price option and data removed.
We have removed \n
We summarize in Table 20 for each bar price structure the average percentage change of local new best precision+recall rates with the number of bar erased.
\nBar price structure | \n1000 bars erased | \n2000 bars erased | \n3000 bars erased | \n
---|---|---|---|
Last | \n3.089 | \n2.166 | \n0.939 | \n
First | \n2.410 | \n3.649 | \n6.727 | \n
Median | \n0.611 | \n0.801 | \n0.781 | \n
Mean | \n1.348 | \n2.149 | \n3.944 | \n
Average absolute percentage change of local best precision+recall rates with the number of bar erased for each bar price structure.
We remark the following:
The sensitivity mentioned by Bodarenko and Anderson does exist.
Its amplitude is not very big, at least for best precision+recall rate, as the maximum change is about 6%.
Median bar price structure is far less sensitive than other price structure.
In Table 21 we summarize for each bar price structure the average percentabe change of the initial local best precision+recall rates with the number of bar erased.
\nBar price structure | \n1000 bars erased | \n2000 bars erased | \n3000 bars erased | \n
---|---|---|---|
Last | \n1.192 | \n1.171 | \n1.067 | \n
First | \n1.612 | \n1.725 | \n1.049 | \n
Median | \n3.514 | \n3.137 | \n1.396 | \n
Mean | \n2.648 | \n3.180 | \n2.489 | \n
Average absolute percentage change of local best precision+recall rates with the number of bar erased for each bar price structure.
We remark the following:
Again the amplitude of the sensitivity is not very large as the maximum change is about 3.5%.
Last bar price structure is less sensitive than other price structure to this phenomenon.
In this last section, we present first a general summary of our findings. Then we propose new suggestion of research concerning this precise subject.
\nWe found that:
VPIN has interesting predictive power (i.e., better than a naive algorithm and at least of local prediction+recall maximum higher than 1.2) for flash events of lower amplitude than flash crashes (about 1.5%) for a certain class of instruments, where flash crashes are at least present (which is not the case for currency Euro FX or Energy Light Crude NYMEX).
VPIN is sensitive to the starting point of computation, but the amplitude of this sensitivity is not really high. For practice, which means not changing local best parameters while erasing some data, last bar price structure is the least sensitive to this phenomenon.
For further studies, this might be worth analyzing:
Define a bigger constraint to capture crashes taking into account, for example, their V-shape. It would indeed filter out more events and enable analyzing more accurately which kind of crash VPIN predicts better.
Benchmark within this framework other predictive tools between them (VIX with a naive algorithm, with VPIN, etc.).
Analyze VPIN time-clock version predictive power.
If previous predictive power of lower amplitude flash events is interesting for practitioners, analyze more precisely parameters that would be interesting for them.
Describe more precisely to which class of financial instrument VPIN predictive power is most effective (if such one is worth being more studied for practitioners).
Define a normalization of events defining crash events within a whole cluster of instruments. It is not easy to put in place as instruments are more or less correlated by crashes and response times are not trivial to analyze, but it would be also interesting indeed to assess prediction quality on common events shared by different instruments of a same cluster. It would make it possible to see whether or not VPIN predictive power is effective beyond different financial instruments embedding different aspects of the financial world to which VPIN is sensitive to.
This area of research studies a very particular class of events: those that are potentially very rare. Taking into account this setting and that the algorithms used are fed with previous information and are sensitive to the starting point of computation, is it possible to build a consistent cross-validation approach? This aspect has not been treated yet as others needed to be first addressed, but it is still important to be studied.
See Table 22.
\nSymbol | \nDescription | \nExchange | \nClass | \nVolume | \n
---|---|---|---|---|
ES | \nS&P500 E-mini | \nCME | \nEquity | \n478,029 | \n
EC | \nEuro FX | \nCME | \nCurrency | \n188,837 | \n
CL | \nLight Crude NYMEX | \nNYMEX | \nEnergy | \n165,208 | \n
YM | \nDow Jones E-mini | \nCBOT | \nEquity | \n110,122 | \n
NQ | \nNasdaq 100 | \nCME | \nEquity | \n173,211 | \n
List of future contracts and their total volume of trades from January 2007 to July 2012.
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Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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Saxena",hash:"d92a4085627bab25ddc7942fbf44cf05",volumeInSeries:2,fullTitle:"Current Perspectives in Human Papillomavirus",editors:[{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Bacterial Infectious Diseases",value:3,count:2},{group:"subseries",caption:"Parasitic Infectious Diseases",value:5,count:4},{group:"subseries",caption:"Viral Infectious Diseases",value:6,count:7}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:2},{group:"publicationYear",caption:"2021",value:2021,count:4},{group:"publicationYear",caption:"2020",value:2020,count:3},{group:"publicationYear",caption:"2019",value:2019,count:3},{group:"publicationYear",caption:"2018",value:2018,count:1}],authors:{paginationCount:148,paginationItems:[{id:"165328",title:"Dr.",name:"Vahid",middleName:null,surname:"Asadpour",slug:"vahid-asadpour",fullName:"Vahid Asadpour",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/165328/images/system/165328.jpg",biography:"Vahid Asadpour, MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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