Feedback strategies. CFi: Crystallization factor for item i.
\r\n\tFurther development of geophysical methods in the direction of constructing more and more adequate models of media and phenomena necessarily leads to more and more complex problems of mathematical geophysics, for which not only inverse, but also direct problems become significantly incorrect. In this regard, it is necessary to develop a new concept of regularization for simultaneously solving a system of heterogeneous operator equations.
\r\n\r\n\tCurrently, the study of processes associated not only with geophysics and astrophysics but also with biology and medicine requires even more complication of interpretation models from non-linear and heterogeneous to hierarchical. This book will be devoted to the creation of new mathematical theories for solving ill-posed problems for complicated models.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"d93195bb64405dd9e917801649f991b3",bookSignature:"Prof. Olga Alexandrovna Hachay",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/8253.jpg",keywords:"Ill-Posed, Inverse Problems, Geophysics, Seismic, Electromagnetic, Thermal, Magnetic, Medicine, \r\nMathematical, Algorithms, Hierarchical, Nonlinear, Historical Description, Regularization",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 7th 2019",dateEndSecondStepPublish:"March 27th 2020",dateEndThirdStepPublish:"May 26th 2020",dateEndFourthStepPublish:"August 14th 2020",dateEndFifthStepPublish:"October 13th 2020",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"150801",title:"Prof.",name:"Olga",middleName:"Alexandrovna",surname:"Hachay",slug:"olga-hachay",fullName:"Olga Hachay",profilePictureURL:"https://mts.intechopen.com/storage/users/150801/images/system/150801.jpg",biography:"Dr. Olga A. Hachay graduated with a degree in Astrophysics from Ural State University in 1969. She obtained her PhD from the Pushkov Institute of Terrestrial Magnetism, Ionosphere and Radiowave Propagation of the Russian Academy of Sciences (IZMIRAN) in 1979 with her thesis 'The inverse problem for electromagnetic research of one-dimensional medium.”\nSince 1969, she has been a scientific member of the Institute of Geophysics Ural Branch of Russian Academy of Sciences (UB RAS), Ekaterinburg, Russia. From 1995 to 2004, she served as chief of the group of seismic and electromagnetic research. Her research interests include developing new methods for searching the structure and the state of the Earth’s upper crust, as well as elaborating a new theory of interpretation of electromagnetic and seismic fields. From 2002, she has been the main scientific researcher of the Institute of geophysics UB RAS. Since 2008, she has been a lead scientific researcher for UB RAS in the laboratory of borehole geophysics. 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This work approaches a new class of problems in which the objective function is discrete but the parameters are continuous. This type of problem arises in rotational irregular packing problems. It is necessary to place multiple items inside a container such that there is no collision between the items, while minimizing the items occupied area. A feedback is proposed to control the next candidate probability distribution, in order to increase the number of accepted solutions. The probability distribution is controlled by the so called crystallization factor. The proposed algorithm modifies only one parameter at a time. If the new configuration is accepted then a positive feedback is executed to result in larger modifications. Different types of positive feedbacks are studied herein. If the new configuration is rejected, then a negative feedback is executed to result in smaller modifications. For each non-placed item, a limited depth binary search is performed to find a scale factor that, when applied to the item, allows it to be fitted in the layout. The proposed algorithm was used to solve two different rotational puzzles. A geometrical cooling schedule is used. Consequently, the proposed algorithm can be classified as simulated quenching.
This work is structured as follows. Section 2 presents some simulated annealing and simulated quenching key concepts. In section 3 the objective function with discrete values and continuous parameters is explained. Section 4 explains the proposed adaptive neighborhood based on the crystallization factor. Section 5 explains the computational experiments and section 6 presents the results. Finally, section 7 rounds up the work with the conclusions.
Simulated annealing is a probabilistic meta-heuristic with a capacity of escape from local minima. It came from the Metropolis algorithm and it was originally proposed in the area of combinatorial optimization [9], that is, when the objective function is defined in a discrete domain. The simulated annealing was modified in order to apply to the optimization of multimodal functions defined on continuous domain [4]. The choices of the cooling schedule and of the next candidate distribution are the most important decisions in the definition of a simulated annealing algorithm [13]. The next candidate distribution for continuous variables is discussed herein.
In the discrete domain, such as the traveling salesman and computer circuit design problems, the parameters must have discrete values; the next point candidate xk+1 corresponds to a permutation in the list of cities to be visited, interchanges of circuit elements, or other discrete operation. In the continuous application of simulated annealing a new choice of the next point candidate must be executed. Bohachevsky et al. [1] proposed that the next candidate xk+1 can be obtained by first generating a random direction vector u, with |u| = 1, then multiplying it by a fixed step size Δr, and summing the resulting vector to the current candidate point xk.
Brooks &Verdini [2] showed that the selection of Δr is a critical choice. They observed that an appropriate choice of this parameter is strictly dependent on the objective function F(x), and the appropriate value can be determined by presampling the objective function.
The directions in [1] are randomly sampled from the uniform distribution and the step size is the same in each direction. In this way, the feasible region is explored in an isotropic way and the objective function is assumed to behave in the same way in each direction. But this is not often the case. The step size to define the next candidate point xk+1 should not be equal for all the directions, but different directions should have different step sizes; i.e. the space should be searched in an anisotropic way. Corana et al. [4] explored the concept of anisotropic search; they proposed a self-tuning simulated annealing algorithm in which the step size is configured in order to maintain a number of accepted solutions. At each iteration k, a single variable of xkis modified in order to obtain a new candidate point xk+1, and iterations are subdivided into cycles of n iterations during which each variable is modified. The new candidate point is obtained from xkin the following form xk+1 = xk+ v Δri ei. Where v is a uniform random number in [-1, 1], and Δriis the step size along direction eiof the i-th axis. The anisotropy is obtained by choosing different values of Δrifor all the directions. The step size is kept fixed for a certain number of cycles of variables, and the fraction of accepted moves in direction ei is calculated. If the fraction of accepted moves generated in the same direction is below 0.4, then the step size Δrialong eiis decreased. It is assumed that the algorithm is using too large steps along eithus causing many moves to be rejected. If the fraction is between 0.4 and 0.6 the step size is left unchanged. If the fraction is above 0.6 then Δriis increased. It is assumed that the step size is too small thus causing many moves to be accepted.
This procedure may not be the best possible to process the different behavior of the objective function along different axes. Ingber [7] proposed that the random variable should follow a Cauchy distribution with different sensitivities at different temperatures. The maximum step size is kept constant during the algorithm and it allows escaping from local minima even at low temperatures. The parameter space can have completely different sensitivities for each dimension, therefore the use of different temperatures for each dimension is suggested. This method is often referred to as very fast simulated re-annealing (VFSR) or adaptive simulated annealing (ASA). The sensitivity of each parameter is given by the partial derivative of the function with relation to the i-th dimension [3].
Irregular packing problems arise in the industry whenever one must place multiple items inside a container such that there is no collision between the items, while minimizing the area occupied by the items. It can be shown that even restricted versions of this problem (for instance, limiting the polygon shape to rectangles only) are NP complete, which means that all algorithms currently known for optimal solutions require a number of computational steps that grow exponentially with the problem size rather than according to a polynomial function [5]. Usually probabilistic heuristics relax the original constraints of the problem, allowing the search to go through points outside the space of valid solutions and applying penalization to their cost. This technique is known as external penalization. The most adopted penalization heuristic for external solutions of packing problems is to apply a penalization based on the overlapping area of colliding items. While this heuristic leads to very computationally efficient iterations of the optimization process, the layout with objective function in minimum value may have overlapped items [6].
Fig. 1 shows an example in which the cost function is the non–occupied space inside the container. As this space can change only by adding or removing areas of items, the cost function can assume only a finite set of values, becoming discontinuous. This particularity of the primal problem makes it difficult to evaluate the sensibility of the cost function related to the optimization variables.
Objective function behavior.
Recently, researchers used the collision free region (CFR) concept to ensure feasible layouts; i.e. layouts in which the items do not overlap and fit inside the container [11]. This way, the solution has discrete and continuous components. The discrete part represents the order of placement (a permutation of the items indexes - this permutation dictates the order of placements) and the translation that is a vertex from the CFR perimeter. The continuous part represents the rotations (a sequence of angles of rotations to be applied to each item). The translation parameter is converted to a placement point at the perimeter of the CFR for its respective item. Fig. 2 shows the connection between the CFR and the translation parameter. Notice that the rotation parameter is cyclic in nature. All arithmetic operations concerning this parameter is performed in modulus 1 (so they always remain inside the interval [0, 1[).
Consider that the container is rectangular and items P1, P2, P3 and P4 are already placed. Item P5 is the next to be placed and to avoid collisions; it is placed at its CFR boundary. Its translation parameter has a value of 0.5. Both figures have P4 placed at different positions, and consequently P5 is also placed in different positions although the translation parameter is the same.
The wasted area that represents the cost function assumes only discrete values, while its variables (the rotations for each item) are continuous. To solve this type of problem, Martins & Tsuzuki [10] proposed a simulated quenching with a new heuristic to determine the next candidate that managed to solve this type of problem.
The objective function is the wasted space in the container and is discrete, depending on which items have been placed. In order to improve the sensibility of the cost function, intermediate levels can be generated by scaling one of the unplaced items, and attempting to insert the reduced version of the item into the layout. Hence, for each unplaced item, a scale factor between [0, 1] is applied, and the algorithm attempts to place the item, if it fits, the scaled area of the item is subtracted from the objective function. Scale factor was determined by a finite fixed depth binary search, restricted to the interval [0, 1].
The proposed algorithm is shown in Fig. 3. The main modification is shown in the inner loop, where the choice is to swap two items in the placement sequence (discrete parameters) or to modify the rotation or translation of an item (continuous parameter).
The proposed algorithm. Different types of positive feedbacks are studied in this work.
The main concept is that rejected solutions do not contribute to the progress of the optimization process. Therefore, the distribution of the step size for each individual continuous parameter is adapted in order to increase the number of accepted solutions. This is accomplished by the adoption of a feedback on the proposed algorithm. The next candidate is generated by the modification of a single parameter, adding to it a summation of ci random numbers with a uniform distribution.
whereiis the index of the modified parameter and ci is its crystallization factor. The resulted modification follows a Bates distribution [8, sec. 26.9] centered on 0 with amplitude 1/2. Its standard deviation is given by
For ci = 1, as all operations on parameters are performed in modulus 1; the modification is the equivalent of taking a completely new parameter uniformly distributed in the interval [0, 1[. As ci increases, the expected amplitude of the modification decreases. When at a given iteration, the modification applied to a parameter leads to a rejected solution; the probability distribution (crystallization factor) for that specific parameter is modified in order to have its standard deviation reduced (resulting in lower modification amplitude), this is the negative feedback. When the modification leads to an accepted solution, the distribution (crystallization factor) for that parameter is modified to increase its standard deviation (resulting in larger modification amplitude), this is the positive feedback. Different positive feedbacks are studied in this work (see Table 1). As can be seen, the higher the crystallization factor for a given parameter, the smaller the modification this parameter will receive during the proposed algorithm. The parameter is said to be crystallized.
Crystallization factor ci controls the standard deviation of the Bates distribution. When a solution is rejected, a negative feedback is applied and the corresponding ci is increased, causing a decrease in the parameter standard deviation. Accordingly, positive feedback is applied when a solution is accepted, increasing ci. In the studied problems, placement was restricted to vertexes of the CFR and thus the only continuous parameter is the rotation. Adopted negative feedback consists of incrementing the crystallization factor. For the positive feedback, the four different strategies in Table 1 were tested.
Feedback Method | Positive Feedback | Negative Feedback |
A | CFi→ CFi− 1 | CFi→ CFi+ 1 |
B | CFi→ CFi/2 | CFi→ CFi+ 1 |
C | CFi→ CFi/4 | CFi→ CFi+ 1 |
D | CFi→1 | CFi→ CFi+ 1 |
Feedback strategies. CFi: Crystallization factor for item i.
The convergence of the algorithm is reached when, at a given temperature, all accepted solutions are equivalent to the best found. This is the global stop condition of the algorithm in Fig. 3. Although a solution as good as the final one is found in less iterations, allowing the algorithm to reach the global convergence is the only generic way to ensure that a solution is the best. The local stop condition shown in Fig. 3 is reached when a predefined number of solutions are accepted.
All problem instances studied here have a solution in which all items can be fitted in the container. Two puzzles cases were considered: Tangram and Larger First Fails (LF Fails). Tangram is a classic problem and LF Fails consists of a problem which cannot be solved using the larger first heuristic. This heuristic determines that the larger items are placed always ahead of the smaller ones. Fig. 4 shows possible solutions to these problems.
a) Unique solution for problem LF Fails. (b)-(d) Solutions for the Tangram problem.
The algorithm was implemented in C++ and compiled by GCC 4.4.4. Computational tests were performed using an i7 860 processor with 4GB RAM. Each case was evaluated 100 times. The proposed algorithm is a simulated quenching algorithm which has the following parameters:
T0: Initial temperature.
α: geometric cooling schedule factor.
Nacc: Number of accepted solutions at a given temperature.
Value of T0 is calculated such that the number of rejected solutions at initial temperature is approximately 10% of the total number of generated solutions. Parameter α is set to 0.99 and Naccis 800.
Table 2 shows results obtained using each of the proposed feedback strategy, for each problem instance. For the Tangram problem, it can be observed that strategy A has a low convergencepercentage, when compared to other feedback strategies, 0.09 less than the rate obtained usingthe feedback C method. In the case of the LF fails puzzle, results showed similar performanceand convergence rate.
Fig. 5 shows the minimum, maximum and average costs explored by the proposed algorithm loop for the LF Fails, for all feedback strategies. The cost function discrete behavior is observable, and it is possible to notice that the global minimum is reached only at low temperatures. In all graphics, the optimum layout was found. One can note that, in Fig. 5.(b) and Fig. 5.(c), the best solution (cost equals zero) was found before reaching convergence. Variation of cost is shown in Fig. 6 and all graphs are very similar independently of the used positive feedback. The rotation crystallization factor for the largest item is displayed in Fig. 7. Possibility of accepting a higher cost solution is lower at low temperatures. As temperature decreases, the crystallization factor is expected to increase, which is confirmed by the graphics in Fig 7. Positive feedback A is very stable, showing that it is less exploratory. Because of the small number of items, it was not necessary to use the scale factor. Fig. 8 shows the specific heat for each case considered. The specific heat is calculated as [14]
whereT is temperature, σ2(T) is the variation of cost, kB is a constant. A phase transition occurs at a temperature at which specific heat is maximum, and this triggers the change instate ordering. In several processes, it represents the transition from the exploratory to therefining phase. However, in this specific case, this transition is not observable.
For the Tangram problem, the minimum, maximum and average costs explored by thealgorithm in one execution are shown in Fig. 9. The increase in allowable cost function valuescan be observed. In each of these executions the global minimum was reached only at low temperatures. Independently of the used positive feedback, the proposed algorithm reachedlocal minima at lower temperatures, but successfully escaped from them. Cost variance isdisplayed in Fig. 10. The rotation crystallization factor evolution is shown in Fig. 11, for oneof the large triangles. It is possible to observe, when adopting feedback strategies A and C,that there are two distinguishable levels. The final level, at lower temperatures, is very high,indicating that the rotation parameter of the item is crystallized. Again, feedback A is morestable when compared to the others, showing that is less exploratory. As the convergence rateis very poor, the scale factor should be used. Fig. 12 shows the specific heats obtained. In theTangram casem, it seems that a peak is present. However, further investigations need to bedone.
Minimum, maximum and average costs for the LF Fails with different feedbacks. (a) Feedback A. (b) Feedback B. (c) Feedback C. (d) Feedback D.
Problem Feedback Method | NconvNmin TconvPconv |
A | 228935 188814 17.48 1.00 |
LF fails B | 235986 197038 17.78 0.99 |
C | 235595 195377 17.64 1.00 |
D | 235481 194394 17.67 1.00 |
A | 303517 255611 64.33 0.56 |
Tangram B | 315019 268996 69.07 0.65 |
C | 319440 270484 69.27 0.62 |
D | 317403 267057 71.09 0.61 |
Statistics for the LF fails and Tangram puzzles. The columns respectively represent the adopted problem instance, the feedback method, number of iterations to converge, number of iterations to reach the minimum, time in seconds to converge, and the percentage of runs that converged to the global optimum.
Variation of cost for the LF Fails with different feedbacks. (a) Feedback A. (b) Feedback B. (c) Feedback C. (d) Feedback D.
Crystallization factor for the largest item of the LF Fails problem, with different feedbacks. (a) Feedback A. (b) Feedback B. (c) Feedback C. (d) Feedback D.
Specific heat for the LF Fails with different feedbacks. (a) Feedback A. (b) Feedback B. (c) Feedback C. (d) Feedback D.
Minimum, maximum and average costs for the Tangram with different feedbacks. (a)Feedback A. (b) Feedback B. (c) Feedback C. (d) Feedback D.
Feedback Method | NconvNmin TconvPconv |
A | 370667 290044 222.54 0.78 |
B | 351141 299052 227.39 0.91 |
C | 343652 327037 228.40 0.98 |
D | 338394 312867 213.91 0.97 |
Statistics for the Tangram puzzles using a binary search with unitary depth. The columnsrespectively represent the feedback method, number of iterations to converge, number of iterations toreach the minimum, time in seconds to converge, and the percentage of runs that converged to the globaloptimum.
Variation of cost for the Tangram with different feedbacks. (a) Feedback A. (b) Feedback B. (c)Feedback C. (d) Feedback D.
Crystallization factor for one of the large item of the Tangram problem, with differentfeedbacks. (a) Feedback A. (b) Feedback B. (c) Feedback C. (d) Feedback D.
Specific heat for the Tangram with different feedbacks. (a) Feedback A. (b) Feedback B. (c)Feedback C. (d) Feedback D.
Binary search is used to improve the sensibility of the discrete objective function, aimingto obtain a higher percentage of convergence for puzzle problems. Its application is notnecessary in the case of the LF Fails problem, as almost all executions converged. As aconsequence, the binary search was employed only in the Tangram problem. The fixed searchdepth was set to 1. Table 3 shows the results of the tests. Comparing with the results obtainedin Table 2, the convergence rate is observed to be considerably higher when binary search isadopted, reaching 98% in the best case. Drawback is the large number of iterations neededto converge, resulting in longer execution times, approximately 3.5 times higher. As with theprevious tests, feedback strategy A obtained less optimum solutions.
The behavior of the optimization process is illustrated through cost function (energy)histograms of the search while the temperature diminishes. For a given temperature, agray-level histogram of the distribution of the cost function at that temperature is plotted.The resulting graph shows a plot of cost histograms (horizontal bars) and temperature (dots)versus the number of iterations. Darker horizontal bars in the histogram, indicate a higherfrequency of occurrence of a particular level of energy at a given temperature. Fig. 13 showsthe histogram of the objective function value during the course of the algorithm, without theuse of the binary search. Fig. 14 shows the same type of histogram employing the binarysearch with depth 1. Observing both graphics, one can note the extra levels of energy whichappears in Fig. 14. Higher values of the search depth were tested, however the convergencerate deteriorates.
From the studied problems, it is possible to observe that positive and negative feedbacks mustnot be opposites. The negative feedback increases the crystallization factor by a unit and thepositive feedback needs to decrease the crystallization factor at a faster speed. If this is not thecase, the parameters might get crystallized.
Histogram for the Tangram, not employing binary search.
Histogram for the Tangram, employing binary search with fixed depth equal to 1.
This work proposed a new simulated quenching algorithm with adaptive neighborhood, inwhich the sensibility of each continuous parameter is evaluated at each iteration increasing thenumber of accepted solutions. The proposed simulated quenching was successfully applied toother types of problems: robot path planning [14] and electrical impedance tomography [12].The placement of an item is controlled by the following simulated quenching parameters:rotation, translation and sequence of placement.
AK Sato was supported by FAPESP (Grant 2010/19646-0). MSG Tsuzuki was partially supported by the CNPq (Grant 309.570/2010–7). This research was supported by FAPESP (Grants 2008/13127–2 and 2010/18913–4).
‘Emotional Finance’, as inaugurated by Richard Taffler and David Tuckett, received a warm reception in the early 2010s from regulators, the financial press, and investment management industry’s main professional body, the CFA Institute. Yet outside their immediate social and professional circles, the wider academic community largely ignored both Emotional Finance’s challenge to Behavioural Finance and its theoretical and methodological approach, which is, at its core, an application of Kleinian psychoanalysis to interpreting the individual psychology of traders and to describing the group psychology of financial markets. A decade after the financial crisis, industry professionals rarely talk about ‘Emotional Finance’ at all, except insofar as they see ‘emotional biases’ as a sub-species of behavioural biases, which hardly amounts to a successful challenge to Behavioural Finance [1]. Because Taffler and Tuckett’s approach is inherently interdisciplinary, few practicing psychoanalysts have felt equipped to comment upon their characterisation of financial markets, and yet fewer finance academics have the training or inclination to reckon with Taffler and Tuckett’s idiosyncratic handling of psychoanalytic theory.
Emotional Finance, as an intellectual project, rests on four related claims: first, that financial assets are categorically different from other kinds of commodities; second, that financial innovation peculiarly lends itself to being experienced as a ‘phantastic object’; third, that regulators can and should design institutional mechanisms that acknowledge the psychodynamics of the dealing room; and fourth, that the recent financial crisis ought to be handled in a manner similar to the post-Apartheid South African regime’s Truth and Reconciliation Commission. While agreeing that financial bubbles are banal and that emotional responses to the vicissitudes of the market are nothing remarkable, this chapter argues that financial assets are not categorically distinct; that the term ‘phantastic object’ is superfluous and its application is a variant of the ‘sharpshooter’ fallacy; that narrative causation is not formally equivalent to causality; that the relationship between group psychology and individual psychodynamics is under-theorised; and that financial instability is not the same as financial bubbles. Finally, there are other bigger threats to financial stability than those identified by the techniques employed by advocates of Emotional Finance, especially given the periods of relative calm in financial markets between the Great Financial Crisis (2007-2008) and its sequelae in the Eurozone Debt Crisis (2009-2012) and the recent crisis caused by the global coronavirus pandemic in Q2 2020.
By far the most problematic claim of Emotional Finance is that financial assets are somehow a special category. In Minding the Markets, Tuckett claims there are three characteristics of financial assets that make them unusually amenable to obtaining the status of a ‘phantastic object’: their volatility, their abstract quality, and the difficulty in determining whether or not a manager’s success was attributable to skill or luck ([2], p. xvii). Of these, the volatility argument is the most straightforward and also most peculiar. While it is certainly true that individual listed securities, futures contracts, or financial derivatives contracts may be highly volatile, financial markets themselves are notable for their lack of volatility. This is straightforward to demonstrate empirically because the volatility of the ‘S&P 500’ (as measured by the implied volatility of index options for the following 30 days) is itself a tradable index called the VIX or CBOE Volatility Index. Its performance since its inception in 1985 can be seen below in Figure 1.
CBOE Volatility Index (VIX) from December 1985 to October 2020 (daily closings). http://www.cboe.com/products/vix-index-volatility/vix-options-and-futures/vix-index/vix-historical-data (CBOE - Chicago Board Options Exchange).
For most of the last thirty-five years, the VIX has hovered around 15 to 25, with a high of 150 during the October 1987 ‘flash crash’. In the fourth quarter of 2008, after the Lehman collapse, it peaked at around 80. What does this mean? The VIX is a measure of volatility over a 30-day period, annualised, such that a VIX of 20 means that the market is expected to move up or down by 5.8% (or 20/√12) over the next 30 days. Even a VIX of 80 in means that the market is expected to move 23.12% up or down over the next 30 days, which most recently occurred in Q2 2020 with the outbreak of the COVID-19 pandemic. The daily volatility, by contrast, is calculated by dividing the number by √256, or the number of trading days in an average year, which for a VIX of 20, means that the market is expected to move up or down by 1.25% daily.
How does this compare to other commodities, say crude oil, or even to gold, that supposedly safe haven? Figure 2 answers this question. In the case of crude oil, the CBOE also publishes an Oil Volatility Index, which is markedly higher than the S&P 500, which compares favourably to a volatility index for gold over the thirty-year period from 1990-2020.
Comparison of Oil, Stocks, and Gold Volatility Indices 1990-2020. http://www.cboe.com/products/vix-index-volatility/volatility-on-etfs/cboe-crude-oil-etf-volatility-index-ovx (CBOE - Chicago Board Options Exchange); http://www.cboe.com/products/vix-index-volatility/volatility-on-etfs/cboe-gold-etf-volatility-index-gvz (CBOE - Chicago Board Options Exchange); http://www.cboe.com/products/vix-index-volatility/vix-options-and-futures/vix-index/vix-historical-data (CBOE - Chicago Board Options Exchange).
Some might complain that this is because oil and gold are exchange-traded and thus subject to added volatility, but, in fact, there is good evidence that exchange-based futures trading lowers volatility rather than amplifies it. We know this because of a natural experiment afforded by the Onion Futures Act of 1958, which banned futures trading in onions [3]. The price volatility of onions is considerably greater than that of either the S&P 500 or of crude oil. The charts in Figure 3 would not surprise economists or finance academics.
Onions vs Crude Oil, Onions vs S&P 500. (a and b) https://www.investing.com/indices/us-spx-500-historical-data (Investing.com Historical Data); https://www.indexmundi.com/commodities/?commodity=crude-oil&months=360 (Index Mundi data archive); https://usda.library.cornell.edu/concern/publications/k643b116n?locale=en (USDA Data Library); https://bsic.it/the-onion-paradox-or-why-futures-are-good-for-the-economy/ (Bocconi Student Investors Club Blog).
As acknowledged above, individual stocks may move more than the market as a whole, but most investment managers hedge this risk by holding a portfolio composed of a variety of different asset classes, let alone constituents among them. Retail investors, rather than professional money managers, may find themselves credit-constrained and forced to sell if a security falls quickly in value, but most fund managers have either automated stop-losses or the discretion with which to cut their losses. Moreover, over longer time horizons, financial assets are not particularly volatile compared to house prices in major markets in last fifteen years as shown in Figure 4.
Annual House Price Inflation Rates, 2006-2020. https://appsso.eurostat.ec.europa.eu/nui/setupDownloads.do (Eurostat Data Archive); https://apps.bea.gov/iTable/iTable.cfm?reqid=19&step=3&isuri=1&1921=survey&1903=11#reqid=19&step=3&isuri=1&1921=survey&1903=11 (BEA, National Data).
The volatility of the stock market against the risk-free rate is not especially significant even during the Dotcom Crashes. As many commentators noticed at the time of the Lehman collapse, the problem was that most fund managers had spent the majority of their career chasing returns and had little experience of market snaps of any kind. Additionally, market snaps of the kind experienced during the 2008 crash or even the Eurobond crisis of 2011-12 pale in comparison to economic shocks such as that experienced during the coronavirus pandemic in Q1-Q2 2020 as illustrated in Figure 5.
VIX to 10 yr. Treasury Note Yield Ratio, 1990-2020. https://www.macrotrends.net/2016/10-year-treasury-bond-rate-yield-chart (Macrotrends Data Archive); http://www.cboe.com/products/vix-index-volatility/vix-options-and-futures/vix-index/vix-historical-data (CBOE - Chicago Board Options Exchange).
This is why financial engineers worked so hard in the period from 2003 to 2006 to create instruments, like mortgage-backed securities and collateralised debt obligations, that gave investors leveraged access to a much more volatile housing market.
There is no reason to contest much of Tuckett’s characterisation of the problems with the ‘efficient-markets hypothesis’ except to say that it is was originally developed as a simplifying assumption that made certain classes of models tractable. The slippage that allowed it to become a (flawed) description of reality, let alone an operative ideal and normative regulatory goal, is ideological and not the consequence of the internal logic of the idea. Some markets are efficient and do not forecast prices reliably, as Holbrook Working discovered when he explored North American grain markets in the interwar period or securities in the postwar moment [4, 5].
The second quality of financial assets, identified by Tuckett, which makes them amenable to the psychodynamic processes that he describes is their putative ‘abstractness’. This is surely in the eyes of the beholder. As early as Adam Smith [6], economic writers identified two (or three) distinct forms of value: value-in-use and value-in-exchange. Value-in-use can be further distinguished as value-in-consumption or in the income generated by a particular asset, i.e. you could live in a house or rent it out, you could eat the produce of your garden or sell it, etc. Value-in-exchange is what it commands either on an open market or in a barter transaction. For financial assets, there is often little consumable ‘value-in-use’ except at the margins, insofar as some investors may buy Class A or B shares in Berkshire Hathaway in order to meet Warren Buffett at his annual junket in Omaha and other investors (especially aggressive hedge funds) may purchase shares in order to control a company, oust its leadership, merge it with another, or even liquidate it. Most often, however, financial assets either generate income (which Tuckett suggests is usually calculated through a capital asset pricing model) or are sold on for speculative gains or losses. Modern finance theory holds that efficient markets should arbitrage value-in-use (income) and value-in-exchange, so that the dividend is ‘priced into’ the share, but in practice many investors are driven by a combination of dividend income and capital gains, and the protection of the latter in the U.S. and UK tax codes has been distorting investment behaviour these last thirty years or so.
Yet, in practice, these instruments are not abstract to those who trade them. Bonds have par values, generate coupon payments, have interest rates and calculable yields based on their prices. Equities may or may not pay dividends, but can be valued on that basis or on the book value of the firm. A variety of options, including the right to buy or sell a security at a particular price, have been well known for over 500 years ([7], p. 2-3). More complex financial derivatives were not abstract so much as they were opaque, in that they were tied to underlying assets that were, themselves, difficult to value. But even if we were to call that ‘abstraction’, it is by no means obvious that this is the sort of abstraction that lends itself to phantasy. It is equally plausible that relatively simple, graspable items in everyday life are the stuff of phantasies. The very complexity of some of the more recent species of financial assets (especially collateralised debt obligations that were tied to securitised mortgages) made them difficult to value, but the problem was not that investment managers fantasised about them, but rather that rating agencies were put under pressure to score them more highly than they deserved.
Elsewhere Tuckett contrasts financial assets with a television set, where ‘a “rational” consumer can consult a range of information about the price and quality and on that basis make a decision’ ([2], p. 21). He goes on to argue that the buyer might notice he got a bad deal, might observe the prices of televisions fluctuates as models sell out quickly or not at all, or as new models appear, and he might have buyer’s remorse, and even sell it on the second-hand market. Yet Tuckett maintains that ‘with financial assets the situation is very different [from television sets] as they have no intrinsic value but one determined by ambiguous information and varying expectations about an uncertain future that plays out in time’ ([2], p. 21). This is simply untrue. Bonds represent claims, either preferential or subordinated, on the business revenue or tax revenue of the firm or sovereign that issued them, equities represent residual claims against the book value of a firm, whereas financial derivatives (swaps, options, etc) represent contractual arrangements that can, and have, been litigated. The fortunes made by vulture funds that purchased collateralised debt obligations composed of subprime mortgages or of junior Greek debt is an indication that these financial assets do have values that are calculable.
What is more difficult to calculate, and is indeed often uncertain, is the depth of the secondary market at a given time, and hence the liquidity risk. Tuckett commits the same error as proponents of the efficient-markets hypothesis do when he ignores what is known in the trade as “the limits of arbitrage”, in that he assumes that buyers are not credit constrained and only buy or sell because of their sense of the direction of the market. The reality is very different, in that people and institutions can be forced to sell for a range of reasons (to raise money to meet current obligations) and institutions, like pension funds, can be forced to purchase risky assets because they have to match their assets with their liabilities to generate the returns needed to meet obligations that are years away. Liquidity risk is particularly acute in a financial crisis where people are not buying or selling anything at any price, but anyone who has tried to sell a television set near the end of the month can tell you that the used market also depends on the proximity of the average consumer to a weekly or monthly pay day, depending on the price level. Sensitivity to liquidity risk is difficult to know ex-ante, but it is not analytically difficult to grasp.
Moreover, the distinction between risk and uncertainty in Tuckett’s account is problematic. Risk is calculable based on an ergodic assumption that the future will be like the past. To a surprising degree, this assumption holds in financial markets, as the ‘equity risk premium’ (the premium paid to investors for buying equities over government debt securities) has not changed much in 150 years, and, as the finance literature has decisively shown, has made owners of shares better off than those who eschew the risks attendant to them [8, 9].
Moreover, as Figure 6 illustrates, the majority of the ‘total return’ from stocks comes from dividends not from price appreciation, which belies the idea that shares do not have a ‘value-in-use’ or income component that is tangible and real.
Nominal and Real Returns from Stocks and the Stock Index, 1927-2020. (a) https://www.statista.com/statistics/1032048/value-us-dollar-since-1640/ (Statista archive); https://www.macrotrends.net/1333/historical-gold-prices-100-year-charthttps://onlygold.com/gold-prices/historical-gold-prices/ (Macrotrends); https://fred.stlouisfed.org/series/DGS20#0https://www.multpl.com/s-p-500-historical-prices/table/by-year (St Louis Federal Reserve Archive); https://seekingalpha.com/article/4311451-stocks-bonds-bills-and-inflation-returns-for-94-years-ending-december-2019 (Seeking Alpha Blog). (b) https://finance.yahoo.com/quote/%5EGSPC/history?period1=-1325635200&period2=1603238400&interval=1mo&filter=history&frequency=1mo&includeAdjustedClose=true; https://www.officialdata.org/us/stocks/s-p-500/1927?amount=100&endYear=2020 (Yahoo Finance); https://www.in2013dollars.com/us/inflation/1927#:~:text=In%20other%20words%2C%20%24100%20in,inflation%20rate%20was%20%2D1.69%25. (CPI Inflation Calculator).
The returns above are for U.S. equities as an asset class (i.e. they represent ‘beta’), and say nothing about particular securities or vintages. Yet it is precisely because retail investors and professional money managers can buy (and sell) index-funds that active managers have to try to ‘beat’ the market. That is where the pressure comes, to generate ‘alpha’, which Tuckett correctly notices is ephemeral, and, according to adherents of the efficient-markets hypothesis, at best idiosyncratic and at worst a statistical mirage. The money managers that Tuckett identified have an incentive to depict their performance as a result of their skill, but given the survivorship bias (firms that get unlucky fail and disappear), the charge that even the big winners are probably ‘lucky monkeys’ is not without some real plausibility.
So, yes, returns are ‘uncertain,’ but what does this mean? ‘Uncertainty’ refers to the ‘unknown unknowns’ of Knight and Keynes, but hardly matters much to the everyday operation of markets, which have remained remarkably continuous and well-funded in all but a handful of cases (some of the more exotic CDOs still do not trade at any price) through the last crisis. The reason that discussions of ‘uncertainty’ were back in vogue in the 2010s is that markets in 2003-2007 under-priced risk, because the models could not account for uncertainty. Once-in-a-lifetime events (the so-called Black Swans) are important to risk managers, but for Tuckett’s argument to work they have to be an everyday feature of markets, which they, by definition, are not. Tuckett is describing the life of an onion trader not a financial asset manager. This is precisely why more recent work on emotions in financial markets has eschewed the problematic articulated by Taffler and Tuckett in favour of understanding how emotions affect participants in markets under ordinary trading conditions [10].
While a middle class, middle-aged Londoner might indeed see a television as ordinary and mundane and a share as ‘exotic’ and ‘abstract’, but as seen in the London riots of 2011, ordinary people did risk their livelihoods, reputation (and a possible criminal record) and even lives to snatch televisions and trainers from shops. Before globalisation, not so long ago, consumers in emerging markets invested television sets with almost magical properties. If television sets are in the eye of the beholder, it is equally plausible that various financial instruments are mundane, familiar and more or less rationally estimable by people who trade them every day. Is financial innovation, in its first incarnation then, any different?
Tuckett thinks so. He cites tulips, subscription shares (in the South Sea Bubble), and a host of other items ([2], p. 18) as inherently generating outsized excitement because they represented ‘financial innovation’, but these assertions are not proven. In some cases, as with tulips and subscription shares, the characterisation of them as novel financial innovations is just wrong ([7], pp. 2-4), as futures trading in Baltic grain had preceded that in tulips by over half a century, and the Bank of England offered subscription shares to English investors decades before the South Sea Company directors did. In other cases, it is easier to explain the erratic valuations in terms of Akerlof’s lemon problem, which can be summarised as those who cannot tell good wine from bad will overpay for the latter and undervalue the former [11]. This is not to deny that bubbles form in financial markets or that they are further fuelled by fantastic narratives about the value of the assets, which are their focal point. But let us not forget that the ‘bubble’ in the early noughties was not in collateralised debt obligations, but rather in housing market where price rises were fuelled by the advent of subprime mortgages, which are neither especially abstract nor backed by something intangible. The trouble is not with the role of fantasies in bubbles, but rather with the theoretical formulation of these ‘phantastic objects.’ In short, there is nothing unique about financial assets.
The theoretical edifice of Emotional Finance equally depends on the usefulness of the term, ‘phantastic object’, as a plausible unit of analysis. Tuckett [2] gives his most recent definition of a phantastic object as ‘subjectively very attractive “objects” (people, ideas or things) which we find highly exciting and idealise, imagining (feeling rather than thinking) they can satisfy our deepest desires, the meaning of which we are only partially aware’ ([2], p. xi). According to Tuckett, he had ‘coined the term’ as an attempt to explain a situation where ‘a story gets told about an object of apparent desire (such as a dotcom share, a tulip bulb, or a complex financial derivative), which becomes capable of generating excitement in a situation where outcomes are inherently uncertain’ ([2], p. xiv). He reports that ‘the term conjoins “phantasy” as in unconscious phantasy and “object” as in representation.’ What does this mean? Tuckett further explains: ‘the phantasy stimulated is about more than just a story of getting rich. Rather it is a story about participation in an imagined object relationship in which the possessor of the desired object plays with the omnipotent phantasy of having permanent and exclusive access to it and all good things’ ([2], p. xiv). Although Freud and Klein both long ago recognised that all object relationships are ambivalent, Tuckett sees ambivalence (and with it a degree of abstraction, ambiguity and uncertainty) as necessary ingredients of this heady emotional stew [12, 13]. Yet the insistence on ambivalence, for emphasis, is hardly a cardinal sin.
The more serious problem arises, however, when we pick apart this notion of ‘phantasy.’ For Tuckett and Taffler, the usual citation is Freud’s meditation on creative writing and daydreaming [14]. Here Freud develops a theory, no longer accepted even in literary theory, that a child’s fantastical play is very similar to the creative writer, because both the child and the writer are able to distinguish their intensely rich libido-cathected worlds and the characters they create for them from external reality ([14], p. 142-3). Phantasies, both conscious and unconscious, come to replace play, for Freud, as ‘the growing child when he stops playing, gives up nothing but the link with real objects; instead of playing, he now phantasises’ ([14], p. 144). Adults populate their phantasies with their internal objects both in manifest and disguised forms. In that limited sense, Tuckett’s ‘phantastic object’ is simply any object that has found a place in an adult’s unconscious phantasy, or what Freud constructed as the ‘psychical reality’, which is unique to each individual. Exactly how ‘financial assets’ become the paradigmatic ‘phantastic object’ remains to be shown, unless what Tuckett really means is that financial assets evoke some memory of a part-object or maternal breast.
To complicate matters, Freud recognises that children rarely conceal their fantastical play, whereas ‘the adult, on the contrary, is ashamed of his phantasies and hides them from other people. He cherishes them as his most intimate possessions, and as a rule he would rather confess his misdeeds than tell anyone his phantasies ([14], p. 144). If so, it is all the more remarkable that the fund managers whom Tuckett interviewed were willing to tell their ‘phantasies’ to him, over the course of one meeting of 70 minutes or so, unless, if by analogy to Freud’s neurotic patient who hopes for a cure, they confess their phantasies to Tuckett in hopes of absolution for speculative excess ([14], p. 145).
Freud’s explanation of what causes adults to hide their phantasies in shame arises from the fact that they are ‘either ambitious wishes, which serve to elevate the subject’s personality; or they are exotic (sic) [erotic] ones’ ([14], p. 146). But he cautions, ‘we will not lay stress on the opposition between the two trends; we would rather emphasise the fact that they are often united’ and just as often reparative ([14], p. 146-7). Even the most overtly ‘ambitious, egotist wishes’ have some element of sexual gratification involved, if merely auto-erotic in the most narcissistic of states. Freud finishes by comparing ‘phantasies’ to ‘dreams’ and noting their quality of wish fulfilment ([14], p. 148). All of this is very familiar to psychoanalysts, but Tuckett’s neologism has lost the crucial sense, found in Freud, of erotic wish fulfilment, presumably because draining it of the erotic makes the concept more palatable to Tuckett’s audience. The Strachey translation’s tendency to render ‘fantasy’ as ‘phantasy’ (which is now the conventional usage in Britain) further reinforces the impression that it has little to do with sex, however alien the spelling may seem to American readers.
If we allow that conscious and unconscious narratives which adults weave about their internal objects are invested with libido and contain sexual gratification and conquest as elements of their function as wish fulfilment, then there is nothing unusual, let alone alarming, about a particular investment or set of investments acting as the vehicle, in such a fantasy, to unlimited wealth and with those resources the means to sexual conquest. Buyers of lottery tickets do this every day. To the extent that a particular object occupies a stereotyped place in such narratives, such that it becomes ‘very attractive’ and ‘idealised’ to an individual, let alone a group, then we have something closer to a ‘fetish’ or ‘an inanimate object worshipped … for its magical powers or as being inhabited by a spirit’ ([15], p. 57). Fetish objects also provoke the ‘divided states’ that Tuckett describes ([2], p. xi), possess ‘magical powers’ and lead to ‘potency the [fetishist may] otherwise lack’ ([15], p. 57). Whereas many sexual fetishes function by synecdoche (feet, hair, clothes, footwear, etc), others do so by metonymy, offering a substitute object ([16], p. 132). There are two further features that sharpen the similarities between ‘fetish object’ and ‘phantastic object’, namely ‘(a) the fetish has multiple meanings derived by condensation, displacement and symbolisation from other objects, and (b) the fetishist behaves as though [the fetish object] actually were these other objects and is no more disturbed by incongruity or absurdity than a dreamer is while dreaming’ ([15], p. 57). Ironically, Tuckett’s example of Aladdin’s lamp is usually explained as a fetish object rather than a phantastic one. The use of ‘pseudo-psychoanalytic’ language (‘phantastic object’ in place of ‘fetish object’) may be more acceptable to the audience, but it has the consequence of dislocating the concept within a wider psychoanalytic discourse.
Although Tuckett does not acknowledge this in his own discussion, the Emotional Finance presentation of ‘phantastic objects’ also depends on these meanings derived from ‘condensation, displacement and symbolisation’ ([15], p. 57). Before exploring that in detail, it is first worth considering the alternative source of ‘phantastic object’ as offered by Tuckett, namely in the definition of ‘phantasy’ offered by Laplanche and Pontalis in their rival to the Rycroft volume ([17], pp. 317-321), which invokes the principle that ‘the use of the term “phantasy” cannot fail to evoke the distinction between imagination and reality (perception). If this distinction is made into a major psycho-analytic axis of reference, we are brought to define phantasy as a purely illusory production which cannot be sustained when confronted with a correct apprehension of reality’ ([17], p. 315). As they note, ‘certain of Freud’s writings appear to back up this type of approach. Thus in “Formulations on the Two Principles of Mental Functioning” (1911b), Freud sets the internal world, tending towards satisfaction by means of illusion, against an outside world which gradually imposes the reality principle upon the subject through the mediation of the perceptual system’ ([17], p. 315).
For Taffler and Tuckett, the ‘reality’ of financial markets ultimately strips the ‘phantastic objects’ of their value if not their meaning, as the inevitable crash and de-idealisation leads to anger and revulsion [18]. As Laplanche and Pontalis also notice, modern psychoanalytic usage extends ‘phantasy’ to a range of conscious, preconscious and unconscious fantasies, thereby muddling the extent to which repression plays a role ([17], p. 315). They suggest, instead, distinguishing between day-dreams that serve as compromise-formations, common ‘unconscious phantasies’ that appear as precursors to neurotic symptoms, and unconscious fantasies that offer the seeds of wish fulfilment in dreams. With Tuckett’s formulation, the narratives about ‘phantastic objects’ appear to be mostly preconscious, in that the fund managers are not necessarily aware of them until prompted by their interlocutor, but then venture them freely. Whether or not this is plausible in a psychoanalytic sense remains debatable, as Tuckett’s formulation appears to ignore both the roles of secondary revision and of repression.
As with Freud, Laplanche and Pontalis also link phantasies to desire, which does not even merit an index entry in Tuckett [2]. In Laplanche and Portalis, they emphasise the extent to which wish fulfilment evokes the ‘hallucinatory memory of satisfaction’ (1973, p. 318), or the maternal breast, which is a kind of primordial ‘phantasy-object’. Yet they also acknowledge, ‘the relationship between phantasy and desire seems to us to be more complicated than that. Even in their least elaborate forms, phantasies do not appear to be reducible to an intentional aim on the part of the desiring subject …’, and crucially ‘it is not an object that the subject imagines and aims at, so to speak, but rather a sequence in which the subject has his own part to play and which the permutations of roles and attributions are possible’ ([17], p. 318). Read that way, Tuckett’s ‘phantastic object’ is, in effect, a contradiction in terms, in that it is not the object itself that the subject desires, but rather the outcome of the script, i.e. unbridled wealth, beautiful women (or men), and universal gratification. In other words, we’re back to ordinary explanations that turn on greed, lust, and gluttony.
As to the relationship between the internalisation of the so-called ‘phantastic object’ and the processes Rycroft alludes to of ‘condensation, displacement and symbolisation’, these are, in effect, what Tuckett evokes when he describes the ‘divided states’ of idealisation and de-idealisation that he postulates occur in the minds of traders. There may well be a value in thinking about how financial assets relate to Marxian and Freudian notions of ‘fetishism,’ but Tuckett forecloses this possibility with his neologism.
To summarise, there are two separate etymologies of ‘phantastic object’ in Tuckett’s writings with Taffler on the subject. The strain that depends most heavily on Freud is very hard to distinguish from more conventional uses of ‘fetish object’ while the version that depends strictly on Laplanche and Pontalis is oxymoronic. Either ‘phantastic objects’ are essentially fetish objects, denuded of the explicit eroticism, or they are an inherently self-contradictory attempt to bridge the gap between part-objects expressed in paranoid-schizoid states (where what the infant desires is the maternal breast) and vehicles for the realisation of erotic phantasies in less regressed states of mind. The love affair, in short, is not with the financial asset or the car or the suit, but rather still with idea of ‘getting the girl’ or ‘winning the game.’
The latter is simply an instance of superfluity and proliferation of neologisms, which in turn muddles the waters, whereas the former suggests something of the very problematic hermeneutic strategy employed by those who advocate for Emotional Finance.
Interpretation is not explanation; causation is not causality. In the social sciences, this is almost a cliché, but they are important caveats. Causality rests on the identification of a specific mechanism by which X has an effect on Y. Explanations can be realistic in the sense that they try to account for external reality, or epistemic (anti-realist) in the sense that they strive for the internal consistency of the empirical model. Much of Tuckett’s complaint with modern economics is that it strives for the latter, whereas the natural sciences present themselves as interested in the former, except perhaps in cosmology.
In finance, the movement of prices is easy to explain: they rise when there are more buyers than sellers, they fall when there are more sellers than buyers. The willingness to buy or sell is, indeed, partly influenced by individual expectations of future prices, such that for markets to function there has to be heterogeneity of belief. There is nothing at all surprising about that. Predicting the movement of prices is an occult science, whether practiced by ‘chartists’ who do ‘technical analysis’ or by punters who pontificate on the market outlook for a particular stock. Interpreting price behaviour (explaining why markets rose or fell) lies somewhere in between, though much of it depends on normative judgments about ‘value’. To imagine that you are in an asset-price bubble is to imagine that the current prices of an asset have diverged from some ‘rational’ judgement of fundamental value.
Tuckett retains that notion of ‘rationality’, though he attributes it to an uncertain, yet-to-be-experienced ‘objective reality’ rather than to the price discovery mechanisms of the market. Now he is by no means alone in that, as Behavioural Economics speaks of ‘bounded rationality,’ but the problem is whether or not any of this can be apprehended ex-ante. Tuckett identifies the ‘drowning out’ of naysayers as a feature of the euphoria he describes, yet some of these naysayers, like Nouriel Roubini, have successfully predicted ten out of the last three crises, whereas Warren Buffett made an even greater fortune on Berkshire Hathaway’s derivatives book, even as he preached about ‘weapons of mass destruction’. Some people are hypocrites, others are stereotyped market commentators, and even stopped clocks are right twice a day. It should not be forgotten that the people who made the most money in the Great Financial Crisis where those who shorted subprime mortgages, often against the interests of their own clients. The most successful currency trade in modern times, the Black Wednesday bear raid organised by Soros, was, in fact, a mean-reversion trade designed to force sterling out of the European Exchange Rate Mechanism. It just took two billion pounds to do it. In other words, the trick about shorting anything is timing it. To echo the line often mis-ascribed to Keynes, ‘markets can remain irrational longer than you can remain solvent.’
Tuckett [2] variously declaims any attempts at quantification, though his most recent work aims in that direction by attempting to exploit insights from Big Data [19, 20, 21, 22, 23]. Instead, what Tuckett’s approach is offering is an interpretative strategy, which serves mostly as an elaboration of the latter stages of the Minsky-Kindleberger model of an asset-price bubble [24], which identifies states of ‘displacement, new opportunities, boom, euphoria, dismissal, unease, panic, revulsion’ ([2], p. 16). What the Minsky-Kindleberger model describes is not a mechanism of causality but a causal chain. Psychoanalysis, with its roots in Aristotelian casuistry (with the assumption of a relationship, albeit a complex one, between infantile conflicts and adult neuroses), is especially well-suited to such an exercise. Moreover, psychoanalytic theories of causation are also multi-valent. Aristotle identified four cases: material, formal, efficient and final. Freudian psychoanalysis, on the other hand, tends to consider symptoms simultaneously in terms of ‘origins, genesis, function, meaning and expectation’ ([25], pp. 22-36). For example, your euphoria might well have its genesis in the rising price of a stock (or falling if you shorted it), might have its origins in an outpouring of enthusiasm for a new technology in which the particular firm has an advantage, might be a function of the fact that institutional investors have decided they need exposure to that sector, may mean that firms that rely on older technologies will experience hard times, might have been expected given the success of a similar technology in a more advanced country. None of these interpretations of your euphoria have anything to do with causality in any formal sense (that the number of buyers in the market started to outnumber the number of sellers); but rather this strategy reflects a mode of analysis of narrative causation that is liberating because it disrupts established narratives and opens up the possibilities of new ones. This is why Tuckett eventually became interested in ‘conviction narratives’, because that seemed to present a means of moving from causation to casuality [20, 22]. Unfortunately, this strategy creates a hermeneutic circle.
As post-structuralist literary critics subsequently noticed, these narratives generally are organised around one of four tropes: metaphor, metonymy, synecdoche, and irony, which correspond to the emplotments of romance, tragedy, comedy/farce and satire [26]. Tuckett’s case studies of investment managers do all follow similar trajectories, but figural causation is an artefact of the mimetic function of narrative not a feature of reality [27]. The reason that people have not learned from previous crises is because there is less to learn than some might imagine. To suggest otherwise is to suggest that there is some path dependent group psychological structuring of financial bubbles, such that they all have the same denouement, regardless of the particular asset at their core. To the extent that this is true, it is obvious (and just an elaboration of Minksy-Kindleberger), and to the extent that it is not obvious, it is wrong (in that the focal point of bubbles does matter) for reasons that should become clear in the next two sections.
As Tuckett explains, ‘groupfeel’ has replaced his earlier usage of ‘groupthink’ as a way of aggregating the individual emotion states of participants in a market. He is surely correct that groups display elements of ‘consensus seeking, group polarisation, out-group stereotyping, and the suppression of dissent’ ([2], p. 66-67), but what becomes harder to understand is why he sees financial markets as ‘groups’ in the sense that a notion of ‘groupfeel’ would apply. Financial markets are nothing if not competitive arenas, and despite the existence of social spaces in which collusion might occur (c.f. LIBOR-fixing), the notion that the market is subject to these dynamics is implausible. Individual firms may be, which is significant only insofar as some bulge-bracket firms become the dominant dealers in particular financial assets. Tuckett is likewise correct that the structure of the industry means individual managers may be more concerned with short-term performance than with longer-term results, but that has nothing to do with phantastic objects and everything to do with how compensation is structured. This is also one of the few areas in which the market does ‘zero-sum,’ as those managers who outperform the market benchmark are richly rewarded in fees, whereas those who underperform benchmarks get sacked. That said, zero-sum games are not especially known for displaying the sorts of group psychologies found elsewhere.
Behavioural Finance, instead, offers ‘herding’ as a heuristic that mangers use to main-chase the perceived ‘market leaders’. This is not necessarily an emotional response (as one might prefer to think oneself smarter than other traders), but rather a rational one of achieving safe but possibly sub-optimal returns. Even so, it is a simplification to suggest that risk-on/risk-off maps to divided states (paranoid-schizoid and depressive positions), unless one assumes that the ‘market norm’ is one of stagnation, which, at least in equities, is discredited by the data presented in Figure 6. What Tuckett has, in fact, done is taken a typical risk management heuristic of the directors of trading desks on dealing floors, which is to remove from the floor traders who are losing on a given day and to cap the winnings of those who appear to be ‘streaking’, and used it as a synecdoche for the market as a whole. That interpretative strategy makes very little sense, however, when you consider that the reason that the risk manager is pulling the trader is that the frustration and disappointment causes him to exaggerate risk, whereas the success encourages him to underrate risk vis-à-vis the market as a whole. Crucially, in Tuckett’s model, the problem is not the distance between the judgements of individuals and the group, but rather the distance between the phantasies of market participants and his notion of ‘reality’, which can only be apprehended ex-post, but seems in fact to be based upon some notion of equilibrium and rationality that hovers behind the precise notions that he and Taffler try to critique [28], hence the hermeneutic circle.
The final problem with Emotional Finance is the specification of the problem. Financial bubbles are nothing new. One recent work of economic theory written by a former practitioner called them ‘banal’ and ubiquitous [29]. Janeway suggests that problem is not with asset-price bubbles, per se, but rather that some of them are productive whereas others destructive. Asset-price bubbles that focus on ‘general purpose technologies’ or infrastructure (canals, steamships, railways, electrification, information computing technology, etc) tend to be socially beneficial. The rush of speculation generates a tolerance for Schumpterian waste. Once the music stops, individual firms may go bankrupt, but the roads, canals, bridges, and rail lines remain.
Debt-leveraged bubbles, particularly in real estate, can wipe out private wealth and cause contagion to other aspects of the economy, generating great hardship, but those are usually generated by central banks that use household balance sheets to smooth aggregate demand, as happened in the late 1990s and early 2000s. Tuckett’s analysis makes no distinction on the basis of the focal point of the bubble. Bubbles are all equally suspect, in that they are formed around ‘phantastic objects’ that promise fool’s gold. Underlying Tuckett’s work is the peculiar fantasy that it is possible to train regulators to identify asset-price bubbles based on their recognition of a kind of prodromal euphoric state as evidenced in the chatter of traders, particularly on the Bloomberg platform. The idea is then to install circuit-breakers, as a kind of market nanny decides ‘enough is enough.’ The problem with this is that it begs the regulatory equivalent of the Texas sharpshooter fallacy, whereby a gunman sprays the side of a barn with a shotgun and only then steps up to draw the bulls-eye. We can only guess at the number of ‘dangerous’ bubbles that will be so averted!
One final point remains to be made. Tuckett calls for a ‘Truth and Reconciliation’ Commission to investigate the financial crisis in the same manner as the Apartheid-era crimes in South Africa. Leaving aside the question of how successful the latter was, the former is hardly worthy of such an intellectual and moral project. The Great Financial Crisis was not the end of the world as we know it, particularly from the vantage point of 2020 when the effects of COVID-19 lockdowns are so much greater. If the Great Financial Crisis was a searing experience for the Millenial generation, it was not because of economic realities, but because of the political responses to the crisis. Austerity policies which produced widening inequality in the developed world are neither ‘necessary evils’ nor the product of the ‘inner logic of capitalism,’ but rather are ideological and political projects pursued with the blessings of the median-voter.
The most dangerous thing about Tuckett’s proposals for ‘minding markets’ is that they de-politicise the regulatory process, putting it in the realm of regulating human emotion, rather than in the sphere of political economy. For Tuckett, based in the United Kingdom, the irony of the financial crisis is that the neo-liberal experiment in ‘light touch’ regulation and low levels of taxation (especially on capital gains) happened under New Labour. The Tories came to power on the back of New Labour’s mistakes, and have followed the ‘tried and true’ strategy of austerity while blaming the ‘pain’ on their predecessors and on the European Union. The electoral calculus of such a strategy produced Brexit. The glee with which the political elite have pursued these aims and the docility of the electorate in the face them is a much worthier target of study via notions like ‘groupfeel’ and ‘phantasies’ that produce master narratives. Rather than minding impersonal markets, we’d do better to mind our own tendency to deny the damage done by those who used the financial crisis to justify policies that they fully intended to pursue anyway. If 9/11 was George Bush’s excuse for invading Iraq, the collapse of Lehman Brothers in its vicissitudes offered the Tories a pretext for dismantling the welfare state. In the era of COVID-19, the main challenge for regulators and central banks alike will be resisting pressure from politicians the world over to encourage bull markets deliberately in order to maintain consumer and investment confidence in the face of damage to the real economy and especially the attendant job losses. In addressing such questions, ‘Emotional Finance’ has little to offer, despite the sense of Déjà vu involved.
The author would like to acknowledge a number of people with whom she has discussed this subject over the years, including Professor Michelle Baddeley, Dr Stanley H. Shapiro, Dr Frederick Fisher, Dr Margot Waddell, and Dr David Bell, and the members of the London Consortium’s ‘Psychoanalytic Thought, History and Political Life Forum,’ which was where she gave a version of this paper as a seminar in June 2014. Special thanks to the organisers, Dr Shaul Bar-Haim, Dr Benjamin Poore, and Dr Helen Tyson, and particularly to Professor Daniel Pick, Professor Jacqueline Rose, Dr Matt ffytche, Dr Manuel Batsch, and Dr Akshi Singh for their comments. A version was also circulated in September 2015 at the New Imago Forum at Jesus College, Oxford where useful suggestions were made. The author would also like to thank her research assistant, Mr Eskil Välilä, for redrawing the graphs with updated data, which required considerable research, and for rendering them to suit the formatting guidelines for this publication.
The author declares no conflict of interest.
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\n\nThe Corresponding Author agrees to indemnify and hold IntechOpen harmless against all liabilities, costs, expenses, damages and losses and all reasonable legal costs and expenses suffered or incurred by IntechOpen arising out of or in connection with any breach of the aforementioned representations and warranties. This indemnity shall not cover IntechOpen to the extent that a claim under it results from IntechOpen's negligence or willful misconduct.
\n\n4.2 Nothing in this Publication Agreement shall have the effect of excluding or limiting any liability for death or personal injury caused by negligence or any other liability that cannot be excluded or limited by applicable law.
\n\n5. TERMINATION
\n\n5.1 IntechOpen has a right to terminate this Publication Agreement for quality, program, technical or other reasons with immediate effect, including without limitation (i) if the Corresponding Author or any Co-Author commits a material breach of this Publication Agreement; (ii) if the Corresponding Author or any Co-Author (being an individual) is the subject of a bankruptcy petition, application or order; or (iii) if the Corresponding Author or any Co-Author (being a company) commences negotiations with all or any class of its creditors with a view to rescheduling any of its debts, or makes a proposal for or enters into any compromise or arrangement with any of its creditors.
\n\nIn case of termination, IntechOpen will notify the Corresponding Author, in writing, of the decision.
\n\n6. INTECHOPEN’S DUTIES AND RIGHTS
\n\n6.1 Unless prevented from doing so by events outside its reasonable control, IntechOpen, in its discretion, agrees to publish the Chapter attributing it to the Corresponding Author and any Co-Author.
\n\n6.2 IntechOpen has the right to use the Corresponding Author’s and any Co-Author’s names and likeness in connection with scientific dissemination, retrieval, archiving, web hosting and promotion and marketing of the Chapter and has the right to contact the Corresponding Author and any Co-Author until the Chapter is publicly available on any platform owned and/or operated by IntechOpen.
\n\n6.3 IntechOpen is granted the authority to enforce the rights from this Publication Agreement, on behalf of the Corresponding Author and any Co-Author, against third parties (for example in cases of plagiarism or copyright infringements). In respect of any such infringement or suspected infringement of the copyright in the Chapter, IntechOpen shall have absolute discretion in addressing any such infringement which is likely to affect IntechOpen's rights under this Publication Agreement, including issuing and conducting proceedings against the suspected infringer.
\n\n7. MISCELLANEOUS
\n\n7.1 Further Assurance: The Corresponding Author shall and will ensure that any relevant third party (including any Co-Author) shall, execute and deliver whatever further documents or deeds and perform such acts as IntechOpen reasonably requires from time to time for the purpose of giving IntechOpen the full benefit of the provisions of this Publication Agreement.
\n\n7.2 Third Party Rights: A person who is not a party to this Publication Agreement may not enforce any of its provisions under the Contracts (Rights of Third Parties) Act 1999.
\n\n7.3 Entire Agreement: This Publication Agreement constitutes the entire agreement between the parties in relation to its subject matter. It replaces and extinguishes all prior agreements, draft agreements, arrangements, collateral warranties, collateral contracts, statements, assurances, representations and undertakings of any nature made by or on behalf of the parties, whether oral or written, in relation to that subject matter. Each party acknowledges that in entering into this Publication Agreement it has not relied upon any oral or written statements, collateral or other warranties, assurances, representations or undertakings which were made by or on behalf of the other party in relation to the subject matter of this Publication Agreement at any time before its signature (together "Pre-Contractual Statements"), other than those which are set out in this Publication Agreement. Each party hereby waives all rights and remedies which might otherwise be available to it in relation to such Pre-Contractual Statements. Nothing in this clause shall exclude or restrict the liability of either party arising out of its pre-contract fraudulent misrepresentation or fraudulent concealment.
\n\n7.4 Waiver: No failure or delay by a party to exercise any right or remedy provided under this Publication Agreement or by law shall constitute a waiver of that or any other right or remedy, nor shall it preclude or restrict the further exercise of that or any other right or remedy. No single or partial exercise of such right or remedy shall preclude or restrict the further exercise of that or any other right or remedy.
\n\n7.5 Variation: No variation of this Publication Agreement shall be effective unless it is in writing and signed by the parties (or their duly authorized representatives).
\n\n7.6 Severance: If any provision or part-provision of this Publication Agreement is or becomes invalid, illegal or unenforceable, it shall be deemed modified to the minimum extent necessary to make it valid, legal and enforceable. If such modification is not possible, the relevant provision or part-provision shall be deemed deleted.
\n\nAny modification to or deletion of a provision or part-provision under this clause shall not affect the validity and enforceability of the rest of this Publication Agreement.
\n\n7.7 No partnership: Nothing in this Publication Agreement is intended to, or shall be deemed to, establish or create any partnership or joint venture or the relationship of principal and agent or employer and employee between IntechOpen and the Corresponding Author or any Co-Author, nor authorize any party to make or enter into any commitments for or on behalf of any other party.
\n\n7.8 Governing law: This Publication Agreement and any dispute or claim (including non-contractual disputes or claims) arising out of or in connection with it or its subject matter or formation shall be governed by and construed in accordance with the law of England and Wales. The parties submit to the exclusive jurisdiction of the English courts to settle any dispute or claim arising out of or in connection with this Publication Agreement (including any non-contractual disputes or claims).
\n\nLast updated: 2020-11-27
\n\n\n\n
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