Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises

The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques

What is Computational Intelligence and where is it going?

What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed

Review of Nature-Inspired Forecast Combination Techniques

Effective and efficient planning in various areas can be significantly supported by forecasting a variable like an economy growth rate or product demand numbers for a future point in time. More than one forecast for the same variable is often available, leading to the question whether one should choose one of the single models or combine several of them to obtain a forecast with improved accuracy. In the almost 40 years of research in the area of forecast combination, an impressive amount of work has been done. This paper reviews forecast combination techniques that are nonlinear and have in some way been inspired by nature

Human brain evolution and the "Neuroevolutionary Time-depth Principle:" Implications for the Reclassification of fear-circuitry-related traits in DSM-V and for studying resilience to warzone-related posttraumatic stress disorder.

The DSM-III, DSM-IV, DSM-IV-TR and ICD-10 have judiciously minimized discussion of etiologies to distance clinical psychiatry from Freudian psychoanalysis. With this goal mostly achieved, discussion of etiological factors should be reintroduced into the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V). A research agenda for the DSM-V advocated the "development of a pathophysiologically based classification system". The author critically reviews the neuroevolutionary literature on stress-induced and fear circuitry disorders and related amygdala-driven, species-atypical fear behaviors of clinical severity in adult humans. Over 30 empirically testable/falsifiable predictions are presented. It is noted that in DSM-IV-TR and ICD-10, the classification of stress and fear circuitry disorders is neither mode-of-acquisition-based nor brain-evolution-based. For example, snake phobia (innate) and dog phobia (overconsolidational) are clustered together. Similarly, research on blood-injection-injury-type-specific phobia clusters two fears different in their innateness: 1) an arguably ontogenetic memory-trace-overconsolidation-based fear (hospital phobia) and 2) a hardwired (innate) fear of the sight of one's blood or a sharp object penetrating one's skin. Genetic architecture-charting of fear-circuitry-related traits has been challenging. Various, non-phenotype-based architectures can serve as targets for research. In this article, the author will propose one such alternative genetic architecture. This article was inspired by the following: A) Nesse's "Smoke-Detector Principle", B) the increasing suspicion that the "smooth" rather than "lumpy" distribution of complex psychiatric phenotypes (including fear-circuitry disorders) may in some cases be accounted for by oligogenic (and not necessarily polygenic) transmission, and C) insights from the initial sequence of the chimpanzee genome and comparison with the human genome by the Chimpanzee Sequencing and Analysis Consortium published in late 2005. Neuroevolutionary insights relevant to fear circuitry symptoms that primarily emerge overconsolidationally (especially Combat related Posttraumatic Stress Disorder) are presented. Also introduced is a human-evolution-based principle for clustering innate fear traits. The "Neuroevolutionary Time-depth Principle" of innate fears proposed in this article may be useful in the development of a neuroevolution-based taxonomic re-clustering of stress-triggered and fear-circuitry disorders in DSM-V. Four broad clusters of evolved fear circuits are proposed based on their time-depths: 1) Mesozoic (mammalian-wide) circuits hardwired by wild-type alleles driven to fixation by Mesozoic selective sweeps; 2) Cenozoic (simian-wide) circuits relevant to many specific phobias; 3) mid Paleolithic and upper Paleolithic (Homo sapiens-specific) circuits (arguably resulting mostly from mate-choice-driven stabilizing selection); 4) Neolithic circuits (arguably mostly related to stabilizing selection driven by gene-culture co-evolution). More importantly, the author presents evolutionary perspectives on warzone-related PTSD, Combat-Stress Reaction, Combat-related Stress, Operational-Stress, and other deployment-stress-induced symptoms. The Neuroevolutionary Time-depth Principle presented in this article may help explain the dissimilar stress-resilience levels following different types of acute threat to survival of oneself or one's progency (aka DSM-III and DSM-V PTSD Criterion-A events). PTSD rates following exposure to lethal inter-group violence (combat, warzone exposure or intentionally caused disasters such as terrorism) are usually 5-10 times higher than rates following large-scale natural disasters such as forest fires, floods, hurricanes, volcanic eruptions, and earthquakes. The author predicts that both intentionally-caused large-scale bioevent-disasters, as well as natural bioevents such as SARS and avian flu pandemics will be an exception and are likely to be followed by PTSD rates approaching those that follow warzone exposure. During bioevents, Amygdala-driven and locus-coeruleus-driven epidemic pseudosomatic symptoms may be an order of magnitude more common than infection-caused cytokine-driven symptoms. Implications for the red cross and FEMA are discussed. It is also argued that hospital phobia as well as dog phobia, bird phobia and bat phobia require re-taxonomization in DSM-V in a new "overconsolidational disorders" category anchored around PTSD. The overconsolidational spectrum category may be conceptualized as straddling the fear circuitry spectrum disorders and the affective spectrum disorders categories, and may be a category for which Pitman's secondary prevention propranolol regimen may be specifically indicated as a "morning after pill" intervention. Predictions are presented regarding obsessive-compulsive disorder (OCD) (e.g., female-pattern hoarding vs. male-pattern hoarding) and "culture-bound" acute anxiety symptoms (taijin-kyofusho, koro, shuk yang, shook yong, suo yang, rok-joo, jinjinia-bemar, karoshi, gwarosa, Voodoo death). Also discussed are insights relevant to pseudoneurological symptoms and to the forthcoming Dissociative-Conversive disorders category in DSM-V, including what the author terms fright-triggered acute pseudo-localized symptoms (i.e., pseudoparalysis, pseudocerebellar imbalance, psychogenic blindness, pseudoseizures, and epidemic sociogenic illness). Speculations based on studies of the human abnormal-spindle-like, microcephaly-associated (ASPM) gene, the microcephaly primary autosomal recessive (MCPH) gene, and the forkhead box p2 (FOXP2) gene are made and incorporated into what is termed "The pre-FOXP2 Hypothesis of Blood-Injection-Injury Phobia." Finally, the author argues for a non-reductionistic fusion of "distal (evolutionary) neurobiology" with clinical "proximal neurobiology," utilizing neurological heuristics. It is noted that the value of re-clustering fear traits based on behavioral ethology, human-phylogenomics-derived endophenotypes and on ontogenomics (gene-environment interactions) can be confirmed or disconfirmed using epidemiological or twin studies and psychiatric genomics

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

"Can Banks Learn to Be Rational?"

Can banks learn to be rational in their lending activities? The answer depends on the institutionally bounded constraints to learning. From an evolutionary perspective the functionality (for survival) of "learning to be rational" creates strong incentives for such learning without, however, guaranteeing that each member of the particular economic species actually achieves increased fitness. I investigate this issue for a particular economic species, namely, commrercial banks. The purpose of this paper is to illustrate the key issues related to learning in an economic model by proposing a new screening model for bank commercial loans that uses the neuro fuzzy technique. The technical modeling aspect is integrally connected in a rigorous way to the key conceptual and theoretical aspects of the capabilities for learning to be rational in a broad but precise sense. This paper also compares the relative predictability of loan default among three methods of prediction--- discriminant analysis, logit type regression, and neuro fuzzy--- based on the real data obtained from one of the banks in Taiwan.The neuro fuzzy model, in contrast with the other two, incorporates recursive learning in a real world, imprecise linguistic environment. The empirical results show that in addition to its better screening ability, the neuro fuzzy model is superior in explaining the relationship among the variables as well. With further modifications,this model could be used by bank regulatory agencies for loan examination and by bank loan officers for loan review. The main theoretical conclusion to draw from this demonstration is that non-linear learning in a vague semantic world is both possible and useful. Therefore the search for alternatives to the full neoclassical rationality and its equivalent under uncertainty---rational expectations--- is a plausible and desirable search, especially when the probability for convergence to a rational expectations equilibrium is low.

Modelling and trading the Greek stock market with gene expression and genetic programing algorithms

This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter