Application of neuro-fuzzy methods for stock market forecasting: a systematic review

Predicting stock prices is a challenging task owing to the market's chaos and uncertainty. Methods based on traditional approaches are unable to provide a solution to the market predictability issue. Thus, contemporary models using accurate neuro-fuzzy systems are found to be the most effective approach to tackling the problem. However, the existing literature lacks a detailed survey of the application of neuro-fuzzy techniques for stock market prediction. This paper presents a systematic literature review of the use of neuro-fuzzy systems for predicting stock market prices and trends.  On this basis, articles issued in various reputed international journals from 2000 to July 2022 were examined, 11 duplicates and 4 non-exclusive articles were removed and, as consequent, 24 eligible studies were retrieved for inclusion. Thus, analysis and discussions were based on two major viewpoints: predictor techniques and accuracy metrics. The review reveals that the researchers, based on their knowledge and research interests, applied a diverse neuro-fuzzy technique and shown stronger preference for certain neuro-fuzzy methods, such as ANFIS. To draw conclusions about the model performance, researchers chose different statistical and non-statistical metrics according to the technique used. It was finally observed that neuro-fuzzy approaches outperform, within its limits, conventional methods. However, each has its own set of constraints regarding the challenges involved in putting it into practice. The complexity of the presented approaches is the most significant potential obstacle that they face. Therefore, stock market prediction is a difficult undertaking, and multiple elements should be considered for accurate prediction. Yet, despite the subject's prominence, there are still promising new frontiers to explore and develop. Keywords: Fuzzy logic, Artificial neural network, Neuro-fuzzy, stock market forecasting JEL Classification: F37 Paper type: Theoretical Research  Predicting stock prices is a challenging task owing to the market's chaos and uncertainty. Methods based on traditional approaches are unable to provide a solution to the market predictability issue. Thus, contemporary models using accurate neuro-fuzzy systems are found to be the most effective approach to tackling the problem. However, the existing literature lacks a detailed survey of the application of neuro-fuzzy techniques for stock market prediction. This paper presents a systematic literature review of the use of neuro-fuzzy systems for predicting stock market prices and trends.  On this basis, articles issued in various reputed international journals from 2000 to July 2022 were examined, 11 duplicates and 4 non-exclusive articles were removed and, as consequent, 24 eligible studies were retrieved for inclusion. Thus, analysis and discussions were based on two major viewpoints: predictor techniques and accuracy metrics. The review reveals that the researchers, based on their knowledge and research interests, applied a diverse neuro-fuzzy technique and shown stronger preference for certain neuro-fuzzy methods, such as ANFIS. To draw conclusions about the model performance, researchers chose different statistical and non-statistical metrics according to the technique used. It was finally observed that neuro-fuzzy approaches outperform, within its limits, conventional methods. However, each has its own set of constraints regarding the challenges involved in putting it into practice. The complexity of the presented approaches is the most significant potential obstacle that they face. Therefore, stock market prediction is a difficult undertaking, and multiple elements should be considered for accurate prediction. Yet, despite the subject's prominence, there are still promising new frontiers to explore and develop. Keywords: Fuzzy logic, Artificial neural network, Neuro-fuzzy, stock market forecasting JEL Classification: F37 Paper type: Theoretical Research &nbsp

Test de l’hypothèse de la marche au hasard : une étude empirique sur le MASI et le MADEX

The random walk theory or random walk hypothesis is a mathematical model applied in the financial markets and is considered one of the most important models for evaluating the behavior of financial asset prices. It aims to represent the stock market variations, and it is based on the fact that the prices of financial securities are not predictable and that they evolve in a random way. This study aims to test the hypothesis of random walk in the Moroccan financial market over a period that varies between 02/01/2012 and 21/01/2021 or 2252 days, using different econometric tests, Augmented Dikky Fuller (ADF), Philips Perron (PP) and Runs Test. Testing this hypothesis implies testing the hypothesis of informational efficiency in the weak sense. Weak efficiency means that the information reflected in stock prices is information that can be derived from the history of those prices. It is useless to look at past variations to predict future variations. Therefore, technical analysis is useless.  The informational efficiency hypothesis was developed by Fama in 1965 and means that the market reflects all available information and that we cannot take advantage of historical data.  In fact, there are three forms of efficiency, weak, semi-strong and strong. Each form of efficiency has a level of age of the information that the prices reflect.  The results of these tests rejected the random walk hypothesis for the two stock market indices used (MASI and MADEX), and consequently the hypothesis of informational efficiency in the weak sense is also rejected.La théorie de la marche au hasard ou l’hypothèse de la marche au hasard, est un modèle mathématique appliqué dans les marchés financiers, elle est considérée comme l’un des plus importants modèles d’évaluation du comportement des prix des actifs financiers. Elle a pour objectif de représenter les variations boursières, et elle repose sur la non-prévisibilité des prix des titres financiers et que ces derniers évoluent d’une manière aléatoire. Cette étude a pour objectif de tester l’hypothèse de la marche au hasard dans le marché financier marocain sur une période qui varie entre 02/01/2012 et 21/01/2021 soit 2252 jours, en utilisant des différents tests économétriques, Dikky Fuller Augmenté (ADF), Philips Perron (PP) et Runs Test. Le fait de tester cette hypothèse implique de tester l’hypothèse d’efficience informationnelle au sens faible. L’efficience au sens faible signifie que les informations reflétées par les cours des titres sont des informations qu’on peut tirer de l’historique de ces cours. Il est inutile de regarder les variations passées pour prévoir les variations futures. Donc l’analyse technique n’a aucune utilité.  L’hypothèse d’efficience informationnelle a été élaboré par Fama en 1965 signifie que le marché reflète toutes les informations disponibles et que nous ne pouvons pas tirer profit des données historiques.  En effet, il existe trois formes d’efficience, faible, semi-forte et forte. Chaque forme d’efficience présente un niveau d’ancienneté des informations que les prix reflètent.  Les résultats de ces tests ont rejeté l’hypothèse de la marche au hasard pour les deux indices boursiers utilisés (MASI et MADEX), et par conséquent l’hypothèse de l’efficience informationnelle au sens faible est rejetée aussi

Artificial Intelligence & Machine Learning in Finance: A literature review

In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical Researc

Les principaux biais comportementaux et leurs impacts sur les décisions financières et la performance des investisseurs en trading

The aim of this work is to study the main behavioral biases and their impact on the performance of traders in financial markets, as well as the impact of these behavioral biases in the financial decisions of investors, through purely theoretical aspects. The existing literature confirms the existence of behavioral biases and demonstrates their impact on investment and financing decisions. In fact, recent research on behavioral finance has shown that investors are sensitive to the main behavioral biases: cognitive biases that can modify their beliefs and preferences, and emotional biases that emphasize the role of emotions in market decision-making. This work therefore aims to understand the main behavioral biases and their impact on the financial decisions and performance of investors in trading. Theoretical studies show that there are multiple behavioral biases that influence investors' decisions, namely imitation, disposition effect, representativeness, anchoring, framing, overconfidence, aversion, risk aversion, loss aversion and optimism. As such, this work challenges the assumption of investor rationality and inferences about the efficiency of financial market information.   Keywords : Financial decisions ; behavioral finance ; behavioral biases; financial markets; investors. JEL Classification : G41 Paper type : Theoretical research.Le but de ce travail vise à étudier les principaux biais comportementaux et leur impact sur la performance des traders dans les marchés financiers, ainsi que l'impact de ces biais comportementaux sur les décisions financières des investisseurs, à travers des aspects purement théoriques. La littérature existante confirme l'existence de biais comportementaux et démontre leur impact sur les décisions d'investissement et de financement. En fait, des recherches récentes sur la finance comportementale ont montré que les investisseurs sont sensibles aux principaux biais comportementaux : des biais cognitifs qui peuvent modifier leurs croyances et leurs préférences, et des biais émotionnels qui mettent l'accent sur le rôle des émotions dans la prise de décision sur le marché. Ce travail vise donc à comprendre les principaux biais comportementaux, et leur impact sur les décisions financières et les performances des investisseurs en trading. Les études théoriques montrent qu'il existe de multiples biais comportementaux qui influencent les décisions des investisseurs, à savoir l'imitation, l'effet de disposition, la représentativité, l'ancrage, le cadrage, l'excès de confiance, l'aversion, le risque, l'aversion aux pertes et l'optimisme. En tant que tel, ce travail remet en question l'hypothèse de rationalité des investisseurs et les inférences sur l'efficacité de l'information des marchés financiers.   Mots clés : Décisions financières ; finance comportementale ; biais comportementaux ; marchés financiers ; les investisseurs. Classification JEL :  G41 Type de l’article :  Recherche théorique

Etude de différentes méthodes d’analyse de risque crédit : Revue de littérature

La crise financière qui a secoué le monde au cours des dernières années, s’est exprimée par la faillite de grandes banques, elle a induit une remise en cause du modèle de gestion des risques bancaires notamment le risque crédit. Ce risque doit être géré actuellement par des méthodes plus sophistiquées que par des méthodes classiques et qui d’emblée doivent être pertinentes. Notre recherche à pour but d’étudier et de présenter les différentes techniques d’analyse du risque crédit tout en mettant en évidence l’apport de chacune de ces méthodes pour les analystes afin de mieux évaluer le risque et d’éviter l’impact négatif sur la situation des établissements financiers

LE LIEN ENTRE LA CREATION DE VALEUR ET LE GOODWILL

Dans un contexte de croissance externe très marqué pour les entreprises et de montée en puissance de l‘immatériel à l’échelle internationale. La thématique du lien entre le goodwill et la création de valeur persiste au cœur des débats et suscite l’intérêt des chercheurs, ce qui a donné lieu à une abondante littérature. En effet, l’état de la recherche témoigne des études considérables mobilisant des méthodologies et échantillons différents.L’article tente de tester et de quantifier le lien entre la valeur du goodwill et la création de valeur en se basant sur la théorie d’efficience. L’étude nous a permis de déduire que le goodwill contient des éléments de la création de valeur. A travers l’analyse des caractéristiques des acquisitions créatrices de valeur, les résultats obtenus nous permettent de confirmer les corrélations positives entre les synergies financières et la valeur du goodwill

Theoretical bases on the nature of goodwill

Abstract. Goodwill has been for over a decade a subject of debates and discussions trying to define the concept: its definition, its nature, its recognition. Indeed, the state of research witnesses to the abundant studies using different methodologies. Through this article we will try to make an overview to discuss the concept, its composition and classification. Beginning with a historical overview describing the appearance and theories related to goodwill, this article will be an overview that attempts to bring opinions and definitions given by researchers and then analyze the factors that determine the motivations of the managers of an acquiring company to pay more to the acquired firm the goodwill. In all these definitions, we present a critical opinion to obtain a theoretical and understandable basis for goodwill. Keywords. Goodwill; Theory of goodwill; Intangible; Residue; Super profit; Managers; Agency theory; Overpayment; Performance.JEL. G32, O34, G34, N80, O32, O38, M41, M48, H11

BEHAVIORAL FINANCE: HOW ARE TRADERS' FINANCIAL DECISIONS AND PERFORMANCE IMPACTED BY BEHAVIORAL BIASES UNDER UNCERTAINTY?

International audienceBehavioral finance is the application of psychology to finance, dedicated to explaining anomalies in the financial market based on research and analysis of human behavior. This paper aims for studying from a conceptual side the main behavioral biases that impact traders operating in the financial market under uncertain circumstances. The current literature confirms the existence of cognitive and emotional biases, which could be caused by heuristics or framing faults impacting the decision-making process in investment and financing decisions alongside the performance of traders. In this vein, the findings affirm that although it is difficult to change people's emotions and control them completely, moreover the capacity for human introspection is limited, with the understanding of cognitive biases based on the knowledge and beliefs of the trader, the possibility of modifying or changing the individuals' way of reasoning is more or less feasible in order to moderate their behaviors within the market. Behavioral finance admitting a certain degree of inefficiency in the markets, and the existence of factors that influence the behavior of the trader, is calling for a precise set of rules and trading plans (such as money management), besides the mental and psychological control essential to succeed in the financial market. This theoretical informative paper enters into a series of works that challenge investors' rationality assumption and inferences about the efficiency of financial market information