Soft Computing Techniques for Stock Market Prediction: A Literature Survey

Stock market trading is an unending investment exercise globally. It has potentials to generate high returns on investors’ investment. However, it is characterized by high risk of investment hence, having knowledge and ability to predict stock price or market movement is invaluable to investors in the stock market. Over the years, several soft computing techniques have been used to analyze various stock markets to retrieve knowledge to guide investors on when to buy or sell. This paper surveys over 100 published articles that focus on the application of soft computing techniques to forecast stock markets. The aim of this paper is to present a coherent of information on various soft computing techniques employed for stock market prediction. This research work will enable researchers in this field to know the current trend as well as help to inform their future research efforts. From the surveyed articles, it is evident that researchers have firmly focused on the development of hybrid prediction models and substantial work has also been done on the use of social media data for stock market prediction. It is also revealing that most studies have focused on the prediction of stock prices in emerging market

Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

Improving risk-adjusted performance in high frequency trading using interval type-2 fuzzy logic

In this paper, we investigate the ability of higher order fuzzy systems to handle increased uncertainty, mostly induced by the market microstructure noise inherent in a high frequency trading (HFT) scenario. Whilst many former studies comparing type-1 and type-2 Fuzzy Logic Systems (FLSs) focus on error reduction or market direction accuracy, our interest is predominantly risk-adjusted performance and more in line with both trading practitioners and upcoming regulatory regimes. We propose an innovative approach to design an interval type-2 model which is based on a generalisation of the popular type-1 ANFIS model. The significance of this work stems from the contributions as a result of introducing type-2 fuzzy sets in intelligent trading algorithms, with the objective to improve the risk-adjusted performance with minimal increase in the design and computational complexity. Overall, the proposed ANFIS/T2 model scores significant performance improvements when compared to both standard ANFIS and Buy-and-Hold methods. As a further step, we identify a relationship between the increased trading performance benefits of the proposed type-2 model and higher levels of microstructure noise. The results resolve a desirable need for practitioners, researchers and regulators in the design of expert and intelligent systems for better management of risk in the field of HFT

Soft Computing Techniques for Stock Market Prediction: A Literature Survey

Stock market trading is an unending investment exercise globally. It has potentials to generate high returns on investors’ investment. However, it is characterized by high risk of investment hence, having knowledge and ability to predict stock price or market movement is invaluable to investors in the stock market. Over the years, several soft computing techniques have been used to analyze various stock markets to retrieve knowledge to guide investors on when to buy or sell. This paper surveys over 100 published articles that focus on the application of soft computing techniques to forecast stock markets. The aim of this paper is to present a coherent of information on various soft computing techniques employed for stock market prediction. This research work will enable researchers in this field to know the current trend as well as help to inform their future research efforts. From the surveyed articles, it is evident that researchers have firmly focused on the development of hybrid prediction models and substantial work has also been done on the use of social media data for stock market prediction. It is also revealing that most studies have focused on the prediction of stock prices in emerging market

Estimation of flexible fuzzy GARCH models for conditional density estimation

In this work we introduce a new flexible fuzzy GARCH model for conditional density estimation. The model combines two different types of uncertainty, namely fuzziness or linguistic vagueness, and probabilistic uncertainty. The probabilistic uncertainty is modeled through a GARCH model while the fuzziness or linguistic vagueness is present in the antecedent and combination of the rule base system. The fuzzy GARCH model under study allows for a linguistic interpretation of the gradual changes in the output density, providing a simple understanding of the process. Such a system can capture different properties of data, such as fat tails, skewness and multimodality in one single model. This type of models can be useful in many fields such as macroeconomic analysis, quantitative finance and risk management. The relation to existing similar models is discussed, while the properties, interpretation and estimation of the proposed model are provided. The model performance is illustrated in simulated time series data exhibiting complex behavior and a real data application of volatility forecasting for the S&P 500 daily returns series

Система підтримки прийняття рішень для аналізу розвитку фінансових процесів

Магістерська дисертація: 118 с., 15 рис., 25 табл., 1 додаток, 58 джерел. Об’єкт дослідження – процес аналізу розвитку фінансових процесів фондового ринку. Предмет дослідження – математичні моделі і методи опису гетероскедастичних процесів, методи прогнозування часових рядів, оцінювання та аналізу якості побудованих моделей та прогнозів, моделі та методи оцінювання ринкових ризиків, а також методи перевірки якості оцінок ризику. Методи дослідження – теорія моделювання і прогнозування, регресійний аналіз, статистичні методи. Метою роботи є побудова системи підтримки прийняття рішень, яка включає в себе адекватну модель гетероскедастичного процесу для прогнозування волатильності та оцінювання ризику акцій фінансового ринку за її допомогою. В роботі проведено огляд основних підходів до оцінювання ринкових ризиків, розглянуто та проаналізовано метод оцінки Value-at-Risk. Також проведений огляд моделей та їх особливостей для опису динаміки волатильності та її прогнозування. Було проаналізовано результати моделювання та оцінювання за-для обґрунтованого вибору найкращої моделі для оцінки ринкових ризиків. Моделювання процесів на базі умовно гетероскедастичних моделей та на базі рекурентних нейронних мереж для оцінювання ризикової вартості за їх допомогою реалізовано на мові програмування Python.Master`s thesis: 118 p., 15 fig., 25 tab., 1 appendix, 58 sources. Object of research - the process of analyzing the development of financial processes of the stock market. Subject of research - mathematical models and methods of description of heteroscedastic processes, methods of forecasting time series, evaluation and analysis of the quality of built models and forecasts, models and methods of market risk assessment, as well as methods of checking the quality of risk assessments. Research methods - theory of modeling and forecasting, regression analysis, statistical methods. The aim is to build a decision support system that includes an adequate model of the heteroskedastic process for forecasting volatility and assessing the risk of financial market shares with its help. In this paper, it is reviewed of the main approaches to market risk estimation, reviewed and analyzed the method for estimating Value-at—Risk and applied innovative methods for verifying the quality of these estimates. Also reviewed models and their features to describe the dynamics of volatility and its forecasting. Results of modeling, forecasting and evaluation were analyzed for selecting the best model for market risks estimation. Modeling and forecasting of financial and economic processes based on autoregressive conditionally heteroscedastic models and recurrent neural networks estimating the risk with their help are implemented in the programming language Python

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

An Improved Model for Stock Price Prediction using Market Experts Opinion

Several research efforts had been done to forecast stock price based on technical indicators which rely purely on historical stock price data. Nevertheless, their performance is not always satisfactory. However, there are other influential factors which can affect the direction of stock market which form the basis of market experts’ opinion such as interest rate, inflation rate, foreign exchange rate, business sector, management caliber, government policy and political effects among others. In this paper, the effect of using market experts’ opinion in addition to the use of technical and fundamental indicators for stock price prediction is examined. Input variables extracted from these hybrid indicators are fed into a fuzzy-neural network for improved accuracy of stock price prediction. The empirical results obtained with published stock data shows that the proposed model can be effective to improving accuracy of stock price predictio