Designing a Novel Model for Stock Price Prediction Using an Integrated Multi-Stage Structure: The Case of the Bombay Stock Exchange

Stock price prediction is considered a strategic and challenging issue in the stock markets. Considering the complexity of stock market data and price fluctuations, the improvement of effective approaches for stock price prediction is a crucial and essential task. Therefore, in this study, a new model based on “Adaptive Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)” is employed to predict stock price accurately. ANFIS has been utilized to predict stock price trends more precisely. PSO executes towards developing the vector, and GA has been utilized to adjust the decision vectors employing genetic operators. The stock price data of top companies of the Bombay Stock Exchange (BSE) from 2010 to 2020 are employed to analyze the model functionality. Experimental outcomes demonstrated that the average functionality of our model (77.62%) was achieved noticeably better than other methods. The findings verified that the ANFIS-PSO-GA model is an efficient tool in stock price prediction which can be applied in the different financial markets, especially the stock market

Examining Trust and Willingness to Accept AI Recommendation Systems

This paper proposes and tests a conceptual model that identifies the antecedents of trust in AI, which could in turn lead to users’ willingness to accept AI recommendation systems. An online survey was conducted in the context of stock market investment. Responses came from 313 participants with prior investment experiences. Data were analyzed using partial least squares structural equation modeling. Results indicate that attitude towards AI and perceived AI accuracy were positively related to users’ trust in AI. Users’ AI anxiety was negatively related to trust in AI. Furthermore, users’ trust in AI was positively related to their willingness to accept AI recommendation systems. The paper extends previous works by explicating the role of users’ trust in AI and suggests that the uptake of AI systems can be promoted by fostering favorable attitudes, greater perceived AI accuracy, and lowering AI anxiety

Analysis of Nifty 50 index stock market trends using hybrid machine learning model in quantum finance

Predicting equities market trends is one of the most challenging tasks for market participants. This study aims to apply machine learning algorithms to aid in accurate Nifty 50 index trend predictions. The paper compares and contrasts four forecasting methods: artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and random forest (RF). In this study, the eight technical indicators are used, and then the deterministic trend layer is used to translate the indications into trend signals. The principal component analysis (PCA) method is then applied to this deterministic trend signal. This study's main influence is using the PCA technique to find the essential components from multiple technical indicators affecting stock prices to reduce data dimensionality and improve model performance. As a result, a PCA-machine learning (ML) hybrid forecasting model was proposed. The experimental findings suggest that the technical factors are signified as trend signals and that the PCA approach combined with ML models outperforms the comparative models in prediction performance. Utilizing the first three principal components (percentage of explained variance=80%), experiments on the Nifty 50 index show that support vector classifer (SVC) with radial basis function (RBF) kernel achieves good accuracy of (0.9968) and F1-score (0.9969), and the RF model achieves an accuracy of (0.9969) and F1-Score (0.9968). In area under the curve (AUC) performance, SVC (RBF and Linear kernels) and RF have AUC scores of 1

A classification model on tumor cancer disease based mutual information and firefly algorithm

Cancer is a globally recognized cause of death. A proper cancer analysis demands the classification of several types of tumor. Investigations into microarray gene expressions seem to be a successful platform for revising genetic diseases. Although the standard machine learning (ML) approaches have been efficient in the realization of significant genes and in the classification of new types of cancer cases, their medical and logical application has faced several drawbacks such as DNA microarray data analysis limitation, which includes an incredible number of features and the relatively small size of an instance. To achieve a reasonable and efficient DNA microarray dataset information, there is a need to extend the level of interpretability and forecast approach while maintaining a great level of precision. In this work, a novel way of cancer classification based on based gene expression profiles is presented. This method is a combination of both Firefly algorithm and Mutual Information Method. First, the features are used to select the features before using the Firefly algorithm for feature reduction. Finally, the Support Vector Machine is used to classify cancer into types. The performance of the proposed system was evaluated by using it to classify datasets from colon cancer; the results of the evaluation were compared with some recent approaches

Generating Moving Average Trading Rules on the Oil Futures Market with Genetic Algorithms

The crude oil futures market plays a critical role in energy finance. To gain greater investment return, scholars and traders use technical indicators when selecting trading strategies in oil futures market. In this paper, the authors used moving average prices of oil futures with genetic algorithms to generate profitable trading rules. We defined individuals with different combinations of period lengths and calculation methods as moving average trading rules and used genetic algorithms to search for the suitable lengths of moving average periods and the appropriate calculation methods. The authors used daily crude oil prices of NYMEX futures from 1983 to 2013 to evaluate and select moving average rules. We compared the generated trading rules with the buy-and-hold (BH) strategy to determine whether generated moving average trading rules can obtain excess returns in the crude oil futures market. Through 420 experiments, we determine that the generated trading rules help traders make profits when there are obvious price fluctuations. Generated trading rules can realize excess returns when price falls and experiences significant fluctuations, while BH strategy is better when price increases or is smooth with few fluctuations. The results can help traders choose better strategies in different circumstances

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

Designing a Novel Model for Stock Price Prediction Using an Integrated Multi-Stage Structure: The Case of the Bombay Stock Exchange

Keywords: Stock Price Prediction, Technical Analysis, ANFIS, PSO, GA Stock price prediction is considered a strategic and challenging issue in the stock markets. Considering the complexity of stock market data and price fluctuations, the improvement of effective approaches for stock price prediction is a crucial and essential task. Therefore, in this study, a new model based on “Adaptive Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)” is employed to predict stock price accurately. ANFIS has been utilized to predict stock price trends more precisely. PSO executes towards developing the vector, and GA has been utilized to adjust the decision vectors employing genetic operators. The stock price data of top companies of the Bombay Stock Exchange (BSE) from 2010 to 2020 are employed to analyze the model functionality. Experimental outcomes demonstrated that the average functionality of our model (77.62%) was achieved noticeably better than other methods. The findings verified that the ANFIS-PSO-GA model is an efficient tool in stock price prediction which can be applied in the different financial markets, especially the stock market

Development of a Genetic Programming-based GA Methodology for the Prediction of Short-to-Medium-term Stock Markets

This research presents a specialised extension to the genetic algorithms (GA) known as the genetic programming (GP) and gene expression programming (GEP) to explore and investigate the outcome of the GEP criteria on the stock market price prediction. The aim of this research is to model and predict short-to-medium term stock value fluctuations in the market via genetically tuned stock market parameters. The technology proposes a fractional adaptive mutation rate Elitism (GEPFAMR) technique to initiate a balance between varied mutation rates and between varied-fitness chromosomes, thereby improving prediction accuracy and fitness improvement rate. The methodology is evaluated against different dataset and selection methods and showed promising results with a low error-rate in the resultant pattern matching with an overall accuracy of 95.96% for short-term 5-day and 95.35% for medium-term 56-day trading periods

Optimización de estrategias de trading con promedios móviles para futuros de petróleo mediante algoritmos genéticos

The implementation of trading strategies through computational tools and artificial intelligence, such as artificial neural networks (ANN) and genetic algorithms (AG), have presented important advances in the last years, In this paper it is implemented a ag for the optimization of a trading strategy with two moving averages in the inter-day market of crude oil futures. The objective function is the global return of the investment. The document presents the methodology and the design of this investment strategy with consistent results even with out-of-sample data.La implementación de estrategias de trading a través de herramientas computacionales e inteligencia artificial, entre ellas las redes neuronales artificiales (RNA) y los algoritmos genéticos (AG), ha presentado avances importantes en los últimos años. En este trabajo se implementó un AG para optimizar una estrategia de trading basada en dos promedios móviles en el mercado intradiario de futuros de petróleo crudo WTI. La función objetivo es el retorno global de la inversión. En el documento se presenta la metodología y el diseño de esta estrategia de inversión con resultados consistentes incluso fuera de muestra