Prediction of stock markets using artificial intelligence algorithms

Abstract

Forecasting the stock prices for future is a major work of concern newlineas it helps in earning massive profits for stock traders, investors and stock newlinebrokers. There are various features affecting the market prices like demand newlineand supply of the product, company health, sentiments of traders, as well as newlinegovernment policies. These features result in stock market fluctuation and newlineleads to uncertainty in the stock trend curve. This research work aims to newlineprovide a reliable enhanced prediction model by alleviating problems like newlinelook ahead bias, overfitting due to number of input features, and noise in the newlinedata. This research work provides three contributions in an effort to mitigate newlinethe volatility associated with the stock trend curve. The first contribution aims newlineto select the best features for the prediction model to overcome the problem of newlineoverfitting and provides a mathematical method to overcome the local minima newlineproblem encountered during feature selection. This contribution presents a newlinereliable and efficient model for short term prediction. A Levy Flight-based newlinetuning for the prediction model are incorporated in the Support Vector newlineRegression based prediction model. The primary features based on which the newlinemachine learning model predicts the prices are selected by a tailored Levy newlineFlightprobability distribution model instead of random modelling. The values for newlinethe parameters of the modified Support Vector Regression model are tuned by newlinethe Antlion algorithm which specifically selects the best values for the current newlinestock prediction problem taken in hand. The prediction accuracy is found to newline be higher with this metaheuristic-based approach and the tuned parameters newlineyield high confidence value than the basic regression modelling of data. newlineThe Covid-19 pandemic has hit the stock markets and the trends of newlinestock markets have accelerated share prices of few companies and has also newlinebrought freefall to certain companies. Stock markets have voluminous data newlineand are subjected to uncertainty. The second contribution aims to build a newlinepredictio