Design framework of option price forecasting using cascaded machine learning and swarm intelligence

Abstract

ABSTRACT newlinePrediction of the stock market decides the financial future of traders and investors. The volatile nature of stock market trends decreases the financial investment in the market. For the prediction of the stock price, various parametric and non-parametric computational models are used. The non-parametric models are conventional approaches and have bottleneck problems regarding the predication of a stock price. The non-parametric model enriches the prediction capacity of stock price trends. The process of non-parametric models used the methodology of artificial neural network and machine learning algorithms. The primary variation of the stock price depends on the random nature of the stock price attribute. The efficiency of machine learning algorithm moves on next stage of stock price prediction. But the methodology involves in machine learning needs enhancement with the others methods. newlineIn consequence of modification proposed cascaded machine learning algorithm for the stock price prediction. The cascaded machine learning algorithm work with an optimized variance of stock parameters. The process of parameters optimization achieves by particle swarm optimization. The particle swarm optimization is a memory-based and iterative process. newlineIn consequence of improvement of accuracy design cascaded machine learning algorithm for prediction of the stock price. The accurate and precise principle of cascading machine learning improve the ratio of predication and decreases the rate of price variation. The primary issue of stock price variation is the strike price and risk factor of interest. The random behavior of risk factor, strike price and stock price carry on the unstable market situation. The variation of attribute behavior increased the value of data error. It degraded the strength of the market, for the depreciation of attribute random