Abstract
IntroductionNowadays, the most significant challenges in the stock market is to predict the stock prices. The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature.Case descriptionSupport Vector Machines (SVM) and Artificial Neural Networks (ANN) are widely used for prediction of stock prices and its movements. Every algorithm has its way of learning patterns and then predicting. Artificial Neural Network (ANN) is a popular method which also incorporate technical analysis for making predictions in financial markets.Discussion and evaluationMost common techniques used in the forecasting of financial time series are Support Vector Machine (SVM), Support Vector Regression (SVR) and Back Propagation Neural Network (BPNN). In this article, we use neural networks based on three different learning algorithms, i.e., Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared.ConclusionAll three algorithms provide an accuracy of 99.9% using tick data. The accuracy over 15-min dataset drops to 96.2%, 97.0% and 98.9% for LM, SCG and Bayesian Regularization respectively which is significantly poor in comparison with that of results obtained using tick data.
Highlights
Nowadays, the most significant challenges in the stock market is to predict the stock prices
We use neural networks based on three different learning algorithms, i.e., Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared
Artificial neural networks have been used widely to solve many problems due to its versatile nature. (Samek & Varachha, 2013) (Yodele et al, 2012), presented a hybridized approach, i.e., a combination of the variables of fundamental and technical analysis of stock market indicators to predict future stock prices to improve the existing methods, (Yodele et al, 2012) (Y Kara & A Boyacioglu, 2011) discussed stock price index movement using two models based on Artificial Neural Network (ANN) and Support Vector Machine (SVM). They compared the performances of both the models and concluded that the average performance of the ANN model was significantly better than the Support Vector Machines (SVM) model. (Y Kara & A Boyacioglu, 2011) (Qi & Zhang, 2008) investigated the best modeling of trend time series using Neural Network
Summary
The most significant challenges in the stock market is to predict the stock prices. The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature. Case description: Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are widely used for prediction of stock prices and its movements. Artificial Neural Network (ANN) is a popular method which incorporate technical analysis for making predictions in financial markets. In any country stock market is one of the most emerging sectors. With the development of the stock market, people are interested in forecasting stock price. Due to dynamic nature and liable to quick changes in stock price, prediction of the stock price becomes a challenging task. Stock markets are mostly a non-parametric, non-linear, noisy and deterministic chaotic system (Ahangar et al 2010)
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