Comparative Analysis of Impact of Various Global Stock Markets and Determinants on Indian Stock Market Performance - A Case Study Using Multiple Linear Regression and Neural Networks
Globalization and technological advancement has created a highly competitive market in the stock and share market industry. Performance of the industry depends heavily on the accuracy of the decisions made at performance level. The stock market is one of the most popular investing places because of its expected high profit. For prediction, technical analysis approach, that predicts stock prices based on historical prices and volume, basic concepts of trends, price patterns and oscillators, is commonly used by stock investors to aid investment decisions. In recent years, most of the researchers have been concentrating their research work on the future prediction of share market prices by using Statistical & Quantitative tools. But, in this paper we newly propose a methodology in which the Multiple Linear Regression and neural networks is applied to the investor’s financial decision making to invest all type of shares irrespective of the high / low index value of the scripts, in a continuous time frame work. The proposed network has been tested with stock data obtained from the Asian Stock Market Database. Finally, the design, implementation and performance of the proposed multiple linear regression and model of simulated neural network are described.
KeywordsStock Market Performance Multiple Linear Regression NIFTY 50 Artificial Neural Networks
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