STOCK PREDICTOR ANALYSIS USING ML AND DATA SCIENCE
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Abstract
Stock Market is a dynamic market where the prices vary and it becomes difficult for an investor for predicting the prices considering external factors like factors like political situations, public image on the company according to efficient market hypothesis. Stock market is extremely volatile and ever evolving with constant developments and research in machine learning and deep learning. This paper deals with a comparative analysis between the most commonly used prediction methods such as Linear Regression, LSTM, CNN , both statistical and recursive learning models using tensor flow and machine learning to find the best fit for individual companies based on historical data.
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References
Rajashree Dash Pradipta Kishore Dash , A hybrid stock trading framework integrating technical analysis with machine learning techniques, The Journal of Finance and Data Science, Vol. 2, issue 1, 2016, pp. 42-57.
Vatsal H Shah, Machine Learning Techniques for Stock Prediction.