Stock Market Prediction using Machine Learning

Being the exchange where the issuing and trading of equities or stocks of publicly held companies take place, the stock market is one of the most vital components of a market’s economy. Stock market prediction is the process of attempting to predict the future values of a company based on its previous data to enhance the probability of a successful trade for an investor. In a financially volatile stock market, it is important to have a very precise prediction of future trends. Stock prediction involves the prediction in advance on whether the future market will close higher or lower compared to its opening levels. The stock market data is highly noisy, irregular and chaotic in nature. Hence proven to be a daunting task for market researchers and investors to make buy or sell decisions. A number of techniques as well as combinations of algorithms have been proposed over time to try and make a reliable and stable prediction. This paper aims at outlining the research work for Stock Market Prediction with special focus to daily, monthly and yearly stock predictions based on the technical approaches that have been proposed or implemented with varying levels of success rates. The algorithms being studied are implemented on a dataset and their accuracies are compared. Rules are proposed at the end of the implementation process to help a developer make predictions on their computers. -------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 11-08-2020 Date of Acceptance: 27-08-2020 -------------------------------------------------------------------------------------------------------------------------------------