lfcabhi/Forecasting-of-Indian-IT-Sector-Index-using-Machine-Learning-techniques-and-LSTM

Stock market indexes predictions have always been under the radar of stalwarts belonging from the domains of econometrics, statistics, and mathematics. This has been a fascinating challenge to deal with since a major portion of the research community who promotes the idea of the efficient market hypothesis (EMH) believes that no predictive model can accurately predict the fluctuations of the ever-changing market, while recent works in this field using more advanced techniques like statistical modelling and machine learning can be used to demonstrate the gesticulations of a time series data with exceptional levels of accuracy. The stock market of a given country can be divided into its constituent sectors which represent the entire behaviour of a particular domain instead of the performance of individual companies. In this dissertation, it has been proposed how machine learning and deep neural network technique like Long Short Term Memory (LSTM) can be used to obtain fantastic results for prediction of stock value. Here, the IT sector of India has been taken into account to analyse its characteristic features about its ascend and descend according to the trend of the market. Regression techniques have been used to predict the probable indexes of the closing values and classification methods for identifying their pattern of movement. At first, a detailed machine learning approach has been adopted by using all adept methods like ensemble techniques like bagging and boosting, random forest, multivariate regression, decision tree, support vector machines, MARS, logistic regression and artificial neural networks. The application of univariate time series (with 5 input) deep learning model for regression was also implemented which has outperformed all the machine techniques as expected.

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