Published June 30, 2020 | Version v1
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Different Techniques used in Stock Market Prediction

  • 1. Dept. of Computer Science & Engineering, RV College of Engineering, Bangalore, India,
  • 1. Publisher


The stock market has been one of the primary revenue streams for many for years. The stock market is often incalculable and uncertain; therefore predicting the ups and downs of the stock market is an uphill task even for the financial experts, which they been trying to tackle without any little success. But it is now possible to predict stock markets due to rapid improvement in technology which led to better processing speed and more accurate algorithms. It is necessary to forswear the misconception that prediction of stock market is only meant for people who have expertise in finance; hence an application can be developed to guide the user about the tempo of the stock market and risk associated with it.The prediction of prices in stock market is a complicated task, and there are various techniques that are used to solve the problem, this paper investigates some of these techniques and compares the accuracy of each of the methods. Forecasting the time series data is important topic in many economics, statistics, finance and business. Of the many techniques in forecasting time series data such as the Autoregressive, Moving Average, and the Autoregressive Integrated Moving Average, it is the Autoregressive Integrated Moving Average that has higher accuracy and higher precision than other methods. And with recent advancement in computational power of processors and advancement in knowledge of machine learning techniques and deep learning, new algorithms could be made to tackle the problem of predicting the stock market. This paper investigates one of such machine learning algorithms to forecast time series data such as Long Short Term Memory. It is compared with traditional algorithms such as the ARIMA method, to determine how superior the LSTM is compared to the traditional methods for predicting the stock market.



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Journal article: 2249-8958 (ISSN)


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