Sustainable Stock Market Prediction Framework Using Machine Learning Models

1. Introduction

The stock market is a public market where buying, selling, and issuance of shares and other assets of different companies takes place. There are two main stock exchanges that provide the platform for trading in India named as the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). All the important firms of India are listed on both exchanges. The two main Indian market indexes are Sensex and Nifty. Sensex and Nifty are the benchmark index values that are used to measure the performance of the stock market as a whole. The Securities and Exchange Board of India (SEBI) monitors the execution, development, regulation, and supervision of the stock market.

Investments in the stock market are done commonly via stock brokerages and electronic trading platforms. Changes in the stock prices occur due to demand and supply for a particular stock. Higher demand than the supply of stock results in rising of the stock prices whereas less demand and high supply results in fall in stock prices. To earn profit, traders need to invest in stocks at low price and when the price is high, sell it to get maximum return on investment. To be able to do so, they should be aware of the trends in the stock market i.e. the opening and closing price of the stocks because they want to sell the stock at a higher price than the price they have bought the stock at. This is the reason why one should analyse the company whose stocks they are going to invest in to ensure profitable returns. When you are looking to invest in stock there are two ways to evaluate it. One is with fundamental analysis and the other is with technical analysis. The former uses the financial statements reported by the business to calculate the intrinsic value of the business. An intrinsic value is what the company is worth today. Intrinsic value has nothing to do with the stock price so it does not consider the stock price in its calculation. Technical analysis looks at the stock chart to analyze the short-term and long-term trend of the stock to determine where the stock price is most likely going to move to. The movement of the stock is based on supply and demand which is driven by the emotion of greed and fear. In technical analysis, we do not look at anything related to financial statements and we just look at stock charts and indicators.

Most of the investors use both financial and technical analysis in their stock investment to buy a stock when it’s first undervalued and when it’s on an uptrend and in the long term stock price will move towards the intrinsic value of the company that we calculated through fundamental analysis. However, in the short term, price movement is very volatile and driven by emotions and analysed through technical analysis. Technical analysis is done by various data analysts, researchers, and traders to predict the future prices of a stock given the past stock trends and activities. This analysis aims to predict the future stock price values which should be closer to the actual stock price values. Chart patterns and statistical numbers are used extensively by technical analysts and this makes technical analysis strenuous and more complex computation. This is where machine learning comes into play. Machine learning plays a very crucial role in stock price prediction. In simple words, machine learning is the process of adding learning ability to a machine. Machine learning is all about writing an algorithm to pass data and use that data to learn and understand the patterns from the data and use the learned algorithm to make further predictions. Machine learning models are built on sample datasets that are trained to make predictions or decisions to solve problems without specifically being programmed to do so. Various data analysts, researchers use different machine learning algorithms to develop a good decision-making system. The algorithmic method using different algorithmic models helps us to get more accurate results as it eliminates human emotions of fear, greed, etc. A particular machine learning model can be improved by training it again and again if we are not satisfied with the results.