Analyzing the Interdependency of Financial Market’s Instruments within the Indian Economy using Machine Learning Algorithms

Abstract

Over the past decades, researchers around the globe have been trying to understand the volatile behavior of the equity market by analyzing the stock prices, daily percentage returns, traded volumes, bid ask spread of the stock, impact of macroeconomic conditions, impact of the bond market, sentiment analysis of the post and tweets on the various social media platforms. These types of studies have been performed on various stock exchanges like New York Stock Exchange, NASDAQ, London Stock Exchange, and various others. Researchers have found that it is not possible to study one market to derive insights and apply its findings over another market. The primary objective for our study is to analyze the interdependency of the financial market’s instruments within the Indian Economy using Machine Learning Algorithms. The instruments of financial markets could be Equity Market, Bond Market, Derivatives Market, Commodity Market while we focus on the Equity Market and a segment of Bond Market called Corporate Bond Market. Machine Learning Algorithms ranges from Regression Analysis, Classification, Clustering, Association Rule Mining, Support Vector Machines, Random Forest to Deep Learning Techniques such as Recurrent Neural Networks, Convolutional Neural Networks etc. In our study, the major focus is on the Application of Regression Analysis and Generalized Addictive Models with Cubic Regression Spline to develop our model. For Performance Evaluation of our Models, we have restricted ourselves to Root Mean Square Error Loss (RMSE) and Mean Absolute Error Loss (MAE) along with the Correlational Analysis to provide insights at the pre-modeling stage.