Assessing Machine Learning Algorithms Performance in Predicting Stock Market Trends Using Varied Input Combinations

This study presents a thorough investigation of the variables affecting price changes in the Indian Stock Market. The study aims to pinpoint the key elements that significantly influence the closing price of the Indian stock market (Nifty 50) by utilizing three different datasets and two different machine learning algorithms. The results show a notable result: the efficiency of the algorithm did not significantly increase as a result of including economic variables in the analysis. Instead, adding these variables reduced the algorithm's capacity for prediction. As a result, the study contends that technical factors, including historical price patterns, trading volumes, and technical indicators, have a greater impact on the trend prediction of the Indian Stock Market. For investors and other market participants looking to predict future market conditions, the implications of this study are extremely important. Investors can make well-informed investment decisions that could result in higher returns while lowering risks by realizing the predictive value of technical variables. This study emphasizes how crucial it is to combine machine learning algorithms with sophisticated data analysis methods to uncover patterns and insights from market data. Investors can improve their ability to predict market trends and make better investment decisions by incorporating technical variables into predictive models. The study adds to the knowledge by highlighting the necessity of focusing on technical aspects rather than just economic data when forecasting trends in the Indian Stock Market. It implies that the market's supply and demand dynamics, investor attitude, and trading patterns are better reflected when technical factors are taken into consideration. This study highlights the potential advantages of making investment decisions, portfolio allocations, and trading choices proactively based on expected market fluctuations. Overall, the study provides insightful information that might improve the performance and accuracy of forecasting models about the Indian Stock Market.