Comparing different Machine Learning Algorithms in a stock Market Scenario to check which one has the highest efficiency

Abstract Predicting stock market movements using machine learning algorithms is a challenging yet crucial task in financial markets. This study evaluates the efficacy of different machine learning algorithms in predicting stock market trends, utilizing historical stock price data alongside technical indicators as input variables, including Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Random Forest. The study extends the prediction horizon to ten and 30 days into the future, aiming to assess the performance of these algorithms over various timeframes. Results indicate that despite the sophistication of the machine learning models, a simple strategy of always predicting a stock price increase outperforms them, aligning with the random walk theory. This finding contributes to the ongoing discussion on the efficacy of predictive algorithms in financial markets. The implications of these results for stock market prediction and the challenges in accurately forecasting stock price movements are discussed. Ultimately, this study offers valuable perspective on the relative effectiveness of machine learning algorithms within the context of the stock market, illuminating the inherent intricacies involved in forecasting fluctuations in stock market.