Strategic Selection of Machine Learning Models for Short-term Trading Optimization

Abstract

Stock market prediction is a key aspect for a financial analyst and investor, as it facilitates better decisions and improved investment strategies. Towards this end, this project compares how machine learning (ML) models perform in predicting stock market prices with relatively small dataset sizes so as to get the best performing one. This study evaluates the efficiency and accuracy of artificial neural networks, support vector regression, LSTM and decision trees in capturing market trends and forecasting stock movements. The importance of our proposed paper lies in its potential to demystify the complexities of financial markets for students and new entrants in the field of finance. Participants can garner practical insights on how the stock market operates and the application of theoretical ML concepts to financial analytics by identifying the best-performing ML models with limited datasets, which is a common occurrence in real-life situations. This paper aims to analyse the commonly present machine learning models in stock market prediction and choose the most effective model depending upon their performance, which is achieved using limited information (short term data). This would provide an edge in taking small (less-risky) informed financial decisions without extensive study of the stock market.