Evaluating the Effectiveness of Machine Learning Algorithms in Stock Price Prediction Across Different Time Frames

Financial markets, characterized by their volatility, uncertainty, complexity, and ambiguity (VUCA), pose significant challenges for accurate predictions. Investment has become increasingly intertwined with technological advancements, as machine learning models revolutionise the field of stock market trend predictions, offering potential solutions by processing large datasets, identifying trends, and minimizing human bias. While machine learning is increasingly applied in financial forecasting, understanding the relative strengths and weaknesses of different algorithms across varying time frames remains underexplored. This is especially relevant given the rise of algorithmic trading and new stock markets such as cryptocurrencies, underscoring the need for precise, data-driven predictions. The aim of this study is to evaluate the performance of machine learning algorithms in predicting stock prices within Singapore’s banking sector. The study explores how each algorithm performs when trained on different amounts of data, comparing its effectiveness for short-term, mid-term and long-term stock price predictions. To do this, historical stock prices were collected using the Yahoo Finance API, focusing on closing prices as the target variable. Using the data collected from major Singaporean banks, namely DBS, OCBC and UOB, this study evaluated the performance of various machine learning algorithms: Random Forest (RF), Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM). The various models were all trained on different datasets, and its predictions for the closing price on a specific date was recorded. Each model was evaluated using rigorous performance metrics, including percentage error, R² values, mean absolute error, and mean squared error, to determine their efficacy in capturing trends and minimising predictive inaccuracies. Different algorithms have distinct methods of learning patterns and handling data variability, and thus will perform differently under the same conditions. Hence, we hoped to gain greater insight into each model’s performance and assess their adaptability to the various time frames. This study contributes to the growing body of research on AI-driven financial forecasting by providing a comparative analysis of machine learning algorithms in Singapore’s banking sector. It highlights the need for flexibility in one’s approach to algorithmic trading to enhance prediction accuracy across diverse scenarios. The insights gained can aid financial analysts, traders, and decision-makers in developing data-driven strategies for stock market investments, ultimately promoting more informed decision-making and risk management in a volatile financial landscape.