Advancing Financial Risk Prediction and Portfolio Optimization Using Machine Learning Techniques

Аннотация

This study explores the application of machine learning models for predicting financial risk and optimizing portfolio management. We compare various machine learning algorithms, including Random Forest, Gradient Boosting, Long Short-Term Memory (LSTM), and Transformer networks, to assess their effectiveness in forecasting asset returns, managing risk, and enhancing portfolio performance. The results demonstrate that machine learning models significantly outperform traditional financial models in terms of prediction accuracy and risk-adjusted returns. Notably, LSTM and Transformer models excel at capturing long- term dependencies in financial data, leading to more robust predictions and improved portfolio outcomes. Feature selection and preprocessing were crucial in maximizing model performance. Portfolio optimization The American Journal of Management and Economics Innovations 01 https://www.theamericanjournals.com/index.php/tajmei The American Journal of Management and Economics Innovations using machine learning models, when combined with By applying advanced machine learning techniques, this traditional optimization techniques, resulted in paper explores how asset allocation decisions based on superior Sharpe and Sortino ratios. These findings predictive models can improve overall portfolio highlight the potential of machine learning to enhance performance, enabling more dynamic and informed real-time financial decision-making, offering more investment strategies. Ultimately, this research aims to adaptive and resilient strategies for managing bridge the gap between traditional portfolio investment portfolios in dynamic market management practices and the modern advancements environments. This research provides valuable insights in machine learning, offering insights into how financial into the integration of machine learning for financial risk prediction can be improved in real-time for optimal risk prediction and portfolio management, with portfolio construction. implications for future advancements in the field. LITERATURE REVIEW The integration of machine learning models into Keywords: Machine learning, financial risk prediction, financial risk prediction and portfolio optimization has portfolio optimization, asset returns forecasting, risk- become a significant area of research in the past adjusted returns, LSTM, Transformer networks, decade. The financial markets' complexity and feature selection, model evaluation, investment unpredictability have spurred interest in exploring non- strategies, financial decision-making, Sharpe ratio, traditional approaches to risk management and Sortino ratio, deep learning, predictive analytics, investment strategies. Machine learning models have financial modeling. shown great promise in overcoming the limitations of traditional models by providing more accurate forecasts and adaptive portfolio strategies. Introduction: In the field of finance, accurately Machine Learning in Financial Risk Prediction predicting financial risk and managing investment portfolios are two of the most crucial tasks for both Financial risk prediction, particularly in the context of individual and institutional investors. Traditional stock market volatility and asset price movements, has portfolio management methods, based on historical been an area of growing interest. Early approaches to price data and static assumptions, have proven to be risk prediction in finance mainly relied on statistical insufficient in handling the complexities and volatility methods, such as Value at Risk (VaR) and GARCH of modern financial markets. Over the past few years, models, which assume a constant volatility over time machine learning (ML) techniques have gained (Engle, 2001). However, these models often fail to significant traction in financial analysis due to their capture the non-linearity and dynamic nature of ability to learn from vast amounts of data, uncover financial markets.In contrast, machine learning complex patterns, and generate more accurate methods have demonstrated superior predictive power predictions. Machine learning models such as Logistic due to their ability to learn from complex data patterns Regression, Random Forest, Gradient Boosting, Long and adapt to changes in the market. For instance, Short-Term Memory (LSTM), and Transformer decision trees and ensemble methods like Random networks have been successfully applied to various Forest and Gradient Boosting have been employed to domains, including financial risk prediction, asset predict stock price movements and assess market risk. pricing, and portfolio optimization. These models can Studies by Buhlmann and Hothorn (2007) and Chen et potentially improve asset selection, enhance risk- al. (2018) showed that these models could outperform adjusted returns, and provide more resilient traditional methods in terms of accuracy and predictive investment strategies in volatile market environments. capability. More advanced techniques