Stock health analysis and forecasting using risk-adjusted metrics and LSTM-guided Monte Carlo simulation

The complex and nonlinear behaviors of financial markets make it exceedingly challenging for average investors to evaluate the performance of a specific asset or make meaningful predictions about its future behavior. Investing in stocks involves various evaluation techniques. However, many of these techniques are restricted to a small set of variables or measurements that are static and historical, limiting their ability to assess the evolving risk-return tradeoff. The objective of this research is to develop a more sophisticated technique that integrates stock evaluation and forecasting by combining traditional financial performance indicators and machine learning time-series forecasting techniques. The result is a system that provides a more organized, simplified, and transparent representation of asset performance and prospective trends. The system provides risk-adjusted performance health scores based on traditional financial metrics such as annualized return, volatility, Sharpe ratio, and drawdown, and supplements this assessment with a Long Short-Term Memory (LSTM) network, which is trained on historical return patterns to generate the predictive signal. Monte Carlo simulation technique is implemented in this work to expect future price paths which, on one hand, are mathematically constrained by the financial limits and, on the other hand, are statistically based on the past distributions. Besides, the framework applies explainable AI methods to interpret the forecasted variations in terms of publicly available market and macroeconomic data, ultimately aiming at opening the "black box" to the users and building their trust. The suggested method offers consistent performance evaluation, stable long-term forecasts, and comprehensible insights, which makes it appropriate for decision-support applications in retail investment analysis, according to experimental evaluation using Indian equity and mutual fund data.