Predicting financial asset prices with neural network: a comparative study of neural network’s effectiveness in financial decision-making

Investing requires deep knowledge of complex financial markets, making it incredibly tedious ‎to predict inflation and deflation. Predictive conventional models like ARIMA and GARCH ‎do not accurately capture the non-linearity and volatility presented in financial datasets. This ‎research examines the different forms of predictive assets, real estate, stocks, commodities, ‎bonds, and cryptocurrency using Long Short-Term Memory (LSTM) Neural networks. The ‎primary focus of this research is to assess the valuable prediction capabilities of LSTM across ‎assets and its integration with financial decision-making. According to the empirical results, ‎deep learning LSTM models give better outcomes with equities and gold, with the R2 ‎indicator reaching over 99% alongside a low RMSE. LSTMs had an over 100% MPE ‎prediction error rate for other assets during the test phase, making it harder to predict ‎intensely volatile assets. The model's verification transfers residual autocorrelation, showing ‎that it can enhance forecasting performance with detailed macroeconomic indicators and ‎sentiment analysis data. Studies show that LSTMs are effective in high-frequency markets ‎with non-linear price changes, but they require special attention to balance interpretability and ‎overfitting. Despite the progress that has been achieved in utilizing neural networks for ‎financial forecasting, hybrid models integrated with XAI are recommended to improve ‎efficiency and real-world applicability. These results contribute to the growing domain of AI-powered finance by offering additional means for many investors, analysts, and decision-makers who wish to utilize data for market speculation‎.