AI in Financial Forecasting : Improving Accuracy and Strategy
DOI:
https://doi.org/10.55606/optimal.v5i1.6541Keywords:
AI, financial forecasting, LSTM networks, predictive analytics, strategic decision-makingAbstract
Financial forecasting faces growing challenges due to market volatility and the inadequacy of traditional models like ARIMA and linear regression in handling non-linear, high-frequency financial data. Artificial intelligence (AI), particularly models such as long short-term memory (LSTM) networks and transformer-based systems, has demonstrated superior performance in tasks like predicting S&P 500 index movements and assessing corporate credit risk in real time. These models not only improve accuracy but also enable strategic applications—for instance, integrating live sentiment data from financial news to adjust portfolio allocations within milliseconds. AI systems have also been used by investment firms to simulate recession scenarios and guide capital reserve strategies. However, adoption remains hindered by issues such as the “black box” nature of deep learning, inconsistent data quality, and concerns over algorithmic bias. As AI continues to evolve, its value lies not just in forecasting precision but in supporting adaptive, transparent, and forward-looking financial management
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