AI in Algorithmic Trading & Market Prediction
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This paper examines the growing role of artificial intelligence in algorithmic trading and market prediction. The study is motivated by the rapid increase in financial data, the spread of automated trading systems, and the limits of traditional forecasting models when markets become nonlinear, volatile, and information-rich. Building on the initial project question, the paper reviews how machine learning, deep learning, reinforcement learning, and natural language processing are being used to generate trading signals, forecast short-term price movements, optimize execution, and support portfolio decisions. It also compares AI-based approaches with classical statistical frameworks such as regression, ARIMA, and volatility models. The review shows that AI can improve pattern recognition and adapt more quickly to changing data structures, especially when large structured and unstructured datasets are available. However, the literature also shows that forecasting success does not automatically translate into consistent abnormal returns once transaction costs, regime shifts, overfitting, and market impact are considered. To address these issues, the paper proposes a transparent empirical methodology based on walk-forward testing, benchmark comparison, and risk-adjusted evaluation. Overall, AI appears to be a powerful enhancement to algorithmic trading, but not a guaranteed replacement for sound financial theory, robust validation, and effective risk control.
Keywords
Algorithmic trading, Artificial Intelligence, financial markets, Machine Learning, market prediction