Understanding AI and Machine Learning in Stock Predictions
Machine learning is a branch of artificial intelligence (AI) that focuses on building methods to learn from and make predictions based on data. Unlike traditional programming, where rules are explicitly coded, machine learning algorithms learn patterns from sample data, known as 'training data'. This is widely used in areas like medicine and finance, where predicting outcomes or decisions is complex.
In stock markets, algorithmic trading has been a staple, utilizing algorithms to make trading decisions. AI's potential in investing isn't just in trading but also in portfolio formation by analyzing vast amounts of data quickly and avoiding human biases.
AI vs. Human Analysts: A Comparative Study
A notable study by Sean Cao and colleagues looked into whether AI could outperform human analysts in predicting stock returns. Their research aimed to identify when human analysts retain their edge over AI, the benefits of combining AI with human forecasting, and what these findings mean for AI's broader application in fields needing skilled decision-making.
The researchers developed an AI model to predict 12-month stock returns, using data like economic indicators and social media updates—excluding human analysts' forecasts to ensure fairness. Their dataset included over a million analyst forecasts, spanning nearly two decades.
Key Findings
- AI analysts outperform around 54.5% of human analysts, largely due to their ability to quickly process diverse information without human biases.
- The AI model achieved a higher return (alpha) than analysts by 50 to 72 basis points monthly, significant in nearly all cases.
- AI models were particularly effective when data was transparent but vast.
- Human analysts excel in scenarios requiring institutional knowledge, like predicting returns for smaller firms or those in rapid change.
- Combining AI with human insights reduced extreme errors and improved predictions.
AI-Powered Mutual Funds: Evaluating Their Performance
Research by Rui Chen and Jinjuan Ren evaluated AI-powered mutual funds over a 26-month period. These funds use machine learning for stock selection, contrasting with quantitative funds (rule-based) and discretionary funds (human-driven).
Findings
- AI-powered funds performed similarly to the market, except for one month, and didn't deliver significant risk-adjusted returns.
- These funds outshone human-managed ones in reducing transaction costs due to lower portfolio turnover.
Investor Takeaways
The study by Cao and colleagues underscores the potential of blending AI with human expertise for better stock predictions. AI can process large data volumes swiftly, while human analysts offer contextual insights and intuition.
The main advantage of AI is in mitigating human biases, leading to more accurate predictions and efficient markets. However, AI hasn't yet proven itself superior in generating risk-adjusted returns.
For investors, the future might lie in AI-human synergy, leveraging the strengths of both to navigate the complexities of stock markets effectively.