(Original Title: AI Won’t Dominate Wall Street: The Challenges of Applying AI to Investments) According to a recent article from the Wall Street Journal, artificial intelligence might not be the silver bullet for Wall Street that many anticipate. While the field of AI-driven investment has seen significant growth, there are still major challenges in applying AI technology to financial markets. Ten years ago, Goldman Sachs faced a setback when their flagship quantitative funds collapsed due to algorithmic trading that failed to deliver expected returns, causing billions in losses. Since then, while advancements in artificial intelligence and machine learning have surged, there are still several critical issues to address before AI can fully dominate Wall Street. One of the biggest concerns is the "black box" nature of many AI systems. These models are often so complex that even their creators struggle to fully comprehend how they make decisions. For instance, neural networks developed by researchers at Washington University can identify wolves in images by associating them with snow, but they may also pick up irrelevant patterns that aren't truly predictive. This leads to the "overfitting" problem, where AI learns from patterns in historical data that don't apply to future scenarios. For example, a model trained on 35 years of declining bond yields might conclude that buying bonds is always a good idea, but this trend isn't guaranteed to continue indefinitely. David Harding, founder of the hedge fund Winton Group, emphasizes that avoiding overfitting is crucial for successful computer-driven investments. He states, "It's about maintaining a mindset that prevents wishful thinking." Similarly, Anthony Ledford, Chief Scientist at Man AHL, notes that more sophisticated AI models can sometimes be less effective because they're too focused on explaining past data rather than predicting future trends. These models need to filter out meaningless noise and focus on identifying significant signals. Many quantitative investors attempt to sidestep overfitting by ensuring their models align with economic or behavioral logic. For instance, if a computer discovers a connection between rainfall in Kansas and rising stock prices of Paris-based oil companies, they would likely dismiss this as coincidental and refrain from basing investment strategies on it. Another major challenge is the lack of transparency in AI systems. Explaining the reasoning behind decisions made by complex AI models is nearly impossible. This opacity raises concerns among regulators and investors alike. The U.S. Department of Defense has even funded projects aimed at creating AI systems capable of explaining their own decisions. Until then, many AI-driven investment strategies remain pilot programs, either managing smaller portions of portfolios or requiring human oversight. Charles Ellis, who recently joined Mediolanum Asset Management, highlights this issue. His team uses a Random Forest Regression Model to avoid overfitting, but the complexity of the model makes it difficult to understand why certain decisions are made. Ellis admits, "It’s kind of like a black box, because you don’t know why that information input produces such a result." He adds, "When such a system fails, the inability to pinpoint the cause can lead to its shutdown. The key distinction between surviving algorithms and those被淘汰 is whether they can be explained clearly." On the other hand, some investors like Jeffrey Tarrant of Protégé Partners aren’t overly concerned about transparency. He believes that the issue doesn’t affect his investment decisions, stating, “It doesn’t bother me at all.†Tarrant invests in funds that use AI technology, often combined with unconventional data sources, and he estimates that while 75 funds claim to use AI, only 25 truly do. Another limitation of AI in finance is its reliance on recent history. High-frequency trading systems might have enough historical data to adjust their strategies, but long-term investments require understanding broader historical contexts. As Sushil Wadhwani, a pioneer in using machine learning for trading, points out, "The machine will have a hard time recognizing that unless it knows what happened during the economic crises of the past." Without access to decades of historical data, AI risks repeating past mistakes. In conclusion, while AI holds immense potential for transforming Wall Street, its application is fraught with complexities. Overfitting, lack of transparency, and reliance on recent data are just a few of the hurdles that must be overcome before AI can fully replace human judgment in financial markets.
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