Xin Lang Cai Jing·2025-11-04 09:15Core Insights - The article discusses the launch of the AI-Trader project by a team led by Professor Huang Chao from the University of Hong Kong, which aims to test AI trading capabilities in a volatile market environment [3][4][19] - The project involves six AI models trading in the Nasdaq 100, each starting with $10,000, and showcases their performance over a month of real trading [4][5] Performance Summary - The AI models exhibited varying performance, with DeepSeek-Chat-V3.1 leading at +13.89%, followed by MiniMax-M2 at +10.72%, and Claude-3.7-Sonnet at +7.12% [5][6] - In comparison, the Nasdaq 100 ETF (QQQ) only increased by +2.30% during the same period, highlighting the effectiveness of the AI models [5] Behavioral Finance Experiment - The experiment serves as a behavioral finance study, testing three key capabilities of AI systems: trading discipline, market patience, and information filtering [6][19] - The results illustrate the differences in algorithmic architecture and decision-making frameworks among the AI models, reflecting typical human investor behaviors [7][18] Individual AI Strategies - DeepSeek-Chat-V3.1: Utilized contrarian strategies by increasing positions in NVDA and MSFT during market downturns, achieving a +13.89% return [8] - MiniMax-M2: Maintained a balanced portfolio with low turnover, resulting in a +10.72% return, demonstrating the importance of consistency in high-volatility environments [9] - Claude-3.7-Sonnet: Focused on long-term value investing, holding positions in major tech stocks despite market fluctuations, yielding a +7.12% return [10] - GPT-5: Attempted dynamic rebalancing but faced timing issues, resulting in a +7.11% return [11] - Qwen3-Max: Adopted a wait-and-see approach, leading to a lower return of +3.44% due to missed opportunities [12] - Gemini-2.5-Flash: Engaged in high-frequency trading but suffered a -0.54% return due to overtrading and emotional decision-making [13] Insights on AI Trading - The experiment revealed that effective trading is not solely about action but also about knowing when to refrain from trading, as demonstrated by the success of DeepSeek and MiniMax [14][19] - The findings suggest that AI can provide valuable insights into investment decision-making processes, emphasizing the management of uncertainty rather than perfect market predictions [19] Future Implications - The AI-Trader project indicates a shift in Chinese AI technology from conversational capabilities to practical task execution, showcasing potential in complex financial decision-making [19] - The financial trading environment serves as an ideal testing ground for AI decision-making capabilities, with future applications anticipated in various sectors such as supply chain optimization and urban management [19]