平方和投资吕杰勇：AI赋能量化投资的未来在于“人机结合”

Core Insights - The conference highlighted the transformative role of AI in quantitative investment, emphasizing its potential to reshape research paradigms and enhance efficiency in the industry [1][2]. Group 1: AI's Impact on Quantitative Investment - AI's breakthrough, marked by Google's AlphaGo in 2016, has led to increased interest in applying AI technologies in investment, resulting in significant advancements [2]. - The reliance on experienced professionals in traditional quantitative investment has created high entry barriers, but AI and machine learning are reducing this dependency, thus redefining research paradigms [2]. - Despite the advantages, the application of AI is not infallible and requires human expertise for effective implementation [2]. Group 2: Practical Applications and Innovations - AI is becoming a focal point in quantitative trading, with companies like Square and Harmony utilizing deep learning models across various stages, from factor discovery to trade execution [3]. - The emphasis is on "incremental innovation" rather than "substitutive innovation," integrating AI into existing robust strategies while maintaining strict risk control [3]. - A closed-loop system combining model development, backtesting, risk control, and trade execution is essential for translating technological advancements into stable alpha [3]. Group 3: Challenges in AI Implementation - The quant market faces challenges such as strategy homogeneity, weak interpretability of AI models, and insufficient adaptability during extreme market conditions [4]. - The core issue lies in aligning the technical potential of AI with the fundamental nature of investment, which requires a balance between efficiency and risk control [4]. - The noise in financial data complicates predictions, indicating that neither AI nor human strategies are superior alone; instead, a collaborative approach is deemed the optimal resource allocation strategy [5].