In recent times, artificial intelligence has significantly reshaped various sectors of the economy, one of which is the investment and fund management industry. As the capabilities of AI continue to expand, tools like DeepSeek have entered the spotlight, propelling a newfound interest in how these technologies can influence stock market performance and investment strategies. This shift has prompted public fund institutions to integrate AI tools, including various open-source models, into their research and investment procedures, illustrating a growing trend in scientific and quantitative investment analysis.

Fund managers, especially those specializing in quantitative investing, exhibit a particular enthusiasm for utilizing AI, as they believe these technologies can help generate substantial excess returns for their investors. The intersection of AI and quantitative analysis serves a dual purpose: it optimizes decision-making for fund managers and offers new tools for average investors seeking better investment opportunities. This article delves into how AI, particularly through the lens of tools like DeepSeek, is revolutionizing the fund management industry.

An investigation by Chinese brokers reveals that quantitative fund managers are fervently embracing AI in their investment research endeavors. These professionals are dedicated to pioneering AI applications that promise to outperform the market. For instance, several index-enhanced funds utilizing AI technologies, such as those managed by Huitianfu and Anxin, have placed among the top performers in their categories, reflecting the potential AI holds for improving investment returns.

In the realm of index-enhanced funds, the Huitianfu Guozheng 2000 Index Enhanced A Fund stands out with a staggering return of 58.83% over the past year, boasting an excess return of 28.5%. Managers of these funds are committed to advancing their investment methodologies; Huitianfu’s fund management team, for instance, employs advanced machine learning techniques to correct market inefficiencies—believing in the law of large numbers for capturing mispriced assets within the financial landscape.

The success of quantitative funds isn't merely a result of AI's implementation but also of its evolving nature to adapt to changing market dynamics. Fund managers are increasingly recognizing the capacity of AI models to aggregate vast amounts of data and analyze them for actionable insights, ultimately leading to a more informed investment strategy. For example, by employing techniques such as deep learning and tree models, they can extract non-linear information that traditional models may overlook.

Further evidence can be seen with the Anxin Quantitative Select fund, where the managers started fully utilizing AI models as of August 2023. Their approach is designed to leverage algorithms systematically and strategically, placing more importance on data-driven decisions rather than subjective judgment often found in traditional investing approaches.

AI not only enhances the potential for increased returns but also plays a critical role in reshaping the frameworks and methodologies utilized by fund managers for research and analysis. With financial markets becoming increasingly complex, fund managers are prepared to leave behind traditional linear models and adopt more sophisticated AI methodologies that promise greater resilience and adaptability in investment strategies. One significant shift has been in how AI is employed to identify and analyze hidden or non-linear factors that traditional models may fail to capture.

As these cutting-edge technologies proliferate, many firms are not only exploring how to use AI in investment decisions but are also initiating updates to their internal research processes. The Hongde Fund, for example, has identified the potential of DeepSeek to expedite the analysis of vast research documents and extract critical data, significantly increasing productivity among analysts. Meanwhile, Yǒngyíng Fund has reported integrating DeepSeek into various core business scenarios, enhancing their research capabilities.

Looking ahead, it is clear that AI technologies like DeepSeek will continue to influence the field of investment research fundamentally. The convergence of AI and finance isn't merely a trend; it represents a transformative shift in how investment strategies are formulated and executed. This transformation seeks to mitigate the emotional biases often reflected in traditional decision-making processes, creating a more systematic, rule-based investment framework and financing strategy.

The improved data-driven decision-making fosters an environment in which fund managers feel increasingly empowered to adjust their methods, focusing on eliminating the volatility that can often lead to losses. As more professionals from technical backgrounds enter the realm of equity trading and investment management, the integration of machine learning and AI techniques is anticipated to soar, allowing fund managers to better navigate the complexities of today’s financial markets.

Ultimately, the integration of AI into fund management marks a pivotal moment in the finance industry. As researchers, analysts, and fund managers acclimate to these technologies, they set the stage for a new epoch in investment strategy—one that promises greater efficiency, enhanced performance, and an optimistic outlook for investors as they endeavor to navigate the turbulent waters of the stock market.