The escalating interest in artificial intelligence (AI) has made profound waves across diverse sectors, not least in the investment landscapeWith AI rapidly evolving into a formidable force, many public funds are scrambling to incorporate AI technologies like DeepSeek into their analytical and investment processesThis integration represents a significant shift, not only for institutional investors but also for individual stakeholders eager to harness new tools for enhancing their investment portfolios.

As public fund managers explore AI's potential, the pivotal question lingering in the minds of many remains: can AI elevate fund performance? Recent investigations by financial news outlets indicate a particularly fervent enthusiasm among quantitative fund managers for employing AI in their investment researchThis cadre of managers is optimistic that by leveraging AI capabilities, they can generate superior returns for investors, thereby outperforming traditional market benchmarks.

Delving into the mechanics behind AI’s application in public funds, a notable trend has emerged where quantitative fund managers stand at the forefrontManaging actively traded quantitative funds and index-enhanced funds, these managers are increasingly inclined to rely on AI-driven strategies to achieve alpha—or excess returns over a benchmark indexAn illustrative example can be found in funds such as the Huatai-PB Index Enhanced A Fund and the Anxin Quantitative Select CSI 300 Enhanced Fund, both of which have showcased exceptional performance by harnessing AI technologies for superior yield.

The last year saw index-enhanced funds tracking the CSI 300 Index deliver commendable results, with the Anxin Quantitative Select Fund and the Hai Futong CSI 300 Enhanced Fund topping the charts with excess returns reaching 15.55% and 8.77%, respectivelyIn the case of Huatai-PB Index Enhanced A Fund, it reported a staggering 58.83% growth alongside an excess return of 28.5%, establishing itself as the frontrunner in its category

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Such figures are a testament to the concrete advantages that AI can impart within the realm of investment.

Manager Shi Rongsheng of the Anxin Quantitative Select CSI 300 Enhanced Fund articulated his commitment to integrating machine learning and deep learning models into his investment strategy upon his appointment in August 2023. His approach aims to exploit market mispricing through probabilistic advantages and the law of large numbers, ultimately enhancing market efficiency through data-driven decisionsSimilarly, the Hai Futong fund employs advanced AI stock-picking models emphasizing deep-learning multi-factor scoring, effectively merging tree-based models with recurrent neural networks to harness non-linear information.

The enthusiasm for AI extends beyond index funds into actively managed quantitative funds, where several, including the Bosera ESG Quantitative Stock Picking Fund, have reported unprecedented net asset valuesThe vibrant activity within China's A-share market is fostering an environment ripe for quantitative investing, further solidifying AI's expanding role within this domain.

Over the past six months, the continuous breakthroughs in AI technology have galvanized fund managers, particularly those at the helm of quantitative funds, in embracing AI with open armsManager Lin Lihe of the Hai Futong CSI 300 Enhanced Fund emphasized AI's potential to offer more efficient and comprehensive investment analyses, adaptable to rapidly changing market stylesThis iterative learning and adaptability align with the current backdrop of evolving market dynamics where new trends are swiftly emerging.

The revelation brought forth by DeepSeek has intensified fund managers' resolve to dive deeper into AI applications within investmentsShi Rongsheng echoed this sentiment, asserting that advancements in large language models and similar technologies could substantially broaden the horizons of quantitative investment strategiesIn August 2024, after significant enhancements in API cost efficiencies, he began utilizing DeepSeek's services to bolster research efforts, including improving software programming workflows and accelerating the development of machine learning models.

However, despite the promising outlook, there remains an inherent uncertainty tied to AI implementations in investment strategies

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Quantitative investing is not a panacea; challenges such as low signal-to-noise ratios and non-stationary data present persistent hurdlesDecisions regarding algorithm selection, hyperparameter tuning, and data preprocessing significantly impact AI's effectiveness in practical applications.

The pursuit of AI integration has led various fund firms to revolutionize their investment research frameworksFor instance, Bosera Fund, after extensive investigations, recognized DeepSeek's potential in automating code development and logic inference, leading to the deployment of the DeepSeek-V1 model on their internal serversThe subsequent upgrade to DeepSeek-V2 in 2024 solidified its role as the foundation for the company’s intelligent development tools.

Additionally, the Yingying Fund disclosed that DeepSeek is pivotal in several core business scenarios, particularly in investment research, where it efficiently consumes massive data sets to extract pertinent information, thereby conserving time for analysts and enhancing research productivityThe ramifications of AI's integration extend far beyond efficiency; the strategic framework guiding fund managers' investment decisions is also poised for transformation.

While traditional quantitative investments in China's public funds largely rely on linear multi-factor models, a shift is occurring as professionals with relevant expertise increasingly occupy roles within these fundsThe continuous breakthroughs in AI technology are propelling public fund quantitative teams to new heightsMore managers are committed to elevating their investment frameworks, working diligently to minimize emotional volatility's influence on their portfolios.

As echoed by managers from the Huatai-PB Fund, the investment management process should minimize direct influences on portfolio holdingsRather than adopting subjective strategies, they advocate for systematic transitions to rule-based quantitative strategies, cautiously adjusting tactics to manage portfolio allocations

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