主動/被動基金面對未來AI交易的轉型

Abstract

隨著全球金融市場進入數位化與智能化時代，資產管理產業正經歷前所未有的典範轉移。傳統投資環境中，主動型基金長期面臨高昂管理成本與人類行為偏差（人性）的挑戰，導致多數績效難以在長期內超越大盤指標，進而促使低成本、透明度高的被動型基金（如指數型基金、ETF）在過去數十年內迅速崛起並成為市場主流。然而，隨著運算能力（Computing Power）的突破與人工智慧（AI）技術的深化，金融交易的生態系正再次面臨劇變。 本研究旨在探討 AI 技術如何重塑主動與被動投資的界線，並分析在AI交易轉型浪潮下，資產管理產業的未來演進趨勢。研究指出，AI的導入不僅止於優化交易執行，更催生了如量子基金、複雜模型交易及高頻交易等多元化策略，使得「主動」與「被動」的定義逐漸模糊。 研究發現，未來主動基金的轉型路徑在於利用AI捕捉「微Alpha」與非主流市場的超額報酬，並透過技術手段克服人性偏誤；而被動基金則將演化為「智能 Beta」（Smart Beta）的增強模式，利用AI進行動態風險調整。在產品定價策略上，建議實施三級定價模式，從純自動化的零費率產品到高附加價值的績效制收費產品，以因應差異化競爭。 最後，本研究針對投資者、資產管理機構及監管機構提出具體建議。在 AI 交易生態中，投資者需提升 AI 素養以辨識新型風險，監管機構則須在創新鼓勵與市場穩定（防範閃崩與算法共謀）之間取得平衡。總結而言，未來的基金產業將不再是單純的主被動之爭，而是 AI 技術整合能力與數據倫理價值的全面競爭，資產管理業者必須積極擁抱 AI 轉型，方能在演算法主導的市場中立於不敗之地。

As the global financial market enters an era of digitalization and intelligence, the asset management industry is undergoing an unprecedented paradigm shift. In traditional investment environments, active funds have long faced challenges from high management costs and behavioral biases (human nature), leading to the historical reality that most active managers struggle to outperform market benchmarks over the long term. This catalyzed the rise of low-cost, highly transparent passive investment vehicles, such as index funds and ETFs, which have become the market mainstream over the past few decades. However, with breakthroughs in computing power and the deep integration of Artificial Intelligence (AI), the ecosystem of financial trading is once again facing a radical transformation. This research aims to explore how AI technology is reshaping the boundaries between active and passive investment and to analyze the evolutionary trends of the asset management industry under the wave of AI-driven trading. The study indicates that the introduction of AI extends beyond merely optimizing trade execution; it has given birth to diverse trading strategies such as Quantum Funds, complex model-based trading, and High-Frequency Trading (HFT), which are increasingly blurring the definitions of “active” and “passive”

The thesis is structured into six chapters. First, it reviews the historical development and the shifting balance of power between active and passive funds. Second, it analyzes the current applications of AI technologies—including machine learning, big data analytics, and Explainable AI (XAI)—in modern trading. Subsequent chapters delve into the impact of AI trading on market liquidity, volatility, and efficiency, while analyzing how institutional investors are transforming their organizational cultures toward a “Quantitative-First” decision-making model. The findings suggest that the future transformation path for active funds lies in leveraging AI to capture “Micro-Alpha” and excess returns in non-mainstream markets while utilizing technical means to overcome human biases. Conversely, passive funds will evolve into “Enhanced Passive” or “Smart Beta” models, using AI for dynamic risk adjustment. Regarding pricing strategies, the study recommends a three-tier pricing model: ranging from zero-fee fully automated products to high-value-added performance-based fee structures to address differentiated competition. Finally, this research provides specific recommendations for investors, asset management institutions, and regulatory bodies. In the AI trading ecosystem, investors must enhance their “AI Literacy” to identify emerging risks, while regulators must strike a balance between encouraging innovation and maintaining market stability (preventing flash crashes and algorithmic collusion). In conclusion, the future of the fund industry will no longer be a simple rivalry between active and passive strategies, but a comprehensive competition centered on AI integration capabilities and data ethical values. Asset management firms must proactively embrace AI transformation to remain competitive in a market dominated by algorithms.