量化投資策略有效性-基於大數據競爭法分析

金融市場的數據化進程加快，大數據技術的應用在量化投資策略中逐漸成為關鍵因素。量化投資策略通過數據分析與數學模型來制定交易決策，旨在提高投資回報並降低風險。然而，隨著市場競爭的加劇，尤其是在大數據技術廣泛應用的背景下，量化投資策略的有效性面臨著新的挑戰。競爭者之間對市場數據的爭奪，使得部分策略的超額收益可能迅速被市場所消化，進而影響其持續性。本文旨在探討量化投資策略的有效性，並基於大數據競爭法的框架進行分析。首先，本文回顧了量化投資策略的基本原理和發展歷程，分析了大數據如何在量化投資中提供競爭優勢。並且探討市場中大數據競爭對量化投資策略的影響，特別是在信息不對稱、數據擁有權與數據處理能力等方面的競爭態勢利用競爭法分析方法，評估不同類型的量化策略在市場中的競爭力，並討論如何在激烈的市場競爭中保持策略的有效性。最後提出優化量化投資策略的建議，強調需加強數據處理技術、提升策略多樣化，並探索新興市場和資產類別以尋求潛在的超額回報。大數據競爭法框架下的量化投資策略，若能有效應對競爭帶來的信息流動和數據價值消耗，仍具有實現穩定回報的潛力。期望能為投資者和金融機構提供有價值的參考，幫助其在競爭激烈的市場中制定更具競爭力的量化投資策略，實現可持續的投資回報。

關鍵字

金融市場；大數據競爭；信息不對稱；量化投資策略

並列摘要

The acceleration of datafication in financial markets has made big data technology a key factor in quantitative investment strategies. These strategies rely on data analysis and mathematical models to formulate trading decisions, aiming to enhance investment returns while reducing risks. However, with intensifying market competition, particularly in the context of widespread application of big data technologies, the effectiveness of quantitative investment strategies faces new challenges. Competition over market data among participants may lead to the rapid absorption of excess returns by the market, thereby affecting the sustainability of these strategies. This paper aims to explore the effectiveness of quantitative investment strategies through the lens of a big data competition law framework. Firstly, it reviews the fundamental principles and historical development of quantitative investment strategies, analyzing how big data provides competitive advantages in this field. It further examines the impact of big data competition on quantitative investment strategies, focusing on competitive dynamics such as information asymmetry, data ownership, and data processing capabilities. By utilizing competition law analysis methods, the study evaluates the competitiveness of various types of quantitative strategies in the market and discusses how to maintain their effectiveness in a highly competitive environment. Finally, it proposes recommendations for optimizing quantitative investment strategies, emphasizing the need to enhance data processing technologies, diversify strategies, and explore emerging markets and asset classes to uncover potential excess returns. Under the big data competition law framework, quantitative investment strategies that can effectively address the challenges of information flow and data value depletion still hold the potential to achieve stable returns. This study aims to provide valuable insights for investors and financial institutions, helping them to design more competitive quantitative investment strategies in a fiercely competitive market and achieve sustainable investment returns.