Paper: arXiv 2305.01642

Authors: Ke Zhang

Abstract

Unlike developed market, some emerging markets are dominated by retail and unprofessional trading. China A share market is a good and fitting example in last 20 years. Meanwhile, lots of research show professional investor in China A share market continuously generate excess return compare with total market index. Specifically, this excess return mostly come from stock selectivity ability instead of market timing. However for some reason such as fund capacity limit, fund manager change or market regional switch, it is very hard to find a fund could continuously beat market. Therefore, in order to get excess return from mutual fund industry, we use quantitative way to build the sparse portfolio that take advantage of favorite stocks by mutual fund in China A market. Firstly we do the analysis about favourite stocks by mutual fund and compare the different method to construct our portfolio. Then we build a sparse stock portfolio with constraint on both individual stock and industry exposure using portfolio optimizer to closely track the partial equity funds index 930950.CSI with median 0.985 correlation. This problem is much more difficult than tracking full information index or traditional ETF as higher turnover of mutual fund, just first 10 holding of mutual fund available and fund report updated quarterly with 15 days delay. Finally we build another low risk and balanced sparse portfolio that consistently outperform benchmark 930950.CSI.

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents a practical quantitative framework for replicating and outperforming a China A-share mutual fund index using sparse portfolio optimization. While the mathematical formulations rely on standard portfolio optimization techniques rather than novel theoretical derivations, the empirical application addresses real-world constraints such as reporting delays, sparse holdings, and industry tracking errors.

Research Flowchart

  flowchart TD
    A[Research Goal: Construct sparse portfolio using China A-share mutual fund favorite stocks] --> B[Data Inputs: Top 10 quarterly mutual fund holdings & 930950.CSI Index]
    B --> C[Analyze mutual fund favorite stocks & stock selectivity alpha]
    C --> D[Run portfolio optimizer with stock & industry exposure constraints]
    D --> E[Track 930950.CSI Index<br>High correlation: median 0.985]
    D --> F[Outcome: Low-risk balanced sparse portfolio that consistently outperforms 930950.CSI]