Paper: arXiv 2404.16449

Abstract

In this study, we utilize the Kalman-Filter analysis to assess market efficiency in major stock markets. The Kalman-Filter operates in two stages, assuming that the data contains a consistent trendline representing the true market value prior to being affected by noise. Unlike traditional methods, it can forecast stock price movements effectively. Our findings reveal significant portfolio returns in emerging markets such as Korea, Vietnam, and Malaysia, as well as positive returns in developed markets like the UK, Europe, Japan, and Hong Kong. This suggests that the Kalman-Filter-based price reversal indicator yields promising results across various market types.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs a complex recursive algorithm (Kalman Filter) to model market states, showing moderate math complexity, and presents backtests across multiple markets (US, UK, etc.) with specific date ranges and performance data, indicating substantial empirical rigor.

Research Flowchart

  flowchart TD
  Start["Research Goal:<br>Assess market efficiency<br>using Kalman Filter"] --> Data["Data: Main Stock Markets<br>(Developed & Emerging)"]
  Data --> Method["Methodology:<br>Kalman Filter 2-Stage Analysis<br>True Value + Noise Model"]
  Method --> Compute["Computation:<br>Price Reversal Indicator<br>Forecasting Algorithm"]
  Compute --> Outcomes["Key Findings:<br>Significant Returns in Emerging Markets<br>(Korea, Vietnam, Malaysia)<br>Positive Returns in Developed Markets"]

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0 of 20 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.