Temporal Coverage Bias in Financial Panel Data: A Coverage-Aware Structuring Framework with Evidence from the Dhaka Stock Exchange

Paper: arXiv 2603.20237

Authors: Tashreef Muhammad

Abstract

A common practice in empirical finance is to construct calendar-aligned panels that implicitly treat all instruments as having existed for the full observation period. When securities with different listing histories are combined without explicit coverage constraints, price histories can be inadvertently extended before valid trading ever began. This paper formalizes this problem and proposes a coverage-aware structuring framework built around instrument-level observation windows encoded through structured metadata and an availability matrix. Applied to end-of-day data from the Dhaka Stock Exchange spanning October 2012 to January 2026 and covering 486 instruments, the framework reveals substantial distortions from naive temporal alignment. ARIMA-based experiments establish the mechanism through which padded observations corrupt return dynamics, and volatility analysis across 53 instruments shows that forward-filling alone suppresses return volatility by roughly 20% on average, with GARCH unconditional variance distortions exceeding 26% in over 90% of instruments - a lower bound, as backward extension to the panel start produces 36.6% suppression and causes GARCH non-convergence in 41% of instruments. The distortion affects any method requiring calendar alignment of heterogeneous histories, including dynamic time warping, covariance-based portfolio construction, factor model regression, and temporal foundation model fine-tuning. Although demonstrated on financial data, the framework applies to any panel combining entities with heterogeneous entry dates, including sensor networks, clinical cohorts, and country-level economic panels. Listing coverage is not a minor preprocessing detail but a first-order variable in panel construction.

Complexity vs Empirical Score

• Math Complexity: 5.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper formalizes a widespread but under-acknowledged data alignment error (’temporal coverage bias’) in financial panel datasets, demonstrating its severe impact on ARIMA and GARCH volatility estimation using 13+ years of Dhaka Stock Exchange data. The methodology is empirically rigorous, exceptional in clarity, and includes a publicly accessible dataset with explicit coverage metadata, making the findings highly reproducible.

Research Flowchart

  flowchart TD
    A["Research Question: Impact of Naive Temporal Alignment<br/>on Financial Panel Data Distortion"] --> B["Inputs: Dhaka Stock Exchange Data<br/>(2012–2026, 486 Instruments)"]
    B --> C["Framework: Coverage-Aware Structuring<br/>(Instrument Windows & Availability Matrix)"]
    C --> D["Experiments: Compare Naive Alignment vs.<br/>Coverage-Aware Panel (ARIMA, Volatility, GARCH)"]
    D --> E1["Findings: Forward-Fill Suppresses Volatility ~20%;<br/>GARCH Variance Distorted >26% in >90% Instruments"]
    D --> E2["Findings: Backward Extension Suppresses Volatility 36.6%<br/>& Causes 41% GARCH Non-Convergence"]

Cited References (40)

1. The Econometrics of Financial Markets (1997)
2. Analysis of Financial Time Series (2005)
3. Empirical properties of asset returns: stylized facts and statistical issues (2001) — link
4. Emerging markets finance (2003) — link
5. Common risk factors in the returns on stocks and bonds (1993) — link
6. Efficient Capital Markets: II (1991) — link
7. Asset Pricing (2005)
8. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation (1982) — link

0 of 40 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.