Accounting Warning Consensus and Next-Year Corporate Accounting Stress in Bangladesh

Gourav Roy; Susmita Sarker

doi:10.61255/jeemba.v4i6.1898

Authors

Gourav Roy Bangladesh Institute of Capital Market, Bangladesh
Susmita Sarker Department of Accounting, National University, Bangladesh

DOI:

https://doi.org/10.61255/jeemba.v4i6.1898

Keywords:

Financial distress, Accounting warning systems, Distress consensus, Frontier market, Bangladesh

Abstract

Purpose – This study tests whether consensus across partially overlapping accounting-warning systems predicts next-year corporate accounting stress beyond raw fundamentals among provider-covered listed non-financial firms in Bangladesh.

Design/methodology/approach – Of 236 listed non-financial firms, 109 have sufficient Refinitiv/Worldscope coverage to construct the Distress Consensus Index (DCI). The DCI combines Zmijewski, Springate, Piotroski F-score, and Financial Health Composite (FHC) risk ranks for 2012-2024, with outcomes through 2025. Firm and year fixed effects, conditional fixed-effects logit, composition and selection diagnostics, Raw Fundamentals Risk Index (RFRI) benchmarks, severe accounting-stress endpoints, and leakage-controlled temporal validation are used.

Finding/Results – In the preferred provider-covered sample (N = 1,066), a 10-point DCI increase is associated with 6.86 percentage points more next-year accounting stress; the estimate is 7.25 points in complete four-score observations and 4.05 points for severe accounting stress. DCI remains incremental to RFRI, including when FHC is removed from the DCI. Holdout AUC is 0.780 under contemporaneous annual ranking and 0.766 under fully historical frozen scaling; adding DCI to raw fundamentals raises AUC from 0.777 to 0.797.

Originality/Value – DCI is a transparent relative screening feature, not a bankruptcy probability or transferable event-risk estimate. Generalization is limited to provider-covered listed non-financial firms with sufficient accounting data, while measurement composition, score overlap, peer ranking, temporal preprocessing, and calibration drift are explicit inferential boundaries.

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