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Uncertainty-Embedded Financial Data and Stock Returns

Jasmine Zhang & Xiao-Jun Zhang

Foundations and Trends in Accounting2024https://doi.org/10.1561/1400000076article
AJG 3ABDC A
Weight
0.30

Abstract

This monograph investigates the role of conservative accounting in capturing various types of uncertainty in a firm's operations and assesses how the resulting financial data can be harnessed to gauge risk and forecast stock returns. It challenges the conventional approach of employing cross-sectional return regression for empirically identifying financial ratiosas "risk factors," suggesting this methodology is fundamentally unsound. An accounting measure may help estimate the expected stock return of a firm, but it does not necessarily reflect any inherent risk in the firm's operations. The study differentiates between conditional and unconditional conservative accounting practices, high lighting how they capture different facets of uncertainty and thereby lead to varying relationships between financial data and expected stock returns. The monograph further substantiates its claims with empirical evidence based on dissecting the market-to-book premium according to the accounting principles employed.

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https://doi.org/https://doi.org/10.1561/1400000076

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@article{jasmine2024,
  title        = {{Uncertainty-Embedded Financial Data and Stock Returns}},
  author       = {Jasmine Zhang & Xiao-Jun Zhang},
  journal      = {Foundations and Trends in Accounting},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1561/1400000076},
}

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Uncertainty-Embedded Financial Data and Stock Returns

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Evidence weight

0.30

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.