Accrual management and expected stock returns in India
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine whether the stock market in India is efficient in the semi-strong form. Design/methodology/approach The study uses financial and stock market data of 1,135 listed Indian companies (non-financial) during 2003–2011 collected from Capital IQ to estimate discretionary accruals (DA) using modified Jones model (1995). The study also examines using the widely used Mishkin (1983) test to whether equity market prices accruals in India. The study is conducted for profit/loss-making firms separately as well as for a hedge portfolio of firms based on the lowest to highest accruals. Findings The empirical study of DA of 1,135 listed Indian companies (non-financial) during 2003–2011 shows that the estimated average DA of the corporate sector in India comes to 1 percent of the total assets of these firms. An empirical analysis whether equity market prices DA in India finds no evidence of investors/market pricing DA. Empirical evidence also finds that the results are invariant for profit/loss-making firms as well as portfolio of firms based on the lowest to highest accruals in the Indian context. The empirical evidence shows that the Indian equity market is inefficient with regard to the incorporation of accruals in expected returns of stocks. Research limitations/implications This study builds on the previous literature on accrual pricing in the context of the USA and developed markets. The study extends the empirics to the one of the largest emerging market economy – India. This issue is important not only to investors, but also to policy makers and researchers because the mispricing of accruals could potentially lead to misallocation of capital. The study has implications for stock/firm valuations and cost of equity/capital. Originality/value This is the first study for the pricing of accruals and test of semi-strong efficiency of the Indian stock market.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".