Piotroski’s FSCORE: international evidence

Walkshäusl, Christian

doi:10.1057/s41260-020-00157-2

Cited by 21 publications

(11 citation statements)

References 40 publications

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“…M-score model accurately identified the firms manipulating financial statements in Poland (Hołda, 2020). Likewise, many more studies have identified M-score model to be highly accurate in identifying companies falsifying financial statements (Omar et Piotroski's F-score is a composite accounting-based indicator of the corporation's fundamental strength and can be used to identify fundamentally weak firms (Walkshäusl, 2020). Regular F-score monitoring enables proactive risk management.…”

Section: Review Of Literature and Development Of Hypothesismentioning

confidence: 99%

An analysis of Satyam case using bankruptcy and fraud detection models

Yadav,

Patil,

Sengupta

2023

SEC

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Bankruptcy, which occurs due to inability of a business, to repay its debts and obligations has caught the interest of investors and practitioners alike. Predicting bankruptcy prior to the occurrence of event has become crucial in the field of investment and lending, especially in the light of events such as the global financial crisis of 2008. Early bankruptcy prediction models used traditional statistical techniques via financial ratios. Since then there has been a constant endeavour to develop models with enhanced predictive performance. Satyam Inc. was Indian listed business which went bankrupt in 2007. In this study we apply financial models such as F score, M-score and Z-score to show how common/retail investor who cannot use sophisticated financial tool, can benefit from these simple tools and make good investment decisions. Our research adds to the discussion regarding the capability of bankruptcy prediction models. We derive our findings using the data for Satyam Inc., one of the biggest corporate scandalin India. Before the scam, Beinish M-score acted as more efficient predictor of bankruptcy and fraud than Altman Z-score and Peotroski F score. In fact, the usefulness of Z score and F score was average to poor in predicting Satyam’s bankruptcy in advance. This result contradicts outcomes from several researches who had found a great ultility of Z score and F score. From the policy view, the regulator of financial market can protect the financial illiterate investor who makes investment in capital market to take informed investment decision by using the Beinish M-score for making investing decision in the stock of the company. Similarly, these models can be used by banks and financial institutions in case of existing as well as potential corporate borrowers.

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Section: Review Of Literature and Development Of Hypothesismentioning

confidence: 99%

An analysis of Satyam case using bankruptcy and fraud detection models

Yadav,

Patil,

Sengupta

2023

SEC

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“…This can lead investors into the so‐called value trap, as value stocks may be cheap for a fundamentally justified reason 4 . That is why other contributions refine the B/P strategy by including additional metrics of fundamental performance or different proxies for intrinsic value—like Frankel and Lee (1998), Piotroski (2000), Bird and Gerlach (2003), Mohanram (2005), Piotroski and So (2012), Penman and Reggiani (2013, 2018), Lee (2015), Penman et al (2018), Asness et al (2019), Li and Mohanram (2019), Walkshäusl (2020) and Huefner et al (2021). These studies provide evidence for the predictive ability of fundamental quality for future stock returns on the US market before and after the financial crisis.…”

Section: Combining Research On Valuation Accuracy Valuation Bias and ...mentioning

confidence: 99%

Value investing via Bayesian inference

2023

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Classic value investing à la Graham & Dodd (Security analysis: The classic, McGrawHill, New York, 1934) focuses on selecting stocks that seem cheap relative to their intrinsic value and fundamental quality. We use Bayesian inference to account for a large amount of uncertainty within intrinsic value estimation. We find that an undervalued‐minus‐overvalued factor that invests in cheap quality stocks and sells expensive junk stocks selected via Bayesian inference yields high risk‐adjusted returns and Sharpe ratios for equal‐weighted portfolios. We also find that using value‐weighted portfolios introduces size‐based dilutions and shifts the focus away from actual quality characteristics like profitability, payout, safety, and past growth. Our findings suggest that while the relative benefit of accounting for uncertainty via Bayesian inference is not large over shorter holding periods, it pays off for investment horizons longer than a month.

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“…Motivated by recent strong evidence for the FSCORE criterion on a stand-alone basis (e.g., see Choi and Sias 2012;Duong et al 2014;Ng and Shen 2020;Walkshäusl 2020), it might also be worthwhile for future studies to revise the research design so that the FSCORE is the primary criterion, being possibly supplemented by other selection criteria. In addition, the methods employed in this paper could be applied to other stock markets to uncover the extent to which our results based on the German sample data are generalizable.…”

Section: Conclusion and Implications For Further Researchmentioning

confidence: 99%

“…This results in relatively narrow portfolios during the holding periods following such portfolioreformation points, at which the total number of high-FSCORE stocks in the tertile portfolio formed on the primary criterion was low. In accordance with recent studies on the FSCORE (e.g., see Choi and Sias 2012;Piotroski and So 2012;Zhu et al 2019;Ng and Shen 2020;Walkshäusl 2020), we extend the group of high-FSCORE stocks to also include stocks with an FSCORE of 7 to keep the FSCORE-related portfolios sufficiently diversified throughout the sample period. This extension is particularly important because raising the market-cap threshold outstandingly narrows the sample.…”

Section: Introductionmentioning

confidence: 95%

Can the FSCORE add value to anomaly-based portfolios? A reality check in the German stock market

Pätäri

Leivo

Ahmed

2021

Financ Mark Portf Manag

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This paper examines the added value of using financial statement information, particularly that of Piotroski’s (J Account Res 38:1, 2000. https://doi.org/10.2307/2672906) FSCORE, for equity portfolio selection in the German stock market in a realistic research setting in which the critique against the implementability of FSCORE-based trading strategies is taken into account. We show that the performance of annually rebalanced long-only portfolios formed on any of the examined 12 accounting-based primary criteria improves by including the FSCORE as a supplementary criterion. Our study is the first to show that although the FSCORE boost is strongest for the 1-year holding period length, it also holds, on average, for the 3-year holding period. The use of a 3-year updating frequency is particularly beneficial for the low-accrual portfolio that—when supplemented with the high-FSCORE threshold—generates the best overall performance among all 75 portfolios examined. Moreover, we show that a high FSCORE is also an efficient stand-alone criterion for long-only portfolio formation.

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Piotroski’s FSCORE: international evidence

Cited by 21 publications

References 40 publications

An analysis of Satyam case using bankruptcy and fraud detection models

An analysis of Satyam case using bankruptcy and fraud detection models

Value investing via Bayesian inference

Can the FSCORE add value to anomaly-based portfolios? A reality check in the German stock market

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