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Implementability of Trading Strategies Based on Accounting Information: Piotroski (2000) Revisited

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  • Sohyung Kim
  • Cheol Lee

Abstract

The return accumulation approach used in studies on accounting-related anomalies cannot be replicated in a practical context because the number and identity of individual observations within a portfolio are assigned within a research context before the accounting information of all firms in the portfolio would actually be available in real time. We explore this issue by re-examining the results in Piotroski (2000) [Value investing: the use of historical financial statement information to separate winners from losers, Journal of Accounting Research , 38 (supplement), 1 - 44]. We find that the relationship between Piotroski's fundamental signals and subsequent returns is partly driven by the choice of return accumulation periods. Because the method used in Piotroski is typical of those often employed in the accounting literature, this study suggests that evidence of profitable trading strategies and market inefficiency in the literature is likely to be overstated.

Suggested Citation

  • Sohyung Kim & Cheol Lee, 2014. "Implementability of Trading Strategies Based on Accounting Information: Piotroski (2000) Revisited," European Accounting Review, Taylor & Francis Journals, vol. 23(4), pages 553-558, December.
    • Handle: RePEc:taf:euract:v:23:y:2014:i:4:p:553-558
    DOI: 10.1080/09638180.2014.921217
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    Cited by:

    1. Ng, Chi Cheong Allen & Shen, Jianfu, 2016. "Screen winners from losers using simple fundamental analysis in the Pacific-Basin stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 159-177.
    2. I-Cheng Yeh & Yi-Cheng Liu, 2020. "Discovering optimal weights in weighted-scoring stock-picking models: a mixture design approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-28, December.
    3. Eero J. Pätäri & Timo H. Leivo & Sheraz Ahmed, 2022. "Can the FSCORE add value to anomaly-based portfolios? A reality check in the German stock market," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(3), pages 321-367, September.
    4. Vitor Azevedo & Christopher Hoegner, 2023. "Enhancing stock market anomalies with machine learning," Review of Quantitative Finance and Accounting, Springer, vol. 60(1), pages 195-230, January.

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