Xue and Zhang (2011) examined whether institutional investors exploit abnormal returns derived from financial statements and studied how transaction costs and arbitrage risk affect the profitability of the trading strategy. Finally, they studied the impact of institutional investors’ trading behaviour on the profitability of the fundamentals-based trading strategy.
To focus on institutions that are more likely to trade on fundamental signals they followed Bushee’s (2001) findings. For instance, Bushee (2001) examined whether investors exhibit preferences for near-term earnings over the long term and whether this has an impact on stock prices. One of the main assumptions is that managers boost operational and accounting decisions to boost short-term earnings under the pressure of institutional investors referring to the term “myopic”. Findings show that the strongest institutions favour firms with short-term earnings rather than firms with long-term earnings. A suggestion might be that clients are more interested in short-term returns.
Across the literature three categories of institutions are defined: transient, dedicated and quasi-indexers. Findings highlight that Xue and Zhang (2011) expect transient institutions to be more likely to trade on fundamental signals, and also the empirical results hold after controlling for other factors that may affect institutional investors’ trading decisions such as analyst forecast revisions and post-earnings announcement drift.
Additionally, Xue and Zhang’s (2011) found that association of future abnormal returns and fundamental signals increases with transaction cost and arbitrage risk. The final part of their
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analysis examined whether institutions trading on abnormal returns using financial statements have an impact, and in this they made progress by suggesting that transient investors tend to reduce abnormal returns associated with fundamental signals. The results suggest that institutional investors’ trading helps alleviate the market under-reaction to information contained in fundamental signals and improve market efficiency.
Throughout their analysis, they examined 11 financial ratios that measure a firm’s profitability, operating efficiency and liquidity, and many of their fundamental signals concur with those in Piotroski (2000). However, in contrast with Piotroski, who is looking at only distressed firms, Xue and Zhang (2011) chose fundamental signals to describe the financial conditions of ordinary listed firms, and therefore included financial ratio that are most visible to investors. One difference between their fundamental signals and the one highlighted by Piotroski is that their measures are all industry adjusted whereas Piotroski’s ratio are benchmarked against zero. Another difference is that they do not include equity issuance and change in leverage ratio in their selection signals. These ratios are intended to measure changes in capital structure and a firm’s ability to meet future debt obligations.
Therefore, each fundamental signal is assigned a score of one if the ratio is above its industry average in that year, indicating a positive signal about the firm’s outlook, or a zero otherwise. The industry average for each year is calculated using only firms’ 31st December fiscal year-end and industry years with fewer than five observations are deleted. All the 11 signals are aggregated into an F-score following the same observations made by Piotroski (2000). To verify if the F-score has the power to predict future abnormal returns, they constructed an equal weighted investment portfolio each year. The results showed that, of the 22 years from 1982 to 2003, this trading strategy of buying high F-score and shorting low F-score generated positive market-adjusted returns in 18 years, with the average three-month (nine-month) market- adjusted returns of 2.78% (7.62%). The annualized markets adjusted buy and hold returns are around 12%, which is lower than those documented by Piotroski of 23%.
Lastly, they checked for robustness, calculated alternative measures of abnormal returns such as size-adjusted return and size, market beta, book to market and momentum-adjusted return and tested statistically.
Regarding the data sample, they collected information data from Compustat industrial and research files, return data from CRSP monthly stock database for NYSE, AMEX and NASDAQ firms and institutional investment data from institutional investors. The analyst coverage and
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forecast data are obtained from the summary file of the IBES database. Firms’ years missing any of the 11 fundamental signals as well as financial service and utility firms were excluded from the sample. The final sample was 2026 unique companies.
Then, they examined firms’ market-adjusted buy and hold return over a three- and nine- month horizon starting from 1st April after the previous fiscal year-end. As an alternative of abnormal return measures, they calculated size-adjusted return and four-factor-adjusted returns. Size-adjusted return is defined as the raw return less the return of the portfolio of the firms in the same size decile.
Overall, they found that fundamental signals derived from publicly available financial statements have the power to predict future stock returns. This paper looked at abnormal returns by examining the trading behaviour of sophisticated investors, i.e., transient investors, and found that transient investors trade on fundamental signals. This finding is consistent with the explanation that the stock market under-reacts to financial statement information and that sophisticated investors take advantage of this arbitrage anomaly. The authors further explored role and limits to arbitrage and found that abnormal returns to fundamental signals increase with arbitrage cost – transaction cost and arbitrage risk. They provide documentary evidence that transient institutions’ trading and holdings help the stock market more quickly impound information contained in fundamental signals into stock prices.
In summary, the results suggest that the F-score has the power to predict future stock returns and a fundamental analysis based on the F-score can earn abnormal returns.
2.2.2.11 Discussion to see if the F-score can be used to predict