ใครแปลได้ช่วยแปลทีค่ะ
Section 2: Literature Review and Motivation
2.1 High book-to-market investment strategy
This paper examines a refined investment strategy based on a firm’s book-tomarket ratio (BM). Prior research (Rosenberg, Reid, and Lanstein 1984; Fama and French 1992; Lakonishok, Shleifer, and Vishny 1994) shows that a portfolio of high BM firms outperforms a portfolio of low BM firms. Such strong return performance has been attributed to both market efficiency and market inefficiency. In Fama and French (1992), BM is characterized as a variable capturing financial distress, and thus the subsequent returns represent a fair compensation for risk. This interpretation is supported by the consistently low return on equity associated with high BM firms (Fama and French 1995; Penman 1991) and a strong relation between BM, leverage, and other financial measures of risk (Fama and French 1992; Chen and Zhang 1998). A second explanation for the observed return difference between high and low BM firms is market mispricing. In particular, high BM firms represent “neglected” stocks where poor prior performance has led to the formation of “too pessimistic” expectations about future performance (Lakonishok, Shleifer, and Vishny 1994). This pessimism unravels in the future periods, as evidenced by positive earnings surprises at subsequent quarterly earnings announcements (LaPorta et al. 1997).
Ironically, as an investment strategy, analysts do not recommend high BM firms when forming their buy/sell recommendations (Stickel 1998). One potential explanation for this behavior is that, on an individual stock basis, the typical value firm will underperform the market and analysts recognize that the strategy relies on purchasing a complete portfolio of high BM firms.
From a valuation perspective, value stocks are inherently more conducive to financial statement analysis than growth (i.e., glamour) stocks. Growth stock valuations are typically based on long-term forecasts of sales and the resultant cash flows, with most investors heavily relying on nonfinancial information. Moreover, most of the predictability in growth stock returns appears to be momentum driven (Asness 1997). In contrast, the valuation of value stocks should focus on recent changes in firm fundamentals (e.g., financial leverage, liquidity, profitability, and cash flow adequacy). The assessment of these characteristics is most readily accomplished through a careful study of historical financial statements.
2.2 Prior fundamental analysis research
One approach to separate ultimate winners from losers is through the identification of a firm’s intrinsic value and/or systematic errors in market expectations. The strategy presented in Frankel and Lee (1998) requires investors to purchase stocks whose prices appear to be lagging fundamental values. Undervaluation is identified by using analysts’ earnings forecasts in conjunction with an accounting-based valuation model (e.g., residual income model), and the strategy is successful at generating significant positive returns over a three-year investment window. Similarly, Dechow and Sloan (1997) and LaPorta (1996) find that systematic errors in market expectations about long-term earnings growth can partially explain the success of contrarian investment strategies and the book-to-market effect, respectively.
As a set of neglected stocks, high BM firms are not likely to have readily available forecast data. In general, financial analysts are less willing to follow poor performing, low- volume, and small firms (Hayes 1998; McNichols and O’Brien 1997), while managers of distressed firms could face credibility issues when trying to voluntary communicate forward-looking information to the capital markets (Koch 1999; Miller and Piotroski 2002). Therefore, a forecast-based approach, such as Frankel and Lee (1998), has limited application for differentiating value stocks.
Numerous research papers document that investors can benefit from trading on various signals of financial performance. Contrary to a portfolio investment strategy based on equilibrium risk and return characteristics, these approaches seek to earn “abnormal” returns by focusing on the market’s inability to fully process the implications of particular financial signals. Examples of these strategies include, but are not limited to, post–earnings announcement drift (Bernard and Thomas 1989, 1990; Foster, Olsen, and Shevlin 1984), accruals (Sloan 1996), seasoned equity offerings (Loughran and Ritter 1995), share repurchases (Ikenberry, Lakonishok, and Vermaelen 1995), and dividend omissions/decreases (Michaely, Thaler, and Womack 1995).
A more dynamic investment approach involves the use of multiple pieces of information imbedded in the firm’s financial statements. Ou and Penman (1989) show that an array of financial ratios created from historical financial statements can accurately predict future changes in earnings, while Holthausen and Larcker (1992) show that a similar statistical model could be used to successfully predict future excess returns directly. A limitation of these two studies is the use of complex methodologies and a vast amount of historical information to make the necessary predictions. To overcome these calculation costs and avoid overfitting the data, Lev and Thiagarajan (1993) utilize 12 financial signals claimed to be useful to financial analysts. Lev and Thiagarajan (1993) show that these fundamental signals are correlated with contemporaneous returns after controlling for current earnings innovations, firm size, and macroeconomic conditions.
Since the market may not completely impound value-relevant information in a timely manner, Abarbanell and Bushee (1997) investigate the ability of Lev and Thiagarajan’s (1993) signals to predict future changes in earnings and future revisions in analyst earnings forecasts. They find evidence that these factors can explain both future earnings changes and future analyst revisions. Consistent with these findings, Abarbanell and Bushee (1998) document that an investment strategy based on these 12 fundamental signals yields significant abnormal returns.
This paper extends prior research by using context-specific financial performance measures to differentiate strong and weak firms. Instead of examining the relationships between future returns and particular financial signals, I aggregate the information contained in an array of performance measures and form portfolios on the basis of a firm’s overall signal. By focusing on value firms, the benefits to financial statement analysis (1) are investigated in an environment where historical financial reports represent both the best and most relevant source of information about the firm’s financial condition and (2) are maximized through the selection of relevant financial measures given the underlying economic characteristics of these high BM firms.
2.3 Financial performance signals used to differentiate high BM firms
The average high BM firm is financially distressed (e.g., Fama and French 1995; Chen and Zhang 1998). This distress is associated with declining and/or persistently low margins, profits, cash flows, and liquidity and rising and/or high levels of financial leverage. Intuitively, financial variables that reflect changes in these economic conditions should be useful in predicting future firm performance. This logic is used to identify the financial statement signals incorporated in this paper.
I chose nine fundamental signals to measure three areas of the firm’s financial condition: profitability, financial leverage/liquidity, and operating efficiency. The signals used are easy to interpret and implement, and they have broad appeal as summary performance statistics. In this paper, I classify each firm’s signal realization as either “good” or “bad,” depending on the signal’s implication for future prices and profitability. An indicator variable for the signal is equal to one (zero) if the signal’s realization is good (bad). I define the aggregate signal measure, F_SCORE, as the sum of the nine binary signals. The aggregate signal is designed to measure the overall quality, or strength, of the firm’s financial position, and the decision to purchase is ultimately based on the strength of the aggregate signal.
ช่วยแปลภาษาอังกฤษทีค่ะ
Section 2: Literature Review and Motivation
2.1 High book-to-market investment strategy
This paper examines a refined investment strategy based on a firm’s book-tomarket ratio (BM). Prior research (Rosenberg, Reid, and Lanstein 1984; Fama and French 1992; Lakonishok, Shleifer, and Vishny 1994) shows that a portfolio of high BM firms outperforms a portfolio of low BM firms. Such strong return performance has been attributed to both market efficiency and market inefficiency. In Fama and French (1992), BM is characterized as a variable capturing financial distress, and thus the subsequent returns represent a fair compensation for risk. This interpretation is supported by the consistently low return on equity associated with high BM firms (Fama and French 1995; Penman 1991) and a strong relation between BM, leverage, and other financial measures of risk (Fama and French 1992; Chen and Zhang 1998). A second explanation for the observed return difference between high and low BM firms is market mispricing. In particular, high BM firms represent “neglected” stocks where poor prior performance has led to the formation of “too pessimistic” expectations about future performance (Lakonishok, Shleifer, and Vishny 1994). This pessimism unravels in the future periods, as evidenced by positive earnings surprises at subsequent quarterly earnings announcements (LaPorta et al. 1997).
Ironically, as an investment strategy, analysts do not recommend high BM firms when forming their buy/sell recommendations (Stickel 1998). One potential explanation for this behavior is that, on an individual stock basis, the typical value firm will underperform the market and analysts recognize that the strategy relies on purchasing a complete portfolio of high BM firms.
From a valuation perspective, value stocks are inherently more conducive to financial statement analysis than growth (i.e., glamour) stocks. Growth stock valuations are typically based on long-term forecasts of sales and the resultant cash flows, with most investors heavily relying on nonfinancial information. Moreover, most of the predictability in growth stock returns appears to be momentum driven (Asness 1997). In contrast, the valuation of value stocks should focus on recent changes in firm fundamentals (e.g., financial leverage, liquidity, profitability, and cash flow adequacy). The assessment of these characteristics is most readily accomplished through a careful study of historical financial statements.
2.2 Prior fundamental analysis research
One approach to separate ultimate winners from losers is through the identification of a firm’s intrinsic value and/or systematic errors in market expectations. The strategy presented in Frankel and Lee (1998) requires investors to purchase stocks whose prices appear to be lagging fundamental values. Undervaluation is identified by using analysts’ earnings forecasts in conjunction with an accounting-based valuation model (e.g., residual income model), and the strategy is successful at generating significant positive returns over a three-year investment window. Similarly, Dechow and Sloan (1997) and LaPorta (1996) find that systematic errors in market expectations about long-term earnings growth can partially explain the success of contrarian investment strategies and the book-to-market effect, respectively.
As a set of neglected stocks, high BM firms are not likely to have readily available forecast data. In general, financial analysts are less willing to follow poor performing, low- volume, and small firms (Hayes 1998; McNichols and O’Brien 1997), while managers of distressed firms could face credibility issues when trying to voluntary communicate forward-looking information to the capital markets (Koch 1999; Miller and Piotroski 2002). Therefore, a forecast-based approach, such as Frankel and Lee (1998), has limited application for differentiating value stocks.
Numerous research papers document that investors can benefit from trading on various signals of financial performance. Contrary to a portfolio investment strategy based on equilibrium risk and return characteristics, these approaches seek to earn “abnormal” returns by focusing on the market’s inability to fully process the implications of particular financial signals. Examples of these strategies include, but are not limited to, post–earnings announcement drift (Bernard and Thomas 1989, 1990; Foster, Olsen, and Shevlin 1984), accruals (Sloan 1996), seasoned equity offerings (Loughran and Ritter 1995), share repurchases (Ikenberry, Lakonishok, and Vermaelen 1995), and dividend omissions/decreases (Michaely, Thaler, and Womack 1995).
A more dynamic investment approach involves the use of multiple pieces of information imbedded in the firm’s financial statements. Ou and Penman (1989) show that an array of financial ratios created from historical financial statements can accurately predict future changes in earnings, while Holthausen and Larcker (1992) show that a similar statistical model could be used to successfully predict future excess returns directly. A limitation of these two studies is the use of complex methodologies and a vast amount of historical information to make the necessary predictions. To overcome these calculation costs and avoid overfitting the data, Lev and Thiagarajan (1993) utilize 12 financial signals claimed to be useful to financial analysts. Lev and Thiagarajan (1993) show that these fundamental signals are correlated with contemporaneous returns after controlling for current earnings innovations, firm size, and macroeconomic conditions.
Since the market may not completely impound value-relevant information in a timely manner, Abarbanell and Bushee (1997) investigate the ability of Lev and Thiagarajan’s (1993) signals to predict future changes in earnings and future revisions in analyst earnings forecasts. They find evidence that these factors can explain both future earnings changes and future analyst revisions. Consistent with these findings, Abarbanell and Bushee (1998) document that an investment strategy based on these 12 fundamental signals yields significant abnormal returns.
This paper extends prior research by using context-specific financial performance measures to differentiate strong and weak firms. Instead of examining the relationships between future returns and particular financial signals, I aggregate the information contained in an array of performance measures and form portfolios on the basis of a firm’s overall signal. By focusing on value firms, the benefits to financial statement analysis (1) are investigated in an environment where historical financial reports represent both the best and most relevant source of information about the firm’s financial condition and (2) are maximized through the selection of relevant financial measures given the underlying economic characteristics of these high BM firms.
2.3 Financial performance signals used to differentiate high BM firms
The average high BM firm is financially distressed (e.g., Fama and French 1995; Chen and Zhang 1998). This distress is associated with declining and/or persistently low margins, profits, cash flows, and liquidity and rising and/or high levels of financial leverage. Intuitively, financial variables that reflect changes in these economic conditions should be useful in predicting future firm performance. This logic is used to identify the financial statement signals incorporated in this paper.
I chose nine fundamental signals to measure three areas of the firm’s financial condition: profitability, financial leverage/liquidity, and operating efficiency. The signals used are easy to interpret and implement, and they have broad appeal as summary performance statistics. In this paper, I classify each firm’s signal realization as either “good” or “bad,” depending on the signal’s implication for future prices and profitability. An indicator variable for the signal is equal to one (zero) if the signal’s realization is good (bad). I define the aggregate signal measure, F_SCORE, as the sum of the nine binary signals. The aggregate signal is designed to measure the overall quality, or strength, of the firm’s financial position, and the decision to purchase is ultimately based on the strength of the aggregate signal.