Market Inefficiencies and Pricing Heuristics: Evidence from the PEG Ratio Stephan Fafatas *, Assistant Professor of Accounting Washington and Lee University Phil Shane, Professor of Accounting University of Colorado ABSTRACT This study investigates the use of a simple trading heuristic, the PEG ratio, to value stocks. Our results indicate that a trading strategy implementing accounting based intrinsic value calculations yields greater one year stock returns for a subsample of firms priced fairly according to the PEG as compared to returns from the same strategy applied to the overall market. Thus, identifying stocks priced according to the PEG heuristic creates a sample of firms which are more likely to have fundamental values that diverge from actual stock prices. These results provide preliminary evidence regarding the relationship between heuristics and market inefficiencies. Investors can profit by applying intrinsic value based trading strategies to stocks currently priced in accordance with the PEG. INTRODUCTION Prior research finds that intrinsic (i.e. earnings and cash flows based) valuation models are useful in identifying mispriced stocks and predicting future returns. 1 The basis behind an intrinsic value based pricing approach is that the fair value of an investment should reflect the present value of the cash flows that the investment generates. As Warren Buffet summarizes: Intrinsic value is an all important concept that offers the only logical approach to evaluating the relative attractiveness of investments and businesses. 2 Despite the fundamental nature of intrinsic value models, many analysts and investors appear to base recommendations and trading decisions on relatively simple heuristics. One heuristic commonly cited in the financial press as a useful tool for evaluating stocks is the PEG, or price earnings growth, ratio. The foundation of the PEG as a pricing tool is rather straightforward, relying on the premise that the price earnings (P/E) ratio of any company that is fairly valued will equal its expected earnings growth rate multiplied by 100. Thus, stocks trading at a PEG equal to 1.0 are considered fairly valued, or holds. Stocks trading at a PEG of less than 1.0 are considered under valued (i.e. buys ) and stocks trading at a PEG of greater than 1.0 are considered over valued (i.e. sells ). The greater the distance from 1.0, the more or less attractive the stock becomes. In line with the growth at reasonable price (GARP) philosophy, the idea is to target those stocks which are undervalued relative to their future earnings growth potential. Given estimates of growth and future earnings, one can calculate a fair price according to the PEG: where P is the current stock price and LTG is long term growth. P = E*LTG*100 (1) * Emails of Stephan Fafatas and Phil Shane are sfafatas@wlu.edu and phil.shane@colorado.edu respectively. 1 For example, Frankel and Lee (1998) study returns between 1979 and 1991 and find that a strategy of trading stocks ranked by ratios formed by dividing earnings based valuations by current market prices generated an average three year holding return of 31%. 2 Easton et al. (2010), pg 1 2.
While the nature of the PEG is clearly simplistic the ratio has become a popular investment tool among Wall Street analysts. In fact, as recent studies by Mark Bradshaw (2002, 2004) show, many sellside analysts use this heuristic, rather than relying on intrinsic value models, to calculate firm target prices and ultimately generate their buy/sell/hold stock recommendations. This finding is consistent with comments from fund managers and other investment advisors which have appeared in the popular press. For example, a 2001 Business Week article refers to the PEG is a useful statistic for comparing busted growth stocks and cites investment fund managers who avidly support the ratio (Braham, 2001). More recently a 2008 piece appearing on Forbes.com describes the PEG as a tool that helps investors sniff out potential bargains, often indicated by a PEG below 1.0 (Murdock, 2008). Indeed, there is some empirical evidence of the PEG s ability to generate abnormal trading profits (Schatzberg and Vora, 2009). However, due to the simplistic nature of this heuristic, instances exist where the PEG approximates 1.0, but the stock is actually incorrectly valued and, likewise, cases where the PEG deviates from 1.0 when the stock is actually correctly priced (Trombley, 2008). Bradshaw (2004) investigates the issue of whether PEG or intrinsic value pricing models are more useful predictors of future returns. His results show that trading strategies based on intrinsic value models yield greater one year ahead returns than strategies based solely on analyst recommendations. Importantly, the Bradshaw (2002, 2004) studies suggest that 1) analysts appear to use basic heuristics such as the PEG ratio to set target prices and 2) recommendations based on these target prices are outperformed by a trading strategy that follows intrinsic value calculations. The Bradshaw findings present a quandary as, although it appears investors and market analysts use the PEG to evaluate firms, the heuristic clearly does not present enough value relevant information to compete against more fundamental approaches to pricing stocks. Indeed academic texts often refer to the PEG as a rule of thumb with little theoretical foundation, (Stickney et al. 2007), and a good tool for sell side analysts because it is an easy method to justify their target prices (Lundholm and Sloan, 2007). Analysts apparent reliance on a ratio that has no solid basis in theory (Hoover, 2006) to make buy sell recommendations is especially troubling, because they are an integral component of the forces which support capital market efficiency. As Stickney et al. (2007) note: The degree of efficiency with respect to complete and quick reactions in an informationefficient capital market depends on analysts and financial statement analysis. Analysts study accounting information to assess appropriate values for stocks and to take positions in underpriced or overpriced securities, thereby driving stock market prices to efficient levels. The speed with which analysts can forecast, anticipate, analyze, and react to accounting information causes prices to move before accounting information is released and to react quickly to surprises in the information when it is released.analysts are driving forces involved in identifying and correcting security mispricing. Thus, a natural question arises as to the pricing inefficiencies created when analysts and other market participants rely on a simple heuristic, as opposed to an accounting based intrinsic value method, to value stocks. To identify inefficiencies linked to the PEG ratio we investigate situations where investors appear to rely on the PEG to price stocks. Specifically, in cases where stocks are priced according to the PEG (i.e. PEGs close to 1.0) we calculate one year returns generated from a trading strategy that takes long and short positions based on ratios of their intrinsic values to current prices (V/P). We then compare these hedge portfolio returns to a similar investment approach across the broad market. We state our primary question addressed through this approach as: Q1: Are returns from an intrinsic value based trading strategy greater for stocks priced in accordance to the PEG as compared to stocks in the broad market?
If reliance on the PEG is costly to investors, we expect our V/P hedge portfolio returns in a subsample of stocks priced in accordance with the PEG (the PEG sample) to exceed the V/P returns from the broad market. This difference in hedge returns would imply that pricing of the PEG sample shares is more inefficient than the pricing of shares in the overall market. Thus, investors bear a cost for relying on the PEG to evaluate stocks, as rather than indicating fair prices, the PEG heuristic actually creates temporary mispricing. Further, using a PEG metric of 1.0 to identify market inefficiencies leads to improved returns from a V/P trading strategy. We find that stock returns based on a V/P trading strategy are greater across the sample of firms priced in accordance with the PEG as compared to returns across the broader market. These results provide preliminary evidence regarding the relationship between heuristics and market inefficiencies. Use of the PEG ratio as an investment tool appears to result in systematic mispricing of stocks and supports the notion that reliance on simple heuristics creates market inefficiencies. Investors who identify these stocks using the PEG measure of 1.0 will experience abnormal V/P trading profits. METHODOLOGY Anecdotal evidence suggests the PEG ratio became a widely used investment tool in the early 1990 s. Peter Lynch, former director of Fidelity s Magellan Fund, first promoted using the PEG to identify undervalued stocks in the bestselling book, One Up on Wall Street, published in 1989. In his investment guide, Lynch touts the PEG as a useful tool and claims that Fidelity managers use this measure all the time in analyzing stocks for mutual funds. Popularity for the PEG grew during the decade as the ratio became a key variable cited by the financial press and used in popular investment guides such as those produced by the Motley Fool (Gardner and Gardner, 1997). Because of the apparent growth in the PEG s interest, our analysis focuses on the period 1990 1994. We begin with 1990 as this represents the first year following the release of Lynch s text. We use 1994 as the ending year of our data analysis to prevent any abnormal results due to the extraordinary gains from the dot.com bubble in the late 1990 s, or the resulting bust between 2000 and 2002. The reduced volatility of the market between 1983 and 1994 should yield results which are easier to generalize to other periods. 3 The purpose of our study is to investigate whether over reliance on the PEG results in increased market inefficiencies. To study this question, we must first identify stocks which appear to be fairly measured under the PEG approach (i.e. Holds ). We classify a stock as a Hold if the PEG ratio is close to 1.0, or more specifically, if the ratio is between 0.9 and 1.1. This approach allows us to focus on companies that are most likely to be priced by market participants who use the PEG heuristic to estimate value. The second step in our approach is to estimate intrinsic firm value based on a residual income approach. The approach we use is based on methods presented in studies by Frankel and Lee (1998), Liu et al. (2002) and Bradshaw (2004). These studies show that residual income based pricing models are useful in both: 1) explaining current stock prices, and 2) predicting future market returns. In our approach, a stock s intrinsic value (V) is based on a residual income valuation with a three year forecast horizon and a terminal value with a perpetuity assumption. Specifically, V is calculated as: V t = B t + ((FROE t r)/(1 + r))*b t + ((FROE t+1 r)/(1+r) 2 )*B t+1 + ((FROE t+2 r)/(1+r) 2 r)*b t+2 (2) where FROE is forecasted return on equity (ROE), B is book value, and r is the cost of equity capital. Industry cost of capital (r) is calculated using industry risk premia in Fama and French (1997) and the 3 For example, average annual returns for the Dow Jones Index equaled 7% between the years 1990 1994, compared to a staggering 25% for the 1995 1999 period and then 10% during 2000 2002. The hedge portfolio approach is used to produce equal dollar value investments in long and short positions. While this approach is not entirely risk neutral it does reduce an investment s exposure to overall market risk.
average risk free rate over the sample period (6.2%). A company s intrinsic value is measured on the same day as it s PEG to reduce the risk of comparing estimates which have changed over time. Next, intrinsic value is compared to the firm s current trading price to calculate the value to price (V/P) ratio. To implement our V/P approach, we rank stocks in quintiles according to their V/P ratio. Stocks in the highest quintile are the most under valued as the intrinsic values of these firms exceed actual prices by the greatest amounts. Likewise stocks in the lowest V/P quintile are the most over valued as these firms have intrinsic values which are much less than their actual trading prices. This approach is based on the notion that market mispricing is temporary and over time, the mis priced stocks will return to their intrinsic, or fair, values. Our key tests are based on the one year buy and hold returns from a V/P investment strategy. These returns are calculated from a hedge portfolio strategy of buying stocks in the highest quintile ranking and selling stocks in the lowest quintile. 4 To address Q1 we follow Frankel and Lee (1998) and Bradshaw (2004) and compare stock returns from a V/P trading strategy across the broad market with those from a V/P strategy across firms priced in accordance to the PEG. As previously stated, a result showing that the V/P strategy produces greater returns across the PEG sample as compared to the broad market sample will suggest that the PEG increases market inefficiencies. 5 In essence, these stocks, which look like fairly priced holds according to the PEG, actually constitute a sample with greater over and under valued firms as compared to the overall market. The PEG ratio suggests these firms are fairly valued, when in fact a more fundamental valuation approach reveals greater mispricing than firms in the broad market. Thus, stocks in the PEG sample are more likely to be temporarily mispriced. As an additional test we create a model in which one year buy and hold returns are regressed on the V/P quintile ranking and additional control variables which may be associated with future stock returns: RET = β 0 + β 1 QR + β 2 LMV + β 3 LBM + β 4 YEAR + ε (3) where: RET = one year buy and hold returns beginning one day following the PEG calculation date, QR = annual quintile V/P ranking scaled to range between 0 and 1: ((quintile 1)/4), 6 LMV = log of market value of equity at the end of the prior fiscal year LBM = log of book to market ratio measured at the end of the prior fiscal year end, and YEAR = indicator variable for calendar years 1991 1994. In this model, the coefficient on the QR variable equals the hedge portfolio return from our V/P trading strategy. Consistent with prior research, we expect a positive coefficient on the QR variable which suggests a V/P trading strategy is effective in generating future stock returns. Measures of firm size (LMV) and the stock s current book to market ratio (LBM) are added as additional risk factors and calendar year indicator variables (YEAR) are included to control for changes in market returns across time. 4 We follow the Frankel and Lee (1998) and Bradshaw (2004) approaches to analyzing returns based on quintile portfolios. While our portfolios do not control for firm specific risk per se, we partially control for risk differences by limiting the sample to a set of high growth, small cap companies. Additional controls for risk are included in model (3). While a more detailed analysis of firm specific risk is beyond the scope of the current study, we acknowledge that such an analysis may impact the strength of our results. 5 We measure buy and hold returns beginning on the day following the PEG ratio calculation date. 6 Gardner and Gardner (1997) suggest the PEG is more helpful with smaller companies as these firms tend to be valued off the power from a given service, or product line as compared to large cap companies. In addition, Gardner and Gardner (1997) refer to their version of the PEG as the Fool Ratio and they take some credit in its initial development as an investment tool.
We investigate whether the results from model (3) differ between a sample of all firms in the broad market as compared to a sample of firms where the PEG ratio indicates a Hold. Specifically at issue is whether the coefficient on QR is greater in the PEG sample as compared to the sample of all firms. A higher QR in the PEG sample indicates the heuristic is costly in the sense that investors are inappropriately relying on the ratio to price stocks. DATA We measure the PEG ratio as (Price/EPS)/LTG, using the first one year ahead EPS (earnings pershare) and LTG (long term growth) forecasts following the annual earnings release and the stock price as of the date of the EPS and LTG forecasts. All of these data items come from the I/B/E/S database. Following the Motley Fool (Gardner and Gardner, 1997) suggestion that the PEG is best applied to smalland mid cap stocks with high growth potential, only companies with a market cap less than or equal to $4 billion and those belonging to industries with median growth rates greater than or equal to 15% are included. 7 To reduce the effects of outliers, we omit firms with PEGs greater than 5 or less than 0. This approach results in a total of 4,579 observations across 1990 1994. One year buy and hold stock returns come from CRSP and begin one day after the PEG calculation date. Additional control variables in model (3) come from COMPUSTAT and the total resulting sample sizes are 2,983 for the overall market and 602 for firms with PEGs indicating Hold. Table 1 reports statistics for the year and industry composition of our sample data. Panel A shows that our sample distribution increases over time, with the largest percentage of data (27.2%) coming from 1994. This likely reflects increased availability of I/B/E/S analyst data over our time period. Panel B reports the total distribution among our sample s top five industries, as defined by Fama and French (1997). Our sample is fairly evenly distributed among industries with the Retail (14.9%) and Business Services (13.4%) lines yielding the greatest percentage of firms. RESULTS Our empirical analysis investigates Q1: whether the increased reliance on the PEG heuristic in the early 1990 s led to more market mispricing during those years. Table 2 presents the one year buy andhold returns from a V/P trading strategy across two samples of firms from 1990 1994. Stocks are first ranked according to V/P calculations and then returns are calculated from a hedge portfolio trading strategy that buys the highest ranking quintile of V/P firms (High V/P) and sells the lowest ranking V/P quintile (Low V/P). The same approach is then applied to a portfolio of stocks for which the firm s PEG ratio indicates Hold. Consistent with Frankel and Lee (1998), the average returns to stocks across both portfolios trend higher as we move from the lowest V/P ranking (Quinitile1) to the highest V/P ranking (Quintile5) and the difference in stock returns between the highest and lowest quintiles is positive and significant at the 5% level (one tailed). 8 This suggests that our V/P method for ranking stocks is informative and in line with the results of both Frankel and Lee (1998) and Bradshaw (2004). Table 2 further shows that across the full market sample, the mean return from our hedge portfolio strategy is 6.5%. In comparison, the mean hedge portfolio return to the sample of firms which are fairly priced according to the PEG is 13.3%, or roughly double the returns across the broad market. Thus, Table 2 provides initial evidence that investors reliance on the PEG ratio to price stocks results in increased market inefficiencies. While firms in the Hold portfolio are adequately priced according to the PEG, these same firms are actually shown to be associated with stronger mispricing in the short term as compared to a portfolio of firms in the broad market. To more formally test the association between PEG ratios and future stock V/P returns we estimate model (3) and provide the results in Table 3. This model estimates the return from a V/P strategy after including time period controls, as well as variables to capture firm size and book to market ratios. 7 Since it is expected that our V/P trading strategy will yield positive returns we use one tailed t statistics to evaluate the significance of returns to the trading strategies. 8 Years prior to 1985 are deleted due to missing data constraints.
The variable of interest is in model (3) is QR. The coefficient on QR (β 1 ) estimates the one year buy andhold returns from a V/P hedge portfolio trading strategy. The results from estimating this model are similar to those reported in Table 3, as the β 1 estimate is greater for the Hold sample as compared to the broad market. Specifically, the one year buy and hold V/P returns for firms in the Hold sample are 14.2%, compared to 6.5% for the full sample of firms. Both of these statistics are significant at the 1% level (one tailed). As with the results in Table 3, the estimated hedge portfolio return from a V/P strategy in the PEG=Hold sample is over double the amount in the full sample. Thus, after controlling for additional variables associated with future stock performance, the V/P strategy still produces greater returns to the sample of firms priced in accordance to the PEG as compared to the overall market. The results from Tables 2 and 3 provide an answer to Q1 as returns from an intrinsic value based trading strategy are indeed greater for stocks priced in accordance to the PEG as compared to stocks in the broad market. More specifically, our results offer initial evidence that firms priced according to the PEG are associated with increased stock market inefficiencies. Investors who use the PEG ratio to price stocks incur a cost as, rather than being fairly priced, these firms are associated with stronger cases of mispricing than the market as a whole. Compared to the full market the PEG firms face increased temporary mispricing as revealed by the profitability of a trading strategy based on the discrepancy between intrinsic values calculated using an earnings based valuation model and prices consistent with the PEG heuristic. Alternatively, V/P investors can enhance their returns by focusing on the subset of firms with PEG ratios around 1.0. As an additional test, we estimate model (3) during a pre PEG period defined as 1985 1988. 9 If the PEG was not a popular pricing heuristic during these years, we should not obtain greater returns from a V/P strategy for the PEG=Hold firms as compared to the overall sample. Results from estimating model (3) for the earlier sample period are presented in Table 4. Similar to the results in Table 3, the coefficient on QR suggests annual returns to the V/P strategy across the broad market are 6.1%. Thus, the accounting based intrinsic value calculation is useful during this period, as well. In addition, contrary to the results presented in Table 3, the coefficient on QR is insignificant for the PEG=Hold sample. Thus a V/P strategy which focused on a PEG=Hold sample of firms would not have generated significant returns in the late 1980 s. Results from Table 4 provide additional support for the notion that the popularity of the PEG as a trading heuristic increased in the early 1990 s and this popularity in the PEG is associated with increased market inefficiencies during our sample period. 10 CONCLUSION AND LIMITATIONS This study investigates the stock market s use of a simple trading heuristic, the PEG ratio, to value stocks. We provide evidence that reliance on the PEG as a pricing tool increases short term market inefficiencies. These inefficiencies result in opportunities for investors to generate gains when the PEG heuristic is viewed in combination with an intrinsic value based trading approach. Specifically, an investment approach which buys stocks in the highest quintile of V/P rankings and sells stocks in the lowest V/P quintile generates greater one year returns across a sub sample of stocks priced in accordance with the PEG as compared to a V/P approach applied to the broad market. Thus, a key contribution of our 9 Results from additional tests (not reported) show that the percentage of stocks priced in accordance with the PEG (i.e. stocks considered Holds) increased from 12% during 1983 1989 to 18% during 1990 1994 which is also consistent with the market relying more on this heuristic during the 1990 s time period. However, caution must be taken when examining percentage changes in Hold firms across the 80 s and 90 s as it is not possible to attribute the causality of this change directly to the market s increased use of the PEG. 10 Indeed it is possible to use a set of assumptions such that both the PEG and intrinsic value residual income) approaches yield the same estimated stock price. See Hoover (2006) and Lundholm and Sloan (2007) for in depth descriptions on the sets of the assumptions which must be satisfied for the PEG to yield similar results as those achieved from more fundamental valuation models.
study is that we find investors can significantly augment their V/P trading strategy by focusing on firms priced according to a market heuristic. This is not to suggest that all proponents of the PEG, or any other heuristic for that matter, use only a single metric to analyze securities. 11 Yet, it is clear from earlier studies (Bradshaw (2002, 2004) that analysts and investors rely heavily on this heuristic to evaluate stocks. Our results imply that overreliance on the PEG weakens short term market efficiency and that investors may profit if they are able to identify those firms considered fairly priced according to this heuristic. Our study provides initial findings related to inefficiencies which result from market reliance on stock price heuristics, and as such, is subject to limitations including the following: Our sample covers a relatively brief time period, from 1990 1994, We measure only short term stock returns over a one year period, and We focus on a set of small companies with high growth rates as suggested by Gardner and Gardner (1997). These limitations mean that our results may not generalize across other market settings. However, the primary goal of our research was to investigate a specific sample where the PEG ratio was most likely used by investors in order to determine whether a popular heuristic leads to market inefficiencies and creates outlets to improve returns from a V/P ratio. While we find evidence that reliance on the PEG leads to pricing inefficiencies and V/P investors can take advantage of these inefficiencies to generate higher returns, additional issues remain unexplored. For example, did reliance on simple heuristics lead to over pricing during the internet boom? Has the stock market instability witnessed over the latter part of the decade caused investors to reevaluate methods for pricing securities, particularly growth firms? Finally, does reliance on alternative pricing heuristics, such as general industry level P/E and B/M ratios also present areas where investors can increase profits from a V/P trading strategy? We leave these important questions for future research.
Table 1 Sample Statistics Panel A: Distribution of Sample across Years Frequency Year n % 1990 417 14.0 1991 486 16.3 1992 545 18.3 1993 725 24.3 1994 810 27.2 Total Observations 2,983 100% Panel B: Distribution of Sample across Industries a Frequency Industry n % Retail 444 14.9 Business Services 401 13.4 Computers 215 7.2 Electronic Equipment 245 8.2 Medical Equipment 173 5.8 Other 1,505 50.5 Total Observations 2,983 100% a Firms are assigned to industries following the Fama and French (1997) method which uses four digit SIC codes to group companies.
Table 2 Differences in stock returns between highest and lowest quintile of V/P stocks PEG = Hold Mean Returns Quintile1 (Low V/P) 0.109 0.124 Qunitile2 0.069 0.141 Quintile3 0.118 0.108 Quintile4 0.139 0.180 Quintile5 (High V/P) 0.242 0.189 Full Sample Q5 Q1 Diff. 0.133** 0.065** n (total) 602 2,983 n (Q5 Q1) 243 1,194 PEG equals a stock s price earnings growth ratio. PEG ratios are calculated as (Price/EPS)/(LTG*100) using the first EPS (earnings per share) and LTG (long term growth) forecasts following the annual earnings release. Price is measured at the date of the EPS and LTG forecasts. Observations with PEGs greater than 5 or less than 0 have been deleted. Only companies with a market cap less than or equal to $4 billion and those belonging to industries with median growth rates greater than or equal to 15% are included in the sample. A PEG that signals a Hold indicates the ratio is between 0.90 and 1.10. Returns are calculated during 1990 1994 and represent the one year buy and hold return beginning one day after the price earnings growth (PEG) calculation date. V/P is a stock s intrinsic value (V) to stock price (P) ratio measured on the same date as the PEG. Intrinsic value is based on a residual income valuation with a three year forecast horizon and a terminal value with a perpetuity assumption. Specifically, V is calculated as in Frankel and Lee (1998): V t = B t + ((FROE t r)/(1 + r))*b t + ((FROE t+1 r)/(1+r) 2 )*B t+1 + ((FROE t+2 r)/(1+r) 2 r)*b t+2 where FROE is forecasted return on equity (ROE), B is book value, and r is the cost of equity capital. Industry cost of capital (r) is claculated using the industry risk premia in Fama and French (1997) and the average risk free rate over the sample period (.062). This model reflects a fundamental earnings based approach to valuing a company s stock. The PEG=Hold sample consists of stocks with PEG ratios between 0.90 and 1.10. ** Denotes significance at the 0.05 level based on one tailed tests.
Table 3 Returns from a V/P strategy focusing on stocks with PEGs reflecting a Hold RET = β 0 + β 1 QR + β 2 LMV + β 3 LBM + β 4 YEAR + ε PEG= Hold Full Sample β 0 0.295 0.154 (2.39)** (2.65)*** β 1 0.142 0.065 (2.59)*** (2.43)*** β 2 0.005 0.004 (0.35) (0.54) β 3 0.044 0.065 (0.55) (1.94)** n 602 2,983 Adj. R 2 0.030 0.020 This table presents the regression results from an intrinsic value to price (V/P) investment strategy between the years 1990 1994 (see Table 2 for a complete definition of the V/P and PEG ratio). The models include observations which fall into the highest or lowest quintile of stocks ranked according to the V/P ratio. RET is the one year buy and hold return beginning one day after the PEG calculation date. QR is the quintile V/P ranking scaled to range between 0 and 1 ((quintile 1)/4). Thus, the results capture the average one year hedge portfolio return from buying (selling) the highest (lowest) ranked stocks according to a V/P ranking. LMV is the log of the market value of equity at the end of the prior fiscal year. LBM is the log of the book to market ratio measured at the end of the prior fiscal year end. YEAR is an indicator variable for calendar years 1991 1994. ***, ** Denote significance at the 0.01 and 0.05 levels based on one tailed (two tailed) tests for t statistics on estimated variables (regression intercepts).
Table 4 Returns from a V/P strategy in the pre PEG time period (1985 1988) RET = β 0 + β 1 QR + β 2 LMV + β 3 LBM + β 4 YEAR + ε PEG= Hold Full Sample β 0 0.244 0.056 (1.82)* (0.88) β 1 0.040 0.061 (0.69) (1.91)** β 2 0.011 0.037 (0.62) (4.18)*** β 3 0.112 0.110 (1.12) (2.37)** n 183 1,120 Adj. R 2 0.156 0.129 This table presents the regression results from an intrinsic value to price (V/P) investment strategy between the years 1980 1988. See Tables 2 and 3 for a complete definition of variable descriptions. ***, **, * Denote significance at the 0.01, 0.05, and 0.10 levels based on one tailed (two tailed) tests for t statistics on estimated variables (regression intercepts).
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