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Earnings Estimates, Predictor Specification, and Measurement Error 205 estimation include equal-weighted least-squares regression and monotone regression." If the inclusion of flash data has explanatory power beyond that provided by consensus data, the coefficient c in Eq. (6.3) will be significantly different from zero. This would indicate that the relationship between returns and predictors based upon flash data di fers from that between returns and predictors based on consensus data. Furthermore, a positive and significant coefficient would sug gest that companies with a positive flash-consensus differential would be expected to have higher excess returns, on average, than companies with a flash mean below the consensus mean. Such a finding would imply not only that the relationship betwe& returns and predictor is sensitive to specification, but also that the relationship between returns and flash data is stronger than the relationship between returns and consensus data. A priori, one might expect this to be the case, since flash data are more timely, and hen likely have higher information content than consensus data. Tables 6-4 and 6-5 present the results from the estimated models The evidence in Table 6-4 pertaining to forecast E/P suggests that the return-predictor relationship is sensitive to specification. With leastsquares estimation, the coefficient c for the flash-consensus differential is positive, with a p value of less than 0.0001.'2 This suggests that the incremental effect of flash data is highly significant, and that one may expect differences between the flash consensus forecast E/Ps for a given company to lead to differences return. Other things equal, those companies with flash forecast E/Ps higher than their consensus E/Ps wil tend to enjoy higher returns than those companies with flash E/Ps lower than their consensus E/Ps. The regression estimate of 0.2210 for the flashconsensus forecast E/P differential suggests that, with a 1 standard deviation increase in the differential, average excess return can be expected to increase by around 22 basis points, other things being equal. Our results suggest that the use of consensus E/P can capture the positive relationship between returns and forecast E/P. Other things equal, average excess return increases by around 34 basis points with a 1 standard deviation increase in exposure to forecast E/P. But the use of flash E/P can lead to even higher returns. The other predictors included in model-controversy the and neglect-
206 selecting securities TABLE 6-4 Incremental Effect of the Flash Forecast W-Regression Results for 3000-Stock Univers+ApriI1990 to December 1 996 Incremental Consensus Flash Forecast Forecast EP W Controversy Neglect Least-Squsms Regmsslon Mean 0.3353 . 0.2210 -0.0448 -0.1540 Standard error mean 0.1009 0.0428 0.0924 0.0962 t-statistic 3.3230 5.1618 -0.4845 -1.6006 p value 0.0014 0.0000 0.6294 0.1134 Monotone Rsnk Regmsslon Mean 0.0412 0.0235 -0,0155 -0.0258 Standard error mean 0.0090 0.0040 0.0068 0.0091 t-statistic 4.5710 5.8112 -2.2622 -2.8224 TABLE 6-5 Incremental Effect of the Flash Forecast Trend-Regression Results for 3000-Stock Universe-April 1990 to December 1996 Incremental Consensus Flash Trend Trend Controversy Negle Least-Squsms Regmsslon Mean 0.3859 0.0134 -0.1289 -0.1704 Standard error mean 0.0606 0.0341 0.0872 0.1001 1-statistic 6.3696 0.3922 -1.4785 -1.7025 p value 0.0000 0.6960 0.1432 0.0925 Monotone Rank Regmsslon Mean 0.0456 0.0031 -0.01 15 -0.0284 Standard error mean 0.0062 0.0022 0.0065 0.0094 t-statistic 7.4019 1.4320 -1.7692 -3.0297 pvalue 0.0000 0.1 560 6.0807 0.0033
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EQUITY MANAGEMENT.
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EQUITY MANAGEMENT Quantitative Anal
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To Ilene, Lauren, Julie, Sam, and E
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CONTENTS Foreword by Harry M. Marko
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Contents ix Chapter 4 Calendar Anom
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Contents xi Implications 272 High-D
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FOREWORD by Harry M. Markowitz, Nob
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Foreword xv In practice, of course,
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Foreword xvii that an RDM would con
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xix
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INTRODUCTION Life on the Leading Ed
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Life on the Leading Edge 3 tion abo
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Life on the Leading Edge 5 clude th
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Life on the Edge Leading 7 stocks t
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Life on the Leading Edge 9 . Quanti
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Life on the Leading Edge 11 By the
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Life on the Leading Edge 13 ple, st
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Life on the Leading Edge 15 find be
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Life on the Leading Edge 17 equal t
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P A R T ONE Selecting Securities A
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Selectine Securities 21 stocks. Suc
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selecting secunties 23 unmoved by s
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CHAPTER 1 The Complexity of the Sto
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The Complexity of the Stock Market
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The Complexity of the St& Market 31
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The Complexity of the St& Market 37
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The of the Complexity Stock Market
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The Comdexitv of the Stock Market 4
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V 7
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Disentangling Equity Return Regular
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Disentangling Equity Rehun Regulari
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Disentangling Equity Rehun Regulari
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64 selecting securities aged 59 bas
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66 selecting Securities FIGURE 2-2
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68 selecting securities FIGURE 2-3
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70 selecting securities tic of 17.8
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72 selecting securities Some Implic
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74 selecting securities Table 2-2 d
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I l
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82 Seleaine Securities these time-s
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84 selecting securities of 9.7. The
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86 selecting securities 2. There ar
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and deferring gains [as per Constan
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90 selecting securities 35. For exa
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92 selecting securities 49. Before
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94 selecting securities Ball, R. 19
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96 selecting securities Iowa, Ames.
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98 Seleaine securities James, W. an
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100 selecting securities Nicholson,
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102 selecting securities Tinic, S.
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104 selecting securities VALUE AND
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106 selecting securities Street Jou
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108 Selectine Securities This is no
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110 selecting securities plaining s
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112
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114 selecting securities transient
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116 selecting securities Na'ive Exp
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118 selecting securities (1988c)l.
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l20 selecting securities average of
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124 selecting securities We have de
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126 selecting securities movements
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l28 selecting securities 34. Pensio
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130 selecting securities Einhom, S.
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132 Selectine securities Poterba, J
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136 selecting securities The availa
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138 selecting securities subsequent
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142 Selectine securities FIGURE 4-2
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144 selecting securities others usi
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146 Selectine Securities FIGURE 4-3
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l48 SelectirlE securities a market
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150 . selecringsecurities There is
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152 selecting securities 1981 and 1
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256 Managing Portfolios manager‘s
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258 Managing Portfolios FIGURE S 4
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260 Managing Portfolios Imposition
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264 Managing Portfolios Recent stud
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High-Definition Style Rotation 267
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0 N
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272 Managing Portfolios TABLE 10-2
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274 Managing Portfolios ing pure re
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276 Managing Portfolios HIGH-DEFINI
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278 Managing Portfolios - FIGURE 10
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280 Managing Portfolios FIGURE 10-S
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282 Managing Portfolios REFERENCES
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284 Expanding Opportunities and “
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286 Expanding Opportunities such as
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290 ExpandingOpprtunities vided sho
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292 Expandingoppo~ties F I G U R E
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2% ExpandingOpportunities FIGURE 11
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298 Expanding Opportunities Typical
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300 Expandingopportunities F I G U
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302 Expanding Opportunities news st
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308 Expanding Opportunities FIGURE
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310 Expanding oppo~ties -and- . 199
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312 ExpandingOpportunities applied
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314 Expanding Opportunities A secur
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316 ExpandingOpportunities in 1987,
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318 Expanding Opportunities the $4.
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320 Expanding Opportunities portfol
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322 Expanding Opportunities managem
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324 Expanding Opportunities The sec
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326 Expanding Opportunities Overall
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328 Expanding Opportunities risen o
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330 Expanding Opportunities lio and
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332 Expanding Opportunities FIGURE
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334 Expanding Opportunities , use o
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The Long and Short on Long-Short 33
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340
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Uptick rules can delay, or in some
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344 Expanding Opportunities expecte
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Nonsymmetrical distributions of sec
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11. We assume a futures margin of 4
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350 Expanding Opportunities see, lo
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352 Expanding Opportunities case, t
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Trading Costs Some other costs of l
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356 Expanding Opportunities We use
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358 Expanding The optimal portfolio
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360 Expanding Opportunities N H = C
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362 Expanding Opportunities . Consi
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364 Expanding Opportunities mark se
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366 Expanding Opportunities not be
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370 Expanding Opportunities securit
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372 Expanding Opportunities maximiz
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374 ExpandingOpportunities uity ret
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376 Expanding Opportunities may be
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378 Expanding Opportunities from se
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hance good performance, but they ca
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382 Name Index Clarke, Roger G., 25
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384 Name Index Levis, M., 174,175,1
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386 Name Index Swanson, H., 183,188
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388 Subject Index Disentangling equ
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390 Subied Index MAE, 170,182 Marke
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392 Subiect Jndex Security analysis
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