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The Complexity of the Stock Market 41 modeling the sue effect, but we limited our investigation to these six valuation variables. While we found the VAR model to fithesize effect quite well in-sample, it provided poor forecasts out-of-sample. Because it has a large number of coefficients available to explain a small number of observations, a VAR model can explain historical data But well. it is likely to "overfit" the data. That is, it will fit not only systematic or stable relationships, but also random or merely circumstantial ones. The latter are of no use in forecasting, and may be misleading? One solution to the overfitting problem of vector time-series approaches is to incorporate economic theory. Such structural econometric models include only those variables and relationships suggested by theory. Simple .theories, however, are no more descriptive of the economy than they are of the stock market, and structural models generally have not performed well. An alternative solution involves a novel Bayesian technique. BAYESIAN RANDOM WALK FORECASTING Many economic measures are difficult to predict, but their behavior can often be approximated by a random walk. A random walk model for interest rates assumes it is equally likely that rates will rise or fall. Hence, a random walk forecast of next month's interest rate would be simply this month's rate of interest. That it is difficult to predict stock returns is no secret. But stock prices;like other economic data, can be approximated by a random walk. As early as 1900, Bachelier proposed a theory of random walks in security prices. A random is walk thus an eminently sensible .first approximation, or "prior belief," for modeling security re- tlNns.'O Prior beliefs about the coefficients of a forecast model can be specified in many ways. One Bayesian specification imposes a ran dom walk prior on the coefficients a of VAR model. This prior belief acts as a filter for extracting signals (meaningful relationships in data), while leaving accidental relationships behind. Such a speci cation results in.a powerful forecasting tool. The results of modeling the size effect with a Bayesian random walk prior belief are displayed in Figure 1-4. The upper chart shows cumulative pure retums to small size'for the period January 1982
42 selecting securities FIGURE 1 4 Forecasting Returns to Small Size 1982 1983 1984 1985 1986 1987 Cumulative Return to Small Sue 1982 1983 1984 1985 1986 1987 Bayesian Model Forecasts
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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
Page 12 and 13: Contents xi Implications 272 High-D
Page 14 and 15: FOREWORD by Harry M. Markowitz, Nob
Page 16 and 17: Foreword xv In practice, of course,
Page 18 and 19: Foreword xvii that an RDM would con
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Page 22 and 23: INTRODUCTION Life on the Leading Ed
Page 24 and 25: Life on the Leading Edge 3 tion abo
Page 26 and 27: Life on the Leading Edge 5 clude th
Page 28 and 29: Life on the Edge Leading 7 stocks t
Page 30 and 31: Life on the Leading Edge 9 . Quanti
Page 32 and 33: Life on the Leading Edge 11 By the
Page 34 and 35: Life on the Leading Edge 13 ple, st
Page 36 and 37: Life on the Leading Edge 15 find be
Page 38 and 39: Life on the Leading Edge 17 equal t
Page 40 and 41: P A R T ONE Selecting Securities A
Page 42 and 43: Selectine Securities 21 stocks. Suc
Page 44 and 45: selecting secunties 23 unmoved by s
Page 46 and 47: CHAPTER 1 The Complexity of the Sto
Page 48 and 49: The Complexity of the Stock Market
Page 52 and 53: The Complexity of the St& Market 31
Page 58 and 59: The Complexity of the St& Market 37
Page 60 and 61: The of the Complexity Stock Market
Page 64 and 65: The Comdexitv of the Stock Market 4
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Page 70 and 71: Disentangling Equity Return Regular
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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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122 selecting securities FIGURE 3-1
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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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140 selecting securities FIGURE 4-1
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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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154 seleding securities Chari,V., R
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156 Selectine securities McNichols,
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160 selecting securities that betwe
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162 selecting securities Handa, Kot
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164 selecting securities risk does
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166 selecting securities returns ac
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168 selecting securities ket return
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~ more 170 Selectine securities tiv
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176 selecting securities hibit feed
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178 Seledine securities multivariat
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-2 1982 1983 1984 1985 1986 1987 18
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182 selecting securities rate produ
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184 Seledine Securities 11. Further
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186 Seleaine securities REFERENCES
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188 selecting securities Fama, E. a
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190 selecting securities Miller, E.
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194 Selectine Securities modeling p
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196 selecting securities surement e
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Earnings Estimates, Predictor Speaf
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Earnings Estimates, Predictor Speci
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0 a I "F c2 . 2 0
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216 selecting securities The Return
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U) J U) C Q) U) C S r jo-0 eke I C
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?68% $1-0 I 220
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222 Seleaine Securitiff ings data f
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224 selecting securities 4. The dif
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226 selecting securities 20. Note t
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230 Managing Portfolios have concen
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232 Managing Portfolios portfolios.
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234 Manaeine Portfolios of investme
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236 Managing Portfolios There is, n
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238 Managing Portfolios bound to ig
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240 Managing Portfolios expected to
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242 Managing Portfolios mance attri
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244 Managing Portfolios those inves
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246 Managing Portfolios vide a bigg
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248 Managing Portfolios If the firm
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250 Managing Portfolios REFERENCE J
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252 Managing Portfolios This invest
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254 Managing Portfolios FIGURE S-l
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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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302 Expanding Opportunities news st
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304 ExpandingOpportunities dinate l
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306 ExpandingOpportunities Also, ma
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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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