Do Industry-Specific Performance Measures Predict Returns? The Case of Same-Store Sales. Halla Yang March 16, 2007

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Do Industry-Specific Performance Measures

Predict Returns? The Case of Same-Store Sales.

Halla Yang

March 16, 2007

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Introduction

• Each industry has its own natural performance metrics.

–

Revenue-passenger miles, total equivalent units shipped, deposits per branch, same-store

sales.

• These metrics provide information about future profitability that is not

neatly captured by accounting data.

–

Revenue, earnings-per-share, EBITDA do not capture differences in industry-specific

performance metrics.

• Market prices may be inefficient with respect to industry-specific metrics.

–

Retailers, restaurants can grow by adding new locations or by increasing sales in existing

locations.

–

Investors may not always differentiate between high quality growth (same-store sales) and

low quality growth (new locations), e.g. the earnings accruals anomaly.

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Introduction

• I construct a data set of performance metrics for retailers and restaurants.

–

Retailers issue monthly sales growth and monthly same-store sales (“comp sales”) growth

figures in the first ten days of the following month.

–

Data collected by PR Newswire from 1998-2006.

–

Restaurant same-stores sales data collected from 10-K filings in SEC Edgar, 1994-2006.

–

Sample includes 71 firms and 372 firm-year observations for the retail industry.

–

Sample includes 72 firms and 411 firm-year observations for the restaurant industry.

• To test for market inefficiency, I use this data set to address two empirical

questions.

–

Does same-store sales growth forecast returns?

• Firm-level Fama-MacBeth regressions • Spreads in portfolio alphas

–

Does same-store sales growth contain information about future profitability?

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Summary of Findings

• Same-store sales growth forecasts firm-level returns in Fama-MacBeth

regressions.

–

Same-store sales growth forecasts firm-level equity returns, with or without controls for

dividends, size, value, ROA, equity sales, and momentum.

• Sorting firms into value-weighted portfolios by same-store sales growth

quartile generates a spread in returns.

–

A zero-cost factor that was long the highest quartile and short the lowest quartile of retail

stores generated an alpha of 2.1% per month with t-statistic of 2.75, after controlling for

the Fama-French four-factor model (from 1998-2006).

–

A zero-cost factor that was long the highest quartile and short the lowest quartile of

restaurants generated an alpha of 1.2% per month with t-statistic of 1.68, after controlling

for the Fama-French four-factor model (from 1997-2006).

• Same-store sales growth forecasts year-ahead firm-level ROA.

–

Suggests that same-store sales growth contains information about future profitability.

–

In the retail sector, a control for total sales growth has a negative and significant

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Related Literature

• Investor inattention may generate predictability of returns.

–

Huberman and Regev (2001) – EntreMed.

–

Ramnath (2002) – earnings surprises of firms within same industry.

–

DellaVigna and Pollet (2005, 2006) – demographic shifts, Friday news releases.

–

Cohen and Frazzini (2006) – industry links (customers/suppliers).

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Outline

1. Introduction

2. Data and Summary Statistics

3. Firm-Level Fama-MacBeth Regressions

4. Portfolio Returns

5. ROA Forecasting Regressions

6. Conclusion

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Retail Data

• PR Newswire compiles monthly sales reports from public news sources.

–

50-70 firms per report. February 1998 through December 2006.

–

Large retailers such as Wal-Mart, Costco, and BJ’s, as well as smaller specialty stores like

Wilson’s Leather, Pacific Sunwear, Gymboree.

–

Reports issued between one and two weeks after close of month.

–

Reports include monthly sales growth (compared to 12 months prior), year-to-date sales

growth, monthly same-store sales growth, year-to-date same-store sales growth.

• Firm financial data from Compustat, returns data from CRSP.

–

Exclude REITS, ADRS, etc.

–

Use only firms with 12 months of historical returns data.

–

Use returns only if firm’s closing price in previous month was at least $5.

–

Keep firms with at least $10 MM in assets, equity.

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Retail Summary Statistics

• The sample contains 71 firms, with 372 firm-year observations, spanning

fiscal years 1997 through 2005.

37 313,335 8,582 1,777 1,000 439 2005 48 286,103 6,906 1,833 928 304 2004 46 257,157 6,872 1,456 661 176 2003 42 245,308 8,445 1,811 713 162 2002 43 218,529 7,489 1,640 689 276 2001 43 192,003 8,818 1,685 589 168 2000 41 165,639 8,795 1,684 605 143 1999 37 137,634 8,012 1,847 357 107 1998 35 117,958 7,997 1,995 418 100 1997 N Max P75 Median P25 Min Sales ($MM)

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• The sample contains 71 firms, with 372 firm-year observations, spanning

fiscal years 1997 through 2005.

37 194,851 7,950 1,861 654 67 2005 48 223,686 4,748 1,421 538 71 2004 46 229,589 7,074 1,189 410 37 2003 42 222,949 6,174 1,144 387 32 2002 43 256,505 6,313 874 341 101 2001 43 237,274 7,067 981 254 28 2000 41 307,865 5,193 999 305 85 1999 37 181,073 7,553 868 205 44 1998 35 88,573 5,599 685 200 28 1997 N Max P75 Median P25 Min Mkt Cap ($MM)

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• The sample contains 71 firms, with 372 firm-year observations, spanning

fiscal years 1997 through 2005.

37 1.53 0.65 0.39 0.30 0.10 2005 48 1.35 0.72 0.50 0.29 0.10 2004 46 2.19 0.67 0.51 0.28 0.12 2003 42 2.27 0.89 0.59 0.34 0.14 2002 43 2.61 0.74 0.42 0.22 0.12 2001 43 4.77 1.02 0.50 0.23 0.11 2000 41 1.74 0.76 0.52 0.18 0.06 1999 37 1.93 0.76 0.44 0.21 0.05 1998 35 1.34 0.77 0.36 0.25 0.11 1997 N Max P75 Median P25 Min BE/ME

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• The sample contains 71 firms, with 372 firm-year observations, spanning

fiscal years 1997 through 2005.

37 24.0 8.0 4.2 1.3 -36.0 2005 48 17.1 5.4 1.5 -1.0 -15.0 2004 46 17.9 7.8 3.8 -1.0 -16.0 2003 42 19.0 4.8 1.0 -1.9 -7.0 2002 43 39.0 6.3 2.0 -3.0 -16.6 2001 43 39.0 4.0 -0.3 -5.4 -17.0 2000 41 47.4 7.0 3.4 -0.9 -15.4 1999 37 18.7 9.4 5.8 3.0 -2.0 1998 35 46.4 11.4 7.0 3.6 -9.0 1997 N Max P75 Median P25 Min Comp Sales

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• A value-weighted index of the retail firms in the sample seems to loosely

track the market.

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

Jul-98 Apr-01 Feb-04 Dec-06

RetailRf MktRf SMB HML

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Restaurant Data

• Many restaurant chains (SIC 5812) report same-store sales growth in their

10-K filings.

–

Collected from SEC Edgar, 1994-2006, for all firms with SIC 5812 in Compustat.

–

Large retailers such as Yum Brands, McDonald’s, smaller chains such as Applebee’s,

Nathan’s Famous.

–

Filing dates listed in SEC Edgar database.

• Firm financial data from Compustat, returns data from CRSP.

–

Exclude REITS, ADRS, etc.

–

Use only firms with 12 months of historical returns data.

–

Use returns only if firm’s closing price in previous month was at least $5.

–

Keep firms with at least $10 MM in assets, equity.

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Restaurant Summary Statistics

• The sample contains 72 firms, with 411 firm-year observations, spanning

fiscal years 1996 through 2005.

39 20,460 1,518 623 184 49 2005 47 19,065 1,112 479 137 33 2004 39 17,141 1,413 591 190 33 2003 37 15,406 1,091 585 235 32 2002 36 14,870 1,448 551 200 65 2001 34 14,243 1,024 499 190 104 2000 41 13,259 797 359 153 48 1999 44 12,421 695 327 160 42 1998 46 11,409 600 265 105 28 1997 48 10,687 519 254 110 19 1996 N Max P75 Median P25 Min Sales ($MM)

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• The sample contains 72 firms, with 411 firm-year observations, spanning

fiscal years 1996 through 2005.

39 42,439 1,704 803 119 20 2005 47 40,306 1,694 555 118 14 2004 39 31,513 1,894 605 119 13 2003 37 20,411 1,285 589 133 11 2002 36 34,026 1,471 634 187 26 2001 34 44,584 1,033 315 135 17 2000 41 54,584 703 255 87 20 1999 44 51,968 646 240 77 20 1998 46 32,889 590 239 75 19 1997 48 31,659 676 201 62 22 1996 N Max P75 Median P25 Min Mkt Cap ($MM)

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• The sample contains 72 firms, with 411 firm-year observations, spanning

fiscal years 1996 through 2005.

39 1.01 0.50 0.38 0.24 0.09 2005 47 1.14 0.68 0.38 0.26 0.10 2004 39 1.36 0.63 0.41 0.27 0.11 2003 37 1.53 0.74 0.51 0.32 0.08 2002 36 2.11 0.64 0.40 0.27 0.01 2001 34 2.15 0.91 0.45 0.31 0.14 2000 41 1.75 1.01 0.56 0.37 0.14 1999 44 2.12 0.93 0.53 0.35 0.16 1998 46 1.42 0.81 0.53 0.36 0.15 1997 48 1.38 0.79 0.50 0.29 0.05 1996 N Max P75 Median P25 Min BE/ME

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• The sample contains 72 firms, with 411 firm-year observations, spanning

fiscal years 1996 through 2005.

39 9.9 5.1 2.8 -0.5 -3.6 2005 47 12.3 6.5 3.8 2.3 -2.0 2004 39 12.3 3.2 1.6 -0.3 -6.9 2003 37 6.6 4.1 1.7 -0.1 -6.7 2002 36 11.2 3.9 2.5 1.3 -2.9 2001 34 11.7 5.0 3.1 1.8 -3.9 2000 41 12.0 5.9 3.6 2.7 -7.6 1999 44 8.6 4.7 3.1 0.7 -9.3 1998 46 10.7 4.7 2.3 -1.0 -11.0 1997 48 11.9 4.3 0.7 -1.3 -7.4 1996 N Max P75 Median P25 Min Comp Sales

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• A value-weighted index of the restaurant firms in the sample seems to

loosely track the market.

0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9

Jul-97 Nov-99 Apr-02 Aug-04 Dec-06 RestRf MktRf SMB HML

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Outline

1. Introduction

2. Data and Summary Statistics

3. Firm-Level Fama-MacBeth Regressions

4. Portfolio Returns

5. ROA Forecasting Regressions

6. Conclusion

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Retail Firm-Level Fama-MacBeth Regressions

• Measure firm characteristics for fiscal year t using Compustat.

–

Book-to-market, return-on-assets, dividends, proceeds from equity sales, growth in total

assets.

• Measure firm’s same-store sales growth in May of year t+1 (figures

released in early June of year t+1), firm’s size in June of year t+1 (based on

market capitalization).

• Run cross-sectional regressions of firm level equity returns for each month

from July of year t+1 through June of year t+2 on set of characteristics.

• Average the coefficient estimates across the 102 months in the sample, and

compute t-statistics.

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Retail Firm-Level Fama-MacBeth Regressions -0.028 Ret1 [-2.02]** [-1.58]

(in total assets)

-0.028 -0.020 Growth [0.96] [1.24]

(OpInc-Earnings over Assets)

7.04 9.00 Accruals [1.70] [1.19] [2.00]**

(Dividends over market cap)

30.9 20.6 35.8 Dividend Yield [0.07] [0.40] [-0.04] [0.32]

(Log book to market)

0.035 0.210 -0.015 0.120 Value [-2.19]** [-1.86] [-1.88] [-1.44] [-1.69] (Log market) -0.369 -0.314 -0.308 -0.233 -0.237 Size [3.04]*** [2.30]** [2.32]** [2.30]** [2.48]**

(Same-store sales growth)

0.140 0.096 0.098 0.094 0.099 Mcomp (5) (4) (3) (2) (1) Beta-adjusted monthly returns

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Retail Firm-Level Fama-MacBeth Regressions [-0.28]

(Lagged 2-12 month return)

-0.002 Ret212 [-1.50]

(Lagged 1 month return)

-0.033 Ret1 [-1.81] [-1.33]

(in total assets)

-0.024 -0.017 Growth [0.87] [1.10]

6.65 8.28 Accruals [1.36] [0.97] [1.84]

25.0 17.0 33.3 Dividend Yield [-0.05] [0.25] [-0.36] [-0.03]

-0.027 0.124 -0.134 -0.012 Value [-2.31]** [-1.86] [-1.95] [-1.53] [-1.61] (Log market) -0.365 -0.295 -0.301 -0.234 -0.216 Size [3.25]*** [2.52]** [2.52]** [2.51]** [2.71]**

0.145 0.103 0.105 0.102 0.110 Mcomp (5) (4) (3) (2) (1) Unadjusted monthly returns

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Retail Firm-Level Fama-MacBeth Regressions 1.00 0.06 -0.22 -0.34 0.04 0.24 Growth

1.00 -0.15 -0.38 0.03 0.09 Accruals

1.00 0.11 0.17 -0.13 Dividend Yield

1.00 -0.50 -0.31 Value (Log market) 1.00 0.06 Size

1.00 Mcomp Growth Accruals Div Yield Value Size Mcomp Correlations

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Restaurant Firm-Level Fama-MacBeth Regressions

• Measure firm characteristics for fiscal year t using Compustat.

–

Book-to-market, return-on-assets, dividends, proceeds from equity sales, growth in total

assets.

• Measure firm’s same-store sales growth for most recent 10-K filed before

June of year t+1, firm’s size in June of year t+1.

• Run cross-sectional regressions of firm level equity returns for each month

from July of year t+1 through June of year t+2 on set of characteristics.

• Average the coefficient estimates across the 114 months in the sample, and

compute t-statistics.

• Consider both risk-adjusted and unadjusted returns.

• Number of firm-month observations: 4,383.

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Restaurant Firm-Level Fama-MacBeth Regressions -0.106 Ret1 [-3.06]*** [-3.76]***

(in total assets)

-0.037 -0.041 Growth [3.33]*** [2.93]**

16.1 13.4 Accruals [-1.64] [-1.43] [-0.64]

-27.3 -24.7 -8.82 Dividend Yield [2.09]** [1.79] [2.53]** [2.67]**

0.840 0.73 0.871 0.898 Value [1.03] [0.73] [0.97] [1.00] [-0.30] (Log market) 0.161 0.116 0.144 0.146 -0.037 Size [2.42]** [2.59]** [1.98] [2.27]** [1.24]

0.152 0.155 0.113 0.125 0.063 Compsales (5) (4) (3) (2) (1) Beta-adjusted monthly returns

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Restaurant Firm-Level Fama-MacBeth Regressions [1.32]

(Lagged 2-12 month return)

0.009 Ret212 [-4.94]***

(Lagged 1 month return)

-0.110 Ret1 [-2.59]** [-3.09]***

(in total assets)

-0.029 -0.032 Growth [3.43]*** [3.05]***

15.8 13.4 Accruals [-1.79] [-1.70] [-1.12]

-29.4 -28.9 -15.2 Dividend Yield [2.20]** [1.85] [2.60]** [2.67]**

0.824 0.714 0.832 0.840 Value [1.89] [1.53] [1.86] [1.88] [0.61] (Log market) 0.282 0.234 0.261 0.259 0.077 Size [2.03]** [2.19]** [1.62] [1.92] [0.91]

0.127 0.130 0.092 0.106 0.046 Compsales (5) (4) (3) (2) (1) Unadjusted monthly returns

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Restaurant Firm-Level Fama-MacBeth Regressions 1.00 -0.24 -0.15 -0.19 0.05 0.12 Growth (OpInc-Earnings over Assets) 1.00 -0.08 -0.22 0.05 -0.02 Accruals

1.00 0.20 -0.04 -0.15 Dividend Yield

1.00 -0.61 -0.33 Value (Log market) 1.00 0.09 Size

1.00 Compsales Growth Accruals Div Yield Value Size Compsales Correlations

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Outline

1. Introduction

2. Data and Summary Statistics

3. Firm-Level Fama-MacBeth Regressions

4. Portfolio Returns

5. ROA Forecasting Regressions

6. Conclusion

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Retail Value-Weighted Portfolios

• Sort firms into quartiles based on same-store sales growth in May of year t.

–

Q1 (lowest growth), Q4 (highest growth)

• Form value-weighted portfolios for each quartile, starting July of year t

through June of year t+1.

Portfolio Returns by Year

Loadings on Fama-French Factors

[-0.00] [-0.94] [2.30]** [4.36]*** -0.001 -0.18 0.45 0.99 HML [-0.17] [-3.79]*** [1.63] [4.12]*** -0.031 -0.59 0.26 0.77 SMB [5.61]*** [5.17]*** [5.66]*** [4.70]*** 1.02 0.80 0.91 0.88 MktRf Q4 Q3 Q2 Q1 36.82 7.97 34.50 55.58 2003 -18.57 -17.22 -7.23 -20.69 2002 26.41 5.67 35.89 -37.93 2001 -28.26 -10.20 -6.62 -24.33 2000 27.41 49.30 -12.52 -31.03 1999 25.42 27.39 -10.49 -17.90 1998 Q4 Q3 Q2 Q1 yr

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Long-Short Retail Factors, VW and EW

• A long-short VW factor generates monthly alpha of 2.1%.

–

Factor is long the highest quartile, short the lowest quartile.

–

Alpha (after controlling for FF 4-factor model) has t-statistic of 2.75.

–

Equal-weighted factor and monthly turnover strategy also have similar alphas.

[2.23]** [3.31]*** [2.75]** 1.63 2.17 2.13 alpha [4.49]*** [2.02]** [3.91]*** 0.57 0.23 0.52 UMD [-2.54]** [-2.65]** [-4.05]*** -0.59 -0.55 -0.99 HML [-3.87]*** [-2.46]** [-3.98]*** -0.74 -0.42 -0.80 SMB [1.10] [2.06]** [0.69] 0.21 0.35 0.14 MktRf MTO Fac EW Fac VW Fac 0.5 0.7 0.9 1.1 1.3 1.5 1.7 1.9 2.1 2.3

Jul-98 Nov-99 Apr-01 Sep-02 Feb-04 Jul-05 Dec-06

CompVW/2 CompEW/2 CompMTO/2 MktRf

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Long-Short Retail Factors, VW and EW

• The alphas look better than raw factor returns.

–

Factors load negatively on SMB, HML, explaining part of recent underperformance.

–

Factors load positively on momentum.

–

Effects seem strongest in Q3, consistent with slow information incorporation.

[0.17] [0.13] [0.55] 0.25 0.19 0.96 Q1 mean [1.76] [1.21] [1.00] 2.96 1.85 1.75 Q4 mean [1.19] [2.82]** [2.07]** 1.78 3.37 3.43 Q3 mean MTO Fac EW Fac VW Fac 0.9 1.1 1.3 1.5 1.7 1.9 2.1 2.3 AlphaVW/2

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Restaurant Value-Weighted Portfolios

• Sort firms into quartiles based on same-store sales growth in most recent

report filed on or before June of year t.

–

Q1 (lowest growth), Q4 (highest growth)

• Form VW quartile portfolios, hold July of year t through June of year t+1.

Portfolio Returns by Year

Loadings on Fama-French Factors

0.51 0.63 0.52 -0.70 alpha [-0.78] [-2.63]** [-0.02] [0.79] -0.09 -0.26 0.00 0.07 UMD [2.03]** [3.27]*** [3.94]*** [4.44]*** 0.44 0.61 0.67 0.75 HML [0.14] [1.23] [1.07] [-1.30] 0.02 0.18 0.14 -0.18 SMB [4.86]*** [4.46]*** [4.89]*** [8.38]*** 0.84 0.66 0.66 1.12 MktRf Q4 Q3 Q2 Q1 Loadings 0.99 -10.76 1.67 1.72 2005 54.72 23.46 16.54 19.82 2004 39.85 41.17 27.01 45.33 2003 -2.05 -7.66 -1.88 -30.71 2002 5.91 35.42 7.63 -20.52 2001 20.64 -6.59 50.38 -12.38 2000 -20.95 22.60 -4.65 -29.94 1999 20.46 14.92 22.75 44.32 1998 -5.70 -1.53 6.92 -6.03 1997 Q4 Q3 Q2 Q1 yr

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Long-Short Restaurant Factors, VW and EW

• A long-short factor generates monthly alpha of 1.2%.

–

Factor is long the highest quartile, short the lowest quartile.

–

Alpha (after controlling for FF 4-factor model) has t-statistic of 1.68.

0.87 1.21 alpha [-0.76] [-1.32] -0.06 -0.16 UMD [-1.23] [-1.34] -0.19 -0.31 HML [0.00] [1.08] 0.00 0.20 SMB [0.00] [-1.55] 0.00 -0.28 MktRf EW Fac VW Fac 1.00 1.25 1.50 1.75 2.00 CompVW/2 CompEW/2

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Long-Short Restaurant Factors, VW and EW

• Alphas look somewhat better than raw factor returns.

–

Factors load negatively on market, value, momentum.

–

No discernible differences among quarters.

0.50 0.75 1.00 1.25 1.50 1.75 2.00

May-97 Oct-99 Mar-02 Jul-04 Dec-06

AlphaVW/2 AlphaEW/2 MktRf [1.66] [0.06] 1.35 0.08 Q2 mean [0.82] [0.73] 0.79 1.01 Q1 mean [0.48] [1.34] 0.47 1.60 Q4 mean [0.32] [0.44] 0.30 0.67 Q3 mean EW Fac VW Fac

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Outline

1. Introduction

2. Data and Summary Statistics

3. Firm-Level Fama-MacBeth Regressions

4. Portfolio Returns

5. ROA Forecasting Regressions

6. Conclusion

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Retail Earnings Predictability

• Same-store sales growth positively forecasts future ROA and changes in

future ROA in random effects regressions.

[-2.83]** [-2.17]**

(Total sales growth)

-5.78E-04 -4.13E-04

Mgrwth, Last month, year t-1

[7.07]*** [6.44]** [7.45]*** [7.37]*** (Same-store sales growth)

0.003 0.002 0.003 0.002 Mcomp, Last month, year t-1

[-1.66] [-1.48] [1.07] (ROA t-1 – ROA t-2) -0.102 -0.093 0.07 ΔROA, year t-1 [15.87]*** [15.94]*** [15.96]***

(OpInc over Assets)

0.717 0.71 0.74 ROA, year t-1 [-0.30] [-2.07]** [-0.14] [-0.16] [-1.51] [-0.03] (Assets t-1 / Assets t-2) -5.28E-05 -3.20E-04 -2.13E-05 -2.65E-05 2.15E-04 -4.86E-06 Growth, year t-1 [1.72] [1.63] [0.80] [-1.80] [-2.02]** [-2.67]** (Log Book-to-Market) 0.007 0.007 0.003 -0.010 -0.011 -0.015 Value, year t-1 ΔROA(t) ΔROA(t) ΔROA(t) ROA(t) ROA(t) ROA(t) Dependent Variable (6) (5) (4) (3) (2) (1)

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Restaurant Earnings Predictability

• Comp sales (weakly) forecasts future ROA in random-effects regressions.

–

z-statistics in brackets.

1.08E-03 1.10E-03 6.44E-04 6.38E-04 CompSales, year t-1 [-0.55] [-0.30] [0.28] (ROA t-1 – ROA t-2) -0.031 -0.016 0.014 ΔROA, year t-1 [21.01]*** [20.43]*** [20.27]***

(OpInc over Assets)

0.73 0.72 0.714 ROA, year t-1 [-0.83] [-0.39] [-0.09] [-1.10] [-1.06] [-1.02] (Assets t-1 / Assets t-2) -1.17E-04 -3.87E-05 -9.06E-06 -1.26E-05 -9.38E-05 -9.05E-05 Growth, year t-1 [-1.04] [-1.04] [-1.44] [-4.24]*** [-4.26]*** [-4.79]*** (Log Book-to-Market) -0.003 -0.003 -0.005 -0.015 -0.015 -0.017 Value, year t-1 ΔROA(t) ΔROA(t) ΔROA(t) ROA(t) ROA(t) ROA(t) Dependent Variable (6) (5) (4) (3) (2) (1)

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Announcement Effects

• Returns for equal-weighted retail portfolio are strong in first post-formation

announcement period (t-1 to t+1).

• Effects non-existent in restaurant industry.

[0.32] [2.80]** 0.07 0.54 Total [1.10] [0.41] 0.43 0.09 Q2 [1.04] [1.70] 0.30 0.67 Q1 [0.08] [0.89] 0.04 0.36 Q4 [-1.14] [2.26]** -0.45 0.99 Q3 Restaurants, EW Retail, EW

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Outline

1. Introduction

2. Data and Summary Statistics

3. Firm-Level Fama-MacBeth Regressions

4. Portfolio Returns

5. ROA Forecasting Regressions

6. Conclusion

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Conclusion