Do Industry-Specific Performance Measures Predict Returns? The Case of Same-Store Sales. Halla Yang March 16, 2007
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1 Do Industry-Specific Performance Measures Predict Returns? The Case of Same-Store Sales. Halla Yang March 16, 2007
2 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.
3 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 Restaurant same-stores sales data collected from 10-K filings in SEC Edgar, 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? Forecasting ROA Earnings announcement period returns
4 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 ). 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 ). 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 coefficient when same-store sales growth is included as a control.
5 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). Hong, Torous, and Valkanov (2005) industries lead the stock market.
6 Outline 1. Introduction 2. Data and Summary Statistics 3. Firm-Level Fama-MacBeth Regressions 4. Portfolio Returns 5. ROA Forecasting Regressions 6. Conclusion
7 Retail Data PR Newswire compiles monthly sales reports from public news sources firms per report. February 1998 through December 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 $10 MM in assets, equity.
8 Retail Summary Statistics The sample contains 71 firms, with 372 firm-year observations, spanning fiscal years 1997 through Sales ($MM) Min P25 Median P75 Max N ,995 7, , ,847 8, , ,684 8, , ,685 8, , ,640 7, , ,811 8, , ,456 6, , ,833 6, , ,000 1,777 8, ,335 37
9 Retail Summary Statistics The sample contains 71 firms, with 372 firm-year observations, spanning fiscal years 1997 through Mkt Cap ($MM) Min P25 Median P75 Max N ,599 88, , , , , , , , , ,144 6, , ,189 7, , ,421 4, , ,861 7, ,851 37
10 Retail Summary Statistics The sample contains 71 firms, with 372 firm-year observations, spanning fiscal years 1997 through BE/ME Min P25 Median P75 Max N
11 Retail Summary Statistics The sample contains 71 firms, with 372 firm-year observations, spanning fiscal years 1997 through Comp Sales Min P25 Median P75 Max N
12 Retail Summary Statistics A value-weighted index of the retail firms in the sample seems to loosely track the market RetailRf MktRf 0.2 SMB HML 0 Jul-98 Apr-01 Feb-04 Dec-06 (Cumulative sum of returns)
13 Restaurant Data Many restaurant chains (SIC 5812) report same-store sales growth in their 10-K filings. Collected from SEC Edgar, , 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 $10 MM in assets, equity.
14 Restaurant Summary Statistics The sample contains 72 firms, with 411 firm-year observations, spanning fiscal years 1996 through Sales ($MM) Min P25 Median P75 Max N , , , , ,024 14, ,448 14, ,091 15, ,413 17, ,112 19, ,518 20,460 39
15 Restaurant Summary Statistics The sample contains 72 firms, with 411 firm-year observations, spanning fiscal years 1996 through Mkt Cap ($MM) Min P25 Median P75 Max N , , , , ,033 44, ,471 34, ,285 20, ,894 31, ,694 40, ,704 42,439 39
16 Restaurant Summary Statistics The sample contains 72 firms, with 411 firm-year observations, spanning fiscal years 1996 through BE/ME Min P25 Median P75 Max N
17 Restaurant Summary Statistics The sample contains 72 firms, with 411 firm-year observations, spanning fiscal years 1996 through Comp Sales Min P25 Median P75 Max N
18 Restaurant Summary Statistics A value-weighted index of the restaurant firms in the sample seems to loosely track the market RestRf 0.7 MktRf SMB HML 0.5 Jul-97 Nov-99 Apr-02 Aug-04 Dec-06 (Cumulative sum of returns)
19 Outline 1. Introduction 2. Data and Summary Statistics 3. Firm-Level Fama-MacBeth Regressions 4. Portfolio Returns 5. ROA Forecasting Regressions 6. Conclusion
20 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. Consider both risk-adjusted and unadjusted returns. Number of firm-month observations: 4,067.
21 Retail Firm-Level Fama-MacBeth Regressions Beta-adjusted monthly returns (1) (2) (3) (4) (5) Mcomp (Same-store sales growth) [2.48]** [2.30]** [2.32]** [2.30]** [3.04]*** Size (Log market) [-1.69] [-1.44] [-1.88] [-1.86] [-2.19]** Value (Log book to market) [0.32] [-0.04] [0.40] [0.07] Dividend Yield (Dividends over market cap) [2.00]** [1.19] [1.70] Accruals (OpInc-Earnings over Assets) [1.24] [0.96] Growth (in total assets) [-1.58] [-2.02]** Ret (Lagged 1 month return) [-1.23] Ret (Lagged 2-12 month return) [-0.22]
22 Retail Firm-Level Fama-MacBeth Regressions Unadjusted monthly returns (1) (2) (3) (4) (5) Mcomp (Same-store sales growth) [2.71]** [2.51]** [2.52]** [2.52]** [3.25]*** Size (Log market) [-1.61] [-1.53] [-1.95] [-1.86] [-2.31]** Value (Log book to market) [-0.03] [-0.36] [0.25] [-0.05] Dividend Yield (Dividends over market cap) [1.84] [0.97] [1.36] Accruals (OpInc-Earnings over Assets) [1.10] [0.87] Growth (in total assets) [-1.33] [-1.81] Ret (Lagged 1 month return) [-1.50] Ret (Lagged 2-12 month return) [-0.28]
23 Retail Firm-Level Fama-MacBeth Regressions Correlations Mcomp Size Value Div Yield Accruals Growth Mcomp 1.00 (Same-store sales growth) Size (Log market) Value (Log book to market) Dividend Yield (Dividends over market cap) Accruals (OpInc-Earnings over Assets) Growth (in total assets)
24 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.
25 Restaurant Firm-Level Fama-MacBeth Regressions Beta-adjusted monthly returns (1) (2) (3) (4) (5) Compsales (Same-store sales growth) [1.24] [2.27]** [1.98] [2.59]** [2.42]** Size (Log market) [-0.30] [1.00] [0.97] [0.73] [1.03] Value (Log book to market) [2.67]** [2.53]** [1.79] [2.09]** Dividend Yield (Dividends over market cap) [-0.64] [-1.43] [-1.64] Accruals (OpInc-Earnings over Assets) [2.93]** [3.33]*** Growth (in total assets) [-3.76]*** [-3.06]*** Ret (Lagged 1 month return) [-4.47]*** Ret (Lagged 2-12 month return) [1.26]
26 Restaurant Firm-Level Fama-MacBeth Regressions Unadjusted monthly returns (1) (2) (3) (4) (5) Compsales (Same-store sales growth) [0.91] [1.92] [1.62] [2.19]** [2.03]** Size (Log market) [0.61] [1.88] [1.86] [1.53] [1.89] Value (Log book to market) [2.67]** [2.60]** [1.85] [2.20]** Dividend Yield (Dividends over market cap) [-1.12] [-1.70] [-1.79] Accruals (OpInc-Earnings over Assets) [3.05]*** [3.43]*** Growth (in total assets) [-3.09]*** [-2.59]** Ret (Lagged 1 month return) [-4.94]*** Ret (Lagged 2-12 month return) [1.32]
27 Restaurant Firm-Level Fama-MacBeth Regressions Correlations Compsales Size Value Div Yield Accruals Growth Compsales 1.00 (Same-store sales growth) Size (Log market) Value (Log book to market) Dividend Yield (Dividends over market cap) Accruals (OpInc-Earnings over Assets) Growth (in total assets)
28 Outline 1. Introduction 2. Data and Summary Statistics 3. Firm-Level Fama-MacBeth Regressions 4. Portfolio Returns 5. ROA Forecasting Regressions 6. Conclusion
29 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 yr Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q MktRf [4.70]*** [5.66]*** [5.17]*** [5.61]*** SMB [4.12]*** [1.63] [-3.79]*** [-0.17] HML [4.36]*** [2.30]** [-0.94] [-0.00] UMD [-6.27]*** [-3.51]*** [0.54] [-2.13]** alpha CAGR [-1.26] [0.50] [1.49] [1.76]
30 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 Equal-weighted factor and monthly turnover strategy also have similar alphas. VW Fac EW Fac MTO Fac CompVW/2 0.9 CompEW/2 0.7 CompMTO/2 MktRf 0.5 Jul-98 Nov-99 Apr-01 Sep-02 Feb-04 Jul-05 Dec-06 MktRf SMB HML UMD alpha 0.14 [0.69] [-3.98]*** [-4.05]*** 0.52 [3.91]*** 2.13 [2.75]** 0.35 [2.06]** [-2.46]** [-2.65]** 0.23 [2.02]** 2.17 [3.31]*** 0.21 [1.10] [-3.87]*** [-2.54]** 0.57 [4.49]*** 1.63 [2.23]**
31 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. VW Fac EW Fac MTO Fac 2.3 Q3 mean [2.07]** [2.82]** [1.19] Q4 mean [1.00] [1.21] [1.76] AlphaVW/2 AlphaEW/2 0.7 AlphaMTO/2 0.5 MktRf Jul-98 Nov-99 Apr-01 Sep-02 Feb-04 Jul-05 Dec-06 Q1 mean Q2 mean 0.96 [0.55] 0.08 [0.05] 0.19 [0.13] 2.14 [1.85] 0.25 [0.17] 0.43 [0.25]
32 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 yr Q1 Q2 Q3 Q4 Loadings Q1 Q2 Q3 Q MktRf [8.38]*** [4.89]*** [4.46]*** [4.86]*** SMB [-1.30] [1.07] [1.23] [0.14] HML [4.44]*** [3.94]*** [3.27]*** [2.03]** UMD [0.79] [-0.02] [-2.63]** [-0.78] alpha [-1.33] [0.98] [1.09] [0.75] CAGR
33 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 MktRf VW Fac EW Fac SMB [-1.55] 0.20 [0.00] [1.08] [0.00] 1.25 HML [-1.34] [-1.23] CompVW/2 CompEW/2 MktRf UMD alpha [-1.32] 1.21 [1.68] [-0.76] 0.87 [1.77] 0.50 Jul-97 Nov-99 Mar-02 Jul-04 Dec-06
34 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 Q3 mean VW Fac 0.67 EW Fac [0.44] [0.32] AlphaVW/2 AlphaEW/2 MktRf May-97 Oct-99 Mar-02 Jul-04 Dec-06 Q4 mean Q1 mean Q2 mean 1.60 [1.34] 1.01 [0.73] 0.08 [0.06] 0.47 [0.48] 0.79 [0.82] 1.35 [1.66]
35 Outline 1. Introduction 2. Data and Summary Statistics 3. Firm-Level Fama-MacBeth Regressions 4. Portfolio Returns 5. ROA Forecasting Regressions 6. Conclusion
36 Retail Earnings Predictability Same-store sales growth positively forecasts future ROA and changes in future ROA in random effects regressions. Dependent Variable Value, year t-1 (Log Book-to-Market) Growth, year t-1 (Assets t-1 / Assets t-2) (1) ROA(t) [-2.67]** -4.86E-06 [-0.03] (2) ROA(t) [-2.02]** 2.15E-04 [-1.51] (3) ROA(t) [-1.80] -2.65E-05 [-0.16] (4) ΔROA(t) [0.80] -2.13E-05 [-0.14] (5) ΔROA(t) [1.63] -3.20E-04 [-2.07]** (6) ΔROA(t) [1.72] -5.28E-05 [-0.30] ROA, year t (OpInc over Assets) [15.96]*** [15.94]*** [15.87]*** ΔROA, year t (ROA t-1 ROA t-2) [1.07] [-1.48] [-1.66] Mcomp, Last month, year t (Same-store sales growth) [7.37]*** [7.45]*** [6.44]** [7.07]*** Mgrwth, Last month, year t e e-04 (Total sales growth) [-2.17]** [-2.83]**
37 Restaurant Earnings Predictability Comp sales (weakly) forecasts future ROA in random-effects regressions. z-statistics in brackets. (1) (2) (3) (4) (5) (6) Dependent Variable ROA(t) ROA(t) ROA(t) ΔROA(t) ΔROA(t) ΔROA(t) Value, year t (Log Book-to-Market) [-4.79]*** [-4.26]*** [-4.24]*** [-1.44] [-1.04] [-1.04] Growth, year t e e e e e e-04 (Assets t-1 / Assets t-2) [-1.02] [-1.06] [-1.10] [-0.09] [-0.39] [-0.83] ROA, year t (OpInc over Assets) [20.27]*** [20.43]*** [21.01]*** ΔROA, year t (ROA t-1 ROA t-2) [0.28] [-0.30] [-0.55] CompSales, year t E E E E-03 (Same-store sales growth) [1.21] [1.21] [1.82] [1.80] Annual sales growth, year t E E-04 (Total sales growth) [0.38] [0.78]
38 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. Q3 Q4 Q1 Q2 Total Retail, EW 0.99 [2.26]** 0.36 [0.89] 0.67 [1.70] 0.09 [0.41] 0.54 [2.80]** Restaurants, EW [-1.14] 0.04 [0.08] 0.30 [1.04] 0.43 [1.10] 0.07 [0.32]
39 Outline 1. Introduction 2. Data and Summary Statistics 3. Firm-Level Fama-MacBeth Regressions 4. Portfolio Returns 5. ROA Forecasting Regressions 6. Conclusion
40 Conclusion Same-store sales growth has forecasting ability in firm-level cross-sectional regressions. Long-short portfolios of firms sorted by same-store sales growth can generate alphas. Same-store sales growth forecasts future firm profits and announcement period returns in the retail industry. Provides support for behavioral hypothesis that investors unable to fully differentiate between high-quality and low-quality sales growth. May be generalizable to more industries. Hotels, casinos, airlines E.g. sales-to-assets is a strong forecaster across all industries ( poor man's SSSG )
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