FIN 5550 - Investments - Winter I 2020 Project 2 This project is worth 15% of your fimal grade. This project must be completed individually. The objectives of this exercise: 1. To perform multiple regression with real financial data. 2. To estimate the Fama - French 3-factor model for a list of twenty stocks and interpret the regression output. 3. To discem whether individual stocks are small, mid or large cap and value, neutral or growth. Learning Outcomes: Students should be able to: 1. Perform Ordinary Least Squares regression to estimate the Fama - French 3-factor model. 2. Perform proper inference with correct interpretation of t-statistics or p-values. 3. Discem whether a stock return significantly loads on one or more of the three factors. 4. Tabulate regression results and discuss empirical findings. Guidelines: 1. You will be assigned twenty (20) stocks from the current S&P 500 index. You must use the same 20 stocks (or, more precisely, the ticker symbols) you used for Project 1. The last month of return data should be December 2019. You must use end-of-month data! (This data should be what you already collected from Yahoo Finance for Project 1.) 2. You must estimate the Fama and French 3-factor model. Thirty-six months of the three factors along with the risk-free-rate in decimal form will be provided in the accompanying Excel spreadsheet. Use these factors and the risk-free rate provided to you. The factors are MRP (BKM call this RM), SMB and HML. Note, you need excess returns for your stocks. Estimating the Fama and French 3-factor model requires the use of regression software. Answer the following question: a. How, exactly, do Fama and French construct their SMB and HML factors? 3. For each firm, perform a multiple regression to estimate the parameters in equation (10.11) of BKM 9th ed. Then complete a Table: (Sample Data) Refer to Stocks assigned picture Beta- Beta- Beta - t (Beta-HML) Firm Alpha t (Alpha) MRP t (Beta-MRP) SMB t (Beta-SMB) HML ABC DEF GHI JKL Etc. Note that t(Alpha) above means the t-statistic for the alpha estimate and that t(Beta-MRP) means the t-statistic for the beta coefficient estimated for the MRP factor, etc. 4. Then, using the results from your multiple regressions, identify the "style" for each of your 20 firms. In your write-up, be sure to explain how you identified the various styles. Styles include Large-Neutral, Large-Value, Large-Growth, Mid-Neutral, Mid-Value, Mid-Growth, Small-Neutral, Small-Value, Small-Growth: (Sample Data) Refer to Stocks assigned picture Firm Style ABC DEF GHI JKL Etc. Output: You must submit a brief write-up of your results. Your paper should be detailed enough so that someone else could pick up your paper and replicate your results. So, you will need a brief introduction describing your data sources, how you manipulated the data, how you calculated the Fama-French betas, 2 Tables, and a brief summary and discussion of your findings. In total, you must submit: 1. Your write-up as a Word file. 2. An Excel spreadsheet (one sheet) containing the two Tables detailed above. 3. Another Excel spreadsheet detailing how, exactly, you calculated the results in your Table (i.e., replicate the detailed findings for your stocks). Note that the Excel spreadsheets in points 2 and 3 should be in one file with two different labeled worksheet tabs. Alternatively, you may use 20 Excel sheets named with the ticker symbol if you wish to combine the date, ticker symbol, adjusted closing prices, and calculations and/or regressions separately for each of your 20 companies. FamaFrenchFactors December 31 2019 for Project 2 Date 201701 MRP SMB 0.0194 -0.0104 HML -0.0278 RF Obs No. 0.0004 201702 0.0357 -0.0202 201703 0.0017 0.0120 201704 0.0109 -0.0179 0.0004 -0.0317 0.0003 0.0071 -0.0187 0.0005 201705 0.0106 -0.0254 -0.0378 0.0006 12375 2 3 4 5 201706 0.0078 0.0217 0.0135 0.0006 6 201707 0.0187 -0.0141 -0.0029 0.0007 7 201708 0.0016 -0.0167 -0.0224 0.0009 8 201709 0.0251 0.0455 0.0303 0.0009 9 201710 0.0225 -0.0194 -0.0005 0.0009 10 201711 0.0312 -0.0065 -0.0004 0.0008 11 201712 0.0106 -0.0128 0.0014 0.0009 12 201801 0.0558 -0.0303 -0.0137 0.0011 13 201802 -0.0365 0.0026 -0.0119 0.0011 14 201803 -0.0235 0.0395 -0.0012 0.0012 15 201804 0.0029 0.0112 0.0054 0.0014 16 201805 0.0265 0.0523 -0.0316 0.0014 17 201806 0.0048 201807 201808 0.0118 -0.0238 0.0014 18 0.0319 -0.0218 0.0040 0.0016 19 0.0344 0.0115 -0.0408 0.0016 20 201809 0.0006 -0.0237 -0.0130 0.0015 21 201810 -0.0768 -0.0476 0.0344 0.0019 22 201811 0.0169 -0.0079 0.0025 0.0018 23 201812 -0.0955 -0.0263 -0.0147 0.0019 24 201901 0.0841 0.0301 -0.0062 0.0021 25 201902 0.0340 0.0206 -0.0284 0.0018 26 201903 0.0110 -0.0313 -0.0407 0.0019 27 201904 0.0396 -0.0168 0.0193 0.0021 28 201905 -0.0694 -0.0120 -0.0239 0.0021 29 201906 0.0693 0.0033 -0.0108 0.0018 30 201907 0.0119 -0.0207 0.0014 0.0019 31 201908 -0.0258 -0.0241 -0.0499 0.0016 32 201909 0.0144 -0.0090 0.0671 0.0018 33 201910 0.0206 0.0025 -0.0207 0.0015 34 201911 0.0387 0.0087 -0.0186 0.0012 35 201912 0.0277 0.0069 0.0182 0.0014 36 No Stock 1 GWW Yahoo Beta Calculated Beta Intercept RSQ 1.01 1.19 0.970262899 -0.00145 0.206135 2 HOG 3 HBI 4 HIG 0.59 5 HAS 1.00 6 HCA 7 HP 0.96 1.64 8 HSIC 0.80 9HES 2.03 10 HPE 1.54 11 HFC 12 HLT 13 HOLX 14 HD 0.97 15 HON 1.15 16 HRL -0.05 17 HAL 1.60 18 HSY 0.02 1.152079397 -0.01678 0.270509 1.11 1.161161062 -0.02015 0.184198 0.570076783 0.001791 0.179603 0.92810465 0.001812 0.180387 0.967259247 0.005436 0.213952 1.693920379 -0.01603 0.294397 0.901383037 -0.00153 0.256425 2.03163423 -0.0139 0.393044 1.561974412 -0.00198 0.364169 0.06 1.231448113 -0.00321 0.133003 0.05 1.170957074 0.002341 0.352977 0.84 0.788874299 0.002592 0.219629 0.967602576 0.00561 0.438144 1.040419814 0.001875 0.605566 -0.11540129 0.011532 0.004898 1.680818348 -0.02166 0.361945 0.013041538 0.009012 6.79E-05