1 of 5 Finance Trading Strategies FNCE5007 Semester 1, 2020 GROUP PROJECT This Project is to be completed individually or in pairs. Issue date: 17 March 2020 Submission details – • Due time and date: 10 am, Tuesday, 28 April 2020. • Your analysis is to be submitted to the Turnitin portal under the as- sessments tab of BlackBoard. If you have any concerns or difficulties with the Turnitin submission, you should email your submission to your Unit Coordinator, John Gould ([email protected]). Do not upload/email spreadsheets. • Attach this page as a cover sheet: enter your personal details in the space provided and sign and date the declaration. This Project is worth 35 marks in total and contributes 35% towards your overall grade for Finance Trading Strategies. GROUP MEMBER FAMILY NAME GIVEN NAME STUDENT NUMBER 1 2 I/we declare that this submission is my/our own original work. Signature 1: Date: Signature 2: Date: 2 of 5 Disclaimer! As demonstrated in lectures, you can use Excel to run your regression anal- yses. However, Excel is not a specialist statistics program, consequently you are cautioned against relying solely on it for any professional interpretation of the regressions. In particular, Excel’s regression coefficient t-statistics and p-values are unadjusted for residual heteroscedasticity and autocorrelation and may be biased towards significance. BACKGROUND You are tasked with identifying and reviewing the existence (or non-exist- ence) of some stock return “regularities” for a sample of ASX-listed stocks for the period February 2006 to January 2020. The sample is comprised of the top 20 stocks by market capitalisation of the S&P/ASX200 Index, identified and updated on the last trading day of January each year from 2006 to 2019 (resulting in a sample of 20×14=280 stock-years). The sample data is pro- vided in the file FinTradStrat_ProjectData_2020sem1.xlsx. ANALYSIS A) Regression analysis Run the following regression model: = + 1 max�−1 , 0� + 2 min�−1 , 0� +3 max�[−20,−2] , 0� + 4 min�[−20,−2] , 0� +5 max�[−220,−21] , 0� + 6 min�[−220,−21] , 0� +7[−20,−1] + 8[−20,−1] +9 + 10 + 11″” 1) for daily returns occurring in months other than June, and 2) for daily returns occurring in June months only where, for each stock-year sample, i: the dependent variable is daily abnormal return, = � − � − [−200,−1] ( − ); is the daily (continuously compounded) return; is the daily Reserve Bank of Australia cash rate; is the daily market return for the S&P/ASX200 Index; [−200,−1] is the market beta estimated over the [-200,-1] trading day window; −1 = (−1 − −1 ) is the one-day lagged excess return; [−20,−2] and [−220,−21] are the cumulative excess returns for the [-20,-2] and [-220,-21] trading day windows respectively; [−20,−1] and [−20,−1] are the daily abnormal (idiosyn- cratic) return volatility and skewness, respectively, over the [-20,-1] trading day window; ( ) is a dummy variable that equals 1 when the prior day’s closing stock price has crossed up (down) through the average daily VWAP for the [-20,-1] trading day window, and zero otherwise; and “” is a dummy variable that equals 1 for stocks with a ticker symbol begin- ning with “C”, and zero otherwise. 3 of 5 Present a table of your regression coefficients with indication of their statis- tical significance (4 marks – see Table 1 for an example). B) End-of-financial year event study analysis Setting the first trading day of July as event day zero, calculate cumulative average abnormal returns (CAARs) for event days t=−15 to t=+15, where daily abnormal return is calculated as: = � − � − [−220,−21] ( − ). Additionally calculate CAARs separately for: 1) “past winners” and “past losers” subsamples; and 2) “past winners in up-markets”, “past winners in down-markets”, “past los- ers in up-markets” and “past losers in down-markets” subsamples. Identify past winners and losers as those stocks for which trailing cumulative excess return, [−220,−21] = ∑ ( − )−21=−220 , is >0 and <0 respectively. Identify past up-markets and down-markets as those event-years for which trailing cumulative market excess return, [−220,−21] = ∑ ( − )−21=−220 , is >0 and <0 respectively. i) Present graphs of CAARs for event window [-16,+15] (set CAAR to zero at trading day t=−16). (6 marks - see Figure 1 for an example) ii) Present tables of CAARs for event windows [-15,-1], [0,+15] and [-15,+15] together with their t-statistics and indication of their significance. (6 marks - see Table 2 for an example) C) Recommended trading strategies Based on your event study results obtained in the Section B analysis, recom- mend an end-of-financial year trading strategy. Assume there are no short- selling constraints. (6 marks) D) Discussion/review of results • Discuss the abnormal return predictability implied by the Section A anal- ysis. (6 marks) • Discuss the predictability of end-of-financial year returns as indicated by the Section B analysis with specific reference to the “turn-of-the-year ef- fect”, “tax-loss selling”, and “window dressing”. (6 marks) • Identify the practical limitations to implementing the trading strategies you recommended for Section C. (1 mark) 4 of 5 Table 1 Regression coefficients and t-statistics (in parentheses) for regression of daily abnormal returns, , for Feb- ruary 2006 to January 2020 for the top 20 stocks by market capitalisation of the S&P/ASX200 Index (identified and updated at the end of January each year from 2006 to 2019), as per the regression model specified in Section A. Significance at the 5% and 1% levels are respectively indicated by * and **. Coefficient term Non-June daily returns June daily returns (1) (2) Intercept 0.0001 -0.0000 (0.7178) (-0.0856) 1 max�−1 , 0� -0.0181** ? (-3.1306) (?) 2 min�−1 , 0� 0.0414** ? (7.0118) (?) 3 max�[−20,−2] , 0� -0.0030* ? (-1.8283) (?) 4 min�[−20,−2] , 0� -0.0131** ? (-8.1788) (?) 5 max�[−220,−21] , 0� 0.0007 ? (1.4578) (?) 6 min�[−220,−21] , 0� -0.0015** ? (-2.8669) (?) 7 [−20,−1] -0.0178 ? (-1.8543) (?) 8 [−20,−1] 0.0003** ? (4.0859) (?) 9 0.0006* ? (2.2506) (?) 10 -0.0005 ? (-1.8976) (?) 11 "" 0.0006** ? (2.9761) (?) Obs. 64,050 5,612 Adj. R-squared 0.0028 0.0041 5 of 5 Table 2 Cumulative average abnormal returns (CAARs) and t-statistics (in parentheses) for [-15,-1], [0,+15] and [-15,+15] trading day event windows for June/July with event day zero being the first trading day of July, for June/July 2006 to June/July 2019 for the top 20 stocks by market capitalisation of the S&P/ASX200 Index (identified and updated at the end of January each year from 2006 to 2019). Daily abnormal returns, , are calculated as specified in Section B. CAARs are calculated for “all” stocks, and for subsamples of: (a) “past winners” and “past losers”; and (b) “past winners in up-markets”, “past winners in down-markets”, “past losers in up-markets” and “past losers in down-markets”, as specified in Section B. Significance at the 10%, 5% and 1% levels are respectively indicated by *, ** and ***. Obs. [-15,-1] [0,+15] [-15,+15] Ju ne /Ju ly e ve nt w in do w s All 276 -0.0025 -0.0033 -0.0058* (-0.7788) (-1.1961) (-1.6724) (a ) Past winners [−220,−21] >0 191 0.0046 -0.0050 -0.0004 (1.2808) (-1.5925) (-0.0933) Past losers [−220,−21] <0 85 -0.0185*** 0.0005 -0.0181** (-3.0040) (0.0803) (-2.5689) (b ) Past winners in up-markets [−220,−21] >0 & [−220,−21] >0 ? ? ? ? (?) (?) (?) Past winners in down-markets [−220,−21] >0 & [−220,−21] <0 ? ? ? ? (?) (?) (?) Past losers in up-markets [−220,−21] <0 & [−220,−21] >0 ? ? ? ? (?) (?) (?) Past losers in down-
markets [−220,−21] <0 & [−220,−21] <0 ? ? ? ? (?) (?) (?) Figure 1 Daily cumulative average abnormal returns (CAARs) for June/July with event day zero being the first trading day of July for June/July 2006 to June/July 2019 for the top 20 stocks by market capitalisation of the S&P/ASX200 Index (identified and updated at the end of January each year from 2006 to 2019). Daily abnor- mal returns, , are calculated as specified in Section B. CAARs are graphed for “all” stocks, and for sub- samples of: (Panel a) “past winners” and “past losers”; and (Panel b) “past winners in up-markets”, “past winners in down-markets”, “past losers in up-markets” and “past losers in down-markets”, as specified in Section B. Panel a: Panel b: …
