代写BAFI/FNCE 429: Investment Management Spring 2024代写Web开发

日期：2024-04-20 06:23

BAFI/FNCE 429: Investment Management

Group Assignment IV

Spring 2024

Instructions:

>The general instructions in the course syllabus are applied for this group assignment and you are required to follow them.

>The due date for this group assignment is 11:59pm on Wednesday, April 17th, 2024. Please upload 1) a PDF file of your group report with answers to the list of questions for Part I and 2) two Excel workbooks that show your work for Part I and Part II, respectively, to the Canvas. For All, make sure that all of your group members’ names and CWRU IDs and your group number are shown on the cover  page of the report and first spreadsheets of the two Excel workbooks. For each, also make sure that only one copy is submitted since duplicates make confusion and delay in grading.

Introduction:

This group assignment consists of two parts: Part I and Part II. The Part I implements tests for timing of each of various market-wide variables, including the market liquidity and market volatility. These tests can be considered as extensions of the market timing tests that we discussed in Lecture 5. The Part I also conducts the pricing test of illiquidity level using the Fama-MacBeth (FM) regressions as covered in Lectures 6 and 7. The Part II asks students to implement their chosen trading strategy among three well-known market anomalies for their managed portfolios in the Stock|Trak trading game. The total points for this group assignment are 48 points.

PART I: (34 points)

Data

In the Excel workbook “GA4_Data1.xlsx”, three different hedge fund datasets are provided. Each dataset has different sample period. All variables in these datasets have monthly frequency and they have the following definitions.

> RET is the value-weighted average return of portfolios managed by each of three hedge funds.

> MKTRF, SMB, and HML are the factors under the Fama-French three-factor model (FF3M).

> RF is the return on the one-month U.S. Treasury bill, which is a proxy for risk-free rate.

> LIQ is the mean-adjusted market-wide liquidity measure by Pastor and Stambaugh (2003).

> MKTRF_VOL is the standard deviation of daily values of MKTRF under the FF3M, which is a proxy for the volatility of market.

The Excel workbook “GA4_Data2.xlsx” contains randomly selected individual stocks from the U.S. stock universe that have the selected variables explained below. These variables are constructed based on the financial data from the Center for Research in Security Prices (CRSP) and they have monthly frequency. The sample period starts from January, 2003 and ends at November, 2016. For firm i (identified by permno #s) in month t, the names of spreadsheets are based on the following variables:

> EXRET is the excess return,

> LOGAMIHUD is a proxy for illiquidity cost proposed by Amihud (2002), whose definition is provided in Lecture 7, in logarithm.

List of Questions:

1.   (8pts) Using the return time-series of Hedge Fund 142 (available in the first spreadsheet of GA4_Data1.xlsx), implement the two market timing tests described in Panels B and C of Slide 17 in Lecture 5. In your tests, include SMB and HML factors as controls. For each market timing test, provide 1) the regression model, 2) the slope coefficients of all independent variables, 3) their t-statistics and statistical significance at the 5% level, and 4) your conclusion on the fund manager’s market timing ability.

2.   (9pts) Using the return time-series of Hedge Fund 26 (available in the second spreadsheet of GA4_Data1.xlsx), implement the following market liquidity timing test as in Lecture 7.

Specifically, run the following time-series regression: in month t for fund p,

rp,t 一 rf,t  = ap + {p p * LIQt } * (rm,t 一 rf,t) + βSMB,p * SMBt + βHML,p * HMLt + εp,t = ap + p * (rm,t  一 rf,t) p * LIQt * (rm,t 一 rf,t) + βSMB,p * SMBt + βHML,p     * HMLt + εp,t,

where LIQt is the mean-adjusted market-wide liquidity proxy proposed by Pastor and Stambaugh (2003). Note that the high value of LIQt means high liquidity available in market. Thus the hedge fund manager has incentives to increase the fund’s exposure to market when the market liquidity is relatively high. Tabulate 1) the slope coefficients of all independent variables and 2) their t-statistics and statistical significance at the 5% level, and 3) draw your conclusion on the fund manager’s market liquidity timing ability.

3.   (9pts) Using the return time-series of Hedge Fund 21 (available in the third spreadsheet of

GA4_Data1.xlsx), implement the following market volatility timing test. Specifically, run the following time-series regression: in month t for fund p,

rp,t 一 rf,t  = ap + {p

+ βSMB,p * SMBt + βHML,p * HMLt + εp,t

= ap + p * (rm,t  一 rf,t)  + p * MKTRF_VOLt * (rm,t 一 rf,t)

+ βSMB,p * SMBt + βHML,p * HMLt + εp,t,

where MKTRF_VOLt is a proxy for market-wide volatility. Note that the high value of MKTRF_VOLt means high volatility or uncertainty in market. Thus the hedge fund manager has incentives to increase the fund’s exposure to market when the market volatility is relatively low. Tabulate 1) the slope coefficients of all independent variables and 2) their t-statistics and statistical significance at the 5% level, and 3) draw your conclusion on the fund manager’s market volatility timing ability.

4.   (8pts) Suppose that using GA4_Data2.xlsx, you want to run the following FM cross-sectional regression (with observed variable) each month to test whether there is a positive illiquidity premium as in Slide 24 of Lecture 7: for stock i in month t,

ri,t 一 rf,t  = Y0,t + YILLIQ,tLOGAMIHUDi,t一1 + Ei,t,

where LOGAMIHUDi,t一1is the logarithm of the illiquidity measure proposed by Amihud (2002) in month t- 1. Tabulate 1) the intercept and slope coefficient and 2) their t-statistics and statistical significance at the 5% level, and 3) draw your conclusion on the pricing of illiquidity level in the cross-section of stock returns.

PART II: (14 points)

Determine how much of your money will be invested in this strategy. But it should be larger than 15% and smaller than 25% of your initial cash (=$5,000,000). Choose only one of the following three suggested market anomalies and implement its long side (=buying side) with your chosen set of  stocks. These three market anomalies are size, value, and momentum effects, whose detailed description is provided below.

>Size Effect: Small firms tend to have higher average returns than large firms.

Construct a portfolio of small stocks, where small stocks are defined as the stocks that have relatively low market capitalization (=price times the number of shares outstanding). To identify these small stocks, refer to the corresponding table below and use the bottom 20%.

When you consider the size effect, note that the returns of small stocks tend to be more volatile than those of large stocks historically.

Value Effect: Value firms (with higher BM ratios) tend to have higher average returns than growth firms (with lower BM ratios).

Construct a portfolio of value stocks, where value stocks are defined as the stocks that have relatively high book-to-market (BM) ratios. To identify these value stocks, refer to the corresponding table below and use the top 20%.

Momentum Effect: Past winner stocks tend to continue to win over the subsequent three to twelve months, while past loser stocks tend to continue to lose.

Construct a portfolio of past winner stocks that have performed well over the past one year, where the past winner stocks are defined as the stocks that have relatively high cumulative returns over the past one year. To identify these momentum winner stocks, refer to the corresponding table below and use the top 20%.

Then you can keep the size, value, or momentum portfolio either value-weighted or equal-weighted.

That is, you can hold each stock in the corresponding portfolio either relative to its market capitalization or in equal amounts as the other stocks. Note that an equal-weighted portfolio might require rebalancing in order to keep the weights in the stocks equal. There is NO restriction on the number of stocks to be included in each portfolio as long as the general rules in our StockTrak trading game is met. You can add or subtract stocks to each portfolio as the trading period progresses. Note that good resources from which you can collect past market capitalization, book-to-market, and return data are the CRSP database and Financial Ratios Suite available in the WRDS (After logging into the  WRDS, for the CSRP database, go to CRSP è Stock/Security Files è Monthly Stock File. For the Financial Ratios Suite, go to Financial Ratios Suite by WRDS è Financial Ratios Firm Level)

Tasks: As an evidence of your team’s work, please upload an Excel workbook that contains 1) the list of your chosen stocks, 2) their past values of the corresponding variables, 3) their weights in your constructed portfolio, and 4) the corresponding StockTrak trading records that show your actual implementation to the Canvas.

Market Capitalization Percentiles as of December, 2023

The table contains every twentieth percentile of market capitalization from 20% to 100%.

Percentile (%)	Size (=Market capitalization in $Million)
20	867.08
40	2788.5
60	6127.14
80	18320.66
100	778545.6

Book-to-market (BM) Percentiles as of December, 2023

The table contains every twentieth percentile of BM from 20% to 100%.

Percentile (%)	Book-to-market ratio (BM)
20	0.222
40	0.385
60	0.594
80	0.887
100	6.123

Cumulative Return Percentiles as of December, 2023

The table contains every twentieth percentile of cumulative returns over the past one year from 20% to 100%.

Percentile (%)	Cumulative Return (%)
20	- 19.56
40	-2.70
60	11.83
80	34.09
100	560.76