#### Step 1. Place your order

Fill in the order form and provide all details of your assignment.

#### Step 2. Make Payment

Choose the payment system that suits you most.

#### Step 3. Receive your paper

# Section 1: Economic Factor Models and Statistical Arbitrage: For this section, w

**Place your order now for a similar assignment and have exceptional work written by our team of experts, At affordable rates**

**For This or a Similar Paper Click To Order Now**

Section 1: Economic Factor Models and Statistical Arbitrage:

For this section, we are going to focus on the following six

stocks: Walmart (WMT), JP Morgan (JPM), Coca-Cola (KO), Microsoft (MSFT),

Alaska Air (ALK), Lululemon. (LULU).

We are going to estimate the alphas and factor loadings of

these stocks using the four factor model as the benchmark.

The general idea of this section is to estimate the alphas

and risks of these stocks using January 2012 – December 2016 as an in-sample

estimation period,

create a portfolio with no systematic risk based on these

estimates, and then measure the ex-post out-of-sample risk of this portfolio

during the period January 2017 – December 2021.

1.1. Retrieve monthly factor returns for the four

factor model. The data is available at:

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html

Click on U.S. Research Returns” on the left side of

the webpage. Y

You will need to download the Fama-French factors (MKT-RF

HML, SMB) in one file, and then separately download the momentum factor.

Make sure you properly align the monthly return observations

on your six stocks with the returns on all four factors. (4 points)

1.2. Retrieve total return data from the Charting Tab

in FactSet for the six stocks.

Compute monthly

excess returns for each of the six stocks.

Note that the Fama-French data conveniently includes a

column for the deannualized risk-free rate expressed as a percentage. (4

points)

1.3. Using data for January 31, 2012 through December

31, 2016, estimate the alphas and factor loadings for each of the stocks.

Do any of your stocks have statistically significant

estimates for alpha, and if so, are these stocks candidates for overweight or

underweight positions? (8 points)

1.4. Using the Excel Solver and the estimates from

(1.4), compute the set of weights required to form a portfolio from the six

stocks that has zero factor risk, an expected alpha of 1% per month, and

satisfies the full investment constraint (weights sum to one). This portfolio

is an arbitrage portfolio as it is expected to generate alpha without any risk

exposure. Include a screen shot of the Solver Dialog box you used to solve for

the portfolio weights (4 points).

1.5. Using the set of weights from (1.4), compute the

monthly excess return on your portfolio during the period January 2017 –

December 2021. To maintain risk neutrality, the portfolio must be rebalanced

back to these weights each month, so the monthly portfolio return is simply rpt

= Σi wirit, where wi are the weights from (1.4). Calculate the total risk (variance)

of this supposed arbitrage portfolio. (5 points)

1.6. The portfolio defined in (1.4) is an arbitrage

portfolio that offers (in-sample) a positive alpha with no systematic risk

exposure. It is created based on information available at the end of December

2016, and we assume that you create and hold this portfolio from January 2017

through December 2021. Calculate the alpha and the factor loadings of your

portfolio using returns for January 2017 through December 2021 to measure its

out-of-sample risk. Do the alpha and factor loadings of the out of sample

arbitrage portfolio match your expectations? Are these results (alpha and

factor loadings) significant and statistically reliable? (5 points)

1.7. Calculate the alpha and the factor loadings of the

6 stocks using returns for January 2017 through December 2021. Compare the

results with your in-sample results from 1.4. How did each stock behave

differently between the two periods i.e. can you explain why your 1.6 results

are different from what out expected in 1.4? (8 points)

1.8. For each month in the out-of-sample period, use

the returns on the four factors and your estimated loadings to calculate the

systematic risk (as explained by the factor loadings i.e. factor loading from

1.6 times factor returns for each month) and idiosyncratic risk (unexplained by

the 4 factor model) on your portfolio. What percentage of the total risk

(calculated in 1.5) of your portfolio was idiosyncratic risk and what portion

was due to factor risk? Hint #1: Use variances. (5 points)

1.9. Compare the percentage of systematic risk to the

portfolio regression R-square from 1.6.

1.10.

Are they the same? Should they be? (2 points)

Section 2: Estimating a Fundamental Factor Model:

We are now going to build and backtest a fundamental factor

model based on the four variables that you choose. The first task is to

estimate the factor premia on your fundamental factors. We are going to estimate

the premia at three different points in time by using three different backtest

dates (06/30/2020, 12/31/2019 and 06/30/2019) with data accessed through

FactSet Screening. Be certain that the formulas you use are lagged properly to

avoid a look-ahead bias. One way to avoid the look-ahead bias is by using

quantitative factors specifically built by FactSet for backtesting. To estimate

the premia, you need to obtain the monthly return FOLLOWING your backtest date.

For example, use the July 2019 return for the 06/30/2019 backtest date. Use the

S&P1500 Composite as your universe.

2.1. Select 4 factors to use in your model. What are

your factors? Why did you choose these factors? What signs (exposure) do you

expect for each of the factor premia i.e. is a low value desirable or not? Each

factor must be from a different factor category (e.g. do not pick 2 valuation

factors like P/E and B/P). (8 points)

2.2. Include the Excel export of the summary page from

Universal Screener showing your factor formulas as well as your total return

formula. (2 points)

2.3. For each of the three backtest dates, estimate

the standardized fundamental factor model to obtain factor premia. Use next

month raw returns as the dependent variable. Each of the following sub-step must

be clearly identified and not combined i.e. I should be able to see the raw

data, the data (2.3.1.), with the outliers issue addressed (2.3.3) and the

standardized data (2.3.4).

2.3.1 What percentage of the universe is left after

filtering out missing data for each factor? How did you handle these missing

data points? Why did you use this approach? Hint: If you are missing more than

10% of the universe, this is probably not a good factor. (3 points)

2.3.2 Using the raw exposures to each factor extracted from

FactSet, compute the following statistics: min, max, mean, median, standard

deviation. How different are these statistics for each of the three backtest

dates? (4 points)

2.3.3 Describe how you handled it and solved the outliers

issue. If you decide to winsorize the data, use the PERCENTILE function to

obtain the 1% and 99% thresholds. (5 points)

2.3.4 Compute each stock standardized exposure (i.e.

z-scores) to each factor and provide the following summary statistics for the

distribution of standardized exposures: min, max, mean, median, standard

deviation. Hint: The monthly returns should not be standardized (9 points)

2.3.5 For each of the three backtest date, calculate the

equal weighted benchmark return and each factor premia using a cross-sectional

regression using your standardized exposures from 2.3.4 against the 1 month

holding return for the universe. What are each of the factor premia, and are

the estimates consistent with your expectations from 2.1? (9 points).

2.4. You now have three estimates for each of your

factor premia (one from estimating the standardized model for each backtest

date from 2.3.5). Average the three estimates for each factor and consider

these to be your expected factor premia. Explain what these numbers represent i.e.

how to interpret them. Be precise and specific. Hint: factor premia are not

betas. (5 points)

Section 3: Backtesting Your Fundamental Factor Model (Alpha

Testing)

Now it is time to backtest your fundamental model using

Alpha Testing (@AT) in FactSet. Import your screen and set both the universe

and the benchmark to be the same as your screening universe. Include inactive

securities, but exclude secondary listings and non-equity securities. Set the

backtest period to 12/31/2011 through 12/31/2021 and use monthly frequency for

rebalancing. Import your raw exposures as factors and then setup a multifactor

rank (MFR) based on z-scores as your signal for expected returns. The weights

assigned to each component of the multifactor ranking should be the expected

factor premia from Section 2[1]. Make sure you handle outliers for each factor

when setting up each factor and the MFR. Set the risk-free rate to be the

91-day T-Bill yield

3.1. Run your model and export the following reports

from Alpha Testing to Excel: a) Workspace, b) Summary: Single Factor, c)

Summary: All Factors, d) Periods, and e) the portion of the Constituents report

containing data for December 2016 (do not submit the entire Constituents

report). (8 points)

3.2. Using the Summary and Period output, evaluate

3.2.1.

Does you model work overall? (2 points)

3.2.2.

How much monthly return does your alpha signal generate? (2 points)

3.2.3.

How much alpha does your best quintile (F1) generate? Can this number be

trusted? (2 points)

3.2.4.

Which factor contributed the most (had the highest impact) to your MFR model?

(2 points)

3.2.5.

Is the contribution of the factor identified in 3.2.4 due to the return or the

weight of the factor? (2 points)

3.2.6.

What was the best monthly return for your best quintile (F1) and what was the

date? (1 point)

3.2.7.

What was the worst monthly return for your worst quintile (F5) and what was the

date? (1 point)

3.3. For your theorical best quintile (F1), 1)

calculate the geometric return average, 2) geometric average active return

(excess return over the benchmark/universe), 3) the total risk and 4) the

tracking error. The data needed for this step is in the Periods Report. Hint:

One risk measure is based on total return and one on active return (4 points)

3.4. Using the data you computed in Step 3.3:

3.4.1. calculate the Annual Sharpe Ratio and

Information Ratio of your F1 (theorical best) quintile. The data from Step

3.3.is expressed on a monthly so will need to convert them to be able the

calculate the data on an annualized Annualize the average monthly return

geometrically (i.e. (1+r)12−1) and annualize monthly risk measures by

multiplying by

. Hint: If you calculate a SR and IR for each month, you are

doing it wrong. (4 points)

3.4.2.

How does your Information Ratio compare to the information on the Summary Report

(single factor)? (1 point)

3.5. Using a linear regression, estimate the alpha and

beta of your F1 (theorical best) quintile relative to your benchmark/universe

(data in the Periods Report). Does this agree with your conclusion on 3.2.3? (4

points)

3.6. Using a linear regression, estimate the alpha and

beta of your F5 (theorical worst) quintile relative to your benchmark/universe

(data in the Periods Report). (3 points)

3.7. Using the data from Steps 3.5 and 3.6, calculate

the weights needed in each quintile to create a long-short market neutral

strategy that has zero exposure to the benchmark i.e. beta = 0 and a total

weight of 1. Calculate the geometric

average return and alpha on your market neutral strategy. Hint #1: geometric

average return and beta are simple weighted average. Hint #2: Don’t turn this

into a difficult problem, it is not meant to be one. If you are not doing basic

algebra computation, you are overthinking it. DO NOT USE SOLVER. (4 points)

3.8. Using the constituents report for the period

December 2016, compute the Spearman rank correlation between your MFR (alpha

signal) and realized returns (universe returns). Does your estimate for the IC

match the FactSet computed IC for the period (available for the period return)?

(6 points)

3.9. Consider the six stocks from Section 1. Compare

the alphas estimated for these stocks during the period January 2017 through

December 2021 in 1.7 to their MFR values available in the December 2016

Constituents report. Did your fundamental model correctly identify stocks with

positive or negative risk-adjusted returns? Discuss the efficacy of your model

as a predictor for future alpha. (4 points)

Section 4: Clarity and Data Presentation

4.1 Present your data and conclusions for each section

in a clear and easy way to understand. I should be able to easily identify what

question you are answering and follow your logic with ease. Data should not be

copied from one sheet to another but referenced. Conclusions should clear and

properly supported. (10 points)

[1] If the weights you computed in (2.4) are not reasonable,

you can override those estimates and use your own set of weights. Just be sure

to explain in your report why you have made this choice.

**Place your order now for a similar assignment and have exceptional work written by our team of experts, At affordable rates**