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FINC3023 Behavioural Finance

创建日期：2024-05-29 14:32:22

所属分类：金融Finance

标签： FINC3023

Behavioural Finance

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FINC3023 Behavioural Finance

Assignment
In this assignment you are tasked with investigating the value of trading based on a ‘factor’ or
‘anomaly.’ As an investment analyst, you are tasked with assessing the trading potential of a
strategy. You will then be asked to critically evaluate the potential of the trading strategy for
investment purposes.
Ken French’s website contains data on a number of different portfolios, where portfolios are formed
on the basis of proposed mispricing factors. The data has conveniently been arranged for you, with
portfolios formed into quintiles or deciles.
In this assignment, the aim is to determine how these portfolio formation factors have returned over
time, and understand the rationale behind why these factors might work as investment strategies.
Consider the audience of your report to be potential clients of your investment fund. They are
interested in knowing whether a strategy would be feasible or not.
Task.
Choose one of the factors beneath the following headings.
Sorts involving Accruals, Market Beta, Net Share Issues, Daily
Variance, and Daily Residual Variance
1. [6 marks]
Explanation of the theory of the trading strategy.
a. What is the logic behind the portfolio formation strategy? Present some academic /
practitioner evidence towards the performance of the strategy. (Note: if the strategy had
not been proposed elsewhere it is unlikely to appear on the site).
b. Does there appear to be some ‘behavioural’ driver of this portfolio formation strategy? Or
is the strategy purely framed as a risk-based pricing factor?
2. [6 marks]
Univariate portfolio performance evaluation.
Using monthly value-weighted returns, examine the portfolio performance across deciles.
a. What is the average return to each of the deciles? Present a graph of the
performance of the top and bottom deiciles on the same set of axes over the period
196307 (or whenever the first month of available data appears) until 202302.
b. What is the standard deviation of performance across the deciles? Do the observed
return differences seem to be reflected in differences in standard deviation?
c. Construct a portfolio that “goes long” in the higher performing of decile 1 / decile 10
and short in the other portfolio (based on average returns). Examine some of the
characteristics of the portfolio’s returns.
i. Is the portfolio performance positive and significant? Perform an
appropriate statistical test?
ii. Is the distribution of portfolio returns normally distributed (or
approximately normally distributed)? Use a histogram and/or test of
normality (e.g. Jarque-Bera) to demonstrate.
iii. What proportion of the returns are positive / negative? What is the average
return conditional on the return being positive / negative? Is there any
indication of downside (skewness) risk?
iv. How does the performance of the portfolio compare over two periods: From
the start of the data until Dec. 1992, vs. January 1993 until the most recent
data point (Feb 2023)? Does the performance of the strategy appear to have
been reduced over time?
v. Are there any seasonalities? For example, are the returns to the portfolio
higher in a specific calendar month (e.g. January) than other months?
3. [6 marks]
Combine your portfolio with data on the factors from Ken French’s website (Use data under the
heading Fama/French 3 Factors). Link up the monthly factors (plus risk-free rate) from 196307
(or the first month of your data) until the most recent month (Feb 2023)
a. Make sure that the Analysis Toolpak is added to Excel. You should see it under the
‘Data’ Tab at the far-right (with Solver if you aren’t sure of its location). If it is not
there, please add it in, using File -> Options -> Add-ins -> Analysis Toolpak. Note – if
you don’t see it, and it is enabled, disable Analysis Toolpak and then re-enable it.
b. Construct the ‘excess’ returns to your long-short anomaly strategy (i.e. strategy
return less the risk-free rate)
c. Build a one factor (CAPM) model to explain your anomaly’s returns. Is the anomaly
return (alpha) significant after controlling for risk in this model? What stocks appear
to be riskier by CAPM-beta, if beta is significant (i.e., does the long or short
component of the portfolio appears riskier).
d. Now, build a Fama-French three-factor model to explain your anomaly portfolio’s
returns. Compared with the results from part 3c, does the model’s explanatory
power increase substantially (use an appropriate R2 measure to explain)? What
factors are significant (if any) in explaining the anomaly portfolio’s returns? Based on
the result of this regression, does it appear that the anomaly can be reasonably
explained by ‘risk.’
e. Take the next row of data below your original portfolio. For instance, if you selected
“Portfolios Formed on Accruals,” select “25 Portfolios Formed on Size and Accruals.”
Now, use this data to explore further the interaction between size and your anomaly
portfolio’s return. Does taking a portfolio that is long “Small stocks with positive
anomaly characteristic” (e.g. small stocks with low accruals) and short “Big stocks
with negative anomaly characteristic” appear to provide superior performance?
What about the other ‘extreme corner’ of the portfolio (small stocks with high
accruals vs big stocks with low accruals)?
4. [12 marks]
Finding a useful time-series predictor / market state variable.
Next, find one (or more) time-series variables that could be useful for forecasting the
portfolio returns (going back to the original 10 portfolios sorted on the anomaly factor). This
could be considered a ‘leading indicator’ for the purposes of forecasting stock market
returns / economic activity. There are several on the FRED website
• Inflation (consumer price index, producer price index),
• credit spreads (average yield on BAA bonds less AAA bonds, or yield on BAA
bonds minus 10-year Treasury Bonds)
• yield curve slope (yield on 10-year bonds less yield on short-term rates (e.g. T-
bills)),

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