Empircal methods in accounting and finance

The work should not exceed 3,500 words. Excessive assignments will be penalised
according to section 9.13 of Regulation 9 Regulation Governing Postgraduate Taught
Awards: “Assessed work which exceeds a specified maximum permitted length will be
subject to a penalty deduction of marks equivalent to the percentage of additional words
over the limit. The limit excludes bibliographies, diagrams and tables, footnotes, tables

of contents and appendices of data.”

Introduction

The mean-variance relationship has long been a focus in finance literature. Traditional
financial theories propose a positive mean-variance relationship, or risk-return tradeoff
(Merton, 1973), ie. bearing high (low) risk should be rewarded by high (low) returns,
empirical studies document at best inconclusive evidence with three mainstreams due
to different economic settings and volatility model selection. French et al. (1987),
Scruggs (1998), Ghysels et al. (2005), Lundblad (2007), Pastor et al. (2008), Brandt and
Wang (2010), and Rossi and Timmermann (2015), among others find the risk-return
tradeoff despite being less significant in some cases. On the other hand, Nelson (1991),
Brandt and Kang (2004), Baker et al. (2011), Fiore and Saha (2015), and Booth et al.
(2016), among others, document a negative mean-variance relationship. Turner et al.
(1989), Glosten et al. (1993), Sun et al. (2017), and Wang et al. (2017), among others,

report both positive and negative relationship between risk and returns.

A wide range of theories are proposed to explain the weak risk-return tradeoff, such as
investor sentiment (Yu and Yuan, 2011; Wang, 2018a&b; Wang and Duxbury, 2021)

and differences in overnight and intraday returns (Wang, 2021).

In line with the above, answer the following requirements:
Required:

1.

Discuss the empirical designs in (i) Yu and Yuan (2011) and Wang (2018a), and
(ii) Wang (2018b) and Wang and Duxbury (2021)
[20 marks]
Critically review literature, and summarise and evaluate approaches to construct
proxies for investor sentiment.
[12 marks]
Suppose that you decide to extend Wang (2021) to another developed market.
Select the market and justify your selection.
[8 marks]
For the selected market, show present and interpret descriptive statistics of stock
returns for the whole sample period, along with high- and low-sentiment periods.
[15 marks]
Use the rolling window method to filter conditional volatility. Present and
interpret descriptive statistics of conditional volatility for the whole sample
period, along with high- and low-sentiment periods.
[20 marks]
Examine the mean-variance relation for the whole sample period, along with
high- and low-sentiment periods. Interpret.
[25 marks]

While attempting requirements you should follow academic writing style format relying

on journal articles. Failing to do so leads to a 30-mark deduction.
Guideline coverage of issues/answers expectations:

Requirement 1:
1. Provide empirical designs in Yu and Yuan (2011) and Wang (2018b).
2. Provide empirical designs in Wang (2018b) and Wang and Duxbury (2021).

3. Make comparison between (i) and (ii).

Requirement 2:
1. Provide reasons why proxies are needed for investor sentiment.
2. Summarise main types of proxies for investor sentiment

3. Critically evaluate each type.

Requirement 3:
1. Select a developed market.
2. Justify your choice from at least two main perspectives: (i) Provide criteria used
to label the selected market as a developed market; (ii) Explain reasons why the
research question as in Wang (2021) is of particular interest in your selected

market.

Requirement 4:
1. Present and interpret descriptive statistics of stock returns for the whole sample
period.
2. Explain how high- and low-sentiment periods are determined.
3. Present and interpret descriptive statistics of stock returns for high- and low-

sentiment period.

Requirement 5:

1. Filter conditional volatility.
2. Present and interpret descriptive statistics of conditional volatility for the three

periods.

Requirement 6:

1. Examine the mean-variance relation for the three periods.

2. Interpret.
Relevant References (You may use these references to help to produce your work)

Baker, M., Wurgler, J., 2006. Investor sentiment and the cross-section of stock returns.
Journal of Finance 61 (4), 1645-1680.
Baker, M., Wurgler, J., 2007. Investor sentiment in the stock market. Journal of
Economic Perspectives 21 (2), 129-151.
Brown, G.W., Cliff, M.T., 2005. Investor sentiment and asset valuation. Journal of
Business 78 (2), 405-440.
Daniel, K., Hirshleifer, D., Subrahmanyam, A., 1998. Investor psychology and security
market under- and overreactions. Journal of Finance 53 (6), 1839-1886.
French, K.R., Schwert, G.W., Stambaugh, R.F., 1987. Expected stock returns and
volatility. Journal of Financial Economics 19 (1), 3-29.

Hendershott, T., Livdan, D., Résch D., 2020. Asset pricing: A tale of night and day.
Journal of Financial Economics 138 (3), 635-662.

Lou, D., Polk, C., Skouras, S., A tug of war: Overnight versus intraday expected returns.
Journal of Financial Economics 134 (1), 192-213.

Qiu, L., Welch, I, 2006. Investor sentiment measures. Working paper, National Bureau
of Economic Research.

Schmeling, M., 2009. Investor sentiment and stock returns: Some international
evidence. Journal of Empirical Finance 16 (3), 394-408.

Tetlock, P.C., 2007. Giving content to investor sentiment: The role of media in the stock
market. Journal of Finance 62 (3), 1139-1168.

Wang, W., 2018a. Investor sentiment and the mean-variance relationship: European
evidence. Research in International Business and Finance 46, 227-239.

Wang, W., 2018b. The mean-variance relation and the role of institutional investor
sentiment. Economics Letters 168, 61-64.

Wang, W., 2020. Institutional investor sentiment, beta, and stock returns. Finance
Research Letters 37, 1-7.

Wang, W., 2021. The mean-variance relation: A 24-hour story. Economics Letters 208,
1-3.

Wang, W., Duxbury, D., 2021. Institutional investor sentiment and the mean-variance
relationship: Global evidence. Journal of Economic Behavior and Organization
191, 415-441.

Wang, Y.H., Keswani, A., Taylor, S.J., 2006. The relationships between sentiment,
returns and volatility. International Journal of Forecasting 22 (1), 109-123.

Yu, J., Yuan, Y., 2011. Investor sentiment and the mean-variance relation. Journal of
Financial Economics 100 (2), 367-381.
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