AFE6014-B: Empirical Methods in Accounting and Finance
FoMLSS Accounting, Finance, and Economics
AFE6014-B: Empirical Methods in Accounting and Finance
Submission date: 22nd Jan 2021 at 3.00 pm [UK time]
TYPE OF ASSESSMENT: Individual Assessed Coursework
(3,000 words maximum excluding tables, figures, and references)
- Failure to submit your coursework by this deadline will result in a mark of 0%
- The assignment must be submitted in electronic format to the ‘Turnitin’ drop-box in the Canvas site for this module
- You are advised to plan your work carefully and back-up your work. Computing problems will NOT be accepted as reasons for non-submission
- Along with the main report, you also need to submit the original dataset and screenshots of results from SPSS or EViews
Individual Assessed Coursework Brief
Under the revised assessment requirements due to COVID-19, this module will be assessed via a written assessed coursework. This is an individual assignment containing seven different requirements. Along with the main report, you also need to submit the original dataset and screenshots of results from SPSS or EViews.
The written report should not exceed 3,000 words and should be submitted by 22nd Jan 2021 at 3.00 pm (UK time) via Turnitin. 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.”
The mean-variance relationship has long been a focus in finance literature. Traditional financial theories propose a positive mean-variance relationship (Merton, 1973), i.e. 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), Pástor 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.
Behavior financial theories highlight investor sentiment in influencing stock prices, despite the traditional ones positing that stock prices are the discounted future cash flows and arbitrage leaves little space for investor sentiment (Fama, 1965). De Long et al. (1990) argue that sentiment investors trading together brings systematic risk into stock markets. The risk originated from the stochastic shifts in investor sentiment imposes arbitrage limits on rational investors, impeding them from trading against noise investors. As a result, the mispricing caused by sentiment investors is persistent. Baker and Wurgler (2006) state two routes whereby investor sentiment can cause persistent impact on stock prices: (i) uninformed demand shocks, and (ii) limits on arbitrage. Uninformed demand shocks naturally persist in that irrational investors’ misbeliefs could be further strengthened by others ‘joining on the bandwagon’ (Brown and Cliff, 2005, p. 407). Limits on arbitrage demotivate arbitrageurs from relieving the impact of investor sentiment since they are commonly subject to relatively restricted investment horizons and can hardly accurately forecast how the impact will persist. Therefore, one can observe that high levels of optimism (pessimism) would cause high (low) concurrent returns, and given the mean-reversion property, overpricing (underpricing) would be corrected and followed by low (high) subsequent returns.
Combining two streams of literature, Yu and Yuan (2011), by sampling the US stock market, evidence the risk-return tradeoff amid low-sentiment periods but not over high-sentiment periods.
In line with the above-mentioned points, please prepare a report with a specific emphasis on the following seven requirements:
- Discuss the theoretical underpinnings for empirical findings of Yu and Yuan (2011). [6 marks]
- Suppose that you decide to extend the US evidence from Yu and Yuan (2011) to another market. Select a market and motivate your selection. [8 marks]
- Critically review related literature, and summarise and evaluate approaches to construct proxies for investor sentiment. [12 marks]
- Determine a proxy for investor sentiment in your selected market, and elaborate motivation for your selection. [8 marks]
- Present descriptive statistics of (i) market returns of the selected market and (ii) investor sentiment. [15 marks]
- Select one method to filter conditional volatility of market returns, and present descriptive statistics of conditional volatility. [15 marks]
- Examine (i) the relation between market returns and investor sentiment, and (ii) the relation between market returns and conditional volatility. Discuss potential limitations of your work. [36 marks]
While attempting requirements 1–7 you should follow academic writing style format relying on journal articles. Failing to do so will lead to a FAIL in this module.
Guideline coverage of issues/answers expectations:
- Provide theoretical underpinnings of the findings in Yu and Yuan (2011).
- Select a non-US market.
- Motivate your choice.
- Specific reasons why proxies are required for investor sentiment.
- Summarise main types proxies for investor sentiment
- Evaluate merits and drawbacks of each type.
- Select a proxy for investor sentiment.
- Motivate your selection.
- Present descriptive statistics of market returns of the selected market.
- Present descriptive statistics of investor sentiment.
- Select the method to filter conditional volatility.
- Motivate your selection.
- Present descriptive statistics of conditional volatility.
- Examine the relation between market returns and investor sentiment.
- Examine the relation between market returns and conditional volatility.
- Discuss limitations of your analysis.
Along with the main report, you also need to submit the original dataset and screenshots of results from SPSS or EViews.
Relevant References (You may use these references to help to produce your report)
Baker, M., Stein, J.C., 2004. Market liquidity as a sentiment indicator. Journal of Financial Markets 7 (3), 271−299.
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.
Black, F., 1986. Noise. Journal of Finance 41 (3), 528–543.
Brown, G.W., 1999. Volatility, sentiment, and noise traders. Financial Analysts Journal 55 (2), 82−90.
Brown, G.W., Cliff, M.T., 2005. Investor sentiment and asset valuation. Journal of Business 78 (2), 405−440.
Campbell, J.Y., Hentschel, L. 1992. No news is good news: An asymmetric model of changing volatility in stock returns. Journal of Financial Economics 31 (3), 281–318.
Daniel, K., Hirshleifer, D., Subrahmanyam, A., 1998. Investor psychology and security market under- and overreactions. Journal of Finance 53 (6), 1839−1886.
De Long, J.B., Shleifer, A., Summers, L.H., Waldmann, R.J., 1990. Noise trader risk in financial markets. Journal of Political Economy 98 (4), 703−738.
Engle, R.F., Ng, V.K., 1993. Measuring and testing the impact of news on volatility. Journal of Finance 48 (5), 1749−1778.
Fama, E.F., 1965. The behavior of stock-market prices. Journal of Business 38 (1), 34−105.
French, K.R., Schwert, G.W., Stambaugh, R.F., 1987. Expected stock returns and volatility. Journal of Financial Economics 19 (1), 3−29.
Nofsinger, J., 2005. Social mood and financial economics. Journal of Behavioural Finance 6 (3), 144–160.
Ofek, E., Richardson, M., Whitelaw, R.F., 2004. Limited arbitrage and short sales restrictions: Evidence from the options markets. Journal of Financial Economics 74 (2), 305−342.
Pan, L., Tang, Y., Xu, J., 2016. Speculative trading and stock returns. Review of Finance 20 (5), 1835–1865.
Qiu, L., Welch, I., 2006. Investor sentiment measures. Working paper, National Bureau of Economic Research.
Scheinkman, J.A., Xiong, W., 2009. Overconfidence and speculative bubbles. Journal of Political Economy 111 (6), 1183−1219.
Schmeling, M., 2009. Investor sentiment and stock returns: Some international evidence. Journal of Empirical Finance 16 (3), 394−408.
Shleifer, A., Vishny, R.W., 1997. The limits of arbitrage. Journal of Finance 52 (1), 35−55.
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., 2019. Institutional investor sentiment, beta, and stock returns. Finance Research Letters, in press.
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.
Yuan, Y., 2015. Market-wide attention, trading, and stock returns. Journal of Financial Economics 116 (3), 548–564.
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