AFE6014-B: Empirical Methods in Accounting and Finance: Individual Assessed Coursework

FoMLSS Accounting, Finance and Economics

AFE6014-B: Empirical Methods in Accounting and Finance

 

 

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 30 July 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.”

 

Introduction

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 bring 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. The theoretical analysis is supported by evidence drawn from the US market (Brown and Cliff, 2005) as well as international markets (Schmeling, 2009; Bathia and Bredin, 2013).

In line with the above-mentioned points, please prepare a report with a specific emphasis on the following seven requirements:

 

 

 

Required:

  1. Discuss the rationale behind the cross-sectional impact of investor sentiment on stock returns.                                                                                                 [10 marks]
  2. Discuss the impact of investor sentiment on stock returns conditional on economic conditions.                                                             [10 marks]
  3. Suppose that you decide to extend the evidence on the impact of investor sentiment on stock returns to one emerging market. Select a market and motivate your selection.                                                                         [8 marks]
  4. Critically review related literature and evaluate survey-based investor sentiment proxies and market-based investor sentiment proxies.                                                                                                                                                             [15 marks]
  5. Find two proxies for investor sentiment in your selected market, and elaborate motivation for your selection.                                     [12 marks]
  6. Present descriptive statistics of (i) market returns of the selected market and (ii) investor sentiment.                                                                                     [15 marks]
  7. Examine (i) the impact of investor sentiment on stock market returns, and (ii) the impact of investor sentiment on stock market returns conditional on economic conditions. Discuss potential limitations of your work. [30 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:

Requirement 1:

  1. Discuss the rationale behind the cross-sectional impact of investor sentiment on stock returns.

 

Requirement 2:

  1. Discuss the impact of investor sentiment on stock returns conditional on economic conditions.

 

Requirement 3:

  1. Select one emerging market.
  2. Motivate your selection.

 

Requirement 4:

  1. Assess merits and flaws of survey-based investor sentiment proxies.
  2. Assess merits and flaws of market-based investor sentiment proxies.

 

Requirement 5:

  1. Find two proxies for your selected market.
  2. Motivate your selection.

 

Requirement 6:

  1. Present descriptive statistics of market returns and two series of investor sentiment.

Requirement 7:

  1. Examine the relation between market returns and investor sentiment.
  2. Examine the relation between market returns and investor sentiment across different economic conditions.
  3. 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

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.

Bathia, D., Bredin, D., An examination of investor sentiment effect on G7 stock market returns. European Journal of Finance 19 (9), 909–937.

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., 2004. Investor sentiment and the near-term stock market. Journal of Empirical Finance 11 (1), 1–27.

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.

Chung, S., Hung, C., Yeh, C., When does investor sentiment predict stock returns? Journal of Empirical Finance 19 (2), 217–240.

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

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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