Individual Assessed Coursework Brief
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.
The written report should not exceed 3,000 words.
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.