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Measuring the Bull and the Bear

John Maynard Keynes (1936) argued that markets can fluctuate wildly under the influence of investors' “animal spirits,” which move prices in a way unrelated to fundamentals.

After Keynes, many other authors have considered the possibility that a significant presence of sentiment-driven investors can cause prices to depart from fundamental values.

The classic argument against sentiment effects is that they would be eliminated by rational traders seeking to exploit the profit opportunities created by mispricing.

If rational traders cannot fully exploit such opportunities, however, then sentiment effects become more likely.

Thus, we can easily say that sentiment plays an important role in asset pricing and it can affect the market.

“Nowadays the question is no longer, as it was a few decades ago, whether investor sentiment affects stock prices, but rather how to measure investor sentiment and quantify its effects.” (Baker, Wurgler 2007)


Measure the investor sentiment: some approaches.

Measuring the sentiment might be a difficult task. There are various indicators and studies that assess markets, individual investors, and macroeconomic factors and that try to understand the feelings of specific markets.

Hereby I present some of the most popular approaches used by researches to measure the Bull and the Bear.

Financial market-based measures

Under the first approach, empiricists proxy for investor sentiment with market-based measures such as trading volume, closed-end fund discount, initial public offering (IPO) first-day returns, IPO volume, option implied volatilities (VIX), or mutual fund flows.

Baker and Wurgler (2007) offers one of the most influential models based on financial markets’ data:



In particular, they use 6 proxies, such as:

·       trading volume as measured by NYSE turnover (TURN)

·       dividend premium (PDND)

·       closed-end fund discount (CEFD)

·       the number of IPOs (NIPO)

·       first-day returns on IPOs (RIPO)

·       the equity share in new issues (S)

One of the key points is that waves of sentiment have clearly discernible, important, and regular effects on individual firms and on the stock market as a whole.

Although market-based measures have the advantage of being readily available at a relatively high frequency, they have the disadvantage of being the equilibrium outcome of many economic forces other than investor sentiment. Qiu and Welch (2006) put it succinctly: “How does one test a theory that is about inputs → outputs with an output measure?”

Survey-based sentiment indexes

Perhaps just by asking investors how optimistic they are, we can gain insight into the marginal irrational investor. 

One of the most known indexes is the UBS/Gallup survey, in which a randomly-selected sample of 1000 households with total investments equal or higher than $10,000 are interviewed to construct Index of Investor Optimism. Another famous index is the  “University of Michigan Consumer Sentiment Index” based on at least 500 telephone interviews with fifty core questions. Brown and Cliff (2005) use the latter to forecast market returns.

However, according to Da et al. (2014), using such sentiment indexes can have significant restrictions.

First, most of the survey-based data sets are available at weekly or monthly frequency.

Second, there is a little incentive for respondents to answer question in such surveys carefully and truthfully (Singer (2002).

Thus, survey-based sentiment indexes can be helpful in predicting financial indicators. However, the usage of such indexes has specific drawbacks and can be limited in some cases.

 

Textual sentiment data from specialized on-line resources

Researchers propose to use text mining and sentiment analysis algorithms to extract information about investors’ mood from social networks, media platforms, blogs, newspaper articles, and other relevant sources of textual data. A precious source of information can be Twitter for example. Zhang et al. (2011) found that emotional tweet percentage is significantly negatively correlated with Dow Jones, NASDAQ and S&P 500, but displayed significant positive correlation to VIX. It therefore seems that just checking on Twitter for emotional outbursts of any kind gives a predictor of how the stock market will be doing the next day.

However, important to notice that it is relatively more difficult to collect such type of data (in most cases a researcher needs a special software to “scrape the web”)

Internet search behavior

Herbert Simon in 1955 was already claiming that “people start their decision making process by gathering relevant information”.

Can we use Households Internet Search Behavior to predict market sentiment? Dimpf and Jank (2012), for example, find a strong co-movement of the Dow Jones’ realized volatility and the volume of search queries for its name.



Search data has the potential to objectively and directly reveal to empiricists the underlying beliefs of an entire population of households. Internet search behavior of households is relatively new and promising proxy for investor sentiment: such type of data does not require additional information from other sources and can be used in scientific studies independently.

Non-economic factors

Many other factors may affect our everyday mood, which is then reflected into the stock market.

For example, given that psychological evidence and casual intuition predict that sunny weather is associated with upbeat mood, Hirshleifer and Shumway (2003) show that stock returns are affected by the weather across the world.

Edmans, Garcia, and Norli (2007) associate the outcomes of sporting events, such as the World Cup, to drops in the stock market when the country loses a game.

There might be many other non-economic factors, even though in this cases a question naturally arises: does correlation always mean causation? Many of these non-economic factors does not seem to have a sufficient economic rationale to support them.

Conclusion

Investor sentiment is not straightforward to measure, but there is no fundamental reason why one cannot find imperfect proxies that remain useful over time.

Such considerations suggest that the practical approach is to combine several imperfect measures.

It should also be recognized that investor sentiment is only one of many forces on the market. Stock prices are of course determined by supply and demand, and there are numerous factors that affect these, such as fundamental factors, legal, tax-related, demographic, technological, international, as well as other psychological factors related to attention, regret, anchoring, and availability.

Indexes of stock market senitment can only play a supportive role in trying to understand market events.



References

Keynes, John Maynard, 1883-1946. The General Theory of Employment, Interest and Money. London :Macmillan, 1936.

Baker, Malcolm, and Jeffrey Wurgler. 2007. "Investor Sentiment in the Stock Market." Journal of Economic Perspectives, 21 (2): 129-152.

Qiu, Lily Xiaoli and Welch, Ivo, Investor Sentiment Measures (2006)

Brown, G. W., & Cliff, M. T. (2005). Investor Sentiment and Asset Valuation. Journal of Business, 78, 405-440.

Da, Zhi; Engelberg, Joseph; Gao, Pengjie (2014). "The Sum of All FEARS Investor Sentiment and Asset Prices". Review of Financial Studies. 28 (1): 1–32.

Singer, Eleanor (2002). "The Use of Incentives to Reduce Nonresponse in Household Surveys".

Zhang, Xue; Fuehres, Hauke; Gloor, Peter A. (2011). "Predicting Stock Market Indicators Through Twitter "I hope it is not as bad as I fear"". Procedia - Social and Behavioral Sciences

Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69, 99–118.

Dimpfl, Thomas; Jank, Stephan (2012). "Can Internet Search Queries Help to Predict Stock Market Volatility?". Rochester, NY: Social Science Research Network.

Hirshleifer, David; Shumway, Tyler (2003). "Good Day Sunshine: Stock Returns and the Weather". The Journal of Finance. 58 (3): 1009–1032

Edmans, Alex; García, Diego; Norli, Øyvind (2007). "Sports Sentiment and Stock Returns". The Journal of Finance. 62 (4): 1967–1998.


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