The Relationship between Sentiment and Risk in Financial Markets.
| Autor | Paraboni, Ana Luiza |
| Cargo | Report |
Introduction
Kahneman and Tversky (1979) put in check the Expected Utility Theory, which claims that agents are risk averse, take rational decisions and seek utility maximization, which gave rise to Prospect Theory, and subsequently the field of Behavioral Finance. This approach includes behavior when faced with decision making in situations of risk and observes that regret due to losses is greater than satisfaction due to gains. Therefore it is useful to explain situations where the traditional approach fails (see Al-Nowaihi, Bradley, & Dhami, 2008). Due to this contribution, many research topics have also gained space in the field of finance. One of these is market sentiment.
A series of studies verify that this variable is crucial to decision making in financial markets, since it affects distinct characteristics ranging from future information to liquidity. We can cite here the works of Barberis, Shleifer and Vishny (1998), Baek, Bandopadhyaya and Du (2005), Brown and Cliff (2005), Baker and Wurgler (2006), Bradley, Gonas, Highfield and Roskelley (2009), Feldman (2010), Hassan and Mertens (2011), Kuo and Chen (2012), Fong and Toh (2014), and Liu (2015). Despite the existence of many ways to compute market sentiment, there is still no consensus. One approach is to consider the quantity of initial public offerings to create a proxy, as has been done by Walker and Lin (2007), and Baker and Wurgler (2007). Another approach is to consider the expectations of future variations in prices (returns), as discussed by Qiu and Welch (2006) and Sturm (2014). Another alternative is investor confidence, which has been used by Lemmon and Portniaguina (2006), and Schmeling (2009).
Based on this content, we can observe that there is an intuitive relationship between market sentiment and risk in financial markets. Indeed, some studies offer arguments for the existence of such an association. We can mention here the works of Charoenrook (2005), Yazdipour (2011), Yazdipour and Neace (2013), Andersen and Nowak (2013), and Fong (2013), where the optimism and pessimism of investors seems to be directly reflected by the behavior of decision making related to risk. An optimistic investor accepts riskier situations than a pessimistic one. However, all these studies are mainly focused on subjective aspects of market risk, such as risk aversion. In contrast to this, the current tendency in market risk management is the development of objective approaches. One fundamental aspect of proper risk management is its measurement. (For a detailed analysis of ways to measure risk, see Righi & Ceretta, 2014). Overestimating risk can lead to a reduction in gains, while underestimating it can result in catastrophic outcomes. Thus, it is crucial to have the best understanding possible of what kind of information affects the measurement of market risk beyond the usual information regarding prices and returns. The first thing to look at would be some market variables related to liquidity, as in Dias (2013). Nonetheless, as we have noted, we must consider behavioral issues, especially market sentiment. Therefore, there is a gap to be filled regarding the relationship between measures of market sentiment and risk.
Thus, we pose the following research question: What is the relationship between measures of market sentiment and risk? The main objective of this article is to estimate the association coefficients between measures of market sentiment and risk. We consider the measures for risk that are most often used in academic studies and industry. For market sentiment, we use a proxy based on investor activity on social networks. The details will be discussed in the next section. Thus, the present study contributes to the literature since, to the best of our knowledge, there is no such parallel. Studies about risk aversion in terms of decision making based on measures of risk, such as Ma and Wong (2010), Wachter and Mazzoni (2013), and Robert and Therond (2014), among others, are based on the paradigm of expected utility, which ignores behavioral aspects. The same can be said about studies of stochastic dominance and preferences, as is the case of Ben-Tal and Taboulle (2007), Ogryczak and Ruszczynski (2002), Bauerle and Muller (2006), Goovaerts, Kaas and Laeven (2010), Denuit, Dhaene, Goovaerts, Kaas and Laeven (2006). This paper is the first to include the behavioral aspect of measuring risk.
Method
As a proxy for market sentiment, we use the Psych Signal technology, which focuses on social networks by considering short message data to elaborate our indicator. This indicator directly reflects the emotions of individuals, bearing in mind that emotions are individual for psychology. The indicator has three main variables that seek to measure individuals' sentiments about the market's future. This indicator is consonant with the approach of Qiu and Welch (2006) and Sturm (2014), who measure market sentiment based on future investor expectations. More specifically, the first variable, called Bullish Intensity, analyzes market optimism according to a scale of 0 to 4, where 0 indicates the absence of this sentiment while 4 indicates the maximum presence of this sentiment. The second variable, called Bearish Intensity, suggests the presence of pessimism on the part of investors, and it is also measured on a scale of 0 to 4. Finally, the third variable, called Bull-Bear, measures the difference between the first two variables in order to give a measure of liquidity.
From an empirical point of view, we have chosen to investigate the U.S. (NASDAQ), German (DAX) and Chinese (SSEC) markets. We have selected these market indices because, in addition to their representativeness and volume in terms of the global economy and their continents, they are the only relevant market indices with the sentiment data that we require. Moreover, they represent distinct economic scenarios, and thus enrich our obtained results. We have collected daily data from quotation and sentiment variables for these markets, considering all information periods that were available through the Psych Signal technology at the time this study was prepared. This paper utilizes data from December 1, 2010 to August 27, 2015. This is the largest possible sample currently available to analyze these three markets.
Turning to the discussion of measures of risk, the risk of financial positions has been more scientifically addressed, ever since the notable work of Markowitz (1952). The use of variability measures, such as standard deviation, became common. Critical events began to be examined by using quantiles, such as Value at Risk (VaR). Duffie and Pan (1997) and Jorion (2007) have examined VaR in their studies. Despite the extensive practical use of VaR, the indiscriminate use of VaR began to be highly criticized because it is not a convex measure, as shown by Artzner, Delbaen, Eber and Heath (1999), which implies that the risk of a diversified position can be greater than the sum of individual risks. Thus, the expected value of losses that exceeds VaR, known as the Expected Shortfall (ES) proposed by Acerbi and Tasche (2002), Rockafellar and Uryasev (2002) and Pflug (2000), was defended as a potential risk measure. However, the variability concept, which is one of the pillars of the...
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