Nonlinear Problems: Mathematical Modeling, Analyzing, and Computing for Finance
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Jiliang Sheng, Jian Wang, Jun Yang, "Regret Theory and Equilibrium Asset Prices", Mathematical Problems in Engineering, vol. 2014, Article ID 912652, 7 pages, 2014. https://doi.org/10.1155/2014/912652
Regret Theory and Equilibrium Asset Prices
Abstract
Regret theory is a behavioral approach to decision making under uncertainty. In this paper we assume that there are two representative investors in a frictionless market, a representative active investor who selects his optimal portfolio based on regret theory and a representative passive investor who invests only in the benchmark portfolio. In a partial equilibrium setting, the objective of the representative active investor is modeled as minimization of the regret about final wealth relative to the benchmark portfolio. In equilibrium this optimal strategy gives rise to a behavioral asset priciting model. We show that the market beta and the benchmark beta that is related to the investor’s regret are the determinants of equilibrium asset prices. We also extend our model to a market with multibenchmark portfolios. Empirical tests using stock price data from Shanghai Stock Exchange show strong support to the asset pricing model based on regret theory.
1. Introduction
The traditional asset pricing models that assume investors are homogeneous cannot explain many anomalies in the financial markets such as the equity premium puzzle [1] and the riskfree rate puzzle [2]. They cannot depict the complex behaviors and ignore the diversification of psychology of different investors.
Behavioral asset pricing theories have emerged and grown during the past two decades in part as a reaction to the phenomena described above. Based on the behavioral theories such as Tversky and Kahneman [3] and assuming investors have heterogeneous beliefs, several behavioral asset pricing models have been proposed that revise the investor’s utility from different perspectives. For example, Bakshi and Chen [4] care about the investor’s relative social status, Constantinides [5], Abel [6] and Campbell and Cochrane [7] consider the habit formation of investors, Barberis et al. [8] focus on investors’ loss aversion, while Abel [9] and Gollier [10] explore the envy between investors. Shefrin and Statman [11] derive a behavioral model based on the noise trading theory. In their model, there are two kinds of traders, information traders and noise traders, who interact and affect asset prices. Many of these studies assume that all investors are the same and do not consider the actual investing process of different types of investors. In this study we assume that investors are heterogeneous: there are two kinds of representative investors in a frictionless market, a representative passive investor and a representative active investor. The representative passive investor invests only in the benchmark and the representative active investor selects his own optimal portfolio based on the regret theory.
Regret theory is developed by Bell [12] and Loomes and Sugden [13]. Regret aversion is a wellestablished psychological theory suggesting that people often have regrets when they see that their decisions turn out to be wrong even if they appeared correct with information available exante. The idea of regret extends naturally to finance by assuming that investors compare their returns with exogenous benchmarks. Clarke et al. [14] argue that investors optimize the tracking error due to regret aversion. Wagner [15] develops an asset selection model assuming the investor’s utility is based on the regret theory. The model is labeled as the meanvariancecovariance (EVC) criterion. Dodonova and Khoroshilov [16] present a theoretical model of asset pricing that analyses how the behavior of stock returns is affected by the presence of regretaverse investors in the market. Gollier and Salanié [17] assume that agents are subject to regret and show that regret reduces the equity premium when the macrorisk is positively skewed. In this study we examine the consequences of investors’ regret aversion on the optimal decisions under risk, the allocation of risk in the economy, and equilibrium asset prices.
Our paper complements and extends the extant literature of Brennan [18], Gómez and Zapatero [19], Cornell and Roll [20], Cuoco and Kaniel [21], and Brennan et al. [22]. In this paper the objective of the representative active investor is modeled as minimization of regret about final wealth relative to the benchmark portfolio in a partial equilibrium setting. Our research differs from the study of Dodonova and Khoroshilov [16] who focus on the impact on volatility and autocorrelation of stock returns and trading volumes.
The rest of the paper is organized as follows. In Section 2 we present our portfolio selection model based on the regret theory. Equilibrium asset prices are analyzed in Section 3. In Section 4 we extend the pricing model to a market with a riskless asset. We also extend the model to multibenchmark in Section 5. Section 6 offers some empirical tests of the asset pricing model. Concluding remarks and possible future research are collected in Section 7.
2. Portfolio Selection Based on Regret Theory
We assume that and are the portfolios of the investor and the benchmark, respectively. The utility of investor is , where and are the final wealth after one period. is twice continuously differentiable. Generally one can measure regret by the change in utility with respect to a change in the hypothetical final wealth . According to Bell [12] and Loomes and Sugden [13], we define regret as According to the classical setting, utility is assumed to be a strictly increasing and concave function of final wealth ; that is, With respect to regret , we assume that the utility function also obeys the following restriction: We assume that there are risky assets, no riskless asset (which will be introduced in Sections 4 and 5), and the return of the risky assets is . The representative investor selects his optimal portfolio using the regret theory. We assume that the utility function of the investor is quadratic. Expanding the utility function as per Taylor series, we get where and are constants because the benchmark portfolio is given exogenously. According to Wagner [15], to maximize the utility, the problem that the investor needs to solve is where is the weight vector of risky assets in the portfolio of investor, is the vector of expected returns, , is the weight vector of risky assets in benchmark portfolio, and and are the coefficients of absolute risk aversion and regret aversion of the representative investor, respectively. The Lagrange function of (5) is where is the Lagrange multiplier. According to the first order condition, we get Substituting for in the constraint condition from (7), we get where and are the expected return and variance of the global minimum variance portfolio of risky assets. We use the notation to represent the global minimum variance portfolio, and and are its expected return and variance. From (7) and (8), we get Equation (9) shows that in the investor’s optimal allocation the weight on the benchmark portfolio increases as his regret aversion increases.
3. Equilibrium Asset Prices
From now on we consider an economy with heterogeneous investors. There are two representative investors in a frictionless market, a representative passive investor and a representative active investor. We assume is the market portfolio, and is the weight vector of risky assets in the market portfolio. is the fraction of the representative passive investor who invests only in the benchmark, and is the fraction of representative active investor who selects his optimal portfolio based on the regret theory. When the market clears, we have Multiplying both sides by the covariance matrix and substituting (9) into (10) yield Premultiplying (11) by gives the variance of the market portfolio: Based on Lemma A.1 as in the Appendix, we have where and are the vectors of individual asset betas with respect to the market portfolio and the benchmark portfolio, respectively, and is the variance of the benchmark return.
Substituting into (13) gives the vector equation that describes the crosssectional relationship between betas and expected returns: where . The th entry in the system of (14) is where , , and is the beta for asset computed against portfolio .
Equation (15) is the asset pricing model based on regret theory that determines the equilibrium asset prices. From (15) we have the following observations.(1)In equilibrium two types of risk are priced in the market, the market risk and the benchmark risk. is the beta for the benchmark risk. By construction it is orthogonal to the market risk, which deals with the fact that the market portfolio and the benchmark portfolio are likely correlated. In this way the two risk factors are independent from each other. The benchmark beta can be positive or negative. Generally speaking is positive for assets that are more correlated with the market portfolio than with the benchmark.(2)When the benchmark is the same as the market portfolio, and , then (15) is similar to the traditional CAPM.(3)Everything else being equal and assuming , the more passive investors in the market (i.e., the greater ), the greater the impact of the benchmark risk (i.e., the greater ). This is because passive investors invest in the benchmark portfolio only.
4. Asset Pricing Model with Riskless Asset
We do not consider a riskless asset in Section 3. With the introduction of a riskless asset in the frictionless market as Section 2, the optimal portfolio selection problem of the investor is where is the return rate of the riskless asset. The first order condition now becomes When the market clears we obtain Similar to (14), we have The th entry in the system of (19) is Equation (20) is the asset pricing model based on regret theory when there is a riskless asset in the market. If the market portfolio is same as the benchmark, (20) is the traditional CAPM.
5. Pricing Model with MultiBenchmark
In Sections 3 and 4 there is only one benchmark in the market. In this section we investigate the situation when the investor’s wealth of portfolio is measured against two benchmarks and . This is common in the investment industry, for instance, when an investment manager is assessed against a market portfolio as well as an internal benchmark (Wang [23]). The utility function of investor is assumed to be a strictly increasing concave function of final wealth : We define investor’s regret to benchmark and as As the situation with just one benchmark in the market, with respect to regret and , we assume that the utility function obeys the following restrictions: Assuming the utility function is quadratic and expanding it in Taylor series, we obtain Benchmarks and are exogenously given, so , and are constants to investors in their portfolio selection problem.
5.1. Model without Riskless Asset
Similar to the method in Section 2, to maximize his utility, the problem that the active investor needs to solve is where is the weight vector of risky assets in the investor’s portfolio, is the vector of expect return, and are the weight vectors of risky assets in benchmark portfolios and , respectively, is the coefficient of absolute risk aversion, and and are the coefficients of regret aversion of the representative investor to benchmark portfolio and , respectively. The optimal portfolio selection of the investor is We assume that and are the factions of representative passive investors who only invest in benchmark portfolios and , respectively. Then is the faction of the representative active investor who selects his own optimal portfolio based on the regret theory. When the market clears, we get Similar to Section 3, the th entry in the system of (27) is where The structure of (28) is similar to that of (15), but it has one more risk factor. Besides the market risk, in equilibrium the expected return also depends on two benchmark risks, which, by design, are independent of the market risk.
5.2. Model with Riskless Asset
In Section 5.1 we do not consider a riskless asset, but now we assume there is a riskless asset in the frictionless market, and is its return. The optimal portfolio selection problem is The optimal portfolio of the investor is When the market clears we obtain Similar to Section 5.1, we have The th entry in the system of (33) is The structure of (34) is similar to that of (20) but with the inclusion of two benchmark risk factors that are independent of the market risk.
6. Empirical Tests
6.1. Data
We take the Shanghai Stock Exchange (SSE) 180 index as the benchmark portfolio. The SSE Stock Composite Index (CI) weekly return series is taken as the market portfolio return. For the locally riskfree asset, the weekly return series of the threemonth Treasury Bill is used. Our sample begins in January 2006 and extends through December 2012.
As for risky assets, we select the 150 stocks in the SSE 180 index without interruption from January 2006 to December 2012. We select this subsample of SSE 180 to avoid the possible price effect associated with changes in the composition of the index. Thus any abnormal return captured in our test cannot be explained by the assets being added or deleted from the benchmark index.
6.2. Methodology
To test the asset pricing model (20), we take a threestep approach as in Brennan et al. [22] and Gómez and Zapatero [19] in the spirit of Fama and MacBeth [24].
First, in order to eliminate the linear dependence of the market portfolio and the benchmark portfolio, we obtain the residual by means of where and denote the weekly return of the market portfolio and benchmark portfolio, respectively. The residual from regression (35), , represents the component of the benchmark that, by construction, is independent of the market portfolio.
Second, we estimate the betas of the market and benchmark portfolio according to where represents the benchmark beta that, by construction, is orthogonal to the market beta .
Finally, we run a crosssectional regression of stocks’ expected returns on the estimated betas as According to the regret theory, the benchmark risk should be priced in equilibrium stock prices. So we expect to be significant.
Following the methodology in Gómez and Zapatero [19], the 150 stocks in SSE 180 index are sorted into 10 portfolios according to their estimated market index beta. We summarize the empirical results of (37) in Table 1.

6.3. Empirical Results
As we can see in Table 1, is highly significant for all the portfolios, which supports the prediction of the asset pricing models based on regret theory. When the benchmark risk is taken into account, the market risk, as measured by , is no longer significant (with the exception of panel 1). This result is consistent with the findings in Chen et al. [25], Wen and Yang [26], Wu [27], and Morelli [28], who report that in Chinese stock markets the market risk is often not priced when other risks (e.g., size, value, liquidity, and skewness) are also considered.
Shanghai stock market displays some unique characteristics compared to stock markets in many developed countries. Among the over 900 stocks listed at SSE, the SSE 180 Index includes the top companies ranked by market capitalization and trading volume in all ten major industries. As industry leaders, the SSE 180 companies have experienced tremendous growth since the index was established in July 2002. According a report from SSE (http://www.sse.com.cn/market/sseindex/bluechips/introduction/), during the period 2006–2010 SSE 180 index experienced an average annual growth rate of 24.64%, higher than the annual return of 19.32% for the SSE Composite Index. At the year end of 2010, SSE 180 has an average P/E ratio of 18.23, compared to 21.61 for SSE CI. This risk and return profile of SSE 180 index is in contrast to that of stock market indexes in many developed economies. For instance, S&P 500 companies, also as industry leaders as SSE 180 companies, earn a slightly lower average return than the overall market performance (Brennan et al. [22]). This is because S&P 500 companies are usually large and relatively mature, therefore, are generally deemed as safe investments by investors. Moreover, their stocks have stable demand from index investors, who are willing to accept them in their portfolio despite their lower returns. Due to the relatively larger fraction of individual investors in SSE, its trading has displayed a somewhat high degree of speculative behaviors such as chasing new and small companies with the focus on shortterm returns (see Kong [29], a research report sponsored by SSE, and Huang et al. [30]). The empirical results in this study suggest that the benchmark risk is the primary determinant of stock returns at SSE, and this risk factor is independent of the market risk.
7. Conclusion
We assume that there are two investors, a representative passive investor and a representative active investor in a frictionless market. The representative passive investor invests in the benchmark portfolio only and the representative active investor selects his optimal portfolio based on the regret theory. We establish a behavioral asset pricing model when the market clears. The model suggests that the benchmark risk and the market risk are the determinants of capital asset equilibrium returns. The coefficients of absolute risk aversion and regret aversion of the representative investor can affect asset prices. We extend the asset pricing model to situations with a riskless asset and with multiple benchmarks. We test the model with stock price data from Shanghai Stock Exchange. The empirical results show strong support to the asset pricing model based on the regret theory.
Other equilibrium effects (such as on trading volume and price volatility) according to the regret theory are left for future research. It may also be of interest to investigate the impact of some risk constraint (e.g., VaR).
Appendix
Lemma A.1. Assuming is the vector of individual asset betas in portfolio ; that is, the th element of is for individual asset , we have .
Proof. we assume is the weight vector of risky assets in portfolio , the interest rate of risky asset is , and then .
The covariance of and is
The covariance matrix of portfolio multiplied by the vector of is
The th element of is
so , that is, is the vector of covariance between the portfolio and individual assets. From the assumptions in Lemma A.1, we have .
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
This study is supported by the National Natural Science Foundation of China (no. 71161013 and no. 71101024), Social Science Foundation of Ministry of Education of China (no. 10YJC630203), Fundamental Research Funds for the Central Universities in China (no. N110406008), China Postdoctoral Foundation (no. 2012M 510968), and Jiangxi Province (China) Undergraduate Institutions Young Faculty Development and Visiting Scholars Program.
References
 R. Mehra and E. C. Prescott, “The equity premium: a puzzle,” Journal of Monetary Economics, vol. 15, no. 2, pp. 145–161, 1985. View at: Publisher Site  Google Scholar
 P. Weil, “The equity premium puzzle and the riskfree rate puzzle,” Journal of Monetary Economics, vol. 24, no. 3, pp. 401–421, 1989. View at: Publisher Site  Google Scholar
 A. Tversky and D. Kahneman, “Advances in prospect theory: cumulative representation of uncertainty,” Journal of Risk and Uncertainty, vol. 5, no. 4, pp. 297–323, 1992. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 G. S. Bakshi and Z. Chen, “The spirit of capitalism and stockmarket prices,” The American Economic Review, vol. 86, no. 1, pp. 133–157, 1996. View at: Google Scholar
 G. Constantinides, “Habit formation: a resolution of the equity premium puzzle,” Journal of Political Economy, vol. 98, no. 4, pp. 519–543, 1990. View at: Publisher Site  Google Scholar
 A. B. Abel, “Asset prices under habit formation and catching up with the Joneses,” The American Economic Review, vol. 80, no. 2, pp. 38–42, 1990. View at: Google Scholar
 J. Y. Campbell and J. H. Cochrane, “By force of habit: a consumptionbased explanation of aggregate stock market behavior,” Journal of Political Economy, vol. 107, no. 2, pp. 205–251, 1999. View at: Publisher Site  Google Scholar
 N. Barberis, M. Huang, and T. Santos, “Prospect theory and asset prices,” Quarterly Journal of Economics, vol. 116, no. 1, pp. 1–53, 2001. View at: Publisher Site  Google Scholar  Zentralblatt MATH
 A. B. Abel, “Risk premia and term premia in general equilibrium,” Journal of Monetary Economics, vol. 43, no. 1, pp. 3–33, 1999. View at: Publisher Site  Google Scholar
 C. Gollier, “Equilibrium portfolios with heterogeneous consumption externalities,” Working Paper, University of Toulouse, 2003. View at: Google Scholar
 H. Shefrin and M. Statman, “Behavioral capital asset pricing theory,” Financial Management, vol. 22, no. 2, pp. 123–134, 1994. View at: Google Scholar
 D. E. Bell, “Regret in decision making under uncertainty,” Operation Research, vol. 30, no. 5, pp. 961–981, 1982. View at: Publisher Site  Google Scholar
 G. Loomes and R. Sugden, “Regret theory: an alternative theory of rational choice under uncertainty,” The Economic Journal, vol. 92, no. 368, pp. 805–824, 1982. View at: Publisher Site  Google Scholar
 R. Clarke, S. Krase, and M. Statman, “Tracking errors, regret, and tactical asset allocation,” The Journal of Portfolio Management, vol. 20, no. 3, pp. 16–24, 1994. View at: Publisher Site  Google Scholar
 N. Wagner, “On a model of portfolio selection with benchmark,” Journal of Asset Management, vol. 3, no. 1, pp. 55–65, 2002. View at: Publisher Site  Google Scholar
 A. Dodonova and Y. Khoroshilov, “Applications of regret theory to asset pricing,” Working Paper, Telfer School of Management, University of Ottawa, 2005. View at: Google Scholar
 C. Gollier and B. Salanié, “Individual decisions under risk, risk sharing and asset prices with regret,” Working Paper, University of Toulouse, 2006. View at: Google Scholar
 M. Brennan, “Agency and asset pricing,” Working Paper, University of California, Los Angeles, Calif, USA, 1993. View at: Google Scholar
 J.P. Gómez and F. Zapatero, “Asset pricing implications of benchmarking: a twofactor CAPM,” European Journal of Finance, vol. 9, no. 4, pp. 343–357, 2003. View at: Publisher Site  Google Scholar
 B. Cornell and R. Roll, “A delegatedagent assetpricing model,” Financial Analysts Journal, vol. 61, no. 1, pp. 57–69, 2005. View at: Publisher Site  Google Scholar
 D. Cuoco and R. Kaniel, “Equilibrium prices in the presence of delegated portfolio management,” Journal of Financial Economics, vol. 101, no. 2, pp. 264–296, 2011. View at: Publisher Site  Google Scholar
 M. J. Brennan, X. Cheng, and F. Li, “Agency and institutional investment,” European Financial Management, vol. 18, no. 1, pp. 1–27, 2012. View at: Publisher Site  Google Scholar
 M. Y. Wang, “Multiplebenchmark and multipleportfolio optimization,” Financial Analysts Journal, vol. 55, no. 1, pp. 63–72, 1999. View at: Publisher Site  Google Scholar
 E. Fama and J. MacBeth, “Risk, return, and equilibrium: empirical tests,” Journal of Political Economy, vol. 81, no. 3, pp. 607–636, 1973. View at: Publisher Site  Google Scholar
 L. Chen, S. Li, and J. Wang, “Liquidity, skewness and stock returns: evidence from Chinese stock market,” AsiaPacific Financial Markets, vol. 18, no. 4, pp. 405–427, 2011. View at: Publisher Site  Google Scholar
 F. Wen and X. Yang, “Skewness of return distribution and coefficient of risk premium,” Journal of Systems Science & Complexity, vol. 22, no. 3, pp. 360–371, 2009. View at: Publisher Site  Google Scholar  MathSciNet
 H. Wu, “The value and size effect—are there firmspecificrisks in China’s domestic stock markets?” International Journal of Economics and Finance, vol. 3, no. 3, pp. 26–37, 2011. View at: Google Scholar
 D. Morelli, “Security returns, beta, size, and booktomarket equity: evidence from the Shanghai Ashare market,” Review of Quantitative Finance and Accounting, vol. 38, no. 1, pp. 47–60, 2012. View at: Publisher Site  Google Scholar
 D. Kong, “A study on irrational behaviors of stock market investors,” 2013 (Chinese), http://www.sse.com.cn/researchpublications/jointresearch/c/c_20130305_3686460.pdf. View at: Google Scholar
 C. Huang, X. Gong, X. Chen, and F. Wen, “Measuring and forecasting volatility in Chinese stock market using HARCJM model,” Abstract and Applied Analysis, vol. 2013, Article ID 143194, 13 pages, 2013. View at: Publisher Site  Google Scholar  Zentralblatt MATH
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Copyright © 2014 Jiliang Sheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.