Cumulative Prospect Theory Investors Research Articles

Asset Allocation with Crypto: Application of Preferences for Positive Skewness

Bitcoin (BTC) returns exhibit pronounced positive skewness with a third central moment of approximately 150% per year. They are well characterized by a mixture of Normals distribution with one “normal” regime and a small probability of a “bliss” regime where the price appreciation is more than 100 times at the annual horizon. The large right-tail skew induces investors who prefer positive skewness to add significant BTC holdings to equity-bond portfolios. Even when BTC is forecasted to lose half its value in the normal regime, investors with power utility optimally add 3% allocations to BTC when the probability of the bliss regime is around 1%. Cumulative Prospect Theory investors are even more sensitive to positive skewness. They hold BTC allocations of about 3% when the probability of the bliss regime is 0.0006, and the mean of BTC in the normal regime corresponds to a loss of 90%.

Dynamic Money Doctors under Cumulative Prospect Theory

We investigate the interaction between investors and portfolio managers under cumulative prospect theory. We model trust in the manager and the relative anxiety about investing in a risky asset in an original way. Moreover, we study how trust and anxiety affect the manager’s fee and the portfolios of cumulative prospect theory investors. In contrast to previous work using the classical mean-variance preferences, there are two main novelties in our contribution. First, our research relies on cumulative prospect theory (CPT) rather than the classical mean-variance framework. Second, we focus on a dynamic portfolio selection. In other words, we formulate the optimal problem under multi-period setting. Besides, we attain an optimal portfolio choices in multi-period relying on the sub-game perfect investment strategies. Moreover, our research differs from traditional CPT work through an improved value function that accurately characterizes the reduction in anxiety suffered by the CPT investors from bearing risk when assisted by the portfolio managers’ help relative to when they lack such assistance.

Multi-period Investment Strategies with Transaction Costs Under Cumulative Prospect Theory

This paper focuses on optimal investment strategies under cumulative prospect theory (CPT). Considering transaction costs, we investigate CPT investors multi-period optimal portfolios. Our main contributions relative to previous work are expanding a single-period optimization problem to a multi-period optimization problem and investigating the impact of transaction costs on optimal portfolio selections. In a numerical analysis that applied original data on four stocks from the NASDAQ, we examine the effects of different risks on the optimal portfolio. Moreover, in contrast with the results without transaction costs, we come to conclusion that the optimal strategy with transaction costs is less sensitive to risk.

A new method of portfolio optimization under cumulative prospect theory

In this paper, the portfolio selection problem under Cumulative Prospect Theory (CPT) is investigated and a model of portfolio optimization is presented. This model is solved by coupling scenario generation techniques with a genetic algorithm. Moreover, an Adaptive Real-Coded Genetic Algorithm (ARCGA) is developed to find the optimal solution for the proposed model. Computational results show that the proposed method solves the portfolio selection model and that ARCGA is an effective and stable algorithm. We compare the portfolio choices of CPT investors based on various bootstrap techniques for scenario generation and empirically examine the effect of reference points on investment behavior.

One-period pricing strategy of ‘money doctors’ under cumulative prospect theory

We focus on the interaction between investors and portfolio managers, employing a cumulative prospect theory approach to the investor’s preferences. In an original way, we model trust in the manager and the relative anxiety about investing in a risky asset. Moreover, we investigate how trust and anxiety affect the manager’s fee and the portfolios of cumulative prospect theory investors. The novelty of our contribution relative to previous work is that we rely on cumulative prospect theory(CPT) rather than the classical mean-variance framework. Moreover, our research differs from traditional CPT work through an improved value function that accurately characterizes the reduction in anxiety suffered by the CPT investors from bearing risk when assisted by the portfolio managers’ help relative to when they lack such assistance. Our results differ in several respects from those obtained when using on classical preferences. First, the optimal fees are not symmetric. Specially, the dominant managers obtain higher fees than subordinate managers regardless of changes in risk of risky assets (a risky asset) and changes in the dispersion of trust in the population. Another difference is that these fees are not proportional to expected returns. In particular, the optimal fees increase nonlinearly as risk of risky assets (a risky asset) increases and the dispersion of trust in the population increases.

Optimal investment with transaction costs under cumulative prospect theory in discrete time

We study optimal investment problems under the framework of cumulative prospect theory (CPT). A CPT investor makes investment decisions in a single-period financial market with transaction costs. The objective is to seek the optimal investment strategy that maximizes the prospect value of the investor’s final wealth. We obtain the optimal investment strategy explicitly in two examples. An economic analysis is conducted to investigate the impact of the transaction costs and risk aversion on the optimal investment strategy.

Cumulative Prospect Theory, Aggregation, and Pricing

Cumulative Prospect Theory (CPT) has been used as a possible explanation of aggregate pricing anomalies like the equity premium puzzle. This paper shows that, unlike in expected utility models, a complete market is not sufficient to guarantee that the market portfolio is efficient and that the standard representative-agent analysis is valid. The separation or mutual fund theorems hold only under very restrictive conditions for CPT investors. Without them, aggregation breaks down, and assets are not necessarily priced as if there were one investor who behaved according to CPT. Under more limited conditions, the market portfolio can be efficient in a complete market with equally probable states. But in this case, individual CPT investors behave in the aggregate like a standard expected utility investor. Similarly, when faced with elliptically distributed assets, the capital asset pricing model (CAPM) holds for any combination of CPT investors and expected utility maximizers.

Cumulative Prospect Theory and the Variance Premium

Cumulative Prospect Theory (CPT) can explain the variance premium puzzle. We solve a simple equilibrium model with CPT investors and find that probability weighting plays a key role in generating a substantial variance premium, while loss aversion captures the equity premium. Using GMM on a sample of U.S. equity and index-option returns between 1996 and 2010, our estimate of the probability distortion parameter implies that real-world investors in option markets distort probabilities significantly, but less so than subjects in lab experiments. We also show that the CPT model prices the cross-section of out-of-the-money index options well. In a dynamic setting, probability weighting and time-varying equity return volatility combine to match the observed time-series pattern of the variance premium.

Cumulative Prospect Theory with Skewed Return Distribution

We investigate a one-period portfolio optimization problem of a cumulative prospect theory (CPT) investor with multiple risky assets and one risk-free asset. The returns of multiple risky assets follow multivariate generalized hyperbolic (GH) skewed t distribution. We obtain a three-fund separation result of two risky portfolios and risk-free asset. Furthermore, we reduce the high dimensional optimization problem to two 1-dimensional optimization problems and derive the optimal portfolio. We show that the optimal portfolio composition changes as some of investor-specific parameters change. It is observed that the consideration of skewness of stock return distribution has considerable impact on the distribution of CPT investor's wealth deviation, and leads to less total risky investment.

Cumulative Prospect Theory and the Variance Premium

Cumulative Prospect Theory (CPT) can explain the variance premium puzzle. We solve a simple equilibrium model with CPT investors and find that probability weighting plays a key role in generating a substantial variance premium, while loss aversion captures the equity premium. Using GMM on a sample of U.S. equity and index-option returns between 1996 and 2010, our estimate of the probability distortion parameter implies that real-world investors in option markets distort probabilities significantly, but less so than subjects in lab experiments. We also show that the CPT model prices the cross-section of out-of-the-money index options well. In a dynamic setting, probability weighting and time-varying equity return volatility combine to match the observed time-series pattern of the variance premium.

Multi-stock portfolio optimization under prospect theory

We study how a behavioral agent allocates her portfolio. We consider a cumulative prospect theory investor in a single period setting with one riskless bond and multiple risky stocks, which follow a multivariate elliptical distribution. Our main result is a two-fund separation between the riskless bond and a mean–variance-portfolio, up to an exogenous benchmark portfolio. The mean–variance-portfolio, which we derive explicitly, is the same for all agents. Individual risk preferences are mirrored only in the participation in this portfolio. This dependence is illustrated by considering empirical returns. Furthermore we solve ill-posed optimization problems by imposing a regulatory risk constraint. Finally we address specific parameterizations of the value function by studying power, linear, and exponential utility.

On the Optimal Type and Level of Guarantees for Prospect Theory Investors

It is well known that cumulative prospect theory (CPT) can explain a demand for guarantees in investment products. This observation motivates us to study what type of guarantee observed in real–world insurance markets is most attractive to CPT investors. We find that CPT investors favor a simple terminal wealth guarantee compared to more complex ratchet and cliquet guarantees. To obtain this result we determine the optimal specifications of these contracts to CPT investors by means of an extensive simulation study. Optimization further allows to study the desired guarantee level (which is determined endogeneously from preferences), and illustrates what elements of CPT drive the demand for guarantees. CPT investors either desire a guarantee level equal to their reference point or insurance against large losses only. Such a crash insurance can be explained by probability weighting alone, i.e., even in the absence of loss aversion.

Risk Tolerance, Marketing Information and Investment Decision Makings Under Loss Aversion: Theory and Evidence

This study models and examines how changes in marketing information affects the degree of investor’s risk aversion, and in turn, influences investor’s decision-makings process under uncertainty. Under the mixed assumptions, the theoretical evidence in this study indicates that cumulative prospect theory (CPT) investors have propensity to discipline their depreciated assets and to sell their appreciated assets. Further, I find that CPT investors have less incentive to sell their holdings with higher advertising than ones with lower advertising when facing a paper gain or a paper loss. The empirical evidence indicates that advertising can help funds stem cash outflows, and finds investors are less willing to sell high performing investments with high fund family advertising than investments with low fund family advertising, and are more reluctant to redeem losing mutual funds with high fund family advertising than funds with low fund family advertising. For loser funds, a possible explanation from this study is that advertising seems to re-enforce the efficacy of recent investor decisions and adjust their beliefs to confirm past decisions, thus lets investors have more incentive to continue holding losing funds. For winner funds, this study infers that advertising may signal product quality, increase consumer satisfaction, brand equity and consumer loyalty that lead investors satisfied with their past decisions to have a greater propensity to retain their winning investments.

Static portfolio choice under Cumulative Prospect Theory

We derive the optimal portfolio choice for an investor who behaves according to Cumulative Prospect Theory (CPT). The study is done in a one-period economy with one risk-free asset and one risky asset, and the reference point corresponds to the terminal wealth arising when the entire initial wealth is invested into the risk-free asset. When it exists, the optimal holding is a function of a generalized Omega measure of the distribution of the excess return on the risky asset over the risk-free rate. It conceptually resembles Merton’s optimal holding for a CRRA expected-utility maximizer. We derive some properties of the optimal holding and illustrate our results using a simple example where the excess return has a skew-normal distribution. In particular, we show how a CPT investor is highly sensitive to the skewness of the excess return on the risky asset. In the model we adopt, with a piecewise-power value function with different shape parameters, loss aversion might be violated for reasons that are now well-understood in the literature. Nevertheless, we argue that this violation is acceptable.