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We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

The authors use a reduced form state-dependent labor participation decision model to illustrate that parameter stability is achieved only if a model properly takes account the observed sample heterogeneity and unobserved sample heterogeneity provided (external) conditions of a model stay constant. Our analysis of the dynamic response path to a health shock using Australian HILDA panel data from 2002 to 2009 shows that experiencing an event by itself can only have a temporary effects. The long-run equilibrium condition is independent of initial conditions or shocks that do not last. In other words, if experiencing an event does not lead to changes in the response parameters such as the real business cycle (Kydland & Prescott, 1977, 1982) or dynamic stochastic general equilibrium model (DSGE, e.g., Sbordone et al., 2010) assumed, policy change may only change the short-run response path. There is no long-term impact for a policy change. On the other hand, if a policy change leads to changes in the decision rules (e.g., the recent US–China trade friction) as the Lucas critique (1976) implies, then there is no other way to evaluate the impact of a policy except to explicitly model how agents respond to the policy change.

The purpose of this paper is to test whether underpricing can serve as a signal of quality in a financing-investment environment and to compare it under the two institutions for financing offers that are commonly observed in corporate financial markets: take-it-or-leave-it offer (TLO) and the competitive bidding offer (CBO).

The research paper uses experimental economics methodology and laboratory experiments to investigate the research question.

The results suggest that underpricing can serve as a signal of quality but not sustainable as a repeat strategy. Over time, the high-quality firms converge to a pooling strategy rather than bearing the cost of signaling. Additionally, underpricing is lower under CBO than under TLO institution due to competitive bidding. Signaling under CBO institution may be less salient due to possible mimicking by the low-quality firms.

This paper presents a first experimental investigation of the underpricing-signaling hypothesis in a financing-investment environment under asymmetric information. The choice of institution in a financing environment produces qualitatively and strategically different behavior among firms and investors.

The purpose of this paper is to introduce the upcoming symposium on experimental macroeconomics in the November issue.

Experimental, survey of articles in the symposium.

The paper describes how experiments can be used in macroeconomics.

The paper discusses the rationale for using behavioral experiments in macroeconomics, and summarizes the papers in the symposium.

The author proposes an update to the mean variance (MV) framework that replaces a constant risk aversion parameter using a dynamic risk aversion indicator. The contribution to the literature is made through making the static risk aversion parameter operational using an indicator of market sentiment. Results suggest that Sharpe ratios improve when the author replaces the traditional risk aversion parameter with a dynamic sentiment indicator from the behavioral finance literature when allocating between a risky portfolio and a risk-free asset. However, results are mixed when using the behavioral framework to allocate between two risky assets.

The author includes a dynamic risk aversion parameter in the mean variance framework and back test using the traditional and updated behavioral mean variance (BMV) framework to see which framework leads to better performance.

The author finds that the behavioral framework provides superior performance when allocating between a risky and risk-free asset; however, it under performs when allocating between risky assets.

The research is based on back testing; therefore, it cannot be concluded that this strategy will perform well in real-time circumstances.

Portfolio managers may use this strategy to optimize the allocation between a risky portfolio and a risk-free asset.

An improved allocation between risk-free and risky assets that could lead to less leverage in the market.

The study is the first to use such a sentiment indicator in the traditional MV framework and show the math.

Purpose — This study proposes an optimal survey design method for multi-day and multi-period panels that maximizes the statistical power of the parameter of interest under the conditions that non-linear changes in response to a policy intervention over time can be expected.

Design/methodology/approach — The proposed method addresses balances among sample size, survey duration for each wave and frequency of observation. Higher-order polynomial changes in the parameter are also addressed, allowing us to calculate optimal sampling designs for non-linear changes in response to a given policy intervention.

Findings — One of the most important findings is that variation structure in the behaviour of interest strongly influences how surveys are designed to maximize statistical power, while the type of policy to be evaluated does not influence it so much. Empirical results done by using German Mobility Panel data indicate that not only are more data collection waves needed, but longer multi-day periods of behavioural observations per wave are needed as well, with the increase in the non-linearity of the changes in response to a policy intervention.

Originality/value — This study extends previous studies on sampling designs for travel diary survey by dealing with statistical relations between sample size, survey duration for each wave, and frequency of observation, and provides the numerical and empirical results to show how the proposed method works.

Prospect theory is now widely accepted as the dominant model of choice under risk, but has not been fully incorporated into applied research because of uncertainty about how to include population-level parameter estimates. The purpose of this paper is to characterize heterogeneity across people to lay a foundation for future applied research.

The paper uses elicitation data from field experiments in Vietnam to fit a finite Gaussian mixture model using the expectation maximization algorithm. Applied results are simulated for investment allocations under myopic loss aversion.

The authors find that about 20 percent of the sample is classified as extremely loss averse, while the rest of the population is only mildly loss averse. This implies a bimodal distribution of loss aversion in the population.

The data set is only moderately sized: 181 subjects. Future research will be needed to extend these results out of sample, and to other regions.

This paper provides empirical evidence that heterogeneity matters in prospect theory modeling. It highlights how policy makers might be misled by assuming that average prospect theory parameters are typical within the population.