Value Functions for Prospect Theory Investors: An Empirical Evaluation for US Style Portfolios

2015 ◽  
Author(s):  
Spyros I. Spyrou ◽  
Evanthia Zervoudi
Keyword(s):  
Prospect Theory ◽  
Empirical Evaluation ◽  
Value Functions

Evaluating the Role of Three Basic Factors of Prospect Theory in Decision Making

2017 ◽  
Vol 6 (2) ◽  
pp. 1-22
Author(s):  
Evanthia K. Zervoudi
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Loss Aversion ◽  
Functional Form ◽  
Empirical Evaluation ◽  
Value Functions ◽  
Optimal Solutions ◽  
Negative Exponential Function ◽  
Value Functional

The main aim of this paper is to empirically evaluate the role of three significant factors of the Prospect Theory: the S-shaped value function, the loss aversion, and the distortion of probability, in decision making. In order to do this, a general behavioral reward-risk model is firstly setup and an empirical evaluation about the role of each of these factor, separately and in interaction, on the optimal solutions of the problem follows. For the analysis, well known US equity portfolios consisting by stocks listed in NYSE, AMEX, and NASDAQ formed on investment style are employed. The findings indicate that agents differentiate their behavior according to their type of preferences and their loss aversion level but they seem to always prefer high positively skewed assets such as small and value stocks. The attractiveness of positively skewed assets is re-enforced when probability distortion is introduced in the model. The introduction of probability distortion also affects the optimal perspective values of the problem increasing significantly their magnitude. After that, results show that as loss aversion increases agents tend to follow more conservative strategies, with and without probability distortion, while the value functional form has also its role in the model; bounded value functions as the negative exponential function drives agents to more conservative behaviors while unbounded value functions as the piecewise power function give the incentive to agents to undertake great risks and follow more aggressive strategies. The examination of the interaction of these factors indicate that the combination of an unbounded value functional form with a large loss aversion index may reduce agents' aggressiveness and limit (but not alter) the value functional form effect on optimal solutions.

Evaluating the Role of Three Basic Factors of Prospect Theory in Decision Making

2020 ◽  
pp. 585-604
Author(s):  
Evanthia K. Zervoudi
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Loss Aversion ◽  
Functional Form ◽  
Empirical Evaluation ◽  
Value Functions ◽  
Optimal Solutions ◽  
Value Stocks ◽  
Value Functional

The main aim of this paper is to empirically evaluate the role of three significant factors of the Prospect Theory: the S-shaped value function, the loss aversion, and the distortion of probability, in decision making. In order to do this, a general behavioral reward-risk model is firstly setup and an empirical evaluation about the role of each of these factor, separately and in interaction, on the optimal solutions of the problem follows. For the analysis, well known US equity portfolios consisting by stocks listed in NYSE, AMEX, and NASDAQ formed on investment style are employed. The findings indicate that agents differentiate their behavior according to their type of preferences and their loss aversion level but they seem to always prefer high positively skewed assets such as small and value stocks. The attractiveness of positively skewed assets is re-enforced when probability distortion is introduced in the model. The introduction of probability distortion also affects the optimal perspective values of the problem increasing significantly their magnitude. After that, results show that as loss aversion increases agents tend to follow more conservative strategies, with and without probability distortion, while the value functional form has also its role in the model; bounded value functions as the negative exponential function drives agents to more conservative behaviors while unbounded value functions as the piecewise power function give the incentive to agents to undertake great risks and follow more aggressive strategies. The examination of the interaction of these factors indicate that the combination of an unbounded value functional form with a large loss aversion index may reduce agents' aggressiveness and limit (but not alter) the value functional form effect on optimal solutions.

scholarly journals You cannot accurately estimate an individual’s loss aversion using an accept-reject task

2020 ◽  
Author(s):  
Lukasz Walasek ◽  
Neil Stewart
Keyword(s):  
Prospect Theory ◽  
Loss Aversion ◽  
Model Parameters ◽  
Probability Weighting ◽  
Value Functions ◽  
Trade Off ◽  
Simple Version ◽  
The Poor ◽  
Gain Loss ◽  
Linear Probability

Prospect theory's loss aversion is often measured in the accept-reject task, in which participants accept or reject the chance of playing a series of gambles. The gambles are two-branch 50/50 gambles with varying gain and loss amounts (e.g., 50% chance of winning $20 and a 50% chance of losing $10). Prospect theory quantifies loss aversion by scaling losses up by a parameter λ. Here we show that λ suffers from extremely poor parameter recoverability in the accept-reject task. λ cannot be reliably estimated even for a simple version of prospect theory with linear probability weighting and value functions. λ cannot be reliably estimated even in impractically large experiments with participants subject to thousands of choices. The poor recoverability is driven by a trade-off between λ and the other model parameters. However, a measure derived from these parameters is extremely well recovered—and corresponds to estimating the area of gain-loss space in which people accept gambles. This area is equivalent to the number of gambles accepted in a given choice set. That is, simply counting accept decisions is extremely reliably recovered—but using prospect theory to make further use of exactly which gambles were accepted and which were rejected does not work.

scholarly journals Segregation and Integration: A Study of the Behaviors of Investors with Extended Value Functions

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Martin Egozcue ◽  
Wing-Keung Wong
Keyword(s):  
Prospect Theory ◽  
Mental Accounting ◽  
Analytical Theory ◽  
Value Functions ◽  
Multiple Outcomes ◽  
Extended Value ◽  
Hedonic Editing

This paper extends prospect theory, mental accounting, and the hedonic editing model by developing an analytical theory to explain the behavior of investors with extended value functions in segregating or integrating multiple outcomes when evaluating mental accounting.

An Empirical Study on Value Functions of Stock Markets

2011 ◽  
Vol 204-210 ◽  
pp. 899-906
Author(s):  
Feng Hua Wen ◽  
Gui Tian Rao ◽  
Xiao Guang Yang
Keyword(s):  
Prospect Theory ◽  
Stock Markets ◽  
Subjective Experience ◽  
Value Function ◽  
Empirical Studies ◽  
Value Functions ◽  
A Value ◽  
Egarch Model ◽  
The Value Function ◽  
Psychological Experiments

As a core component of the prospect theory, a value function is employed to characterize the subjective experience of a decision-maker’s gain or loss. Previous empirical studies of the prospect theory were largely carried out through psychological experiments on individual decision-makers. In this paper, taking the whole stock market as an entity, we use the flow of information extracted by EGARCH Model as the proxy variable of change in wealth, and then use a two-stage power function as the representation of the value function to study the daily return data from the stock markets of 10 countries or regions. Empirical results show that the value functions of all the 10 stock markets present the shape of inverse-S, instead of the S-Shape of the value function generated by most psychological experiments on individuals.

ESTIMATING PROSPECT THEORY'S DECISION WEIGHTS WITH STOCHASTIC DOMINANCE: THE SMALL PROBABILITY CASE

2012 ◽  
Vol 07 (02) ◽  
pp. 1250006
Author(s):  
HAIM LEVY ◽  
MICHAL ORKAN
Keyword(s):  
Prospect Theory ◽  
Stochastic Dominance ◽  
Cumulative Prospect Theory ◽  
The Other ◽  
Value Functions ◽  
Small Probability ◽  
Point Estimates ◽  
Decision Weights ◽  
First Time ◽  
Small Probabilities

When one prospect is certain and the other uncertain, Cumulative Prospect Theory employs the certainty equivalent methodology to estimate Decision Weights (DW). However, DW may be different with two uncertain prospects. In this study, we neutralize the "certainty effect" and propose Stochastic Dominance (SD) to estimate DW for the first time with small probabilities, which is the raison d'être of the employment of DW. Using SD we provide ranges, rather than point estimates, of DW parameters that are consistent with all possible S-shape value functions. Comparing CE and SD implied DW, we find that DW are situation dependent: DW derived with one certain prospect are much different than those derived with two uncertain prospects.

scholarly journals The dynamics of change in decision making under risk

Psihologija ◽  
2007 ◽  
Vol 40 (1) ◽  
pp. 147-164 ◽  
Author(s):  
Aleksandar Milicevic ◽  
Dubravka Pavlicic ◽  
Aleksandar Kostic
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Expected Utility ◽  
Empirical Evaluation ◽  
Expected Value ◽  
Decision Making Under Risk ◽  
Outcome Probability ◽  
Made In ◽  
The Way

The goal of this study was to investigate the dynamics of decision making under risk. In three experiments this dynamics have been explored with respect to probability of outcome and with respect to frame, i.e. the way the outcomes of the alternatives have been specified. The process of decision making was explored within a framework of expected utility and Prospect theory. The outcomes of alternatives as well as their probabilities were quantitatively specified (so that the expected value of a risk alternative was equal to the value of a non-risk alternative). The results of experiments indicate that the attitude towards risk (risk-proneness vs. risk-averseness) depends on the outcome probability and the way the outcomes were specified (i.e. positive/negative frame). It was also demonstrated that content strongly affects the choices made in decision making. This outcome is somewhat unexpected and requires additional empirical evaluation.

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