Prospect Theory Model Research Articles

The Impact of Prospect Theory on Asset Forecasting: A Comparative Study Based on Random Forest and Linear Regression

This study focuses on the application of predictive models and prospect theory in the field of machine learning to predict financial asset prices. Here, the linear regression and random forest algorithms were employed to forecast the price values and fluctuations of two different financial assets: the S&P 500 index and Bitcoin prices. Meanwhile, this study compared the performance of different financial asset datasets before and after the introduction of prospect theory models. Through calculation and comparative analysis, this article provides an in-depth discussion on the predictive performance of regression models and classification models. The research results indicate that in most cases, simple linear regression models have high prediction accuracy. Meanwhile, this paper also found that introducing prospect theory can effectively improve the accuracy of prediction models for specific financial assets. This result has a positive impact on the financial industry, helping to optimize risk management and investment decision-making, improving efficiency at the market level and promoting information transparency, while also promoting the development of emerging financial formats in financial innovation

Reward Sensitivity and Noise Contribute to Negative Affective Bias: A Learning Signal Detection Theory Approach in Decision-Making.

In patients with mood disorders, negative affective biases - systematically prioritising and interpreting information negatively - are common. A translational cognitive task testing this bias has shown that depressed patients have a reduced preference for a high reward under ambiguous decision-making conditions. The precise mechanisms underscoring this bias are, however, not yet understood. We therefore developed a set of measures to probe the underlying source of the behavioural bias by testing its relationship to a participant's reward sensitivity, value sensitivity and reward learning rate. One-hundred-forty-eight participants completed three online behavioural tasks: the original ambiguous-cue decision-making task probing negative affective bias, a probabilistic reward learning task probing reward sensitivity and reward learning rate, and a gambling task probing value sensitivity. We modelled the learning task through a dynamic signal detection theory model and the gambling task through an expectation-maximisation prospect theory model. Reward sensitivity from the probabilistic reward task (β = 0.131, p = 0.024) and setting noise from the probabilistic reward task (β = -0.187, p = 0.028) both predicted the affective bias score in a logistic regression. Increased negative affective bias, at least on this specific task, may therefore be driven in part by a combination of reduced sensitivity to rewards and more variable responses.

Behavioral biases of cryptocurrency investors: a prospect theory model to explain cryptocurrency returns

PurposeThe study aims to test prospect theory (PT) predictions in the cryptocurrency (CC) market. It proposes a new asset pricing model that explores the potential of prospect theory value (PTV) as a significant predictor of CC returns.Design/methodology/approachThe study comprehensively analyses a large sample set of 1,629 CCs, representing more than 95% of the CC market. The study uses a portfolio analysis approach, employing univariate and bivariate sorting techniques with equal-weighted and value-weighted portfolios. The study also employs ordinary least squares (OLS) regression, panel data methods and quantile regression (QR) to estimate the models.FindingsThis study demonstrates an average inverse relationship between PTV and CC returns. However, this relationship exhibits asymmetry across different quantiles, indicating that investor reactions vary based on market conditions. Moreover, PTV provides more robust predictions for smaller CCs characterized by high volatility and illiquidity. Notably, the findings highlight the dominant role of the probability weighting (PW) component in PT for predicting CC behaviors, suggesting a preference for lottery-like characteristics among CC investors.Originality/valueThe study is one of the early studies on CC price dynamics from the PT perspective. The study is the first to apply a QR approach to analyze the cross-section of CCs using a PT-based asset pricing model. The results shed light on CC investors' decision-making processes and risk perception, offering valuable insights to regulators, policymakers and market participants. From a practical perspective, a trading strategy centered around the PTV effect can be implemented.

Optimal Choice between Defined Contribution and Cash Balance Pension Schemes: Balancing Interests of Employers and Workers

In the context of pension plans, the employer and the worker have distinct interests and face different risks. The worker seeks higher retirement benefits, while the employer aims to minimize the cost of fulfilling his obligations. To address these diverse needs, the defined contribution plan managed with participating life insurance (DC-PL) and the cash balance plan managed with unit-linked insurance (CB-UL) serve as suitable choices. The multi-criteria analysis is conducted using the cumulative prospect theory model to measure the utility of the parties involved toward a mixed product combining these two pension plans. By assigning weights to risk measures and maximizing utilities, the paper employs both additive utility and Nash equilibrium approaches. The results reveal that the CB-UL plan aligns with employers’ interests, offering potential financial gains, while the DC-PL plan attracts workers due to its profit-sharing aspect. Significantly, when equal importance is given to both parties, the CB-UL plan emerges as the prevailing choice. This study contributes to the understanding of pension plan design and decision-making dynamics between employers and workers, providing valuable insights for achieving a balance between retirement benefits and cost management.

Explaining the description-experience gap in risky decision-making: learning and memory retention during experience as causal mechanisms.

When making decisions based on probabilistic outcomes, people guide their behavior using knowledge gathered through both indirect descriptions and direct experience. Paradoxically, how people obtain information significantly impacts apparent preferences. A ubiquitous example is the description-experience gap: individuals seemingly overweight low probability events when probabilities are described yet underweight them when probabilities must be experienced firsthand. A leading explanation for this fundamental gap in decision-making is that probabilities are weighted differently when learned through description relative to experience, yet a formal theoretical account of the mechanism responsible for such weighting differences remains elusive. We demonstrate how various learning and memory retention models incorporating neuroscientifically motivated learning mechanisms can explain why probability weighting and valuation parameters often are found to vary across description and experience. In a simulation study, we show how learning through experience can lead to systematically biased estimates of probability weighting when using a traditional cumulative prospect theory model. We then use hierarchical Bayesian modeling and Bayesian model comparison to show how various learning and memory retention models capture participants' behavior over and above changes in outcome valuation and probability weighting, accounting for description and experience-based decisions in a within-subject experiment. We conclude with a discussion of how substantive models of psychological processes can lead to insights that heuristic statistical models fail to capture.

Dynamic prospect theory: Two core decision theories coexist in the gambling behavior of monkeys and humans.

Research in the multidisciplinary field of neuroeconomics has mainly been driven by two influential theories regarding human economic choice: prospect theory, which describes decision-making under risk, and reinforcement learning theory, which describes learning for decision-making. We hypothesized that these two distinct theories guide decision-making in a comprehensive manner. Here, we propose and test a decision-making theory under uncertainty that combines these highly influential theories. Collecting many gambling decisions from laboratory monkeys allowed for reliable testing of our model and revealed a systematic violation of prospect theory's assumption that probability weighting is static. Using the same experimental paradigm in humans, substantial similarities between these species were uncovered by various econometric analyses of our dynamic prospect theory model, which incorporates decision-by-decision learning dynamics of prediction errors into static prospect theory. Our model provides a unified theoretical framework for exploring a neurobiological model of economic choice in human and nonhuman primates.

Complex economic decisions from simple neurocognitive processes: the role of interactive attention.

Neurocognitive theories of value-based choice propose that people additively accumulate choice attributes when making decisions. These theories cannot explain the emergence of complex multiplicative preferences such as those assumed by prospect theory and other economic models. We investigate an interactive attention mechanism, according to which attention to attributes (like payoffs) depends on other attributes (like probabilities) attended to previously. We formalize this mechanism using a Markov attention model combined with an accumulator decision process, and test our model on eye-tracking and mouse-tracking data in risky choice. Our tests show that interactive attention is necessary to make good choices, that most participants display interactive attention and that allowing for interactive attention in accumulation-based decision models improves their predictions. By equipping established decision models with sophisticated attentional dynamics, we extend these models to describe complex economic choice, and in the process, we unify two prominent theoretical approaches to studying value-based decision making.

Reliability of Decision-Making and Reinforcement Learning Computational Parameters.

Computational models can offer mechanistic insight into cognition and therefore have the potential to transform our understanding of psychiatric disorders and their treatment. For translational efforts to be successful, it is imperative that computational measures capture individual characteristics reliably. Here we examine the reliability of reinforcement learning and economic models derived from two commonly used tasks. Healthy individuals (N = 50) completed a restless four-armed bandit and a calibrated gambling task twice, two weeks apart. Reward and punishment learning rates from the reinforcement learning model showed good reliability and reward and punishment sensitivity from the same model had fair reliability; while risk aversion and loss aversion parameters from a prospect theory model exhibited good and excellent reliability, respectively. Both models were further able to predict future behaviour above chance within individuals. This prediction was better when based on participants' own model parameters than other participants' parameter estimates. These results suggest that reinforcement learning, and particularly prospect theory parameters, as derived from a restless four-armed bandit and a calibrated gambling task, can be measured reliably to assess learning and decision-making mechanisms. Overall, these findings indicate the translational potential of clinically-relevant computational parameters for precision psychiatry.

Risk acceptance and offensive war: The case of Russia under the Putin regime

ABSTRACT Russia’s decision to invade Ukraine in early 2022 was seemingly driven by an unprecedented willingness to accept risks for the Russian regime, state, and society. Developing a generalizable framework, this article analyzes the development of Russian risk acceptance in offensive war initiation. Drawing on policy documents, speeches, expert literature, and various interviews with Russian, Ukrainian, and Western policymakers, the article finds that risk acceptance has continuously risen since the mid-2000s, although the 2022 invasion still evinces some risk aversion. These results are robust when accounting for miscalculation and caution against attributing the 2022 invasion solely to short-term and leader-centric factors. They also provide cues for understanding the crisis behavior of Russia and other major powers, corroborate prospect theory models on cognitive biases in elite decision-making, and indicate the need to revise the theoretical assumption that risk acceptance is an empirically rare and drastic aberration from a risk-neutral or risk-averse normality.

Risk-Based Portfolio Modeling: Kahneman-Tversky Approach

The risk-based portfolio selection problem with investors’ behavioral risk aversion bias under uncertainty is monitored. The main results of this research are developed heuristic approaches for the prospect theory model proposed by Kahneman and Tversky in 1979 as well as an empirical numerical analysis of this model with real data. The main purpose is to impose behavioral features of prospect theory to minimize the risk with a certain level of income to the portfolio selection problem. In this research, the real data of TSE is employed for computational results with regard to the prospect theory model with several stocks as risky assets. In order to investigate empirically the performance of the behaviourally based model, different portfolios are selected with different operating sectors. The aggressive behavior in terms of returns of the prospect theory model with the specific risk has the same output as returns. On the other hand, the certain level of returns as constrained shows minimized risk with the implementation of a behavioral approach in comparison to the conventional approach which is the Markowitz model. The performance of the two behavioral and traditional models are compared and lower risks are obtained by a behavioral approach which shows the optimal portfolio selection and might be used by investors as their investment strategy.

Leveraging behavioural economics in smart nudge design through data-driven prospect-theoretic modelling and context-aware intelligent reasoning: application to smart tip nudging

Behavioural economics and nudge theory have attracted much attention for engineering design in a broader context of sociotechnical systems, which provides new perspectives for engineering design when systems have interactions with human. This paper deals with service system design with a particular application to smart tip nudging. The conventional tip nudging approach that uses a set of default tip options lacks context-awareness and sometimes suggests inappropriate tip options. To address this problem, and to also increase tip revenues potentially, this paper proposes a smart tip nudging mechanism based on prospect theoretic modelling of customer tipping behaviours. The model proposes to use joint prospect values of both customers and service providers, which mimics the trade-off that customers make between their own economic gains and losses and the service provided by service providers. Meanwhile, this study provides a data-driven approach to parameter estimation of the prospect theory model, and it enables tip nudging to be context-aware for tip option adjustment by using intelligent reasoning techniques. One field experiment is conducted to validate the proposed system.

A neuronal prospect theory model in the brain reward circuitry

Prospect theory, arguably the most prominent theory of choice, is an obvious candidate for neural valuation models. How the activity of individual neurons, a possible computational unit, obeys prospect theory remains unknown. Here, we show, with theoretical accuracy equivalent to that of human neuroimaging studies, that single-neuron activity in four core reward-related cortical and subcortical regions represents the subjective valuation of risky gambles in monkeys. The activity of individual neurons in monkeys passively viewing a lottery reflects the desirability of probabilistic rewards parameterized as a multiplicative combination of utility and probability weighting functions, as in the prospect theory framework. The diverse patterns of valuation signals were not localized but distributed throughout most parts of the reward circuitry. A network model aggregating these signals reconstructed the risk preferences and subjective probability weighting revealed by the animals’ choices. Thus, distributed neural coding explains the computation of subjective valuations under risk.

Deterrence or conformity?-An analysis of digital economy taxpayer compliance behavior based on prospect theory.

Taking the tax evasion of live streamers as a reference case, this article aims to establish a tax compliance prospect theory model, in which the reference point is determined by the taxpayer's perceived compliance level. The taxpayer compliance behavior is analyzed from two dimensions of the tax authority's audit deterrence and the taxpayer's conformity mentality. We find that both deterrence effect and conformity effect are existing in the taxpayer compliance behavior, which is affected by the tax authority's enforcement effort. When the enforcement effort is low, the deterrence effect and the conformity effect do not exist, and the taxpayer does not comply at all. When the enforcement effort is moderate, the deterrence effect and the conformity effect coexist and the taxpayer complies partially. Besides, the taxpayer compliance level will not exceed the perceived compliance level, and an increase in the perceived compliance level will prompt the taxpayer to comply more. When the enforcement effort is high, there is only a strong deterrence effect, and the taxpayer completely complies. We use the theoretical analysis result to explain the tax compliance behavior of live streamers and find that it is consistent with the predictions of the model. Finally, we propose some policy recommendations on tax administration in digital economy.

Quantum decision making in automatic driving

The behavior intention estimation and interaction between Autonomous Vehicles (AV) and human traffic participants are the key problems in Automatic Driving System (ADS). When the classical decision theory studies implicitly assume that the behavior of human traffic participants is completely rational. However, according to the booming quantum decision theory in recent years and actual traffic cases, traffic behaviors and other human behaviors are often irrational and violate the assumptions of classical cognitive and decision theory. This paper explores the decision-making problem in the two-car game scene based on quantum decision theory and compares it with the current mainstream method of studying irrational behavior-Cumulative Prospect Theory (CPT) model. The comparative analysis proved that the Quantum Game Theory (QGT) model can explain the separation effect which the classical probability model can’t reveal, and it has more advantages than CPT model in dealing with game scene decision-making. When two cars interact with each other, the QGT model can consider the interests of both sides from the perspective of the other car. Compared with the classical probability model and CPT model, the QGT is more realistic in the behavior decision-making of ADS.

Investor beliefs in the midst of a market crash and the COVID 19 pandemic: Survey and experimental evidence.

In March of 2020, the stock market fell 34%, due to the COVID-19 pandemic. How do market crashes affect the way investors form beliefs regarding future market returns? To investigate, we surveyed two mTurk panels during the March 2020 meltdown. The first survey was completed after the S&P 500 fell 9.5% on March 12, and the second survey was distributed the next day on March 13 after S&P 500 rose by 9.3%. In the midst of extreme market volatility and widespread media reporting of investor panic, most participants reported that beliefs were formed thoughtfully and in line with traditional financial advice. We next report the results of a controlled laboratory experiment where participants provided incentivized forecasts and allocated money between a risky and a riskless asset for 56 periods, with one group experiencing a severe market crash. Comparing participant forecasts against six different classes of belief models, we find the best fitting models are a naive short memory Recency model, and a short memory Cumulative Prospect Theory model, along with an Adaptive Expectations model. The experimental results are consistent with the idea that recent returns encourage extrapolative beliefs. In contrast, the mTurk survey results convey that investors claim to be more thoughtful than short-term extrapolative models suggest. We conclude by suggesting that investors can both be thoughtful and reactionary depending upon the context of the investing environment. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

An inter-temporal CAPM based on First order Stochastic Dominance

The inter-temporal Capital Asset Pricing Model (CAPM) assumes that investors are risk-averse. However, there is a very large body of empirical and experimental evidence documenting that many investors are not globally risk-averse: Prospect Theory and aspiration-level models are two well-known examples of this literature. This paper employs Stochastic Dominance criteria to generalize the inter-temporal CAPM for all investors with increasing utility functions. Another advantage of the proposed approach is its simplicity: it does not require dynamic programming, and it allows for ambiguous investment horizons.

DO shifts in regimes impact the disposition effect implied by prospect theory models?

ABSTRACT We study how market regimes can influence investors’ behaviour under the framework of prospect theory. Two market regimes (bull and bear) are considered, which affect our investors’ reference points and their loss-averse behaviours. Using both theory and empirical evidence, the paper shows the disposition effect in a bull market is stronger than that in a bear market. Investors learn from both domains (gain and loss) in a bear market and more from the gain domain in a bull market. These results also provide an explanation for asymmetric learning regarding the disposition effect.

Currency returns and downside risk: Debt, volatility, and the gap from benchmark values

The paper considers competing portfolio-balance specifications of currency returns, including one based on expected utility theory and another on prospect theory. The prospect theory specification relates downside risk to the gap between the exchange rate and its benchmark value. The empirical analysis uses survey data on exchange rate expectations to test directly the models’ predictions concerning ex ante excess returns. It also relies on the cointegrated VAR framework, which is well suited for testing competing models and dealing with unit roots. Like earlier studies, we find little support for the expected utility theory model in three major currency markets. By contrast, the prospect theory model’s predictions are largely borne out in the data, including those about sign reversals. We find the strongest support for a hybrid model that incorporates the risk factors of both models.

PENGARUH FRAMING, URUTAN BUKTI AUDIT DAN SKEPTISME AUDITOR TERHADAP AUDIT JUDGMENT DENGAN KOMPLEKSITAS TUGAS SEBAGAI VARIABEL MODERASI

This study aims to examine the effect of framing, sequence of audit evidence and auditor skepticism on audit judgment with task complexity as a moderating variable. The theory used to test this research is prospect theory and adjustment model theory. The data used are primary data with the research method used, namely a questionnaire. The nature of this research is a quantitative study because the research conducted to test the proposed hypothesis is to test the effect of the independent variable on the dependent variable. The results of this study indicate all variables have a positive effect on Audit Judgment.
 
 Keywords: Framing; Audit Evidence Sequence; Auditor Skepticism; Audit Judgment and Task Complexity

CPT‐TODIM method for bipolar fuzzy multi‐attribute group decision making and its application to network security service provider selection

Network security is a very important issue in recent years. For many new or traditional enterprises, utilizing internet to store and analyze data has become an indivisible part of their production and operation. But many enterprises do not have enough ability to ensure the security of information, so the choice of the optimal network security service provider has become a practical problem faced by many ordinary companies. In this paper, we establish bipolar fuzzy interactive multi-criteria decision-making (TODIM) based on the cumulative prospect theory (CPT-TODIM) model for dealing with the problem of multi-attribute group decision making, and this special model is applied to the field of network security about the issue of network security service provider selection. The second and third parts of this paper introduce the knowledge of bipolar fuzzy and the initial CPT-TODIM method. In the fourth part, we discuss the bipolar fuzzy CPT-TODIM model in detail. In the last two parts of this article, the method proposed in this paper is applied to the field of network security provider selection and compared with other methods. The results show that bipolar fuzzy CPT-TODIM method with unique advantages in some aspects is reasonable and effective, and can be applied to more uncertain decision fields. It is hoped that this method can help enterprises make better decisions.