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.

scholarly journals Efficiency and Consistency in Group Decisions

2012 ◽  
Vol 4 (1) ◽  
pp. 77-88
Author(s):  
Tapan Biswas
Keyword(s):  
Value Function ◽  
Single Element ◽  
Value Functions ◽  
Group Decisions ◽  
Group Choice ◽  
Borda Rule ◽  
A Value ◽  
Normative Significance ◽  
The Value Function

The paper introduces a concept of “efficiency set" in the context of group decisions and analyses its properties. If the set contains a single element, then the Borda rule finds it. Otherwise, the group needs a value function to choose from the efficient alternatives. Two value functions, with considerations for the number of participants who are badly affected by the choice, have been discussed. It turns out that the consistency axiom of group choice imposes a constraint, on the form of the value function, with questionable normative significance.

scholarly journals The uniqueness of a value function for asymmetric isotope separation

1988 ◽  
Vol 7 (2) ◽  
pp. 83-84
Author(s):  
G. Geldenhuys
Keyword(s):  
Value Function ◽  
Isotope Separation ◽  
Uniqueness Problem ◽  
Value Functions ◽  
Uranium Enrichment ◽  
Separation Processes ◽  
A Value ◽  
Symmetric Separation ◽  
The Value Function

Value functions for isotope separation can be used to calculate the separative power of uranium enrichment cascades. The uniqueness of the value function for symmetric separation processes is well known. The uniqueness problem is discussed and solved for asymmetric processes.

scholarly journals Hill Climbing on Value Estimates for Search-control in Dyna

2019 ◽  
Author(s):  
Yangchen Pan ◽  
Hengshuai Yao ◽  
Amir-massoud Farahmand ◽  
Martha White
Keyword(s):  
Value Function ◽  
Gradient Algorithm ◽  
Hill Climbing ◽  
Value Functions ◽  
Current Estimate ◽  
Sampling Distributions ◽  
Current Value ◽  
Empirical Demonstration ◽  
Search Control ◽  
The Value Function

Dyna is an architecture for model based reinforcement learning (RL), where simulated experience from a model is used to update policies or value functions. A key component of Dyna is search control, the mechanism to generate the state and action from which the agent queries the model, which remains largely unexplored. In this work, we propose to generate such states by using the trajectory obtained from Hill Climbing (HC) the current estimate of the value function. This has the effect of propagating value from high value regions and of preemptively updating value estimates of the regions that the agent is likely to visit next. We derive a noisy projected natural gradient algorithm for hill climbing, and highlight a connection to Langevin dynamics. We provide an empirical demonstration on four classical domains that our algorithm, HC Dyna, can obtain significant sample efficiency improvements. We study the properties of different sampling distributions for search control, and find that there appears to be a benefit specifically from using the samples generated by climbing on current value estimates from low value to high value region.

scholarly journals Dynkin game under g-expectation in continuous time

2020 ◽  
Vol 9 (2) ◽  
pp. 459-470
Author(s):  
Helin Wu ◽  
Yong Ren ◽  
Feng Hu
Keyword(s):  
Differential Equation ◽  
Stochastic Differential Equation ◽  
Continuous Time ◽  
Value Function ◽  
Backward Stochastic Differential Equation ◽  
Saddle Points ◽  
Value Functions ◽  
Dynkin Game ◽  
The Value Function

Abstract In this paper, we investigate some kind of Dynkin game under g-expectation induced by backward stochastic differential equation (short for BSDE). The lower and upper value functions $$\underline{V}_t=ess\sup \nolimits _{\tau \in {\mathcal {T}_t}} ess\inf \nolimits _{\sigma \in {\mathcal {T}_t}}\mathcal {E}^g_t[R(\tau ,\sigma )]$$ V ̲ t = e s s sup τ ∈ T t e s s inf σ ∈ T t E t g [ R ( τ , σ ) ] and $$\overline{V}_t=ess\inf \nolimits _{\sigma \in {\mathcal {T}_t}} ess\sup \nolimits _{\tau \in {\mathcal {T}_t}}\mathcal {E}^g_t[R(\tau ,\sigma )]$$ V ¯ t = e s s inf σ ∈ T t e s s sup τ ∈ T t E t g [ R ( τ , σ ) ] are defined, respectively. Under some suitable assumptions, a pair of saddle points is obtained and the value function of Dynkin game $$V(t)=\underline{V}_t=\overline{V}_t$$ V ( t ) = V ̲ t = V ¯ t follows. Furthermore, we also consider the constrained case of Dynkin game.

scholarly journals A DECISION-MAKING FRAMEWORK BASED ON THE PROSPECT THEORY UNDER AN INTUITIONISTIC FUZZY ENVIRONMENT

2018 ◽  
Vol 24 (6) ◽  
pp. 2374-2396 ◽  
Author(s):  
Jing Gu ◽  
Zijian Wang ◽  
Zeshui Xu ◽  
Xuezheng Chen
Keyword(s):  
Decision Making ◽  
Prospect Theory ◽  
Value Function ◽  
Behavior Pattern ◽  
Weighting Function ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Decision Making Framework ◽  
Decision Making Behavior ◽  
The Value Function

Uncertainty and ambiguity are frequently involved in the decision-making process in our daily life. This paper develops a generalized decision-making framework based on the prospect theory under an intuitionistic fuzzy environment, by closely integrating the prospect theory and the intuitionistic fuzzy sets into our framework. We demonstrate how to compute the intuitionistic fuzzy prospect values as the reference values for decision-making and elaborate a four-step editing phase and a valuation phase with two key functions: the value function and the weighting function. We then conduct experiments to test our decision- making methodology and the key features of our framework. The experimental results show that the shapes of the value function and the weighting function in our framework are in line with those of prospect theory. The methodology proposed in this paper to elicit prospects that are not only under uncertainty but also under ambiguity. We reveal the decision-making behavior pattern through comparing the parameters. People are less risk averse when making decisions under an intuitionistic fuzzy environment than under uncertainty. People still underestimate the probability of the events in our experiment. Further, the choices of participants in the experiments are consistent with the addition and multiplication principles of our framework.

scholarly journals Make Up Your Mind

2018 ◽  
Vol 21 (1) ◽  
Author(s):  
Helena Holmström Olsson ◽  
Jan Bosch
Keyword(s):  
Data Collection ◽  
Embedded Systems ◽  
Customer Value ◽  
Value Function ◽  
Case Study Research ◽  
Value Functions ◽  
A Value ◽  
Long Run ◽  
Data Collection And Analysis ◽  
The Impact

Today, connected software-intensive products permeate virtually every aspect of our lives and the amount of customer and product data that is collected by companies across domains is exploding. In revealing what products we use, when and how we use them and how the product performs, this data has the potential to help companies optimize existing products, prioritize among features and evaluate new innovations. However, despite advanced data collection and analysis techniques, companies struggle with how to effectively extract value from the data they collect and they experience difficulties in defining what values to optimize for. As a result, the impact of data is low and companies run the risk of sub-optimization due to misalignment of the values they optimize for. In this paper, and based on multi-case study research in embedded systems and online companies, we explore data collection and analysis practices in companies in the embedded systems and in the online domain. In particular, we look into how the value that is delivered to customers can be expressed as a value function that combines different factors that are of importance to customers. By expressing customer value as a value function, companies have the opportunity to increase their awareness of key value factors and they can establish an agreement on what to optimize for. Based on our findings, we see that companies in the embedded systems domain suffer from vague and confusing value functions while companies in the online domain use simple and straightforward value functions to inform development. Ideally, and as proposed in this paper, companies should strive for a comprehensive value function that includes all relevant factors without being vague or too simple as is the case in the companies we studied. To achieve this, and to address the difficulties many companies experience, we present a systematic approach to value modelling in which we provide detailed guidance for how to quantify feature value in such a way that it can be systematically validated over time to help avoid sub-optimization that will harm the company in the long run.

The Relationship Between Numeracy and the Value Function and Probability Weighting in Prospect Theory

2012 ◽  
Author(s):  
Mona Gauth ◽  
Maria Henriksson ◽  
Peter Juslin ◽  
Neda Kerimi ◽  
Marcus Lindskog ◽  
...  
Keyword(s):  
Prospect Theory ◽  
Value Function ◽  
Probability Weighting ◽  
The Relationship ◽  
The Value Function

Geometric Asymptotic Approximation of Value Functions

2009 ◽  
Vol 9 (1) ◽  
Author(s):  
Axel Anderson
Keyword(s):  
Value Function ◽  
Payoff Function ◽  
The State ◽  
Second Derivative ◽  
Value Functions ◽  
State Variable ◽  
Specific Formula ◽  
Geometric Term ◽  
Dynamic Stochastic ◽  
The Value Function

This paper characterizes the behavior of value functions in dynamic stochastic discounted programming models near fixed points of the state space. When the second derivative of the flow payoff function is bounded, the value function is proportional to a linear function plus geometric term. A specific formula for the exponent of this geometric term is provided. This exponent continuously falls in the rate of patience.If the state variable is a martingale, the second derivative of the value function is unbounded. If the state variable is instead a strict local submartingale, then the same holds for the first derivative of the value function. Thus, the proposed approximation is more accurate than Taylor series approximation.The approximation result is used to characterize locally optimal policies in several fundamental economic problems.

Bilevel Integer Programs with Stochastic Right-Hand Sides

2021 ◽  
Author(s):  
Junlong Zhang ◽  
Osman Y. Özaltın
Keyword(s):  
Structural Properties ◽  
Large Scale ◽  
Value Function ◽  
Integer Program ◽  
Value Functions ◽  
Integer Programs ◽  
Solution Algorithms ◽  
Right Hand ◽  
Solution Methods ◽  
The Value Function

We develop an exact value function-based approach to solve a class of bilevel integer programs with stochastic right-hand sides. We first study structural properties and design two methods to efficiently construct the value function of a bilevel integer program. Most notably, we generalize the integer complementary slackness theorem to bilevel integer programs. We also show that the value function of a bilevel integer program can be characterized by its values on a set of so-called bilevel minimal vectors. We then solve the value function reformulation of the original bilevel integer program with stochastic right-hand sides using a branch-and-bound algorithm. We demonstrate the performance of our solution methods on a set of randomly generated instances. We also apply the proposed approach to a bilevel facility interdiction problem. Our computational experiments show that the proposed solution methods can efficiently optimize large-scale instances. The performance of our value function-based approach is relatively insensitive to the number of scenarios, but it is sensitive to the number of constraints with stochastic right-hand sides. Summary of Contribution: Bilevel integer programs arise in many different application areas of operations research including supply chain, energy, defense, and revenue management. This paper derives structural properties of the value functions of bilevel integer programs. Furthermore, it proposes exact solution algorithms for a class of bilevel integer programs with stochastic right-hand sides. These algorithms extend the applicability of bilevel integer programs to a larger set of decision-making problems under uncertainty.

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