Risk Aversion Factor Research Articles

Investigating the moderating role of past behavior in the relationship between risk aversion and investment choice in the Tehran stock market

Research aims: The possibility of damaging factors such as risk aversion and past behavior when making investment choices is very high. Therefore, it is crucial to identify and measure risk aversion and the influence of people's past behavior in different conditions of certainty and uncertainty on investors' decisions in the market. The primary goal of this research, which has remained hidden from the perspective of many researchers, is to investigate the moderating effect of past behavior (experiences) on the relationship between risk aversion and investment choice among investors of the Tehran Stock Exchange.Design/Methodology/Approach: Considering the unlimited population size, Morgan's Table was used for sampling, and the sample size for the unlimited population according to Morgan's Table is 384 people. Therefore, 384 community members were selected as samples. The collected data were then analyzed through SPSS and SMART-PLS software, and research hypotheses were investigated using structural equation modeling.Research findings: The results obtained from the research demonstrated that the risk aversion factor yielded a positive and statistically significant effect on investment choice. In contrast, the factor of past behavior (experiences) generated a negative and statistically significant effect on the relationship between risk aversion and investment choice.Theoretical contribution/Originality: This study provides information on how to earn returns for investors and increase the efficiency of securities markets. In addition, there has been minimal research into this field in Iran.Practitioner/Policy implication: The results of this research provide for investors to pay attention to the impact of market efficiency and the optimal and desired use of available resources.Research limitation/Implication: Among the most important limitations, three things can be mentioned: (a) society's lack of familiarity with academic research, (b) failure to provide more variables, and (c) due to some characteristics of the respondents, generalizing the results to other populations must be approached with caution.

An Emotionally Intuitive Fuzzy TODIM Methodology for Decision Making Based on Online Reviews: Insights from Movie Rankings

With the burgeoning growth of the internet, online evaluation systems have become increasingly pivotal in shaping consumer decision making. In this context, this study introduces an intuitionistic fuzzy TODIM (an acronym in Portuguese for interactive and multicriteria decision making) methodology to rank products based on online reviews. Our approach aims to enhance user decision making efficiency and address the prevalent issue of information overload. Initially, we devised a product attribute emotion quantification framework within the confines of the intuitionistic fuzzy paradigm. This allows for the transformation of online reviews into exact functional outputs via our advanced intuitionistic fuzzy scoring mechanism and its associated precise function. Following this, we take into account the inherent correlation among product attributes, leading to the development of an attribute-associated intuitionistic fuzzy model. This model further ascertains the dominance degree of alternative products. Moreover, by integrating the risk aversion factor, we can derive a hierarchical structure for alternative products, aiding in the prioritization process. Finally, this paper validates the proposed method using movie sequencing as a case study. The results show that the proposed method, which takes into account the emotional tendencies of different attributes in a movie and the different preferences of viewers in the attribute weighting and movie selection process, is more reasonable than methods proposed in previous studies.

A dynamic approach to supply chain reconfiguration and ripple effect analysis in an epidemic

The COVID-19 pandemic has illustrated the unprecedented challenges of ensuring the continuity of operations in a supply chain as suppliers’ and their suppliers stop producing due the spread of infection, leading to a degradation of downstream customer service levels in a ripple effect. In this paper, we contextualize a dynamic approach and propose an optimal control model for supply chain reconfiguration and ripple effect analysis integrated with an epidemic dynamics model. We provide supply chain managers with the optimal choice over a planning horizon among subsets of interchangeable suppliers and corresponding orders; this will maximize demand satisfaction given their prices, lead times, exposure to infection, and upstream suppliers’ risk exposure. Numerical illustrations show that our prescriptive forward-looking model can help reconfigure a supply chain and mitigate the ripple effect due to reduced production because of suppliers’ infected workers. A risk aversion factor incorporates a measure of supplier risk exposure at the upstream echelons. We examine three scenarios: (a) infection limits the capacity of suppliers, (b) the pandemic recedes but not at the same pace for all suppliers, and (c) infection waves affect the capacity of some suppliers, while others are in a recovery phase. We illustrate through a case study how our model can be immediately deployed in manufacturing or retail supply chains since the data are readily accessible from suppliers and health authorities. This work opens new avenues for prescriptive models in operations management and the study of viable supply chains by combining optimal control and epidemiological models.

The Share Price Movement Of Indian Banking Shares, can alter the GDP, based on the interest rate

The stock market is the altimeter of Indian budgeting. Trading in the Stock market is subject to market risk, and therefore returns can be affected, although it provides diversification to the portfolio of Retail investors, HNI & FII clients. The investment pattern of each category affects the growth of the country. As investments are reduced due to the risk aversion factor, the GDP fell to 6.0 percent for Q1 FY23, which is the lowest of the two years as per the economic survey of the Ministry of finance, resulting in a domestic slowdown in Q2 FY23. This research work aims to study the effects of an array of fundamental factors like – interest rate risk, counterparty risk, and regulatory risk on the share price movement of Top 5 (SBI, Canara Bank, Union Bank of India, Punjab National Bank, Bank of Baroda) performing public Banking sector stocks listed on the Indian stock exchange. So, the long-term investors should look on to the fundamentals and invest wisely to earn even in the current economic slowdown situation of FY 23. The results suggest that these three factors affect most of the share prices, namely- interest rate risk, counterparty risk, and regulatory risk and explained only interest rate risk taking into consideration.

A Credibility Theory-Based Robust Optimization Model to Hedge Price Uncertainty of DSO with Multiple Transactions

This paper addresses the deregulated electricity market arising in a distribution system with an electricity transaction. Under such an environment, the distribution system operator (DSO) with a distributed generator faces the challenge of electricity price uncertainty in a spot market. In this context, a credibility theory-based robust optimization model with multiple transactions is established to hedge the uncertain spot price of the DSO. Firstly, on the basis of credibility theory, the spot price is taken as a fuzzy variable and a risk aversion-based fuzzy opportunity constraint is proposed. Then, to exploit the resiliency of multiple transactions on hedging against uncertain spot price, the spot market, option contract and bilateral contract integrating power flow constraints are studied, because it is imperative for DSO to consider the operational constraints of the local network in the electricity market. Finally, the clear equivalence class is adopted to transform the risk aversion constraint into a deterministic robust optimization one. Under the premise of considering the expected cost of the DSO, the optimal electricity transaction strategy that maximizes resistance to uncertain spot price is pursued. The rationality and effectiveness of the model are verified with a modified 15-node network. The results show that the introduction of option contracts and bilateral contracts reduces the electricity transaction cost of DSO by USD 28.5. In addition, under the same risk aversion factor, the cost of the proposed model is reduced by USD 195.18 compared with robust optimization, which avoids the over-conservatism of traditional robust optimization.

Risk-based multistage stochastic mixed-integer optimization for biofuel supply chain management under multiple uncertainties

CO2 emissions from the transportation sector account for 22% of global emissions. Blending biofuels with fossil fuels is a promising method for reducing these emissions and ensuring energy security. One of the most important and challenging aspects of the biofuel production process is the design of supply chains that integrate various types of biomasses under multiple uncertainties. The aim of this study is to address this issue by proposing a multi-stage stochastic program for strategic and tactical planning in multi-feedstock biofuel supply chain networks under uncertainties of biomass supply and biofuel demand. The proposed model minimizes the total annualized cost by selecting raw materials, positioning production facilities, placing storage facilities, and defining deferral paths for each feedstock. In addition, a new dynamic method of the conditional value at risk is adopted to consider the risk hedge. Deterministic, stochastic, and risk-based models are compared, and case studies based on the South Korean context show the effectiveness of the model and the algorithm. Computational results indicate that considering risk can increase biofuel expected production cost by up to 88%. It also shows that biomass transportation is the most affected part of the supply chain in risk assessment. Furthermore, it indicates that a value of 0.5 is the most effective risk aversion factor.

Multistage Economic Scheduling Model of Micro-Energy Grids Considering Flexible Capacity Allocation

Micro-energy grids integrating multiple energy sources can realize the efficient use of renewable energy and accelerate the process of energy transition. However, due to the uncertainty of renewable energy, the stability and security of system operations should be taken into account with respect to multi-energy coupling economic operations. Thus, it is essential to make flexible capacity allocations in advance of the actual scheduling of production in the micro-energy grid. With this motivation, this paper constructs a three-stage scheduling model corresponding to the running stage of the spot market. Specifically, the capacity of flexible, active devices is configured in the day-ahead stage; then, the intraday economic operation dispatching scheme is provided according to the capacity configuration. Based on the day-ahead and intraday optimization results, the system power balance is realized through the dispatching process using the reserve capacity of flexible active devices for deviations generated in the real-time stage of renewable energy. For the uncertainty of renewable energy output, the clustering method is applied to realize the clustering analysis of renewable energy output scenarios. In addition, the conditional value at risk (CVaR) theory is introduced to modify the three-stage stochastic optimization model, and the risk values caused by uncertainty are quantitatively evaluated. Finally, we simulate a practical case to verify the effectiveness of the proposed model. The results show that day-ahead flexible capacity allocation enhances the autonomy of the micro-energy grid system, ensures a certain degree of system operational security, and reduces balancing costs in the real-time stage. The higher the risk aversion factor, the more operational costs the system operator pays to avoid the risk. In addition, if the carbon penalty coefficient is higher, the overall carbon emission level of the micro-energy grid will decrease, but it will gradually converge to a minimal level. This paper guides the development of micro-energy grids and has important constructional significance for the construction of multi-energy collaborative mechanisms.

Risk Assessment of Crowd-Gathering in Urban Open Public Spaces Supported by Spatio-Temporal Big Data

The urban open public spaces are the areas where people tend to gather together, which may lead to great crowd-gathering risk. This paper proposes a new method to assess the rank and spatial distribution of crowd-gathering risk in open public spaces in a large urban area. Firstly, a crowd density estimation method based on Tencent user density (TUD) data is built for different times in open public spaces. Then, a reasonable crowd density threshold is delimited to detect critical crowd situations in open public spaces and find out the key open public spaces that need to have intensive crowd-gathering prevention. For estimating the crowd-gathering risk in key open public spaces, the quantified risk assessment approach is conducted based on the classical risk theory that simultaneously considers the probability of an accident occurring, the severity of the accident consequence, and the risk aversion factor. A case study of the area within the Outer-ring Road of Shanghai was conducted to determine the feasibility of the new method. The thematic maps that describe the ranks and spatial distribution of crowd-gathering risk were generated. According to the risk maps, the government can determine the crowd control measures in different areas to reduce the crowd-gathering risk and prevent dangerous events.

Optimal production and financing strategies for capital-constrained closed-loop supply chain under risk aversion

PurposeThe purpose of this paper is to investigate the optimal production and financing strategies for the closed-loop supply chain (CLSC) composed of a capital-constrained original equipment manufacturer (OEM) and a risk-averse authorized remanufacturer (RM).Design/methodology/approachThe authors formulate four models with different scenarios, namely, the OEM has sufficient capital; the OEM has limited capital without financing; the OEM adopts debt financing strategy; and the OEM adopts equity financing strategy. The equilibrium solutions of each scenario are obtained by backward induction method, the influences of risk aversion coefficient on the equilibrium solutions are examined and the OEM's optimal financing strategy is found by comparison analysis.FindingsWhen the OEM's initial capital is limited and the equity dividend ratio is less than a certain threshold, the equity financing strategy is more advantageous for the OEM. However, if the OEM's initial capital is extremely scarce and the dividend proportion is large, the OEM prefers the debt financing strategy. When considering financing, consumer surplus always decreases as the risk aversion factor increases; the debt financing strategy is more environmentally friendly compared with the equity financing strategy. Only the debt financing strategy can make both members in the CLSC achieve a win-win situation in a certain region when the dividend ratio is sufficiently large.Research limitations/implicationsIt will be more fascinating if the model extends to such a case that the production operation situation in the CLSC composed of multiple OEMs in multiple periods. Furthermore, the remanufacturer's risk-averse information is asymmetry may be more realistic in our daily life.Originality/valueThere are three main differences from the existing research. One is that the remanufacturer's risk aversion originates from the uncertain remanufacturing cost instead of the uncertain market demand. Another is that the boundary conditions of the OEM prefer to adopt debt financing is obtained through the envelope theorem with Lagrange multiplier method. Last but not the least, this paper provides a good theoretical reference and practical guidance for the OEM to make the rational financing strategy selection in face of different degree of capital scarcity in the CLSC system. The value of the three aspects provides a theoretical basis for the optimal operation decisions of capital-constrained manufacturer considering the remanufacturer's risk aversion in the CLSC operation system.

Recognising the factors affecting customer trust in online banking services among Iranian banks

The main purpose of this study is to investigate factors impacting customer trust in online banking. The statistical population of the research consisted of all individuals benefiting from online banking and residing in Tehran city in Iran. The samples were selected randomly, and the interview and questionnaire instruments were used for data collection. After collecting the questionnaires, interviewing bank managers and related experts, and examining the research background in this domain, we employed exploratory factor analysis to investigate the customer trust-impacting indices in online banking. Then we started the modelling process, extracted the final model and identified nine effective indices (including main and sub-variables) of customer trust. Regarding the main factors, the results showed that the two factors, including service familiarity and service quality of online banking, had positive and significant effects on customer trust, while the risk aversion factor had a negative and significant effect on customers' trust in online banking.

Emergency Quantity Discount Contract with Suppliers Risk Aversion under Stochastic Price

This paper constructs an emergency quantity discount contract to explore the inherent law of the contract coordinating the supply chain with stochastic market demand and price and the risk-averse supplier. Meanwhile, the conditional value-at-risk (CVaR) risk measure criterion is revised to study the influence of supplier’s risk aversion attitude on supply chain coordination. The results show that supplier risk aversion will cause the bifurcation of the relevant factors in the supply chain under the stochastic price. Within the bifurcation region, the supply chain cannot be coordinated; out of the bifurcation region, the supply chain can achieve coordination. The supply chain related factors’ variation range in the bifurcation region is related to the step size of the risk aversion factor and the normal distribution function’s variance of the market demand, and it increases with the latter.

A new mean-variance-entropy model for uncertain portfolio optimization with liquidity and diversification

Abstract This paper deals with a portfolio optimization problem with uncertain returns. Here, the returns of risky assets are regarded as uncertain variables which are estimated by experienced experts. First, a mean-variance-entropy model for uncertain portfolio optimization problem is presented by taking into account four criteria viz., return, risk, liquidity and diversification degree of portfolio. In our model, the investment return is quantified by uncertain expected value, the investment risk is characterized by uncertain variance and entropy is used to measure the diversification degree of portfolio. Moreover, different from the previous bi-objective optimization model, our model achieves both the maximum return and the minimum risk in a single objective form by introducing a risk aversion factor and the dimensional influence caused by different units is eliminated by normalization method. Then, two auxiliary portfolio selection models are transformed into different equivalent deterministic models. Finally, a numerical simulation is given to verify the effectiveness and practicality of our model.

Trade Credit and Revenue Sharing of Supply Chain with a Risk-Averse Retailer

In this paper, we develop three supply chain game models, i.e., the basic model, the single trade credit model, and the trade credit and revenue sharing collaboration model. Conditional value-at-risk (CVaR) criterion is used as the measure of risk assessment in these models. We analyze the optimal decisions in the centralized and decentralized situations, respectively, and verify that single trade credit cannot coordinate the supply chain. However, the collaboration contract can coordinate the supply chain. Furthermore, this paper explores the influence of risk-aversion factor, trade credit period, revenue sharing coefficient, and other parameters on the optimal decisions and studies the feasible range of Pareto improvement in the collaborative model. In numerical experiments, the results show that the decisions and profits of both the manufacturer and the retailer reply on the degree of the risk aversion, the trade credit period, and the revenue sharing coefficient. The collaborative contract effectively improves supply chain performance and achieves a ‘win-win’ situation for the supply chain members. In addition, we also consider two extensions for our research. One extension shows that the collaborative contract of trade credit and buyback can also coordinate the supply chain in a certain range. The other extension considers the optimal decision of a risk-averse manufacturer with CVaR.

How to govern the big data discriminatory pricing behavior in the platform service supply chain?An examination with a three-party evolutionary game model

In recent years, the big data discriminatory pricing (BDDP) behavior of the service platform has recurrently appeared in the platform service supply chain, which is considered as a scandal that is irresponsible to the consumers. Therefore, how to effectively govern the service platform has become an important research topic. Based on this background, this study considers the strategies of the service platform, the government and the consumers, builds a three-party evolutionary game model, and puts forward a governance mechanism for preventing the service platform from using BDDP. This study has drawn many interesting conclusions. First, when the service platform is risk-neutral, if the government does not supervise the platform, the government should set a high tax rate; conversely, if the government chooses to supervise the platform, the tax rate can remain unchanged, and the government can set a high penalty. Second, considering the risk aversion of the platform, if the government does not supervise, not only high tax rate but also low tax rate will be effective; the specific value of the tax rate at this time is determined by the risk aversion factor. When platforms are supervised by the government, a high penalty can be imposed, but the penalty may decrease as the platform risk aversion factor increases. Third, in situations in which the government has no incentive for long-term supervision since the supervision cost is greater than the supervision revenue, the service platform has the incentive to carry out BDDP and consumers do not give the service platform bad evaluations, if the government sets a high penalty, the service platform will recurrently, but not always, carry out BDDP.

Electricity Retailer Trading Portfolio Optimization Considering Risk Assessment in Chinese Electricity Market

The Chinese electricity market underwent a significant reform in 2015 resulting in its complete liberalization on the sell-side. Electricity retailers now seeking to adapt to the electricity market are focused on trading portfolio optimization based on risk assessment, which can be performed by classifying and combining possible electricity purchases and sales on mid-long-term and spot markets. The scenario method is used in this study to simulate random risk variables (the real-time price and user demand), then a comprehensive decision-making/risk assessment model for electricity trading portfolio optimization is established with the goal of profit maximization. The conditional value-at-risk (CVaR) serves as the risk assessment index for electricity purchases and sales. Four combinations of electricity trading modes are assessed as a case study. The most basic trading mode is significantly affected by the risk aversion factor in regards to purchases scale and expected profit, which validates the proposed model. The time-of-use (TOU) price and real-time price guaranteeing the bottom and top price as a transaction mode are found to affect the scale of electricity purchases and the expected profit of the electricity retailer. Proportional distribution plans for three respective retail transactions are determined according to electricity retailers’ different attitudes toward risk.

Multi-period mean-semi-entropy portfolio management with transaction costs and bankruptcy control

This study investigates a multi-period portfolio management problem under fuzzy settings. For the first time, the newly proposed semi-entropy in the literature is employed as an efficient downside risk measure for risk control in multi-period portfolio optimization. Fuzzy techniques for financial modeling show advantageous performance when future financial market conditions cannot be effectively detected with only historical data. We describe the assert returns by fuzzy variables. Two realistic constraints of transaction costs and bankruptcy events are taken into consideration in our model formulation of a multi-period mean-semi-entropy optimization program. The formulated program is rewritten as a crisp single-objective nonlinear programming by introducing a risk-aversion factor, and final solution to the program is obtained by using genetic algorithm. For the demonstration of computational results, we provide a numerical example with real-life stock data, which illustrates the main modelling concept and the efficiency of genetic algorithm solving method. Comparative analyses over several baseline models show the advantages of adopting fuzzy semi-entropy as an efficient downside risk measure for multi-period portfolio investment optimization.

Wealth distribution models with regulations: Dynamics and equilibria

Simple agent based exchange models are a commonplace in the study of wealth distribution in an artificial economy. Generally, in a system that is composed of many agents characterized by their wealth and risk-aversion factor, two agents are selected sequentially and randomly to exchange wealth, allowing its redistribution. Here we analyze how the effect of social protection policies, which favor agents of lower wealth during the exchange, influences the stability and some relevant economic indicators of the system. On the other hand, we study how periods of interruption of these policies produce, in the short and long term, changes in the system. In all cases, a steady state is reached, but with varying relaxation times. We conclude that regulations may improve economic mobility and reduce inequality. Moreover, our results indicate that the removal of social protection entails a high cost associated with the hysteresis of the distribution of wealth. Economic inequalities increase during the period without social protection and decrease when they are restored. But recovery to the initial equilibrium may take longer than it took to disturb it. In some extreme cases, inequality is irreversible.

Coordinating a dual-channel supply chain with conditional value-at-risk under uncertainties of yield and demand

In a dual-channel supply chain, the coordination of retailer’s risk-averse behavior is a challenging problem under uncertainties of manufacturing yield and market demand. This paper proposes a decision-making model in which Conditional Value-at-Risk (CVaR) criterion is used as the measure of risk assessment, analyzes the optimal decision in the centralized and decentralized situations respectively, and verifies that the joint contracts of revenue sharing contract and buyback contract can coordinate a dual-channel supply chain. The joint contracts proposed in this paper can achieve Pareto improvement for a dual-channel supply chain in which risk-averse retailer is involved under uncertainties of yield and demand. In experiments, this paper explores the effects of yield-demand uncertainty factor, risk-aversion factor and market distribution factor on the expected profits of manufacturer and CVaR value, and studies the feasible range of Pareto improvement in the joint contract model. The results show that the decision and profit of both the manufacturer and the retailer reply on the degree of the risk and uncertainty, and the possibility of the joint contract realizing the coordination becomes greater with the increase of the proportion of the channel’s market share and, at the same time, the decrease of the risk-averse. In addition, the joint contract can coordinate the dual-channel supply chain, effectively improve the dual-channel supply chain performance, and achieve win-win goal for the dual-channel supply chain members.

Stochastic programming model for scheduling demand response aggregators considering uncertain market prices and demands

This paper proposes a stochastic decision making model for a demand response (DR) aggregator as an interface between the market and customers in a competitive environment. The DR aggregator participates in day-ahead (DA) energy and balancing markets as well as offers selling price to the customers to maximize its expected profit, considering the reaction of customers to the rivals’ offering prices. Moreover, the effect of load reduction due to implementing DR contracts on the decision making process of the DR aggregator is assessed. However, the main focus is on the operation of both shiftable and sheddable loads in price-based DR programs with detail. In order to investigate the behavior of different DR actions from the DR aggregator viewpoint, the restrictions imposed by the preferences of customers to the decisions made by the DR aggregators are modeled via a bi-level stochastic programming approach. The upper level represents the decisions made by the DR aggregator, while the lower level models the customers’ behavior. To deal with various uncertainties, a risk-constrained scenario-based stochastic programming framework is presented where the DR aggregator’s risk aversion is modeled using conditional value at risk (CVaR) method. Finally, a detailed illustrative case study based on the Nordic energy market data is provided and the effects of different DR actions and risk aversion factor on the profit of the aggregator are analyzed.

Supply Chain Decision Analysis Based on Risk-Aversion and Cost Information Asymmetry

This paper analyzes the impact of risk aversion and retailer’s cost lied factor on supply chain decision-making in a hybrid channel supply chain system consisting of a risk-averse manufacturer and a retailer. According to the expected utility maximization theory, this paper gets the optimal decision of the manufacturer and retailer. First, it analyzes the impact of risk aversion factor on pricing decisions of supply chain members when the information of the retailer’s cost is public. Next, this paper considers the fact that the retailer has advantages on cost information, analyzes the relations between the retailer’s optimal cost lied factor and each parameter.