Fuzzy mathematical risk preferences based on stochastic production function among medium-scale hog producers

2020 ◽  
Vol 39 (4) ◽  
pp. 4859-4868
Author(s):  
Ning Wang ◽  
Meng Sun ◽  
Liu Yu ◽  
Fazhu Jiang
Keyword(s):  
Risk Aversion ◽  
Production Function ◽  
Risk Preferences ◽  
Relative Price ◽  
Single Element ◽  
Medium Scale ◽  
Input Element ◽  
Stochastic Production ◽  
Degree Of Risk ◽  
Mean Risk

Farmers’ risk preferences and degree of risk aversion affect their production and management decisions. According to Just-Pope stochastic production function model, we get the expression of the single element risk-aversion coefficients that include input element and hog slaughter absolute price, compared with the expression of relative price mean risk-aversion coefficients, it can directly observe the influence of the element and output price on single element risk-aversion coefficients. Based on the regression procedures and the calculation method of the average value of the element risk-aversion coefficients, mean risk-aversion coefficients of per household medium-scale hog producers are calculated in 76 households, 11 counties, Heilongjiang province. The results show that medium-scale hog producers are risk-averse, accounting for 96%; newborn animal weight and feed consumption affect hog producers’ degree of risk aversion. The former is the risk-reducing input element, while the latter is the risk-increasing input element.

scholarly journals ESTIMASI FUNGSI PRODUKSI DAN EFISIENSI ALOKASI SUMBERDAYA PETANI KEDELAI DI LOMBOK BARAT

2006 ◽  
Vol 3 (1) ◽  
pp. 145-148 ◽  
Author(s):  
Nurhayatin Nufus
Keyword(s):  
Technical Efficiency ◽  
Production Function ◽  
Stochastic Frontier ◽  
Allocative Efficiency ◽  
Farm Management ◽  
Management Level ◽  
Stochastic Frontier Production Function ◽  
Stochastic Production ◽  
Frontier Production Function ◽  
Land Size

This research  aims  to analyses  factors  influence  on production  and  resources  allocation  of soybeans  by farmer  at  West Lombok.  Production  function  was estimated  from survey data and technical  efficiency  was used to indicate  farm management  level  through maximum  likelihood,  which  was transformed  into frontier stochastic  production  function.  The land  size,  fertilizer  (urea and  TSP), labor  and pesticide  influence  the production  of soybean  at site.  The technical efficciency  level of Soybean fann was 95,6 percent   The  usage of TSP and pesticide reached allocative efficiency while urea and seeds were al/ocative efficiency yet Key words:  technical  effICiency, allocative  effICiency, and stochastic  frontier  production  function.

AND, INJUSTICE FOR SOME CORRUPT EXCHANGE AND THE RISK-AVERSE OFFICIAL

2004 ◽  
Vol 2 (2) ◽  
Author(s):  
Gary E. Marche
Keyword(s):  
Law Enforcement ◽  
Theoretical Analysis ◽  
Risk Aversion ◽  
Organized Crime ◽  
Risk Preferences ◽  
Risk Averse ◽  
Enforcement Official ◽  
Optimal Law Enforcement

Although corruption and optimal law enforcement literature have addressed the effects of corruption, little has been done to analyze the decision to become corrupt. For example, little is known about risk preferences and how they might affect the nature of a corrupt exchange scheme. To answer this question, a theoretical analysis is developed that considers the noncoercive incentivea and circumstances necessary for a law enforcement official, assumed averse to criminal risk, to choose a corrupt exchange with organized crime that involves murder. Risk-aversion and the severity of the crime involved are shown to reduce the likelihood of detecting the corruption scheme and murder is shown to be optimal. Corruption schemes involving less risk averse offenders are analyzed and compared.

scholarly journals A Machine Learning Integrated Portfolio Rebalance Framework with Risk-Aversion Adjustment

2020 ◽  
Vol 13 (7) ◽  
pp. 155
Author(s):  
Zhenlong Jiang ◽  
Ran Ji ◽  
Kuo-Chu Chang
Keyword(s):  
Machine Learning ◽  
Risk Aversion ◽  
Financial Data ◽  
Rolling Horizon ◽  
Learning Models ◽  
Market Index ◽  
Out Of Sample ◽  
The Mean ◽  
Mean Risk ◽  
Machine Learning Models

We propose a portfolio rebalance framework that integrates machine learning models into the mean-risk portfolios in multi-period settings with risk-aversion adjustment. In each period, the risk-aversion coefficient is adjusted automatically according to market trend movements predicted by machine learning models. We employ Gini’s Mean Difference (GMD) to specify the risk of a portfolio and use a set of technical indicators generated from a market index (e.g., S&P 500 index) to feed the machine learning models to predict market movements. Using a rolling-horizon approach, we conduct a series of computational tests with real financial data to evaluate the performance of the machine learning integrated portfolio rebalance framework. The empirical results show that the XGBoost model provides the best prediction of market movement, while the proposed portfolio rebalance strategy generates portfolios with superior out-of-sample performances in terms of average returns, time-series cumulative returns, and annualized returns compared to the benchmarks.

Risk Preferences and Incentives for Evidence Acquisition and Disclosure

2020 ◽  
Vol 36 (2) ◽  
pp. 314-342
Author(s):  
Erin Giffin ◽  
Erik Lillethun
Keyword(s):  
Decision Making ◽  
High Risk ◽  
Optimal Design ◽  
Risk Aversion ◽  
Risk Preferences ◽  
Decision Rules ◽  
Low Risk ◽  
Risk Averse ◽  
Good Decision ◽  
Legal Rules

Abstract Civil disputes feature parties with biased incentives acquiring evidence with costly effort. Evidence may then be revealed at trial or concealed to persuade a judge or jury. Using a persuasion game, we examine how a litigant’s risk preferences influence evidence acquisition incentives. We find that high risk aversion depresses equilibrium evidence acquisition. We then study the problem of designing legal rules to balance good decision making against the costs of acquisition. We characterize the optimal design, which differs from equilibrium decision rules. Notably, for very risk-averse litigants, the design is “over-incentivized” with stronger rewards and punishments than in equilibrium. We find similar results for various common legal rules, including admissibility of evidence and maximum awards. These results have implications for how rules could differentiate between high risk aversion types (e.g., individuals) and low risk aversion types (e.g., corporations) to improve evidence acquisition efficiency.

Financier Search and Boundaries of the Angel and VC Markets

2018 ◽  
Vol 43 (6) ◽  
pp. 1223-1249
Author(s):  
Gurupdesh Pandher
Keyword(s):  
Risk Aversion ◽  
Risk Preferences ◽  
Venture Capitalists ◽  
Entrepreneurial Finance ◽  
Initial Value ◽  
Agency Model ◽  
Investment Return ◽  
Investment Returns ◽  
Venture Funding ◽  
Funding Cycle

This paper studies how critical entrepreneurial finance outcomes such as the investment return and equity division are shaped by venture characteristics, financier risk preferences, and competitive searching. Our analysis uses a double-hazard agency model in which financiers determine the equity division to maximize the expected utility of their investment return while entrepreneurs search for the best deal. Model results provide new theoretical insights on the venture funding cycle, the coexistence of angels/venture capitalists (VCs) with heterogeneous risk aversion, and risk separation in the entrepreneurial finance market. The model predicts that financiers with higher funding capacity and advisory capabilities (e.g., VC firms) will prefer to fund at later stages as their expected investment return rises with the venture’s initial value and financier productivity. Competitive searching by entrepreneurs enables financiers with a diverse set of risk preferences to coexist profitably by reducing the advantage (disadvantage) of lower (higher) risk aversion financiers and making investment returns more similar. Further, the model shows the emergence of a risk separation cutoff beyond which only angels/VCs with lower levels of risk aversion can profitably fund riskier ventures.

Power Law with Exponential Cut‐off Utility

Metamorphosis ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 26-32
Author(s):  
Afreen Arif H. ◽  
T.P.M. Pakkala
Keyword(s):  
Risk Aversion ◽  
Utility Function ◽  
Power Law ◽  
Risk Preferences ◽  
Portfolio Allocation ◽  
Utility Functions ◽  
Relative Risk Aversion ◽  
Optimal Solutions ◽  
New Class ◽  
Exponential Power

Most of the utility functions studied earlier concentrated on properties of risk aversion. In this article, the authors have introduced a new class of utility function called the Power Law with Exponential Cut-off (PLEC) utility function, which exhibits all the absolute and relative risk aversion and risk loving preferences of individuals, under various conditions. It generalises and encompasses other systems of utility functions like that of exponential power. Certain properties of this utility function are discussed. Sensitivity analysis exhibits different portfolio allocations for various risk preferences. The analysis also shows that arbitrary risk preferences may lead to biased risk response estimates. Performance of PLEC utility function in portfolio allocation problem is demonstrated through numerical examples. This is evaluated through optimal solutions.

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