The objective of this paper is to develop a portfolio optimization technique that is simple enough for an individual with little knowledge of economic theory to systematically determine his own optimized portfolio. A compromise programming approach and a fuzzy logic approach are developed as...

This study examines the performance of logistic regression, artificial neural networks and adaptive neuro-fuzzy inference system in predicting credit default using data from Farm Credit System. Empirical findings show that credit default predictions vary with empirical model used.

Chinese rural household has been always facing credit constraints. Few institutions lend loans to farmers because of financial risks. Farmers have to use usury or other informal sources to meet the financial needs for production. This credit constraint has been forcing farmers to deviate from...

This study investigates nonparametrically the optimizing behavior of a sample of 289 Kansas farms under profit-maximizing and cost-minimizing hypotheses. The results do not support strict adherence to either optimization hypothesis. However, evidence against cost-minimizing behavior seems to be...

Agricultural lenders in today’s environment face many challenges when evaluating the creditworthiness of farm borrowers. To address these challenges, a survey was conducted with financial institutions in Kansas and Indiana where agricultural lenders were asked for their response to...

Given the importance of land valuation to the various stakeholders, the objective of this research is to develop a theoretically sound model that robustly estimates the market value of land in Kansas, accounting for urban influence. The market value of land is estimated using a hedonic model...

This study investigates non-parametrically the optimizing behavior of a sample of 289 Kansas farms under profit-maximization and cost-minimization hypotheses. The study uses both deterministic and stochastic non-parametric tests. The deterministic results do not support strict adherence to...