Reinforcement learning algorithm has been successfully used in prediction and decision making [5,11]. The main contribution of this paper is to provide decision making using reinforcement learning approach to allocate resources optimally in stochastic conditions in a well known example; in the portfolio selection. The modern theories of portfolio selection consider some presumptions. But if the...

In this paper, we consider portfolio selection problem in which security returns are regarded as fuzzy variables rather than random variables. We first introduce a concept of absolute deviation for fuzzy variables and prove some useful properties, which imply that absolute deviation may be used to measure risk well. Then we propose two mean-absolute deviation models by defining risk as abs...

Fibroblast activation protein (FAP) is a cell-surface serine protease highly expressed on cancer-associated fibroblasts of human epithelial carcinomas but not on normal fibroblasts, normal tissues, and cancer cells. We report herein a novel FAP-triggered photodynamic molecular beacon (FAP-PPB) comprising a fluorescent photosensitizer and a black hole quencher 3 linked by a peptide sequence (TSG...

High-technology projects are known as tools that help achieving productive forces through scientific and technological knowledge. These knowledge-based projects are associated with high levels of risks and returns. The process of high-technology project and project portfolio selection has technical complexities and uncertainties. This paper presents a novel two-parted method of high-technology ...

Introduction One of the basic problems of applied finance is the optimal selection of stocks by conflicting objective of maximizing future return and minimizing investment risk. The first systematic treatment of this dilemma is the mean variance approach proposed by Markowitz. Markowitz combined the optimization and probability theory to solve the dilemma. In Markowitz’s mean variance model, th...

In this paper, the Kapur cross-entropy minimization model for portfolio selection problem is discussed under fuzzy environment, which minimizes the divergence of the fuzzy investment return from a priori one. First, three mathematical models are proposed by defining divergence as cross-entropy, average return as expected value and risk as variance, semivariance and chance of bad outcome, respec...

The environment of loan in bank is very complex, there are not only random factors but also fuzzy factors, so the return rates of loan often have fuzzy random characteristic. Mean chance is a measure of fuzzy random variable. This paper proposes two fuzzy random dependentchance programming models of loan portfolio, one is minimize the mean chance of a bad outcome under the certain expected retu...

Optimal control is a very important field of study not only in theory but in applications, and stochastic optimal control is also a significant branch of research in theory and applications. Based on the concept of fuzzy process, a fuzzy optimal control problem presented. Applying Bellman’s Principle of Optimality, the principle of optimality for fuzzy optimal control is derived, and then a fun...

In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management, etc. Among all these, stock selection has long been identified as a challenging...

Optimal control is a very important field of study not only in theory but in applications, and stochastic optimal control is also a significant branch of research in theory and applications. Based on the concept of fuzzy process introduced by Liu, we present a fuzzy optimal control problem. Applying Bellman’s optimal principle, we obtain the principle of optimality for fuzzy optimal control, an...