Volume 17, Issue 3 (8-2020)                   jor 2020, 17(3): 99-117 | Back to browse issues page

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Karimkhani M, Zomorodian G, Aligholi M, Fallah Shams M, Hanifi F. Optimizing Financial Asset Pricing Using Quantitative Behavioral Bias Measurement. jor. 2020; 17 (3) :99-117
URL: http://jamlu.liau.ac.ir/article-1-1917-en.html
Department of Business Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
Abstract:   (1903 Views)
The optimization of pricing financial assets quantitatively is an emerging discipline that attempts to model the impact of biases that investors in asset prices. This article provides an overview of the theoretical foundations and challenges and offers some solutions in this field. The paper is divided into two parts. In the first part of the paper, an overview of the selected literature is presented on key theoretical foundations. Why is this new financial field emerging? What subjects does he study? In the second part, the paper outlines a general plan provides a comprehensive set of resources on approaches to optimize the effectiveness of investment in quantitative modeling the behavior of financial market offers. Recent evidence of financial decision-making under uncertainty suggests that people do not act like neoclassical models. For this reason, investors often act as behavioral biases that have not been described in neoclassical models. Because behavioral bias in investors is a rather vague concept and it is very difficult to define and quantify numerically. Therefore, investor behavioral bias is another constraint that optimizes financial decision making. The problem of optimizing current financial asset pricing models does not take into account the effect of behavioral bias on portfolio valuation. The aim of this study was to optimize financial assets pricing subject to behavioral biases in the measurement of cognitive psychology, factors related to emotional and irrational factors will be discussed.
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Type of Study: Research | Subject: Special
Received: 2019/04/21 | Accepted: 2019/11/24

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