درخت حوزه‌های تخصصی

Portfolio allocation with Deep Reinforcement Learning (DRL) has been the focus of many researchers. In investing, a portfolio optimization strategy is selecting assets that maximize return on investment while minimizing the risk. Asset optimization involves balancing risk and return, where stock returns are profits over time, and risk is the standard deviation value of the asset's return. Many of the existing methods for portfolio optimization are essentially the expansion of diversification methods for assets in the investment. Signiant drawdowns and early entry into the share are still challenging in portfolio construction. The idea is that having a portfolio based on net money flow is less risky than allocating a portfolio based on historical data only and turbulence as risk aversion. This paper proposes a profitable stock recommendation framework for portfolio construction using the DRL model based on the net money flow (MNF) indicator. We develop a new risk indicator based on the intelligent net-flow behavior of smart money to help determine the optimal market timing for buying and selling. The experimental results of real-world trading scenario validation show that the model outperforms all the considered baselines and even the conventional Buy-and-Hold strategy. Moreover, in this paper, the effect of defining different environments made of various information with hyper parameter optimization on the performance of models has been investigated, and the performance of DRL-driven models in different markets and asset positions has been investigated. The empirical results show the dominance of DRL models based on MNF indicators.

Protecting the interests of shareholders is considered one of the main pillars of any capital market, which, while increasing the level of financial transparency, prevents the loss of their interests to those in power in companies. Consistency of laws and regulations on the one hand and the effectiveness of the corporate governance system in supervising the financial and legal operations of the company is considered important factor in respecting contracts and supporting the investments of minority and majority shareholders of companies, which makes investors confident in protecting their investments. Governance structural keywords in the corporate governance system can lead to Investor Protection by arranging effective board oversight mechanisms and motivating investors to have confidence in the structures of capital market companies. The purpose of this study is to interpret Investor Protection under the existence of a Governance Civilization based on interpretive prioritization analysis. In this study, theoretical screening based on similar studies was used to identify the components (Investor Protection) and research propositions (themes of corporate governance civilization). Then, in order to determine the reliability of research components and propositions through the participation of 14 experts and experts in the field of accounting and financial management, Delphi analysis was used. In the quantitative part, the components and propositions identified in the form of matrix questionnaires were evaluated by interpretive analysis by 20 managers of the top 50 companies of the Tehran Stock Exchange in 2009. The results showed that the proposition of sociocracy is considered the most influential theme of corporate governance civilization in capital market companies, which strengthens the effectiveness of internal control as a component of protecting the interests of shareholders. This result shows that the theme of sociocracy enables corporate governance mechanisms by promoting the level of sharing and participatory discourses to increase the effectiveness of board oversight, strengthen the weaknesses of internal controls to enhance the effectiveness of control over financial performance, and enrich the capacity to build trust and confidence in the performance of companies.