Samira Khonsha; Mehdi Agha Sarram; Razieh Sheikhpour
Abstract
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. The task of asset optimization involves balancing risk and return, where ...
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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. The task of asset optimization involves balancing risk and return, where stock returns are profits over a period of 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 a challenge in portfolio construction. The idea here is that having a portfolio based on money net flow is less risky than allocating a portfolio based on historical data only and turbulence as risk aversion. In this paper, we propose a profitable stock recommendation framework for portfolio construction using DRL model based on the money net 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 by real-world trading scenario validation show the model clearly 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 hyperparameter 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 indicator.
Leila Nateghian; saeid Jabbarzadeh Kangarlouei; Jamal Bahri Sales; Parviz Piri
Abstract
Today, choosing the right model for determining the portfolio of investment in financial assets is one of the important issues of attention of analysts and capital market activists, and investing in a portfolio consisting of mutual investment funds is the same. With this statement, the purpose of the ...
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Today, choosing the right model for determining the portfolio of investment in financial assets is one of the important issues of attention of analysts and capital market activists, and investing in a portfolio consisting of mutual investment funds is the same. With this statement, the purpose of the article is to evaluate and compare net assets value (return) of Federation of Asian and European Stock Exchanges (FEAS) member countries with using support machine models in comparison with statistical models. The statistical and sample population included the data of 39 selected traded funds, members of FEAS, from 12 selected countries (including Iran) between 2014 and 2021.
The data related to the mentioned funds were classified and analyzed using spss-modeler, rapid miner, and weka software, and were tested with 24 support machine methods and 11 statistical methods and the results showed that the prediction accuracy of statistical models is lower than that of support machine models. To find out the significance of this difference, the Mann-Whitney test was used. Also the results show that at the 95% confidence level, it can be claimed that the prediction accuracy of machine learning models is higher than statistical models. The average rating of machine learning models was (20.86) which was much higher than statistical models (10.85).
Arezoo Ghafari; Meysam Arabzadeh; mehdi safari gerayli; Hossein jabbary; Yasser Rezaei Pitenoei
Abstract
Corporate integrity is considered as part of the company's development strategies, which in the long run can lead to the increased firms' financial transparency to stakeholders. The purpose of our study is to present a corporate integrity model and, then to investigate its effect on firms' ...
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Corporate integrity is considered as part of the company's development strategies, which in the long run can lead to the increased firms' financial transparency to stakeholders. The purpose of our study is to present a corporate integrity model and, then to investigate its effect on firms' information asymmetry. In this study, to measure the corporate integrity, we use Meta-synthesis and Delphi analysis in the qualitative part. Then, in the quantitative section, the corporate integrity questionnaires were sent to the managers of the sample firms. Subsequently, a total of 138 questionnaires were completed and sent back, which were used as the final samples for analysis. In addition, information asymmetry is measured using the three different proxies, namely bid-ask spread, turnover, and Amihud illiquidity measure. Our findings show a significant and negative effect of corporate integrity on information asymmetry. This results suggest that corporate integrity, by promoting behavioral values based on truthfulness and commitment, the structures will enhance the corporate governance mechanisms and, thereby, firstly motivate the managers to reduce the agency gap and, secondly, implement a more effective level of the supervisions on the firm's performances in front of the stakeholders by accelerating the circulation of information and giving timely and reliable feedback to the stakeholders. This is the first study that presents a corporate integrity model through qualitative analysis and then, investigates the effect of corporate integrity on firms' information asymmetry. Therefore, our study can contribute to the extant literature of this context.