نشریه چشم انداز مدیریت مالی
پیاپی 43 (پاییز 1402)

In today's world, optimizing investment portfolios has received increasing attention. While predicting the expected returns of investment options and incorporating them into the objective function for profit maximization is a common practice, the most significant innovation in current research is the minimization of prediction error as the objective function. This innovation advises investors to emphasize not only on profit and risk but also on the predictability of investment options when forming an investment portfolio. Integrating return prediction from traditional time series models into portfolio formation can enhance the performance of the primary portfolio optimization model. Since machine learning and deep learning models have demonstrated a significant superiority over time series models, this paper combines return prediction in portfolio formation with machine learning models, namely Random Forest, and deep learning model, Long Short-Term Memory (LSTM). To evaluate the performance of the proposed model, five years of historical data from 2017 to 2021 are used for five industry sectors: banking, automotive, pharmaceutical, metal, and petroleum. The experimental results demonstrate that the mean-variance optimization models perform better when return prediction is done using Random Forest

In this study, the relationship between the profitability of insurance companies (based on two key profitability indicators, including the return on assets (ROA) and the return on equity (ROE)) and its influencing factors, including claim ratio, operational expenses, and retention ratio, has been studied. Nineteen insurance companies listed on the Tehran Stock Exchange (TSE) and Iran Fara Bourse (IFB) were included in the research sample. A panel regression analysis was conducted using Eviews software to test hypotheses during 2016-2021. As a result of the estimation of the research models, the variable of loss ratio significantly and negatively affects the return on assets and return on equity of insurance companies listed in the Tehran Stock Exchange and Iran Farabourse. Also, operating costs have a negative and significant effect on the rate of return on assets and return on equity as profitability criteria of the sample companies. On the other hand, the holding ratio variable has had a positive and significant effect on the rate of return on assets and return on equity as profitability measures of insurance companies listed in the Tehran Stock Exchange and Iran Farabourse

To develop the financial literature related to the identification of financial market regimes, the present research proposes a new rule-based method for selecting turning points in business cycles, which eliminates subjectivity in the process of classifying market regimes. The proposed algorithm has a heuristic approach. No conditions are imposed to determine the duration of the regimes or the amplitude of their returns. Also, for the comparison between the proposed algorithm and the algorithms of Pagan, Lunde, White's bootstrap test has been used in various assets, including the Tehran Stock Exchange index, copper and gold metals, and oil commodities, and the Sharpe ratio has been used as a performance measure in out-of-sample data. The results show that the proposed algorithm has better or equal performance with other identification methods in identifying out-of-sample regimes, especially in time series that are different from capital market index data. The obtained results show the success of the proposed algorithm compared to other algorithms. The straightforward structure of the proposed algorithm avoids the potential fluctuations that occur in parameter optimization by providing a specific method and is useful and practical in identifying different regimes in various sets of time series without changing the parameters.

Market liquidity risk is a crucial factor in asset valuation, and understanding the influential factors on liquidity risk holds paramount importance. One of the key influential factors is exchange rate risk, a dimension often overlooked in the existing literature. The objective of this research is to investigate the impact of exchange rate risk on equity market liquidity risk. The study utilizes quarterly data from selected stocks of 32 companies listed on the stock exchange during the years 1386-1400. The liquidity risk is extracted using the Amihud measure (2002), and the risk is calculated through the detrended method. Various dynamic panel data models are employed for estimations, controlling for the effects of exchange rate risk alongside other explanatory variables, including market excess return, product prices, risk-free return, size, and book-to-market ratio. Results from the generalized method of moments (GMM) estimator indicate that an increase in exchange rate risk leads to an elevation in equity market liquidity risk. Additionally, market excess return and the book-to-market ratio exhibit positive effects, while product prices, risk-free return, and size show negative impacts on equity market liquidity risk. The degree of stickiness in liquidity risk is found to be minimal based on the dynamics of the model, and seasonal shocks have limited effects on equity market liquidity risk, suggesting a high level of stability.

Capital market efficiency is considered as one of the foundations of stability and stability in any economic system, which can lead to higher investor confidence. But the existence of disturbances related to macroeconomic variables; The governance of the capital market and the political currents governing the market cause the instability of the capital market, which can have behavioral and macroeconomic consequences for any country. The purpose of this research is the feasibility of capital market future research is from the perspective of evaluating the drivers of instability under the presence of the emotional behavior of investors. The methodology of this study is mixed in terms of data collection and consists of a set of thematic analysis processes in the qualitative part and analyzes related to scenario development in the quantitative part. The participants in the qualitative part were 20 academic experts, while in the quantitative part 25 capital market brokers, who had the necessary experience and knowledge about the behavioral drivers of investors, participated. The results in the qualitative section during 20 interviews indicated the existence of 5 organizing themes and 40 basic themes, and all the themes used in the scenario development section were also confirmed by Delphi analysis. In the quantitative section, it was also determined that the most important scenario related to the drivers of capital market instability is the Senusi scenario or the explanatory phrase (simulation of the literary theme) of the rebellious horse which as the most important factor in the instability of the capital market, the emotional behavior of investors is further intensified under the influence of mass stimulus. The obtained results indicate the fact that one of the most important reasons for the instability of the capital market is the mass behavior of investors and shareholders, and the reason for this is the existence of currency fluctuations, which causes the attractiveness of investing in financial markets such as the stock exchange to be replaced by investing in monetary and foreign exchange markets to bring more returns in the shorter term for investors by avoiding fluctuations in the difference between Rial and international currencies.

The refining industry is one of the most important industries in the Tehran Stock Exchange, and it has a significant impact on the behavior of the existing shares in it due to fluctuations in global oil prices. These effects are in such a way that they also affect the relationship between each share and the others. Therefore, in order to address the impossibility of examining all the shares available in the stock market, the formation of an optimal portfolio of various industries requires the identification of leading shares in this industry. To this end, in this study, using daily data in the period from August 30, 2016, to May 21, 2023, and employing the Asymmetric TVP-VAR method, the relationship between refining industry shares in three states of positive returns, negative returns, and general returns has been examined. The results of this study indicate that there is an asymmetrical relationship between negative and positive returns, with a stronger relationship observed in positive returns. Additionally, Shabna, Shabriz in negative returns, and Shebandar in positive returns are the leading shares in the refining industry.