مجله راهبرد مدیریت مالی
سال دهم شماره 3 (پیاپی 38، پاییز 1401)

Stock markets play a significant role in organizing modern economic systems. Several research projects have been performed in the field of prediction using intelligent techniques. Considering that the accuracy of these techniques is significantly affected by their input features, one of the improvements made in the use of intelligent models, in addition to the application of hybrid models, is the use of dimensionality reduction as a preprocessing for the prediction model. In this study, in order to predict the daily index of the Tehran Stock Exchange, two methods of dimensional reduction (selection and extraction) are used simultaneously to select appropriate features as model inputs. Hence, the MRMR-MID algorithm is used to select features and the PCA algorithm is used to extract features. Then, support vector regression is used as a prediction model. Finally, an algorithm for selecting suitable features is proposed as ISF_ MID, according to the results obtained from the analysis of the use of dimensional reduction techniques in the prediction model. The results show that with the proposed method, with 7 selected features, it is possible to achieve high accuracy in predicting the daily index of the Tehran Stock Exchange. It should be noted that the studied models were evaluated in the implementation stage by the k-fold cross-validation method. MAE, MSE, and RMSE criteria are also used to evaluate the performance of these models.

Financial markets are affected by several factors, one of them is investors. Due to the recent trend of entry and influence of individual investors on the capital market, addressing the effects of the trading behavior of individual investors in various dimensions, including trading volume, has become more important than before. In this regard, the present study examines the effect of real investors' transactions on the value of companies admitted to the Iranian capital market in the period 2014 to 2019. Using two-stage least squares regression and Granger causality test, it was found that the transactions of individual investors have a positive and significant effect on the Firm value, thus that the transactions of real investors increase the value of the Firm. Another result is that the effect of real investors' transactions on the value of the Firm is stronger in good companies than in bad companies; this means that the transactions of real investors increase the value of the Firm by improving the price information content

This paper aims to provide the best approach to optimize the multi-asset portfolio of five asset classes including cryptocurrencies, foreign currencies, gold, stock and investment funds in three groups of fixed income investment funds, stock investment funds and Mutual investment funds. Purposely, the mean-value at risk and mean-conditional value at risk models have been developed and solved using the artificial bee colony algorithm. The performance of the value at risk based models is compared with the mean-variance, mean-semi variance and mean–absolute deviation models. Also, the profitability of the models is evaluated in the presence of three real world constrains, i.e. quantity constrains, class constrains and the both. Our investigation time period is from August 2015 to December 2020. The in-sample and out-of-sample results showed that the conditional value at risk model outperforms the other models, whether in the presence of constraints or not. Also, the artificial bee colony algorithm was superior to the imperialist competitive and particle swarm optimization algorithms in multi asset portfolio management, based on Sharpe, conditional Sharpe and return on risk ratios.

Modern investment management began with Markowitz's theory on how to select an optimal portfolio based on the mean-variance model, and then continued with various optimization methods such as conditional value at risk model, half-variance model, and the mean-absolute deviation model. One of the most important weaknesses of these models is not paying attention to investors' views and relying on past information. The Black Literman model has largely eliminated this weakness by combining a matrix of investor views and past returns. In this research, using fundamental analysis, the matrix of investor views is formed and then the performance of the optimal portfolio of the Black Literman model is compared with existing models. The research period is between 2016 to 2021 and MATLAB software has been used to obtain the optimal portfolio. The results show that using different evaluation criteria, the fundamental Black Literman model performs better than other existing optimization models.

Today, in line with the age of digital technology and the industry 4.0 revolution, using the cyberspace tools and technologies in the money and capital markets is increasing. One of the technologies considered in discussion of digital financial strategy is blockchain technology, that is not possible to formulate a specific strategy for the money and capital markets without analyzing its advantages and challenges. The main problem of this study is the failure to identify the advantages and challenges of blockchain technology in the money and capital markets and their prioritization; Therefore, the purpose of this study is to identify these advantages and challenges in the first step, and their prioritization in the second step. Finally, digital financial strategies for blockchain technology utilization are suggested in the money and capital markets. In order to answer seven questions, this study was conducted using the exploratory sequential mixed method strategy, which in the first step, challenges and benefits of blockchain technology in the money and capital market were identified by referring to the secondary data through metasynthesis strategy, and then in the second step, each obtained benefit and challenge is prioritized with the soft operations research approach and the importance-performance analysis strategy. The obtained results include 17 advantages and 18 challenges of thematic analysis that after prioritization, suggest the digital financial strategies for challenges and advantages such as the strategy of focusing on advantages and management and reduction of challenges, the strategy of non-focusing on low priorities and challenges, the strategy of continuing the desired activity and challenges management, and the strategy of waste avoidance of resources and managing the challenges resulted by blockchain technologies in the money and capital markets.

Dividends are recognized as an important and influential factor in the value of stocks and therefore the market value of the company. It can be said that dividend (cash) has always been of great importance for investors and shareholders. Therefore, in the research, the effect of dividend policy on firm value has been investigated by considering the basic principles of the company and also the effect of company size on this relationship. Finally, the amount of dividends premium in this period is stated.Research In order to achieve the goal of research, 93 companies listed on the Tehran Stock Exchange during a period of 8 years (2013-2020) 744 years-company has been done using the panel data regression method. The relationship between dividend payout and the firm value is positive and significant. As a result, the dividend premium in this period is also positive. Panel data regressions suggest that the dividend premium for firms’ equity and the dividend premium for firms’ assets. Dividend premium in this period is also positive. According to the results, the stock prices of dividend payers are great compared to those of non-payers (retained earnings). The stock price with dividend payout is higher than the shares with accumulated dividend. The positive dividend premium indicates the investors' preference for dividend payout

The desire of firms to maintain the optimal level of cash arises from the fact that keeping less and more than the optimal amount of cash can decrease the firm value. This causes firms to constantly try to adjust the cash holdings ratio. The speed of adjustment depends on various factors at the firm, industry and macroeconomic level. The purpose of this study is to investigate the effect of economic prosperities and recessions on cash holdings speed of adjustment. For this purpose, 2893 observations in the period of 2003-2020 in the form of unbalanced panel data have been used. The ordinary least squares (OLS) estimator is used to estimate static models and the system generalized method of moments estimator (GMM) is used to estimate dynamic models. The research results confirm the existence of an optimal (target) cash holdings level in Iranian firms. In addition, the results show that compared to the recessions, during the prosperities, firms adjust their real cash holdings ratio more quickly to the target ratio. These results are consistent with the concepts in trade-off theory.

This study aims to investigate how and to what extent stock price crash risk is affected by various factors and tries to design a model to predict this relationship in the Tehran Stock Exchange. Accordingly, Besides reviewing the literature on stock price crash risk, 12 experts were selected from the statistical population of the capital market for the qualitative part using the theoretical sampling method and the gradual selection rule. Once the data were collected by referencing documents and interviews, the target model was extracted by MAXQDA18 software. The systematic random sampling was used to select 100 companies from a statistical sample of listed companies on the Tehran Stock Exchange over 2009-2019 to test the research hypothesis. Then, a quantitative model derived through the qualitative method was tested as structural equations using the PLS software.The results indicate that it is possible to achieve a model for predicting the stock price crash risk using a mixed (qualitative and quantitative) approach and providing a method to assess its fitness accuracy. and respectively the seven variables obtained from the qualitative part; Financial variables, business strategies, managerial ability and information asymmetry as internal factors and macroeconomic variables, political relations and social responsibility as external factors affect the stock price crash risk.

Solvency is one of the essential components that illustrates financial status of an insurance company. Therefore, providing a model with considering internal and external factors which could predict decreasing solvency can improve the efficiency of this index. The main purpose of this paper is to present a model for an early warning system based on decreasing probability of solvency from its critical amount in point of supervisory authority view in Iranian insurers. To this end, based on previous studies, a number of indexes, as independent variables affecting solvency, are selected and classified as economic, financial, and corporate governance. In this respect, the empirical model of research was estimated by econometric method of logit panel data for 18 Iranian companies during 1387-1396. The results depict that interest rates and the change in board of directors have the most and the least impact on the probability of falling insurers’ solvency, respectively. Also, the impact of loss ratio differs in various quantities. Moreover, the impact of variables on probability of decreasing of solvency of insurers are verified; including macroeconomic; interest rate, economic growth and international economic sanctions, Financial variables; current ratio, loss ratio, the Herfindahl–Hirschman Index, and change in board of directors.

Maximizing the value of the company requires making the right investment decisions and any striving for value creation and optimal use of investment opportunities is influenced by the behavioral aspects and individual characteristics of the CEO. The present study investigates the effect of narcissism and CEO power on firm's investment efficiency and consistent earnings growth. Accordingly, 110 companies listed in Tehran Stock Exchange were selected by random sampling method and multivariate regression method was used for the period 2012-2019. This research is applied in terms of purpose and using the post-event approach. The results showed that narcissism had a significant positive and negative effect on firm's investment efficiency and consistent earnings growth, with a significance level of 0/006 and 0/111, respectively. Also, CEO power has a significant positive effect on investment efficiency and consistent earnings growth with a significant level of 0/001 and 0/002, respectively. This result reflects the opportunistic behavior of managers in pursuit of personal benefits and increased agency costs, which requires more monitoring of the behavioral characteristics of managers.