نشریه تحقیقات مالی
پیاپی 72 (زمستان 1402)

Investment is one of the powerful levers to achieve socio-economic growth and development. Since investment is often associated with economic growth and prosperity, it can be expected that ignoring this issue can lead to stagnation or a decline in the company's financial situation. Also, risk is associated with managers when they do not make enough efforts to earn profits and attribute any failure in the company's performance to external factors from control or major shareholders. Therefore, based on this argument, the purpose of the current research is to investigate the impact of management characteristics and financial strategies on the risk-taking of companies listed on the Tehran Stock Exchange.

This research is applied in terms of the type of objective and post-event in terms of the experimental research method. In terms of the reasoning method, this research is a type of inductive research that uses the observation of parts of the society (sample) to present a model for the whole society. The current research is theoretically among the proof researches and statistically, it is a correlational study that multivariable linear regression is used to test the research hypotheses. The quantitative data needed to test the research hypotheses were extracted from the audited financial statements published in the publishers' comprehensive information system (Kedal Network) and Rahvard Novin software. The socio-statistics studied in this research included all the companies admitted to the Tehran Stock Exchange during the years 2013 to 2021.

The results obtained from the first hypothesis showed that the characteristics of management (management ability, management overconfidence, political connections, and power of the CEO) reduce the company's risk-taking. Also, the result of the second hypothesis of the research indicates that financial strategies (working capital strategy, investment strategy, and profit-sharing strategy) reduce corporate risk-taking.

In strategic management research, several factors can influence the company's risk-taking, one of which is the characteristics or personality of managers, as well as other factors such as financial strategies. Also, the behavior of managers can lead to different decisions that affect the company and investors. In this regard, the results obtained from the first hypothesis of the research indicated that managerial characteristics reduce the companies' risk-taking. This means that the more a company's managers have high personality traits (for example, capable managers or powerful managers and managers with high politics), they reduce the company's risk for shareholders through their abilities. Considering the second hypothesis of the research, it can be stated that financial strategies have an impact on the companies' risk-taking. Valuation of companies is a necessity of planning for managers and investors. Valuation shows how the strategy and financial structure affect the stock market value of companies. The value of the company is very important for the shareholders, investors, managers, creditors, and other stakeholders of the company in their evaluation of the future of the company and its impact on the estimation of risk and return on investment and stock price. Thus, the acquired outcome suggests that, from a strategic management perspective, financial strategies that establish long-term company goals, make informed decisions aligned with these objectives, and allocate resources accordingly can also mitigate the company's risk exposure by overseeing projects with a net negative impact on the organization. Therefore, appropriate financial strategies can reduce the corporate risk-taking.

Forecasting the asset prices in any market is an inseparable and critical part of research on markets. Obtaining a credible prediction of an asset's potential future price provides valuable information for investors. Besides that, being confronted with real estate price shocks and price fluctuations in alternative markets, determining the best time for the investment may come up as a big challenge for any investor. Tracing the trend of housing price changes in Iran shows that the average housing price has had a general trend close to other prices and price indices; but the significant issue in this market is the various growth processes and dynamics, compared to changes in other economic indicators. This dynamic gets more complex when a variety of quantitative and qualitative data from diverse types and from several markets are added to the model. This diversity along with the unstructured, stochastic, and scattered datasets makes the implementation of the model so hard to do. The main goal of this paper is to build an Artificial Intelligence model with the most flexibility in the input part for the data type heterogeneity and the lowest error in the output part as the predicted price. All the implemented models showed a high accuracy on the real data extracted from the housing market.

Artificial Intelligence models have an enormous capacity to get a broad range of any kind of data and process and concurrently compile them to build a valid output. This characteristic of AI is very useful in financial models to increase the accuracy of output. Our model is based on the Recurrent Neural Networks. Due to its capability to preserve past information, the LSTM algorithm was implemented as a time series forecasting model.

In this study, using diverse types of official datasets, such as the Central Bank of Iran, we distinguish the influencing variables in the housing market and then we could predict the average housing price in Tehran. Our findings indicate that the average housing price demonstrates the strongest correlation with gold prices, foreign exchange rates, the Consumer Price Index, and market liquidity levels. Utilizing these indicators, predictions with very high accuracy were obtained.

Among the four different models of this research, the best prediction belonged to the multivariate stacked-LSTM model, which was empowered by the highly correlated macroeconomic variables. The validation of models was done by the MAPE Indicator. Furthermore, all the results confirmed that the LSTM algorithm was highly effective in utilizing over two decades of actual housing data from Tehran to forecast future prices.

The significance of the capital market in driving the economic growth and development of a country necessitates a thorough examination of this market from multiple perspectives. Participating in this market invariably involves a heightened level of risk, prompting the emergence of various tools aimed at mitigating these risks. One of the main factors affecting investment decisions is the accurate valuation of derivatives, including options. The Black-Scholes model is used to price a wide range of options contracts. The basic assumption in this fixed model is to consider volatility, which reduces the accuracy of calculating the option price. The main purpose of this research is to determine the price of a European call option with stochastic volatility.

The Heston-Nandi model is a closed pricing formula for European options that shares numerous assumptions with the Heston model. The main difference between the Heston-Nandy model and the Black-Scholes model is the use of the variance type when option pricing. The Heston-Nandy model considers the non-normal distribution of returns and random fluctuations more realistically. Since the Heston model is one of the effective models among the random turbulence models, in this study, the option pricing under Heston and Heston Nandi random stochastic is discussed, which has been investigated considering the non-normality of the data distribution.

In this study, data from Iran Khodro was utilized, spanning the period from November 21, 2020, to December 14, 2022. To increase the accuracy, the volatility was calculated using two historical and implied methods. Following the application of option pricing using all three models, namely Black-Scholes, Heston, and Heston-Nandi, and subsequent comparison of the results, it was determined that the Heston-Nandi model exhibited superior performance when compared to the other two models.

The findings of this research indicate that, in both the short, medium, and long terms, the Heston-Nandi model yields prices that closely align with market prices and exhibits lower error rates. Consequently, it can be inferred that the Heston-Nandi model demonstrates a high degree of flexibility. The Heston-Nandi model outperforms the Black-Scholes and Heston models by capturing unusual patterns like skewness and elongation. This makes it a good alternative to those models.

This article aims to forecast Iran Cooperative Development Bank's profit/loss using a two-stage collective learning method. Employing machine learning for profit and loss prediction is a novel approach to numerical computations, aligning with the article's goal of leveraging big data. Hence, we employ a two-stage collective learning method utilizing Support Vector Machines, Decision Trees, and Weighted Averaging models for learning, testing, and predicting the profit/loss of the Cooperative Development Bank.

In the initial stage of machine learning, Support Vector Machines, and Decision Trees serve as the base models, while the second stage employs a weighted averaging approach. The combination of base learning models was utilized to initially test the prediction process on the performance of 35 branches of the Cooperative Development Bank nationwide from 2012 to 2021. The two-stage machine learning method relies on the utilization of 12 variables grouped into 5 factors for the task. These variables encompass interest-free loans and short-term deposits, deposit interest expenses, total customer deposits, income from facilities and investments, common income, cash balances, administrative expenses, long-term deposits, non-common income, asset depreciation expenses, and the bank's size. Relevant data for the study was obtained from the financial statements of 35 branches of the Cooperative Development Bank spanning the years 2012 to 2021. To minimize prediction errors and facilitate ratio comparisons, all indicators mentioned were adjusted relative to the total value of the bank's assets. Furthermore, to minimize prediction errors and enhance the comparability of ratios, all the mentioned indicators were adjusted in proportion to the total value of the bank's assets. In the initial stage of applying this method, two machine learning models - Support Vector Machines and Decision Trees - were employed, followed by a weighted averaging approach in the second stage.

This article contrasts linear regression with machine learning approaches for predicting the Cooperative Development Bank's actual profit/loss. The results reveal notably high-performance accuracy, evidenced by an MAE metric of 5.66 and an MSE metric of 620.34. Additionally, the correlation between training data and predictions from the two-stage collective machine learning stands at 0.9977. Following this method's performance assessment, it is subsequently employed to predict the Cooperative Development Bank's profit/loss for the years 2022 to 2027.

The results, showcasing the high efficiency of the two-stage collective machine learning method, suggest that managers can employ these approaches for profit/loss prediction in banks. Based on this method, and under normal conditions without abnormal or non-normal circumstances, the obtained results indicate a prospective decrease in the accumulated losses of the Cooperative Development Bank in future years, leading to an ultimate increase in profits by the year 1405. The results of data analysis reveal that the average ratio of the net profit or loss of the bank to its total assets has been computed in a manner that reflects the average profitability of the bank branches over the research period.

Disclosure of strategy in annual reports by companies is a method of transmitting information related to the performance of the company's strategy and its effects on stakeholders, and identifying the drivers and factors affecting it is considered important due to the important role of this type of reporting in making correct and informed decisions. The purpose of this study is to investigate the drivers of strategy disclosure in the companies’ annual reports. Revealing a company's corporate strategy to the public is seen as reliable because managers prioritize safeguarding their reputation. This commitment to transparency encourages a company's leadership to make meaningful investments in the strategy. While disclosing forward-looking statements, like the progress of business strategies' implementation, in annual reports offers insight into the company's direction from management to financial analysts, it can also entail certain costs by potentially harming the company's competitive position.
 
In 2021, this study conducted a comprehensive qualitative analysis and meta-synthesis of prior research findings. A total of 191 articles were extracted and analyzed, and after analyzing the desired components, 26 articles were finalized. After identifying and determining the components, Shannon's entropy method was used to validate the meta-synthesis findings. Therefore, a survey was conducted among 15 experts who were selected using the snowball sampling method to comprehensively classify the conceptual codes. Experts were engaged to validate the elements associated with the extracted codes. Consequently, a table depicting the codes and components was presented to them, and their insights and opinions were solicited on this matter. Due to the absence of specific requirements and standards for preparing strategy disclosure reports in Iran, it appears that individuals working in this field may lack adequate theoretical knowledge on the subject. Therefore, we tried to use the opinions of experts who are theoretically familiar with the subject. These individuals were experts, professors, and researchers who had conducted research in the areas of reporting and capital markets over the past few years. They had accumulated a minimum of 15 years of experience in the fields of accounting and finance.
 
The findings indicated that strategy disclosure in annual reports is classified into 12 components and 32 codes. Among the identified components of a company's vision, the mission statement, statement of strategy, naming, scope, and strategic reference were deemed more significant than other components.
 
This research, by comprehensively examining the factors affecting strategy disclosure, tried to provide a new perspective in examining the drivers of annual reports and improve the awareness of the stakeholders in identifying the drivers and factors affecting this type of reporting.

One of the most important risks faced by financial institutions is credit risk, which is formed by the possibility of the receiver's default in repaying the loan or repaying the loan on time. One of the pivotal financial institutions in Iran's economy is the Export Guarantee Fund of Iran. Being the exclusive official export credit insurance entity in Iran, it operates under complete government ownership, shouldering all obligations associated with the fund's coverage. Over the past decade, the Export Guarantee Fund of Iran, the sole provider of export guarantees in the Iranian economy, has undertaken the risk amounting to approximately $15.8 billion through the issuance of diverse guarantees and insurance policies. The most significant volume of claims pertains to credit guarantees. Based on available statistics and official records within the Iranian entity, it can be observed that between the Iranian calendar years of 1387 and 1397, this fund issued guarantees totaling $6.7 billion, with the largest claim, exceeding $317 million, originating from the credit guarantees provided for the benefit of creditors. Recognizing the significance of this matter, the development of an optimal model for evaluating the credit guarantees provided by customers of the Export Guarantee Fund of Iran holds the potential to mitigate credit risk and subsequently decrease the fund's incurred losses. Even a one percent reduction in the fund's losses could result in substantial savings, amounting to at least 1,330 billion Rials. This figure is poised to grow even further as the fund increases the issuance of credit guarantees. Consequently, the primary objective of this research is to identify the most effective method for distinguishing between creditworthy and high-risk customers, ultimately diminishing the default rate of loans extended by the Export Guarantee Fund of Iran.

In this study, an artificial neural network model was employed to create a risk assessment framework for applicants seeking financial facilities and guarantees from this fund. This model exhibits exceptional predictive capabilities, especially in estimating the likelihood of default for the granted facilities. To achieve this objective, a dataset comprising 2,170 credit files granted by this fund was utilized. A total of 69 intra-organizational and extra-organizational variables were initially considered, and their respective information was gathered. Subsequently, six key variables were employed in the ultimate neural network model, encompassing the credit recipient's province, the guarantee beneficiary (creditor bank), personnel count, current ratio, real interest rate, and economic growth rate.

Based on the neural network model, the best models were extracted with three methods Bayesian, Levenberg, and Conjugate Gradient, and with one to 30 neurons in the hidden layer. The best model with all independent variables (i.e., 69 independent variables) was predicted with 96.2% accuracy, which is more than the Probit econometric model.

The findings of this research offer the capability to distinguish between customers with favorable and unfavorable credit profiles. Furthermore, the credit rating of customers can be used as a basis for adjusting the collateral requirements for each customer group, thereby aligning the collateral amount with the creditworthiness of the respective groups. This means that if the customer is one of the customers with good credit and a good credit rating, less collateral can be obtained from them, and if the customer has higher credit risk, more collateral can be used as a guarantee of the repayment obligation. In this case, while measuring the credit risk and separating customers, it is possible to manage the risk and the credit portfolio of the Export Guarantee Fund of Iran optimally.

Behavioral finance research delves into how investors' perceptions of the capital market and the decisions they make are influenced by psychological factors, ideas, and their willingness to take risks. In addition, it tries to identify the investors’ decisions by investigating the psychological factors. The researchers have tried for several decades to explore the direct relationship between the sentiments and total index return by linear methods permanently. However, this research examines the asymmetrical relationship between investors' sentiments and fluctuations in the total index, categorized into recession and boom regimes. Generating instantaneous response functions based on regimes enables a comprehensive investigation of how investors' sentiments impact market fluctuations in distinct market conditions. Investors' perceptions of total index fluctuations revealed significant losses during various periods of market collapse. Consequently, this study explores the connection between the total index's behavior and investors' sentiments.

Four criteria were used in this research to assess the sentiments indirectly. First, the sentiments hybrid index was extracted in the 2011-2021 period using the main components analysis method. Then, the non-linear Markov switching model was used to investigate the asymmetric relationship between investors' sentiments and total index returns and fluctuations. Moreover, instantaneous response functions were used to investigate the effect of shocks on each research variable. Eviews12 and OxMetrics8 software types were used to estimate the research model.

The findings affirm the presence of an asymmetric relationship between investors' sentiments, returns, and total index fluctuations during both recession and boom periods. Investors' reactions to market fluctuations were found to be positive and significant during the upward market trend of the capital market. However, investors' reactions to market fluctuations were negative during the downward market trend. Furthermore, the study revealed that an increase in returns during a bearish market trend elicited a negative response from investors, while in an upward market trend, it triggered a positive reaction. Moreover, extraction results of instantaneous response functions showed that the inserted shocks to the variables in recession and boom periods may lead to different reactions.

As per the research findings, the likelihood of market continuity stands at 41.54% during the boom regime and 58.46% during the recession regime. This shows the fact that the capital market's tendency to persist in the recession period is higher. Moreover, an examination of the derived instantaneous response functions from the model revealed that sentiment shocks can substantially alter stock market returns. The nature of these effects may vary depending on the prevailing market conditions (be it during a recession or a boom). The outcomes of this study enhance the understanding of investors and decision-making institutions regarding the impact of investor sentiments on capital market fluctuations and returns. This insight empowers decision-makers to formulate policies aimed at managing the impact of influential news and rumors, ultimately leading to heightened investor awareness and a reduction in market volatility.