Financial Research Journal
Year:2024 | Volume:26 | Issue:4|Papers Count:10

Objective There are two distinct perspectives regarding the impact of real earnings smoothing on labor investment efficiency through its effect on information asymmetry. On one hand, based on signaling theory, real earnings smoothing can reduce information asymmetry and enhance labor investment efficiency. On the other hand, the opportunistic managerial view highlights the opposite effect. Given this context, the present study aims to investigate the impact of real earnings smoothing on labor investment efficiency, with information asymmetry acting as a mediating variable.   Methods This study falls under the category of applied research due to the applicability of its findings in the decision-making process. The data utilized in this research were collected based on past real information, making it an ex-post-facto study. Additionally, this research is descriptive-correlational in nature, with the primary objective being the examination of relationships between the research variables. To this end, considering the conditions and constraints imposed on the research population, a sample of 106 companies listed on the Tehran Stock Exchange was selected, and the hypotheses were tested using multivariate regression models.   Results The results indicate that an increase in the level of real earnings smoothing leads to improved labor investment efficiency. Furthermore, information asymmetry mediates the relationship between real earnings smoothing and labor investment efficiency, where real earnings smoothing, by reducing information asymmetry, contributes to greater efficiency in labor investment.   Conclusion Based on the findings, it can be concluded that company managers use real earnings smoothing to convey hidden information. This information reflects the company's future outlook, which, through consistent earnings smoothing over the years, presents a clear and positive projection of the company's future to investors and creditors. This supports the view that smoothed earnings are indicative of a bright future for the company, as these earnings are perceived by investors and creditors as sustainable. In fact, by using signaling tools, managers reduce market information asymmetry regarding the company’s stock price, enabling the company to secure the necessary financial resources for labor investment and make efficient decisions. Therefore, based on the signaling theory of private information, it can be concluded that real earnings smoothing conveys managers' private information regarding the company's future revenues, thereby reducing the information asymmetry between companies and external capital providers, ultimately leading to greater labor investment efficiency. This research contributes to the growing body of literature on real earnings smoothing and labor investment efficiency, highlighting the importance and positive impact of real earnings smoothing, while also emphasizing the need for further investigations due to the lack of understanding regarding managerial motivations behind real earnings smoothing.

ObjectiveThis study aims to evaluate the drivers of startup financing in capital market firms through the lens of libertarian or metaphysical strategies. The goal is to develop insights that enhance the appeal of investment in startups, ultimately fostering a more attractive environment for investors interested in this sector. MethodsThis study is exploratory, with practical applications based on the analysis and data collection methods. It employs a mixed-method approach to integrate various data for comprehensive insights. In the qualitative phase, thematic analysis and link matrix analysis were conducted, involving 14 university experts in financial management. For the quantitative phase, 50 managers from capital market firms participated through reciprocal matrices and assessments to determine the data scenarios. In this step, pairwise comparison matrices were created in the form of row " " and column " " to determine the most important drivers of financing startups at the level of capital market firms. Drawing the results of the matrix in the form of a MICMAC diagram the two basic axes for scenario planning were determined. ResultsIn the qualitative phase, the findings indicated that among 12 initial studies and 6 additional studies aimed at determining key dimensions, 8 main themes were identified as factors influencing the sustainability of startup financing drivers. These themes were also validated through the Delphi process. In the quantitative phase, the results yielded four scenarios, each accompanied by explanatory statements: the Market Leadership Matrix, the Intelligent Competition Matrix, the Territory Conquest Matrix, and the Left Behind Matrix. These scenarios serve as a foundation for selecting the most advantageous approach to startup financing from the perspective of libertarian philosophical strategies within capital market firms. As a result of the quantitative analysis, the Territory Conquest scenario emerged as the most significant context for influencing startup financing drivers. This scenario, shaped by the critical thinking strategy as a philosophical foundation rooted in entrepreneurial metaphysics, offers substantial potential for fostering the growth of startups within capital market firms in the future. ConclusionThe findings of this study indicate that advanced technology in the development of startups enhances their sustainability, allowing firms to capture a greater market share in the future. Capital market firms are in the process of changing their production lines, starting new production units, supplying new products to the market, or even entering international markets. According to this matrix, they should focus on technological capabilities to attract financial providers, ensuring that firms do not encounter challenges in developing their startup businesses.

Objective This study investigated the role of commodities as independent investment tools, determined their interrelationships, explored their behavior within hybrid portfolios, and sought to identify suitable relationships for portfolio selection decisions between commodities and the stock index. The primary goal was to examine the diversification and hedging (safe haven) properties of assets and future commodities in Iran's exchange market compared to stocks during "bull and bear periods of the stock market" and to determine the minimum variance portfolio.   Methods This research was applied in purpose and quantitative in methodology. The research approach was deductive (comparative), investigating expected empirical benefits using historical data. The study was theoretically categorized as proof research and, statistically, as a correlational study (econometric models). To this end, regression models of market tests, dynamic conditional correlation (DCC-GARCH), and the Markowitz model (portfolio optimization) were applied to daily data from the stock and commodity markets over 11 years from 2009 to 2020.   Results The findings revealed that commodities, on their own, present a high-risk investment. Gold is the only commodity comparable to the stock market, which shares similar returns and volatility. Except for copper, other commodities have significantly higher volatility but lower efficiency than the stock index. Regarding the market test model, results indicated that some commodities had the appropriate hedging ability as a “safe haven” in different stock market regimes (business cycle phases). In portfolio construction, adding a single commodity to a quantitative stock portfolio resulted in low returns, offset by reduced fluctuations. However, the results showed that a “commodities portfolio” outperformed a “single portfolio”. When adding a commodities portfolio or a commodity index to the hybrid portfolio, the stock index still held the largest weight (around 94%), but the average portfolio risk was notably reduced (approximately 1/557), yielding an improved Sharpe ratio.   Conclusion According to the findings, investing in commodities is a better option for reaping the benefits of diversification. Furthermore, when making investment decisions, the bull and bear periods of the stock market should be considered, as the findings revealed that business cycle phases are a strong indicator for the tactical allocation of commodities. The results of this study also support the evidence that the behavior of different commodity groups varies significantly. Finally, hedging is not always a safe haven for the stock market, and the reverse is also true.

Objective Using cryptocurrencies to hedge against the risk of various types of assets can be considered a useful feature in cryptocurrency investment. In recent years, investment in cryptocurrencies has become more common, and many people have allocated a portion of their portfolios to cryptocurrencies. Understanding the behavior and capabilities of cryptocurrencies can help investors better manage their investments. In this research, we have studied the capability of cryptocurrencies to hedge investment portfolios in Iran's economy. We wanted to determine whether cryptocurrencies can hedge investments in the stock market and gold coins. Accordingly, we have selected two popular cryptocurrencies, namely Bitcoin and Ethereum, to investigate their capability to hedge investments in the stock and gold markets in Iran.   Methods To investigate the risk hedging of common Iranian investments using cryptocurrencies, daily data related to the Tehran Stock Exchange Index and the price of the Bahar Azadi gold coin were utilized. The daily returns of the gold coin and the stock index were calculated over a four-year period from March 2019 to April 2023. Additionally, using the exchange rate of the US dollar in the free market, the daily prices of the most used cryptocurrencies (Bitcoin and Ethereum) were collected, and their daily returns were extracted. To examine the volatility of the variables, the researchers employed the multivariate GARCH autocorrelation model. For evaluating the hedging capability of cryptocurrencies based on the minimum risk approach, they used the following three methods: Constant Conditional Correlation (CCC), Dynamic Conditional Correlation (DCC), and the BEKK Diagonal Correlation Matrix.   Results The results showed that cryptocurrencies can be used to hedge the risk of investments in the gold coin and stock markets in Iran. It should be noted that, based on the results, the hedge ratio of Bitcoin is larger than that of Ethereum, and to hedge the risk of investments in gold coins and stocks, Bitcoin has consistently allocated a higher percentage of the portfolio compared to Ethereum. Furthermore, it was found that the Dynamic Conditional Correlation (DCC) method provided a larger average risk hedge ratio across all portfolios. On the other hand, the results of the BEKK Diagonal Correlation method exhibited more fluctuations compared to the other approaches. Additionally, the findings indicated that during the periods from April to December 2020 and from September to March 2023, alongside the significant increase in the price of the US dollar in the country, the required weight of cryptocurrencies for hedging in investment portfolios composed of Bitcoin and gold coins, Ethereum and gold coins, as well as Bitcoin and stocks, and Ethereum and stocks increased.   Conclusion Bitcoin and Ethereum can hedge investments in the Tehran Stock Exchange and the Iranian Gold Coin market. It should be noted that during times of uncertainty and devaluation of the local currency against the US dollar, greater investment in cryptocurrencies is needed to hedge investments in the gold and stock markets.

Objective The impact of financial and economic shocks and uncertainty is not always limited to the target market and may spread to other markets as well. Empirical research results, such as those by Jurado et al. (2015) and Gabor and Gabota (2020), indicate that the contagion of cross-sectoral uncertainty and the significance of these uncertainties are not constant over time and may change. Traditional time series regression models assume that a relationship with fixed coefficients can be applied across different time periods. The misleading results of this unrealistic assumption have led to the development of dynamic models that better reflect the realities of the external world. The state-space approach is a modeling method for dynamic systems that predicts and analyzes system behavior under these modeling conditions. One of the applications of this approach is to account for structural instability in parameters and to allow coefficients to vary over time. Models of this type are known as time-varying parameter (TVP) models. This research aims to study the reaction of the financial, housing, and macroeconomic sectors in Iran to each other's shocks, with a focus on the effects of uncertainty contagion.   Methods The present study is applied in terms of purpose and correlational analysis in terms of nature and method. It is post-event and utilizes past information. In this study, a library method was used to collect theoretical sources, while an archival method was employed to gather the data needed for hypothesis testing. To examine changes in cross-sectoral uncertainty contagion, the time-varying parameter vector autoregression model (TVP-VAR) is used with monthly data from January 2008 to December 2020. In this context, uncertainty indicators are calculated using GARCH models and then tested using the TVP-VAR approach, along with an analysis of variance of the generalized prediction error of total dynamic connectedness, as well as the directional dynamic connectedness of the indicator pairs.   Results The research results indicate that the primary source of uncertainty is the macroeconomic sector, which acts as the main source and transmitter of uncertainty to the other financial and housing sectors. Additionally, the housing sector is a net recipient of uncertainty from the other two sectors. The findings suggest that the contagion of uncertainty between the financial and housing sectors is bidirectional and conditionally dynamic, while the contagion of uncertainty from the macroeconomic sector to the financial and housing sectors is unidirectional.   Conclusion According to the results, the contagion of cross-sectoral uncertainty and the significance of these uncertainties are not constant and change over time. Therefore, identifying the different channels of contagion between markets and pinpointing the source of contagion can help in selecting policies that reduce vulnerability and enhance the performance of asset portfolio risk management.

ObjectiveThis study aims to present a novel model for predicting the future commitments of insurance companies that can adequately address the potential challenges of traditional methods. Traditionally, insurance companies use the Chain Ladder approach as a statistical tool to forecast the trend of claims development. This statistical method is favored by regulatory authorities in various countries due to its simplicity in assumptions and clear interpretation. However, certain assumptions, such as the stability of data development and linear relationships between variables, can affect the efficiency of this model when faced with internal policies or external factors like the COVID-19 pandemic. Forecasting future commitments close to reality is closely related to the financial stability of insurance companies. The amount that insurance companies allocate to meet their future obligations is identified as reserves. Calculating reserves that are less than the required amounts can pose challenges for insurance companies in fulfilling their commitments while calculating more than necessary amounts can negatively impact the financial statements of insurance companies. MethodsIn this study, a dynamic model based on machine learning algorithms is proposed. The model's output, which combines the number and timing of bodily injury accidents, plays a crucial role in calculating reserves for non-life insurance products. This model is specifically trained to predict the frequency of accidents in Vehicle Third-Party Liability Insurance. It can identify hidden patterns and non-linear, complex relationships within claims data. A Long Short-Term Memory (LSTM) neural network algorithm is employed, recognized for its strong predictive capability in time series data. The model is trained using historical data from Karafarin Insurance Company covering the years 2017 to 2021. ResultsThe performance of the model is highly related to the hyperparameters chosen for the model. Two of the most common approaches for tuning the hyperparameters are tested in this study. These Two models are grid and random search. The Root Mean Square Error (RMSE) is used as a performance metric, and it indicates that the grid search has a lower RMSE than the random search for the training data with a slight difference (16.33 versus 17.4). However, the results for the test data in the grid search have a sign of overfitting. ConclusionThis study recommends using random search for tuning the hyperparameters of the model to predict the frequency of daily incidents. The evaluation of the two approaches for tuning hyperparameters indicates that random search is more suitable for working with unfamiliar data and managing overfitting situations. Overfitting occurs when the model becomes overly influenced by the training data, learning not only the actual patterns but also the noise and minor details of the data. This issue can negatively impact the model's generalization ability.

ObjectiveThe main objective of this article is to examine the relationship between financial leverage and financial performance. After identifying this relationship, the moderating effect of financial distress on the strengths and weaknesses of this relationship will be investigated. Then, due to the importance of the effect of the currency crisis on the financial performance of companies, the effect of this moderating variable on the relationship between financial leverage and financial performance is also examined. MethodsThe statistical population investigated in this research comprises companies listed on the Tehran Stock Exchange during the period from 2013 to 2022. The data needed to analyze the relationships were collected using the library research method, utilizing databases from the Central Bank of Iran, the World Bank, and Codal. Systematic elimination was employed in the population to account for limitations, resulting in a final sample of 114 companies from various sectors of the Tehran Stock Exchange, excluding the financial and investment sectors. In this research, financial leverage is considered the independent variable, while financial performance measures such as return on assets, return on equity, Tobin's q, return on sales (ROS), and return on cash flow are treated as dependent variables. Financial distress and the currency crisis serve as moderating variables. Data were analyzed using correlation analysis and multivariate regression, with the Estimated Generalized Least Squares (EGLS) method applied to fit the model. ResultsThe findings indicate that financial leverage has a significant negative impact on financial performance, and this effect is attenuated in companies that face a higher risk of financial distress. Furthermore, the results suggest that a currency crisis intensifies the negative relationship between financial leverage and financial performance. ConclusionThe negative relationship between financial leverage and a company's financial performance indicates that as a company increases its debt level, the rise in borrowing costs outweighs the benefits. Managers can enhance the company's financial performance by reducing financial leverage levels and planning to increase internal funds, which should be considered an essential strategy to improve performance and mitigate external financing constraints. In high-risk financial distress conditions, the negative effect of financial leverage on financial performance tends to diminish. Management issues and the absence of operational and control systems are more pronounced in companies at a higher risk of financial distress; therefore, monitoring, limiting, and enhancing company efficiency due to high financial leverage will have a significantly positive effect on the financial performance of companies facing a high risk of financial distress (including payment defaults, bankruptcy, and liquidation). It can be asserted that higher financial leverage can improve the financial performance of companies due to its disciplinary role. Additionally, during a currency crisis, which typically results in higher inflation, investment costs and the utilization of loans and financial resources will increase. Reducing financial leverage during such crises, when access to foreign financial resources becomes more expensive and difficult, may lead to improved financial performance for the company.

Objective Hedging the risk caused by price volatility using options relies on an accurate and appropriate valuation of those options. Therefore, the purpose of this research is to value the options traded on the Tehran Stock Exchange using modular neural networks. The study will also compare the performance of these modular neural networks with the most renowned options valuation models, namely the Black-Scholes-Merton model and the multi-layer perceptron neural network model.   Methods For this research, data on call options traded on the Tehran Stock Exchange from March 2018 to March 2022 were utilized. Initially, after removing outlier data, 80% of the dataset was designated as training data, while the remaining 20% was set aside as test data. To facilitate a comparison of results obtained from different models, these two subsets of data remained constant throughout the research. In this study, the theoretical prices generated by each model were compared with the market prices traded on the Tehran Stock Exchange using MSPE, RMSPE, and MAPE statistical criteria. To calculate the prediction error for the Black-Scholes-Merton model, the theoretical price of options was first obtained using its pricing formula. Subsequently, the theoretical prices derived from the Black-Scholes-Merton equation were compared with their corresponding market prices. In the neural network models, option prices were predicted using Python and its machine learning algorithms. Finally, the predicted prices from the models were compared with the market prices of the same options. To assess the significant differences between each model and the others, the Paired Sample Test of the mean percentage of errors was employed.   Results This research showed that, from the perspective of the RMSPE criterion, the developed neural network model with implied volatility has the lowest error and has the best performance in valuing call options across all monetary positions and periods compared to other investigated models. However, the performance of the developed multi-layer perceptron neural network model with implied volatility has been slightly better than that of its modular counterpart. Following this, the neural networks developed with historical volatility, the neural networks with discrete data, the Black-Scholes and Merton model, and the modular neural network model proposed by Gradoevich et al. (2009) have been the most accurate, respectively. From the perspective of the MAPE criterion, the developed neural network model with implied volatility has performed the best; however, among all the neural network models, the multi-layer perceptron neural network has outperformed the modular model.   Conclusion Modular neural network models can outperform the Black-Scholes and Merton models. Incorporating implied volatility enhances the performance of neural networks in options valuation. However, when considering the RMSPE criterion, modular neural networks trained with historical volatility perform better than multi-layer perceptron neural networks. In contrast, for models using implied volatility, the modular neural network does not achieve better performance than the multi-layer perceptron neural network. Overall, neural networks utilizing implied volatility—whether in modular or multi-layer perceptron configurations—exhibit superior performance in long-term periods and in ITM (in-the-money) moneyness situations.

ObjectiveThis study aims to examine the existence and intensity of the disposition effect among individual investors in the Iranian stock market. The disposition effect refers to the tendency of investors to sell winning stocks (stocks with gains) and hold losing stocks (stocks with losses). This behavioral bias can lead to suboptimal decision-making in the market and reduce investors' profitability. The primary objective of this research is to analyze the factors that influence this behavior and to investigate the relationship between market conditions, investors' timing of entry into the market, and the intensity of the disposition effect. Specifically, the study seeks to answer whether factors such as market returns, volatility, and economic uncertainty increase the likelihood of individual investors exhibiting the disposition effect. The study is based on the hypothesis that investors who enter the market during periods of uncertainty and high volatility are more likely to display this behavioral bias. MethodsThis research utilizes detailed trading data of individual investors. The data includes transaction records such as the date, direction, price, and volume of each trade, as well as personal information of the investors, such as age, gender, trading experience, and total account balance. To analyze the disposition effect and assess its intensity among individual investors, survival analysis and regression models are employed. The survival analysis method allows researchers to track the timing of investors' entry into the market and examine how it influences their behavior over time. Additionally, regression models are used to explore the relationship between market-related variables and personal characteristics of investors with the intensity of the disposition effect. These models help researchers understand how factors such as market volatility, market returns, and other economic indicators influence emotional and irrational decision-making among investors. ResultsThe results of the analysis indicate that individual investors who enter the market during periods characterized by low market returns, high volatility, and economic uncertainty are more likely to display the disposition effect. In other words, during times of market instability and heightened economic uncertainty, these investors tend to hold on to losing stocks and sell winning stocks at a higher rate. This finding highlights the significant impact that market conditions have on investors' emotional and irrational decision-making processes. Investors are more prone to making behaviorally driven decisions based on fear and anxiety during periods of economic uncertainty, which ultimately leads to a reduction in their overall returns. Furthermore, the findings suggest that investors' trading experience and personal characteristics such as age and gender can also influence the intensity of the disposition effect. ConclusionThis study demonstrates that the disposition effect not only exists among individual investors but is also influenced by market conditions and personal characteristics. Specifically, investors tend to display a stronger disposition effect during periods of low market returns, high volatility, and economic uncertainty. These results can help investors and policymakers better understand the impact of behavioral factors on investment decision-making and, as a result, adopt more effective strategies to manage emotional biases among investors. Additionally, this research underscores the importance of education and awareness in mitigating the effects of behavioral biases in financial decision-making, thus preventing irrational and detrimental decisions in the market.

Objective One of the most important financial decisions companies make is investment decisions. The purpose of investment decisions is to obtain a higher return, and to obtain a suitable return through investment, accurate knowledge of the effective factors is essential, because fluctuations in investment can come from various sources. According to real option theory, uncertainty plays a crucial role in shaping investment decisions for companies. This is because uncertainty impacts the future cash flows of companies and increases information asymmetry between managers and shareholders. Therefore, uncertainty is a key factor that must be considered in investment decisions, as it plays a decisive role in shaping those decisions. The main goal of the research is to investigate the sensitivity of companies' investment to uncertainty and cash flow in companies listed on the Tehran Stock Exchange. Additionally, this study aims to expand the investment literature by investigating the impact of various forms of uncertainty (firm-specific uncertainty, market uncertainty, economic uncertainty, and Capital Asset Pricing Model (CAPM)-based uncertainty) on investment. Furthermore, this study empirically tests the effect of cash flow on the relationship between different forms of uncertainty and company investment.   Methods This research is applied in terms of its objective and correlational in terms of its nature, specifically causal. In this research, the library method was used to collect information related to the theoretical foundations. To achieve the research objectives, data from 840 firm-year observations during the period from 2014 to 2021 were utilized. Additionally, all models were estimated using the two-step Generalized Method of Moments (GMM) estimator proposed by Arellano and Bond to address the issue of endogeneity.   Results The results indicated that uncertainty is a key factor influencing company investment, with all four forms of uncertainty (company-specific uncertainty, market uncertainty, CAPM-based uncertainty, and economic uncertainty) having a positive and significant impact on corporate investment. Also, the results showed that cash flow intensifies the impact of firm-specific uncertainty and economic uncertainty on investment and reduces the impact of market uncertainty and CAPM-based uncertainty on investment. Therefore, a company's investment level depends not only on its uncertainty but also on the amount of its cash flow.   Conclusion The results of the research confirm the views of the growth real option and the convex function of capital's marginal income, while disconfirming the views of the expectation real option, market structure, and production technology. The findings suggest that an increase in uncertainty encourages investment, as an early investment decision can enhance competitive advantage by gaining growth opportunities over other competitors. In addition, an early investment decision can help companies increase their market share and gain more profit. In other words, greater risk leads to more investment and, consequently, more reinvestment. This is because higher risk is associated with higher expected returns on investment, which in turn encourages greater reinvestment.