نشریه تحقیقات مالی
پیاپی 73 (بهار 1403)

The dynamic relationship between stock prices and housing prices has been an important topic of discussion in the academic and professional literature. The impact of stock prices on housing prices can be examined through two substitution effects and the wealth effect. Real estate and stocks are both assets and can also be considered investment options. The connection between these two assets, housing and stocks, can be interpreted as a substitution effect. This implies that a high return on investment in the stock market may lead investors to divest from the housing market, resulting in a decrease in demand and housing prices. In this case, stock prices will harm housing prices. When housing is considered as a consumer good, the effect of wealth is more pronounced. An increase in income and wealth, including financial assets due to the increase in stock prices, has a positive effect on the total consumption expenditure, including housing costs. More specifically, because homeowners receive windfalls from stocks, the wealth effect of these windfalls increases the purchase of houses as a consumer good. In other words, the wealth effect establishes a positive correlation between stock prices and housing. This is because high returns in the stock market augment the overall wealth of homeowners, enhancing their capacity to invest in additional real estate. Therefore, the stock market index can have a positive or negative effect on housing prices. It is necessary to examine this relationship for each country in detail and based on statistical evidence. Accordingly, in the present study, the effect of the stock market index on housing prices in Iran is investigated.

To reach the research goals, data spanning from 1971 to 2020 is utilized. To test the research hypotheses, the NARDL method was used to identify possible asymmetries effect of positive and negative shocks of the stock price index on the housing price index. 

Findings show that in the short run, positive and negative shocks in the stock market index have no significant effect on the housing price index, but in the long term, positive shocks in the stock market index have a positive effect on the housing price index, while the effect of negative shocks on the housing price index is insignificant. 

According to the obtained results, due to the substantial impact of stock market fluctuations on housing prices in Iran, it is recommended that policymakers take the following into account when formulating stock market policies. First, in the policymaking process for the housing sector, where the anticipation of housing price trends is crucial, careful attention should be given to potential shifts in the stock market index. Next, a long-term boom in the stock market correlates with an increase in housing prices, while the impact of a stock market downturn on housing price increases is deemed insignificant. Consequently, policies promoting a stock market boom can be considered as a contributing factor to the rise in housing prices.

The probability of default is one factor that determines the cost of capital due to its role in credit risk. Dividend as a sign of cash flow or as a sign of ownership of wealth is one of the factors affecting the probability of default. There are two theories of dividend signaling theory and agency theory regarding the effect of dividends on credit risk. The cash profit signaling theory expresses the management’s confidence in the company’s future cash flows and causes market participants to calculate the company’s assets as high and the company’s asset volatility as low, thus reducing information asymmetry, it causes the probability of default calculated by market decreases and there is a negative relationship between dividend and estimated default probability. Conversely, agency theory posits that cash dividends decrease the company’s assets and increase the debt ratio, and this increases the risk of debtholders and the acquisition of their wealth by shareholders, and the conflict of interests between shareholders and debtholders. Therefore, it causes a positive relationship between dividends and estimated default probability. This article aims to examine the relationship between dividends and the probability of default, exploring its alignment with both dividends signaling theory and agency theory.

In this article, Geske’s method, which is an extension of Merton’s method, is used to calculate the probability of default. Subsequently, an unbalanced nonlinear regression is applied to investigate the quadratic relationship between cash interest and the probability of default during the years 2001-2021. The statistical population comprises companies listed on the Tehran Stock Exchange and the Iran Farabourse, with the exclusion of financial companies, those with more than six months of consecutive trading suspension, and those that have experienced trading suspension for more than twenty percent of their market lifespan. Ultimately, 462 companies meet these criteria and are included in the study.

The findings suggest a quadratic U-shaped nonlinear relationship between dividends and the probability of default. Initially, as dividends increase, the probability of default decreases with a diminishing slope. However, beyond a certain threshold of dividend increase, the probability of default starts to rise with an increasing slope.

According to the theory of dividend signaling, as dividends increase up to a certain threshold, the company's future earnings outlook is positively evaluated. The market, in turn, assesses the company's assets at a higher value with lower asset volatility. This positive assessment, attributed to the reduction of information asymmetry, leads to a decrease in the market's estimated probability of default. Once the dividend surpasses this threshold, it becomes advantageous for shareholders. This is due to a reduction in the company's assets, an elevation in the debt ratio, an increased risk for debtholders, and the potential transfer of the company's assets, ultimately leading to the appropriation of wealth from debtholders by shareholders. Therefore, the probability of default increases, which is in accordance with the empirical findings of the quadratic U-shaped relationship between dividend and the probability of default.

In financial literature, there are two well-explored characteristics of volatility. The first pertains to the asymmetric reactions of volatility to positive and negative news, while the second involves the presence of volatility spillover (contagion) between markets and various financial assets. The asymmetric behavior of volatility refers to empirical evidence that a negative return shock causes a greater increase in volatility than a positive return shock of the same size. Also, concerning the asymmetric impact of news on stock volatility, two hypotheses, namely the leverage effect and volatility contagion, have been postulated. Accordingly, this study aims to explore the leverage effect, dynamic conditional correlation, and volatility spillover among ten selected indices of the Tehran Stock Exchange. These ten industry indices collectively constitute over 75 percent of the overall Tehran Stock Exchange index.

In this study, the ARMA (1,1) form was utilized to construct the mean model. Then, the GJR-GARCH model was used to check the leverage effects. Finally, the DCC-GARCH (1,1) framework was employed, which helped to deeply analyze the dynamic linkages in volatility among selected indices of the Tehran Stock Exchange. The daily return data of industry indices, comprising a total of 1117 observations, was utilized during the period from March 25, 2018, to November 16, 2022.

The result of the GJR coefficient, which was positive and significant for all return series -except for the Chemical and Oil Product Indexes- Indicates leverage effects exist. Also, the result of DCC (1,1) indicates the conditional correlation between all variables is positive and volatility spillover among them was strongly confirmed.

Financial markets, particularly the stock market, exhibit varied responses to positive and negative shocks, and these shocks impact the correlation between variables. For this reason, this research aimed to investigate the time frame during which the stock market underwent substantial fluctuations. This approach allowed for a more thorough and accurate examination of leverage effects, volatility spillover, and dynamic conditional correlation between returns. In the first half of 2019, despite the drop in the prices of commodities, oil, and the COVID-19 pandemic, the stock market experienced stunning growth, while investors were excited to buy regardless of the fundamental conditions of the companies. This enthusiasm to buy spread among stock market industries, but from the second half of 2019, the situation was completely reversed, and the market sold their shares at the slightest negative news. Hence, based on the results revealing the presence of leverage effect, dynamic conditional correlation, and volatility contagion, investors and portfolio managers can use these findings to mitigate risks and optimize their portfolios.

Fama (1970) showed that stock markets have weak efficiency and follow the random walk model, so investors cannot achieve abnormal returns by using historical data. It is very important, therefore, to know about the stock price process. The refining and petrochemical industry companies’ stocks are among the most popular ones in the Iranian stock market because they often distribute appropriate dividends to shareholders and sometimes even have good price returns. Also, petrochemical companies are known as the leaders of the stock market, with a great effect on the main stock market index in Iran (TEPIX). This article is to test the random walk hypothesis or weak efficiency of daily stock prices in six petrochemical and three refining Iranian companies.
 
To test the random walk hypothesis, in the first stage, augmented Dickey-Fuller (ADF) unit root tests were conducted using the approaches proposed by Dolado et al. (1990) and Hamilton (1994), along with the Zivot and Andrews (ZA) unit root test incorporating an endogenous structural break. According to the first stage, GARCH (1.1) and Exponential GARCH approaches were used in the second stage to control the fluctuations and leverage effects in evaluating weak efficiency. Daily stock price data (adjusted) were used to test the weak efficiency hypothesis.
 
The results of the augmented Dickey-Fuller (ADF) unit root test and the Zivot and Andrews endogenous structural break showed that Iranian companies of Nouri, Parsan, Pars, Tapico, Shepna and Shetran are pure random walk (weak efficiency). However, Fars, Shepdis, and Shabandar follow a random walk with drift, suggesting the absence of weak efficiency in these companies. In addition, the results of GARCH and exponential GARCH models showed that there is a positive relationship between risk and return for all seven companies. Also, volatility shocks in Fars, Nouri, Pars, and Tapico companies are completely permanent (weak performance). In addition, the shocks observed in Parsan, Shepdis, and Shatran companies are transient, with their effects dissipating over time, and the prices readjusted to the long-term mean, indicating the absence of weak efficiency. The evidence confirms that in these companies, the volatility caused by negative (adverse) news is more than the volatility caused by the same level of positive (favorable) news.
 
According to the findings of the two stages, Nouri, Pars, Tapico, and Shapna companies have weak efficiency which means that the stock price behavior of these companies cannot be predicted. On the other hand, Parsan, Shapedis, Shatran, Fars and Shabandar companies show weak efficiency which means that their stock price behavior is predictable. The results of this article have important implications for investors in the Iranian stock market. Market participants can engage in effective modeling for stocks that lack efficiency and exhibit predictable behavioral patterns to some extent. This allows them to gain insights into future prices and potentially earn profits.

The main purpose of this research is to investigate the effect of stock liquidity on investment returns under financing restrictions and information efficiency in 111 companies listed on the Tehran Stock Exchange from 2014 to 2019. This study examines the effect of the liquidity of companies' shares under two conditions i.e., financial constraints and information asymmetry. The findings align with prior research, indicating a positive correlation between stock liquidity and current and future rates of capital accumulation, productivity, and economic growth.
 
Hypotheses were examined using the multiple regression and ordinary least squares (OLS) method. This study adopts an applied research classification, involving the exploration and analysis of various factors within the Tehran Stock Exchange to either confirm or reject hypotheses. The outcomes of this research carry relevance for the Tehran Stock Exchange, investment firms, and brokerage companies.
 
The main findings of the research proved there is a significant relationship between stock liquidity and investment in companies; a higher liquidity level of shares corresponds to a more effective and profitable investment. Based on the type of model and variables, four hypotheses are discussed in this article, all of which have been confirmed. This research shows that there is a direct relationship between stock liquidity and investment efficiency for younger companies with higher information asymmetry and financial constraints. These results are consistent with the view that increased stock liquidity positively impacts enhancing the information content of stock prices. This, in turn, contributes to the enhancement of market feedback and transparency, enabling managers of newer companies facing information asymmetry and heightened financial constraints to be influenced towards making value-creating decisions.
 
The research findings indicate a significant and negative association between higher stock liquidity and the decrease in investments below the required level for young companies. Furthermore, a negative and statistically significant relationship is observed between higher stock liquidity and lower investment in companies characterized by information asymmetry and greater financial constraints. This study sought to examine the relationship between stock liquidity and investment efficiency of companies listed on the Tehran Stock Exchange. It also tried to investigate whether the effect of stock liquidity on the reduction of investment in companies with information asymmetry and financial constraints is more significant or not. The findings reveal that the impact of increased stock liquidity on the reduction of investment is more pronounced in companies facing information asymmetry and financial constraints. These results are obtained following a thorough examination, incorporating endogeneity tests and a range of measures assessing both stock liquidity and investment efficiency.

The optimization of asset portfolios, taking into account market advancements, has emerged as a pivotal subject in financial economics. Constructing asset portfolios is acknowledged as a critical decision for investors. Consequently, researchers focus on identifying factors that influence the selection of portfolios with high returns and controlled risk. Portfolio optimization, a cornerstone of financial economics, has gained prominence in the face of ever-evolving market dynamics. Investors' decision-making plays a pivotal role in portfolio construction, prompting researchers to explore factors that influence the selection of portfolios with high returns and controlled risk. Numerous models have addressed the optimization problem of stock portfolio management, each tailored to specific conditions and constraints. This research focuses on developing a multi-objective optimization model that incorporates investor memory and behavioral preferences. The literature on portfolio optimization is vast and diverse, encompassing various approaches and methodologies. Traditional optimization models, such as Markowitz's Mean-Variance model, aim to maximize expected returns while minimizing risk. However, these models often fail to capture the complexities of real-world investment decisions, which are often influenced by behavioral factors. Investor memory refers to the tendency of investors to base their current investment decisions on past experiences. This can lead to biases and suboptimal outcomes. Behavioral preferences, on the other hand, encompass a range of psychological factors that influence investor behavior, such as risk aversion, overconfidence, and herding.

We perceive stock portfolio optimization as a multi-objective challenge, considering two primary criteria. The first criterion involves investor memory, which encompasses utilizing historical price data and market trends to anticipate future performance. The second criterion pertains to behavioral preferences, which involves integrating investor risk aversion, overconfidence, and herding behavior into the model. We employ a Genetic Algorithm (GA) to optimize portfolios under both criteria. GA is a robust optimization technique that can effectively handle complex problems with multiple constraints. The study population comprises companies listed on the Tehran Stock Exchange for the year 2021. 

The achieved results suggest that investor memory serves as a more suitable criterion for optimal portfolio construction compared to behavioral preferences because investor memory incorporates market data and trends, providing a more objective basis for decision-making. Additionally, incorporating market return alongside investor memory data yielded superior results than using behavioral preferences with market return. This indicates that the combination of investor memory and market data can lead to more efficient and profitable portfolios. Pairwise comparisons of portfolios created using investor memory and behavioral preference criteria revealed that investor memory consistently outperformed behavioral preferences across different risk levels. This finding highlights the importance of considering investor memory when constructing optimal stock portfolios.

This study contributes to the literature on portfolio optimization by demonstrating the effectiveness of incorporating investor memory and behavioral preferences into the decision-making process. The findings suggest that investor memory is a more suitable criterion for portfolio optimization than behavioral preferences. Moreover, the combination of investor memory and market return data can lead to more efficient and profitable portfolios. The findings of this study have important implications for investors and portfolio managers. Investors should consider incorporating investor memory into their decision-making process when constructing stock portfolios. Additionally, portfolio managers can use the proposed multi-objective optimization model to create more efficient and profitable portfolios for their clients. This study provides a foundation for future research on portfolio optimization. Future studies can explore other factors that influence investor behavior, such as social media sentiment and news sentiment. Additionally, researchers can investigate the application of other optimization techniques, such as machine learning algorithms, to portfolio optimization.

This paper seeks to employ fractional cointegration methodology to model high and low stock prices, as well as the range series, indicating the difference between high and low stock prices. Additionally, it tries to examine the regime-switching characteristic in the cointegrating relationship between price series.

The study utilizes the Fractionally Cointegrated Vector Autoregressive (FCVAR) approach to explore the cointegrating relationships among six key indices of the Tehran Stock Exchange - TEPIX, First Market index, Second Market index, Industry index, Mali index, and Fara Bourse overall index - across various time frequencies from August 14, 2007, to August 15, 2022. To test for the fractional unit root in each price series, the GPH and ELW methods are also employed. Furthermore, the Qu (2011) method is employed to test the true long memory against spurious long memory on the range series. Finally, the threshold effect in the cointegrating relationship between the high and low price series is analyzed by the two regimes' Self-Exciting Threshold Autoregressive approach (SETAR).

In most indices, the estimated fractional parameter of the range series is lower than that of the high and low stock price indices. Moreover, the high and low prices and the range series are affected by regime changes or a smoothly varying trend. The high and low stock price indices are fractionally cointegrated, in the two levels of stationary and non-stationary ranges. Further, the fractional cointegration approach gives a lower measure of dependency in price range series than the fractionally integrated approach. These findings are robust to different time frequencies, including daily, weekly, and monthly. Finally, the results affirm the time-varying cointegrating relationship between high and low stock prices. Thus, the FCVAR framework should be generalized to adjust according to this characteristic.

The empirical results show that, unlike the return, which is stationary and unpredictable, the range prices have characteristics of the long-memory processes, falling into non-stationary and stationary levels with mean-reverting behavior. Accordingly, one can obtain a non-stationary range-based volatility estimator, which is more efficient than a stationary return-based realized volatility estimator. These results imply that traders, investors, and policymakers could predict the future extreme prices of the market indices from past values and exploit such predictions to design investment strategies. The cointegration between high and low prices of indices implies limited arbitrage opportunities for the investors and traders in the Tehran Stock Exchange and Iran Fara Bourse Co. Furthermore, the efficiency of these markets is shown to be unstable across indices. Still, there are robust long-run relationships between the high and low stock price indices. Thus, to design hedge fund strategies containing combinations of the stocks from these markets, the behavior and long-run relations of the indices must be considered. Moreover, to design portfolio allocation and diversification strategies, one should mix assets that haven’t short-term behavior with indices that show long-term relationships. This conclusion is derived from the notion that mispricing and over-hedging may occur in the absence of cointegrating relationships between price series.

Project financing has emerged as a critical mechanism for funding infrastructure and capital-intensive projects across various sectors, both in the public and private domains. Its significance has grown notably in recent decades within the financial market, reflecting its pivotal role in facilitating economic development. Notably, in developing countries, the establishment of robust economic infrastructure is a pressing priority, serving as a cornerstone for overall growth and advancement across multiple economic sectors. However, the insufficient attention given to meeting financing requirements in this domain poses substantial challenges to economic progress. Diving deeper into the realm of project financing, one finds a diverse array of financing mechanisms, each characterized by its unique set of risks and complex capital structures. These risks, if not effectively managed, can exert considerable influence on project outcomes, impacting crucial objectives such as project timelines, cost management, safety protocols, quality standards, and long-term sustainability goals. Understanding these risk factors and their interplay is paramount for stakeholders involved in project financing endeavors.
 
To explore these dynamics, this study employs an objective-oriented approach, leveraging a descriptive-correlational method for data collection. Central to this methodology is the utilization of Structural Equation Modeling (SEM), a powerful analytical tool that allows for the examination of complex relationships among variables. The study's statistical population comprises a diverse range of professionals, including experts, senior experts, supervisors, and managers from mining companies within the country under study. From this pool, a sample of 220 individuals was randomly selected to ensure robustness and representativeness in the analysis.
 
The study's findings shed light on the intricate relationships between various factors and project financing risk. Notably, the analysis unveils the significant impact of financing methods on political risk, with an observed effect intensity of 0.462 and a probability statistic of 0.686. This surpasses the critical t-value at the 5% error level, underscoring the statistically significant influence of financing methods on political risk. This underscores the importance of considering the choice of financing mechanisms in mitigating or exacerbating political risk factors, thereby enhancing the overall risk management strategy of project financing endeavors. Moreover, the study delves into the relationship between customer and management risks. While the overall effect of customer risk on management risk was determined to be 0.046, the calculated significance value of 0.851 fell below the critical t-value at the 5% error level. This indicates a lack of significant observed effect, suggesting that customer risk may not exert a substantial impact on management risk. Nevertheless, understanding these nuanced relationships contributes to a more comprehensive understanding of the multifaceted nature of project financing risk. Beyond these specific findings, the study highlights a myriad of factors that influence project financing risk, including social risk, contractor risk, construction risk, design risk, legal risk, financial risk, and subsidiary contracts, among others. Each of these factors contributes to the overall risk profile of project financing endeavors, necessitating a holistic approach to risk management and mitigation strategies.
 
In conclusion, the study underscores the critical importance of focusing on project organization and management capability, particularly in the context of developing countries. Inadequacies in these areas often result in suboptimal utilization of resources and capital, leading to delays in project commencement, implementation, and operation, ultimately yielding uneconomical outcomes. In light of the prevalent project-oriented approach adopted by organizations, there exists a heightened imperative to design and enhance project organizations to effectively navigate the complexities of project financing endeavors and achieve desired outcomes.