مجله مدیریت دارایی و تامین مالی
سال نهم شماره 4 (پیاپی 35، زمستان 1400)

The purpose of this study is to investigate the ability of past earnings, compared to past cash flows, to predict future cash flows. A sample of 69 companies was selected, during the years 2004 to 2017, and regression models were applied to analyze the data, using cross-sectional data and time series analysis. Also, in this study, Markov random methods are used to predict future cash flows. For this purpose, transfer probability matrices were formed and after examining the basic assumptions of the model, the transfer process was estimated and the findings were extracted. The results of the study show that in predicting future operating cash flows, the ability to explain past operating cash flows has been greater than past operating profits. Findings in this method also indicate the superiority of the cash flow forecasting power over operating profit and loss. Other results show that although accruals, as well as their breakdown into smaller components, lack the predictive power of future cash flows, by adding models from past cash flows, they acquire the power of prediction. In short, the findings indicate the prominent role of cash flow, compared to profit and loss, in predicting future cash flows.

Firms try to bring their cash holdings ratio closer to the optimal ratio. The adjustment speed of actual cash holdings to achieve the target depends on several factors. In this study, the effect of managers' overconfidence on cash holdings speed of adjustment was investigated. For this purpose, the data of 137 firms during 2004-2020 were gathered and the system GMM controlling the effects of years and industries was utilized. The results showed that the managers’ overconfidence reduced the cash holdings speed of adjustment. The severity of the negative effect of managers' overconfidence on the adjustment speed was less in the firms where more than optimal cash was maintained compared to the other firms. The results of the supplementary tests, which confirmed the main findings of the research were consistent with the facts that the trade-off theory and pecking order theory are interacting in explaining changes in cash holdings speed of adjustment.

Daily liquidity to investors is promised by mutual funds, which allows them to redeem their money based on the prevailing Net Asset Value (NAV). A number of factors like trading suspension and daily price limits can cause redeemed NAV to deviate from the intrinsic values of fund units. Such “staleness” in prices can create arbitrage opportunities for the investors, who can invest or redeem their money at the expense of other passive investors. Using hand-collected data from quarterly mutual fund holdings, we documented significant deviations between the announced and “true” NAVs of Iranian mutual funds. We found that the investors reacted to and took advantage of such deviations. The wealth transfer between the fund investors was estimated to be approximately 1.3% of Total Net Asset (TNA), which was 4 times larger than the wealth transfer documented in the previous studies.

Effectiveness and efficiency of investment is related to the use of technical analysis tools to maximize returns and minimize trading risks as the two challenges researchers commonly face. In the present study, the daily stock price information of 135 companies listed on Tehran Stock Exchange (TSE) during the period of 2010-2020 was used to investigate their investment strategies based on technical indicators in both short-term and medium-term periods. Investor behavioral reactions were indicated by the utilized technical indicators. The relationship between the values of the technical indicators and stock returns showed that the signals represented by the three indicators of Moving Average (MA), Exponential Moving Average (EMA), and Relative Strength Index (RSI) over a weekly to 6-month period to buy or sell the stocks (as a strategy) performed better than the other indicators. Therefore, investors can use a combination of these three indicators to determine their strategies of buying and selling so as to obtain more returns.IntroductionBehavioral finance is one of the new topics that has been raised and considered by some financial researchers over the past few decades. The behavioral financial perspective states that most investors’ behaviors depend on judgment, emotional tendency, and informal sources of information. A significant number of studies has shown that investors’ emotional behaviors affect the stock market. According to the traditional view of stock returns, changes in the stock price are related to systematic changes in the firm’s core values. However, recent studies have shown that investor’s emotional tendency plays an important role in setting prices and determining time-series returns. The effectiveness and efficiency of technical analysis tools can be determined by their applications to maximize returns and minimize transaction risks as the challenges researchers frequently face. There are several strategies for stock trading in a financial markets. Most of these strategies are based on some technical indicators. This study examined the performance of 11 technical indicators to find which ones better represent the investors’ behavioral reactions and finally provide an optimal investment strategy by combining them.   Method and DataTo investigate the relationship between the values of the technical indicators and stock returns, we extracted the daily stock price data of 135 companies listed in Tehran Stock Exchange (TSE) during the period of 2010-2020 and accordingly used the regression method and panel data. FindingsThe values of investments in the weekly to 6-month modes showed that the 6-month information led to higher returns compared to the weekly information, indicating significantly increased returns represented by the 11 studied indicators with the increasing time of the period. The indicators of Moving Average (MA), Exponential Moving Average (EMA), and Relative Strength Index (RSI) had the highest return values ​​in both the weekly and monthly information modes. In the mode of 6-month data, the true strength index (TSI) had a lower coefficient of variation, but better performance than the other indicators. Among the 11 indicators studied, the indicators of MA and EMA had a higher coefficient with stock returns in the short- and medium-term investments. The higher coefficient meant that the returns were enhanced by increasing these indicators of buying and selling. Therefore, they had more power or strength than the other indicators and could better represent the stock returns. The relationship between the values of the technical indicators and returns in the short run revealed that the indicator of MA better represented the stock returns compared to the other indicators. In the medium term, the mentioned indicator was a better indicator for representing the stock returns. When comparing performance of the strategies by the use of the technical indicators, moving from a weekly to 6-month period demonstrated more returns related to the mentioned indicators that were significantly different from the returns represented by the other indicators. Conclusion and discussion In all the investment periods, the indicator of MA had more power to influence on the returns and better represented the stock returns as compared to the other indicators. The signals that were given by the three indicators of MA, EMA, and RSI in the different short and medium-term periods to buy or sell the stocks (as a strategy) better showed the investors’ behavioral reactions and could be thus utilized with higher confidence in comparison to the other indicators since leading to higher returns and profitability. As the trend of the returns represented by the mentioned indicators moved from weekly to the 6-month mode, a larger difference was achieved in terms of significance, indicating that the relationship between the values of the technical indicators and stock returns could provide better buying-selling signals in a longer period.

Overconfidence as one of the personality traits of managers affects each of the factors and stages of investment. In this regard, it is possible that financial constraints and ownership type affect the relationships. Hence, the purpose of this study was to investigate the roles of governmental ownership and managerial ownership in the relationship between managers' overconfidence and R&D expenses in the context of financial constraints. In this study, 30 companies were selected from among the companies listed on Tehran Stock Exchange (TSE) during the years of 2002-2018 to test the hypotheses by using the generalized least-squares method. The results indicated a significant and positive relationship between the managers' overconfidence and R&D expenses. This relationship was stronger in the companies with strong financing constraints. IntroductionDecisions of investing in R&D need more courage. Managers with extreme self-confidence are more entrepreneurial than rational managers, thus greatly promoting the company’s internal technological innovation activities. Some researchers believe that with the increase of uncertainty, the corporate investment environment will be more complicated and changeable, but overconfident managers are more interested in arduous tasks. Managers face more severe challenges in the R&D investment in the environment of strong compared to weak financing constraints. Therefore, using more confident managers to play an active role in innovation and compete with similar companies can be effective.On the other hand, the state- compared to non-state-owned companies can have access to internal and external financial resources more easily. This is because the state-owned companies have abundant assets and rich government-related resources. Thus, in the context of strong financial constraints, the former companies can use some special financing channels to obtain financial support. On the other hand, managerial ownership helps to align managers and shareholders’ interests since it has an effective role in controlling managers' behaviors, while causing managers to avoid decisions that lead to over-investment. Accordingly, the purpose of this study was to investigate the relationship between managers' overconfidence and R&D expenses in companies with financial constraints influenced by the type of ownership.   Method and DataIn this study, 30 companies were selected from among the companies listed on Tehran Stock Exchange (TSE) during the years of 2002-2018 to test the research hypotheses by using the generalized least-squares method. FindingsThe results indicated a significant and positive relationship between the managers' overconfidence and R&D expenses. This relationship was stronger in the companies with strong financing constraints. Additionally, the positive relationship between the managers' overconfidence and R&D expenses was stronger in the non-state-owned companies, while managerial ownership had no significant effect on the relationship between them in the context of financial constraints. Conclusion and discussion Based on the findings, the positive relationship between the managers' overconfidence and R&D expenses was stronger in the companies with strong financing constraints. In addition, upon investigating the impact of ownership type on this relationship, the positive relationship between them was found to be stronger in the non-state-owned compared to the state-owned companies in the context of strong financing constraints. On the other hand, managerial ownership did not show a significant effect on the relationship between them in the companies with financial constraints. In fact, the moderating role of state ownership was more effective than managerial ownership because the relationship between the managers' overconfidence and R&D expenses was weaker in the state-owned companies under financial constraints.

Lottery preferences cause investors’ tendency toward high skewed (third moment of return distribution) stocks. Such preferences differ based on various market conditions. In this study, using predicted systematic and idiosyncratic skewness ranks, pricing of the third moment of stock return distribution was empirically tested in Bear/Bullish market conditions. The predicted systematic skewness was shown to be priced, while the idiosyncratic part was not. After considering market states, the predicted systematic skewness had positive and negative risk premiums in the bearish and bullish markets, respectively. In addition, there was significant evidence of pricing idiosyncratic skewness in both bullish and bearish markets. In this study, for the first time, an empirical test of both systematic and idiosyncratic skewness was done based on different market conditions. The evidence of priced idiosyncratic skewness in both bull and bear markets was another contribution of this work.IntroductionThe empirical and theoretical evidence of non-normal return distribution has encouraged researchers toward working on higher moments of return. Skewness as the third moment of this distribution is considered as an indicator of investors’ lottery preferences. In other words, investors tend to have stocks with higher skewness in their portfolios. Similar to other types of risk, skewness has the two systematic and idiosyncratic parts. Since the idiosyncratic part is believed to be removed during diversification, the expected return should be attributed to the systematic part. In this paper, it was investigated whether systematic or idiosyncratic skewness could be priced.Ex-post (historical) skewness is noisy enough to prevent us from testing the relationship between stock return and skewness. The noisy characteristics of skewness can be avoided by having predicted skewness based on firm characteristics. In this work, firm and price characteristics, which are important to investors with lottery preferences, were used to predict both systematic and idiosyncratic skewness separately. The relationship between skewness and stock return is sensitive to market conditions. For example, the higher the unemployment rates are during bearish markets, the lower and higher the investments and investors’ tendency toward lottery stocks will be, respectively. Thus, it is believed that skewness may have a different relationship with stock returns during bullish or bearish markets. In this paper, pricing of systematic and idiosyncratic skewness was done by both considering and not considering market conditions, the results of which were then compared with each other. Method and DataIn this work, skewness was categorized as systematic and idiosyncratic skewness. After predicting each of them based on price variables and firm characteristics and dividing market conditions, we formed the test portfolios based on the predicted skewness ranks. Then, we used portfolio analysis and Fama & MacBeth’s approach (1973) to empirically investigate the returns of the test portfolios in bullish and bearish market conditions together with cumulative return portfolio and separately. The sample used in this study included 268 firms, which were active in Tehran Stock Exchange between the years of 2001 and 2020. We extracted the price variables and firm characteristics for the firms by using TSECLIENT and Rahavard Novin 3 software. FindingsThe results of not considering market conditions showed that the predicted systematic skewness was priced, while the predicted idiosyncratic skewness was not. However, after dividing market states into bullish and bearish markets and separately testing them in each state, not only the predicted systematic skewness, but also the predicted idiosyncratic skewness was priced. In this case, the predicted systematic skewness had positive and negative risk premiums in the bearish and bullish states, respectively. In addition, there was significant evidence of pricing idiosyncratic skewness in both bullish and bearish markets. Conclusion and discussion The findings of this paper first revealed that using predicted skewness led to the results that were more matched with the theories behind skewness and had a relationship with stock return. Thus, it seemed that using predicted skewness significantly decreased the empirical errors made by the noise of this variable. Second, the predicted systematic skewness had an opposite relationship with stock return due to the different market states. It showed that investors acted differently toward high-skewed stocks in a bullish compared to the bearish market. And third, the predicted idiosyncratic skewness was priced, while it was believed to have been removed during diversification.