نشریه تحقیقات مالی
پیاپی 63 (پاییز 1400)

Theoretical corporate finance literature claims that financial policies of firms are related to the nature of their cash flows shocks; that is firms respond to transitory and permanent cash flow shocks differently. However, a limited number of surveys are conducted in the empirical corporate finance literature to verify the validation of these models.  The main objective of this article is to fill this gap.

First of all, the existence of a permanent element in the cash flows of firms will be tested. Secondly, an adjusted filter will be devised to identify short term and permanent shocks to firms' cash flows and estimate their characteristics. This filter is adjusted according to the dynamics of this article's theoretical model. Eventually, the basic axioms of the dynamic framework on which the theoretical model is based, will be analyzed and then the hypotheses will be tested among sample classes.

1- Modeling operational cash flows of companies as a combination of permanent and short-term stochastic processes can eliminate most of the problems assuming a permanent random process governing cash flows. 2- Pieces of evidence show that in firms that are operationally the same, the characteristics of permanent cash flows shock resemble. 3- While it is common to classifying stocks of companies whose sales are not affected by economic cycles as defensive industries, it seems that the separation of short-term and permanent shocks can provide a better criterion or distinguishing tool to interpreting this concept. 4- Firms that are exposed to higher cash flows volatility or experienced a greater permanent growth rate, hold a higher level of cash flows and at any time that they do external financing via equities, issue more stocks.

According to the findings, separating short term and permanent shocks presents a strong intuition to describe firms' financial policies, and most implementations of the theoretical model of this article are acceptable.

In addition to being aware of stock fragility, Investors are considering an Earnings acceleration strategy to understand how companies' financial practices change to achieve the expected returns and the speed of stock price convergence. Therefore, the purpose of this study is to investigate the mediating effect of earnings acceleration on the relationship between stock fragility and the speed of stock price convergence.

The statistical population of this research is the companies listed on the Tehran Stock Exchange, among which data are available for 87 companies and the time horizon from 2009 to 2019. Therefore, 870 years -participation in this study have been examined. Barapratab et al (2015) model has been used to measure the variable of the speed of stock price convergence. Quantum financial theory has been used to measure stock fragility. To test the research hypotheses, the generalized least squares regression model has been used.

The results of the study indicate that there is a positive and significant relationship between stock fragility and the speed of stock price convergence. Expression of earnings also explains the relationship between stock fragility and the speed of stock price convergence.

The convergence of Iranian stock prices has important implications in terms of economy and policy. During periods of stock fragility, stock price convergence generated by one firm rapidly spreads to other subsidiaries in an industry Therefore, it is of special importance to attract investments and reduce the range of risks. Also in emerging markets such as the stock market and the level of investment attitudes in developing societies, as the acceleration in factors such as profits increases, optimistic thinking increases the level of risk-taking and thus generalizes more strongly in the industry or other stocks.

The main purpose of this paper is to investigate a developed stochastic algorithm for modeling financial markets using the Ornstein-uhlenbeck process combined with Levy noise. Using the closing prices of stock markets, it can be concluded that the stochastic model of the Ornstein-uhlenbeck process with time-dependent parameters significantly improves the performance of stock price forecasting.

At first, we study the stochastic differential equation that is composed of Ornstein-uhlenbeck independent processes. Since these processes are extracted by the gamma process, we call it the gamma Ornstein-uhlenbeck process, We used a stochastic differential equation under the combination of two independent processes and simulate the time series data.  The parameter estimation is done using the maximum likelihood estimator.

To illustrate the performance of the proposed model, we apply the desired stochastic differential equation for a set of financial time series from Tehran Oil Refining Company, Saipa Azin, and the Cement of Urmia stock exchanges. The simulated data mimics the original financial time series data. This is observed from the estimates of root mean square error criteria.

Numerical results show that the predicted volatility of these companies is close to the simulated ones. The advantage of this methodology is the fact that the estimates obtained are stable around the true value and also the low errors indicate that the estimation procedure is accurate, therefore producing a higher forecasting accuracy. Thus, the proposed estimation algorithm is suitable with large data sets and has good convergence properties.

The efficient market hypothesis (EMH) states that stock prices reflect all available information and price changes (or return) should be unpredictable. But the lead-lag effect is a phenomenon that provides the predictability of return in the stock market. Examining the lead-lag relationship between stocks, in addition to evaluating market efficiency, provides investors with useful trading strategies.

This study has investigated the lead-lag effect between stocks within 20 industries of the Tehran Stock Exchange (which cover 75% of the market capital) from 2015 to 2020 using the vector autoregression (VAR) model.

From 20 industries in our sample, 13 industries show the lead-lag effect. In 10 industries, the return of small stocks leads to the return of big stocks within the same industry. Whereas, in the other 3 industries, the return of big stocks leads to the return of small stocks within the same industry. In addition, a stronger lead-lag effect has been observed in the computer, transportation, and metal products industries (which are relatively small industries).

These observed lead-lag relationships (or predictable returns) between stocks intra-industry show a systemic inefficiency in the Tehran Stock Exchange market. Also, the results show that in some industries of the Tehran Stock Exchange, there are potentially profitable trading strategies.

The purpose of this study is to investigate the effect of audit indicators on the risk dimensions of banks listed on the Tehran Stock Exchange and OTC with emphasis on the mediating role of the complexity of operations.

The statistical population of the study is all banks listed on the Tehran Stock Exchange and OTC. The statistical sample included 16 banks (144 years-company) in the period 2008 to 2018. In order to analyze the interactive effect of variables, Structural Equation Modeling (SEM) was used and for pattern analysis, the partial least squares (PLS) approach was used using Smart PLS software.

The results showed that among the banks listed on the Tehran Stock Exchange and OTC, auditing indicators have a negative and significant effect on the risk dimensions of banks and the complexity of banks' operations. In addition, using the Subel test, the results showed that the complexity of banks 'operations as an intermediary variable has a positive and significant effect on the relationship between audit indicators and banks' risk dimensions. Another important finding of the study was that using VAF statistics, it was found that 25 percent of the impact of total audit indicators on the dimensions of risk, is indirectly through the mediating variable of banking operations complexity.

According to the research findings, banks' risk is affected by the complexity of operations and audit dimensions affect these two factors. Therefore, the size of the auditor, change and tenure of managers and the audit firm, the percentage of distortions detected and reported, the auditor's expertise and knowledge of the banking industry, reputation and audit quality control index have a significant effect on the complexity of operations and risk of banks. The auditor should be considered by the bank managers. This finding is also important in determining the management and audit team in audit firms.

Financial spillovers are commonly defined as occurrences where fluctuations in the price of an asset in one country (or region) trigger changes in the prices of the same asset or other assets in another country (or region). Our country's economy, since the discovery of oil in it; has always relied on this product. It is common among stock market participants that the price of this product fluctuates; it causes fluctuations in the stock market. The present article seeks to investigate the existence of spillover between the Tehran Stock Exchange and the oil market.

Using daily data on daily returns on the Tehran Stock Exchange index and West Texas Intermediate oil prices during the period December 5, 2008, to November 4, 2019, and the vector autoregressive model we examined spillover between the two markets. This article also examines the Granger causality test between the two markets.

After performing the augmented Dickey-Fuller test and ensuring the stationary of both time-series data; the vector autoregressive model was estimated. Length of optimal lags; With regard to the Akaike information criteria; was determined seven days. According to the results of this article, there is no spillover between the two markets and oil and the stock market are no other cause of fluctuation. In the time series returns on the index; everyday return has a significant relationship with a return of one day; two days; three days; five days and seven days ago. Oil returns have no significant relationship with their amount in any of the previous seven days except the previous day. According to the Granger causality test, the p-value (prob.) in both directions was more than 5%, and therefore the return on oil and the index are not the other cause of the Granger test. Also based on the impulse response function (IRF); the shock to the index and oil returns decays after twelve and eight days, respectively.

Contrary to what was expected; there is no spillover between the two markets. As a result, other factors are involved in changing prices and causing stock market shocks.

The purpose of this study was to investigate that behavioural factors play an important role in decision-making and subsequently formation of markets phenomena.

The population of this study consist of all companies listed in The Tehran Stock Exchange of various industries and groups and samples were collected between the years 2007 and 2017. Using regression analysis, we estimated the influence of institutional investors anchoring bias on the 52-week highest price by the cross-sectional regression model, as well as measuring the effect of increasing proximity of price to the 52-week highest price next abnormal returns in parallel with controlling of the other factors over PEAD.

Given the statistically significant anchorage coefficient, it can be stated that there is a significant relationship at a 95% confidence level between the anchorage bias and the abnormal returns resulting from the profit declaration. Also, the level of institutional ownership has a significant relationship with the abnormal returns of profit declarations. In institutionally-owned companies, the role of the anchor variable is not even significant. However, in companies with low institutional ownership, this role is positively significant with a 0.13 coefficient.

Anchoring bias plays a minor role in explaining the additional returns resulting from the profit declaration in companies that are institutionally owned. Therefore it can be said that the first hypothesis of this research on the minor effect of anchorage variable in case of low institutional ownership is confirmed.