نشریه تحقیقات مالی
پیاپی 65 (بهار 1401)

To produce a proper reaction when confronted with market uncertainties (booms and busts), before making any investment decisions, investors and financial institutions tend to obtain some level of assurance about the market’s future and also the market’s probable feedback on their performance in the future. This study seeks to identify optimized robust portfolios with the best performance in the face of market uncertainties than can minimize the investors’ regret about their portfolio selection.

To create optimal portfolios, in the study, scenarios pertaining to various market situations based on daily returns of the Tehran Stock Exchange Price Index (TEDPIX) were designed, and the particle swarm optimization algorithm and minimax regret criterion were applied. This study also explored the application of multivariate objective functions and the Omega Conditional Value at Risk ratio as the fitting functions in particle mass optimization. To calculate optimal portfolios, the data from 50 companies on Tehran Stock Exchange (TSE) from 2009 to 2016 were analyzed. Also, the data from the year 2017 were evaluated as out of sample data.

Research findings indicated optimized robust portfolios in monthly periods had higher information ratios and lower tracking errors than the benchmark portfolios.

Making market scenarios and applying the minimax regret criterion improves the performance of optimized robust portfolios. Additionally, compared with the semi-variance benchmark model, applying the multi-objective function and Omega-Conditional Value at Risk ratio in portfolio optimization leads to improve performance of the robust portfolios.

One of the concerns of financial market investors is adopting a profitable trading strategy, which is based on profitable turning points (TPs). To achieve this target, it is necessary to predict TPs. The first step for predicting TPs is to detect TPs from the history of the corresponding time series. The profitability of the predicted TPs depends on the profitability of the detected TPs. Hence, the academic literature has always tried to enhance the performance of TPs detection methods or improve the profitability of the detected TPs. Yet, to the best of our knowledge, none of the existing methods can detect the most profitable and optimal TPs. The current paper aims to fill this research gap.

Our proposed method seeks the optimal TPs from the history of time series, by implementing the detection problem in the context of the graph and solving it by searching the longest path.

The proposed graph-based TPs detection method, in contrast to the existing detection methods, is able to detect the optimal TPs from the history of the corresponding financial time series. The optimality of this method is based on its graph-based specific structure. In this method, the only potentially valuable trading points (breakpoints, i.e., BPs) are considered as the corresponding graph's vertices and connections between the entire pair of BPs form the corresponding graph's edge set. Considering the vertex and edge set, the proposed graph-based TPs detection method optimizes the detection process, by finding the longest path existing in the corresponding graph and extracting the vertices existing in the longest path (as the optimal TPs). It is worth noticing that due to the structure of the corresponding graph, the longest path is equal to the most profitable set of BPs and the most profitable trading strategy available in the history of time series. Hence, the vertices existing in the longest path are the most profitable and the optimal TPs.

To evaluate the performance of the proposed graph-based TPs detection method, it was applied to real-world data set, and thereafter its detection results were benchmarked against other detection models. Constraints considered for modeling the proposed graph-based TPs detection method were applied to the comparative detection models, as well. These constraints included the possibility of short-selling the financial asset, the impossibility of detecting consecutive buying or selling TPs, and considering no time value for the investment money. Results from applying the proposed method to NASDAQ and New York Stock Exchange indicated the efficiency of the proposed method in the problem of detecting the optimal TPs. Besides, comparison results revealed the superiority of the proposed graph-based TPs detection method, in comparison with other detection models available in the existing literature.

Iranian mutual funds’ average turnover rate stood at 330% in the years from 2001 to 2007. Such a high rate could lead to excessive trading costs for investors without necessarily bringing in high returns. The goal of this paper is to estimate transaction costs and examine their effect on the performance of the funds.

In addition to the explicit costs of trading such as transaction taxes and commissions, there are other, implicit, components such as bid-ask spread and price impact which are harder to compute and less recognized by investors. This paper used high frequency (tick-by-tick) trade and limit order book data, as well as the hand-collected quarterly holding and trading data for a sample of Iranian equity mutual funds to estimate the aforementioned trading costs. Then, the effect of trading costs on the risk-adjusted fund returns was examined using the four-factor alpha as our proxy.

Funds understudy spend on average 11% of their AUM annually on trading costs which is a considerable amount in comparison to their return (15.6%), during the sample period. The present study found no significant effect of trading costs on the funds’ performance in the sample. However, during the subperiod of 2013-2014, a negative and significant relationship was discovered between them.

As the obtained results of the present study proved, more trading activities of the Iranian fund managers’ do not lead to higher returns; accordingly a high trading volume generates only enough excess return to offset the associated transactions costs.

Behavioral finance and market microstructure studies showed that investor sentiment has a positive and meaningful effect on liquidity. The purpose of this study is to investigate the effect of the Aggregate Sentiment Index (ASI) on liquidity and its volatilities and to examine the symmetry of news (shocks) on the Tehran Stock Exchange (TSE).

The available academic literature on liquidity proposes a wide set of indices and proxies to measure the different characteristics and dimensions of liquidity. In this study, four indices were employed to measure the various dimensions of liquidity including Transaction Cost, Volume-Based Measure (Trading Volume), and price-impact characteristics. Accordingly, the indices of turnover and trading volume were related to the transaction costs dimension, the Amihud illiquidity ratio was related to Volume-Based Measure (Trading Volume), and the High-Low Spread index was related to the price impact characteristic. Irrational Aggregate Sentiment Index (ASI) was formed using nine indirect (institutional) indices for measuring investor sentiment using Principal Component Analysis (PCA) and the GARCH model was used to investigate asymmetry. The E-GARCH and T-GARCH models were also employed to investigate the asymmetry of news (shocks).

The achieved results proved no significant relation between Turnover and Trading Volume indices but the strong impact of Amihud illiquidity ratio on investor sentiment. The findings also indicated the effects of shocks as asymmetric, i.e., optimistic (positive) shocks have greater effects than pessimistic (negative) shocks. Positive shocks cause a decrease in volatilities and an increase in liquidity.

Since Amihud’s illiquidity ratio is mostly affected by investor sentiment, policymakers can increase liquidity and decrease price volatility by controlling investor sentiment.

Stock markets are extremely volatile and contain only a small predictable component that may still be economically justifiable and could be translated into substantial utility gains for a risk-averse investor. Moreover, the degree of return predictability varies over time and the predictive power of some instruments appears to be diminishing in recent decades. Presumably, the structural relationship between economic predictor variables and future stock returns is time-varying or regime-dependent. Identifying the prevailing macroeconomic state and risk regime is thus highly beneficial for meaningful portfolio management. Therefore, the purpose of this paper is to develop a model for switching regimes in the Tehran Stock Exchange (TSE) index return and to determine the transit variables among economic and financial variables.

The approach used in this paper is based on the smooth transition regression model. Therefore, the statistical data of the 2006-2019 period were used, based on the frequency of seasonal data for TEDPIX returns and economic and financial variables including inflation rate, interest rate, GDP, exchange rate, oil price, money supply, price-to-earnings (P/E) ratio, and price-to-book (P/B) ratio.

The fitted model showed that the rate of return on the Tehran Stock Exchange index continuously switches from a lower inefficient regime to an upper inefficient one. This study confirmed the relationship between explanatory variables and stock returns is nonlinear and the existence of multiple regimes governing the, return of the TSE index as well as the importance of accounting for nonlinearity and the cyclical behavior of stock returns.

According to the obtained results index returns and explanatory variables have a nonlinear and asymmetric relationship and among the explanatory variables of economic and financial, the exchange rate is a transition variable. The regression coefficients during the establishment of the low fluctuations regime and higher fluctuations regime are different. Same as previous studies, the current analysis featured a range of important financial and macroeconomic factors for determining the behavior of stock returns. The results provided evidence that the real significance of the influence of factors such as exchange rate is only revealed once regimes are explicitly modeled.

The main purpose of the present research was to compare the performance of pairs trading based on the Vine Copula, Student's t Copula, and Distance approaches. This was done for the first time on the Tehran Stock Exchange (TSE). The main advantage of the Copula approach over other common approaches in pairs trading, such as the Distance approach, is that in this approach it is not necessary for the stock return distribution to follow the Gaussian distribution.

In the Distance approach, for each sample stock, a stock with the same price behavior was selected, the undervalued stock was bought and the rest was sold. In the Copula approach, first, similar stocks were selected using the traditional and geometric approach. Then Copula functions were fitted among stocks. In Vine Copula, different families of Copula were fitted and the best Copula was determined based on the Akaike Information Criterion (AIC). Then, the pairs with the most mean-reverting characteristic was selected and transferred to the trading period, in order to execute appropriate trading positions. Finally, the return, Sharp ratio, Value at Risk (VaR), maximum drawdown of the Distance approach strategies, the Student's t Copula and the Vine Copula, were compared. The performance of the pairs trading strategy was tested according to the downtrend and non-downtrend conditions of the stock market.

Both Copula approach strategies produced higher returns and Sharp ratios than the Distance approach and the Tehran Stock Exchange Index (TEDPIX). However, the Distance approach had a lower performance than the TEDPIX. The annual returns of the Vine Copula strategy, Student's t Copula strategy, and Distance approach stood at 194, 171, and 20 percent, respectively. The Sharp ratios of these three strategies were 0.79, 0.70 and -0.04, respectively. Also, the performance of pairs trading strategy in non-downtrend market conditions was better than downtrend market conditions.

As expected, due to the many advantages of the Copula approach over the Distance approach, both strategies based on the Copula approach produced higher returns and sharper ratio than the Distance approach. Also, due to the greater flexibility of the Vine Copula in modeling the dependence structure, this Copula could produce a higher return and Sharp ratio than the Student's t Copula.

In this study, a market liquidity prediction model is proposed for the Tehran Stock Exchange (TSE) by identifying the factors affecting liquidity.

Based on the data of 154 Tehran Stock Exchange (TSE)-listed companies for the 2009–2020 period, the values of 23 factors were extracted and divided into two clusters. The partial least squares structural equation modeling (PLS-SEM) technique was employed to validate the variables extracted through the evaluation criterion and to determine their power to explain changes.

The relationships among the variables were evaluated by machine learning models. The results indicated the 10 variables of abnormal cash flow, abnormal discretionary expenses, accrual estimation error, the difference between fulfilled and expected working capitals, free float, auditor’s tenure, audit fees, auditor’s market share, conservatism, and change of auditor to have the greatest effect on clustering. Finally, the best machine learning model was selected through training and testing. Applying the logistic regression model showed that for the calculated value of a dependent variable greater than or equal to 3.391, the illiquidity of the company’s shares is definite, and for the calculated value of the dependent variable less than 3/391, the liquidity of shares of the company is definite. Finally, the best machine learning model was selected through training and testing.

Based on library studies, 89 different liquidity criteria have been used around the world in the studies on the subject of liquidity and various classifications, including measures with more or less frequency and one-dimensional or multi-dimensional measures. There is less agreement on the best measure and the correlation among most of these measures indicating that the use of an inappropriate measure may lead to incorrect conclusions. Using Minitab software, the dependent variable was compared with 5 commonly used criteria (measures) known in library studies (Liu measure, Amihud illiquidity measure, zero measure, number of trading day measure, and trading volume measure). The results confirm the statistically significant difference between the widely used and newly obtained measures. It should be noted that the dependent variable was extracted using non-supervisory patterns in machine learning software and a statistically significant difference between the widely used criteria and the obtained variable was proved. Thus, the resulting variable is a criterion for liquidity. According to the results, the independent variables explain more than 72 percent of the changes in liquidity. Moreover, the neural network model was more capable of prediction than the other machine learning models. In fact, it was proved as the best liquidity prediction model with 99.32 percent of the fitness accuracy.