نشریه تحقیقات مالی
پیاپی 49 (بهار 1397)

Presence of the considerable gap between the time of receiving the buy/sell signals and the beginning of the price change trend provides an appropriate situation for implementation of algorithmic trading systems. Tehran stock exchange is one of these markets. A high-frequency trading system has some advantages (exploiting intraday stock market volatility) and disadvantages (high amounts of transaction cost due to the high transaction volume), thus we can augment the advantages and control the disadvantages by designing the system elaborately and modifying the trading regulations.
In this research, the “Local Traders” approach has been utilized to predict the future trend of stock and Reinforcement Learning has been used for dynamic portfolio management. According to the “Local Traders” approach, there is a local trader (an agent) for each stock that is expert at it. It predicts the future trend of its own stock based on stock’s intraday data and their technical indicators by determining how beneficial it is to buy, sell or hold. In this research, 2 models will be proposed based on Local Traders. Based on the first one, trades with fixed lot size were sought by exploiting the local traders’ recommendations. In the second model which is an extension of first model, one can dynamically manages the portfolio using reinforcement learning and local traders’ recommendations.
Results showed that, the proposed models outperformed the Buy and Hold strategy in Normal and Descending markets. Furthermore, in all kinds of markets, the second model outperformed the first one.
Generally, the Buy and Hold strategy works the best in an Ascending market, hence the proposed algorithms are not expected to outperform this strategy. However, the performance of the proposed approach along with Neural Network method to anticipate the future trend of stocks was considerable in Normal and Descending markets. In addition, the implementation of Reinforcement Learning model to dynamically manage the portfolio has improved the results.

The aim of the present study is to analyze and test the power of Conditional Capital Asset Pricing Model (CAPM) with Time Variant Beta against Standard Capital Asset Pricing Model to find the better model to explain expected return of stocks.
Using monthly data, beta value was estimated using standard CAPM and Multivariate GARCH methods for companies included in the statistical sample. Based on these two methods, the expected returns of the next year to test out-of-sample performancewere calculated by eliminating 12 months from the top and adding 12 months from the bottom. The same process was repeated for the following years. Then, the accuracy of each of these models was examined using criteria MAE and MSE.
Using paired t-test and Diebold-Mariano test, we tested the research hypothesesand the results were presented based on MAE and MSE indices. The results showed that according to both criteria in MAE and MSE, the conditional CAPM models, whether based on full rank BEKK or diagonal BEKK, can have better performance than the standard CAPM model.
Regarding the findings and better predictive power of conditional CAPM based on full rank BEKK and/or diagonal BEKK, in terms of MAE and MSE criteria, replacing the standard model with these models can result in higher accuracy.

This study aims at measuring the effect of added valued of unobserved actions of mutual funds.
Using Return Gap criteria– the difference between the return of hypothetical and actual return – the researchers examined the effect of indiscernible activities in mutual funds throughout seasonal reports.
The results showed that unreported activities in mutual funds create just enough value to offset their costs.
We studied the unobserved actions of Iranian mutual funds among quarterly disclosure of holdings. We measured the effect of these unobserved actions using return gap – the difference between the return of a hypothetical constant portfolio matching the fund’s beginning of quarter and the fund’s actual return. The results indicated that these actions create just enough value to offset the costs associated with them; hence, the net effect on returns to fund investors is zero. We further documented that the funds are able to create more value through these activities in bull markets.

The aim of this study is to propose a synthetic approach including Clustering and UTADIS models to form a profitable investment portfolio.

In this research, securities are clustered using K-means method and the ideal number of clusters is determined through certain validation indexes. The results obtained from the Clustering method were used as the input data for the UTADIS model and the securities were classified by UTADIS. After solving the primary model, in order to achieve better resulst, a post-optimality analysis was performed and the classification validity test and the classification error tests were carried out.

After reviewing previous studies in this field and carrying out a survey of professionals from the financial industry, eight key attributes including capital return, beta coefficient, net profit margin, BV/MV, ROA, ROE, P/E and EPS were identified. The investment portfolio consists of Iran tele companies, Khark Petrochemical, Shazand Petrochemical, Fanavaran Petrochemical, Information services, Iran refract, Khouzestan steel, and Iran zinc mines.

The results of study showed that the proposed framework has created a profitable portfolio and capital return is the most important attribute in stock portfolio selection.

To date, the main source of inspiration for behavioral finance scholars has been cognitive psychology. This study builds on this tradition by merging in insights from yet another psychological sub-discipline: personality psychology. We argue that a human being’s personality is a key determinant of his/her behavior and performance. We illustrate, for a subset of five personality traits (locus of control, maximizing tendency, self-monitoring, sensation seeking and type-A/B behavior), how this logic can be applied in the context of the study of traders’ behavior and performance.
we investigated the behavioral and functional components of 380 individual investors in the stock market using survey method and collecting the questionnaire.
The results suggested that Investors with an external locus of control, type-A behavior and high maximizing tendency are busy working on trading more frequently. Investors with an external locus of control, high sensation seeking and high self-monitoring have a less diverse portfolio. Finally, the frequency of transactions is related to better performance, while portfolio diversification does not affect the performance of the individuals.
Different personality traits affect distinct components of trading behavior, and so trading performance.

In manager myopia, long-term profitability of the firm through reducing the research and development activities, marketing and activities which are costly in the early years are determined, have been sacrificed in order to achieve the short-term profit and enhanced current-term stock price. The purpose of this study is to examine the reaction of Capital Market to managerial myopia and theimpacts of the existence of institutional investorson listed companies in Tehran Stock Exchange (TSE).
Using systematic elimination method, 170 companies were selected from among the companies listed in TSE during 2007 to 2016. In this study, reaction of the capital market is measured based on abnormal return criteria.
The results of the first hypothesis showed that the F-value is 2.942, its level of significance is set at 0.000 and the determination coefficient was measured 0.146; besides, T-value and level of significance for managerial myopia were reported 0.165 and 0.869 respectively. Based on the results of the second hypothesis, F-value, level of significance and the determination coefficient for the companies with organizational investors are reported 2.652, 0.000 and 0.085 respectively; and the F-value, level of significance and the determination coefficient for the companies with non-organizational investors are reported 3.181, 0.000 and 0.098. Finally, T-value and level of significance for managerial myopia were reported 1.331 and 0.183 for the first group and 0.776 and 0.436 for the second group, respectively.
The results indicated that the presence or absence of institutional investors among the stakeholders does not have any significant effect on the relationship between abnormal returns and manager’s myopia. In addition, there is no significant effect of capital market reaction on myopia managers. At last, as expected there is a negatively significant relationship between managers’ myopia and abnormal returns.

In this research, the hypothesis of convergence of the stock price indices in selected countries over the period from January 2007 to February 2017 has been investigated.
Cluster analysis method is used for estimation purposes in the present study.
The results did not confirm the overall convergence of the stock markets under investigation. However, there are two convergent clusters and one non-convergent (divergent) cluster among those stock markets. The results also showed that Iran stock market doesn’t behave independently and there is a tendency towards convergence with other international stock markets.
This tendency towards convergence might have taken place through two channels: first; through the effect of international volatility of oil and other commodities on Iran stock market and second; through foreign trade as Iran and its main trading allies lie in the same cluster. We can also claim that financial policymakers should moderate and manage the effects of international financial volatilities on domestic market by implementing policies which can help make domestic market more diversified.