نشریه چشم انداز مدیریت مالی
پیاپی 34 (تابستان 1400)

Implied equity duration has been developed in recent years as a risk measure in capital asset pricing models to explain expected returns. Empirical studies indicate a downwards-sloping pattern in the term structure of equity returns; nevertheless, longer duration implies higher expected returns mostly because longer investment horizon is positively associated with more risks. In this research we employ Dechow’s formula to calculate implied equity duration of a sample of firms listed on Tehran stock exchange from 2008 to 2018. The sample is divided into four portfolios sorted in terms of duration. The results indicate a downwards sloping terms structure of equity return. In the literature a number of reasons are proposed to explain this pattern; we refer to the state’s regulation in the pricing of firm’s products or services. According to the results the more regulatory interference in the pricing of firms’ products and services, the higher stock valuation and the less expected returns; thus our proposed investment strategy is to take short position in the stocks with the most price suppression of the products (high duration stocks) and to take long positions in the stocks with the least price suppression of the products (low duration stocks).

The performance of an intelligent model largely depends on the selection of the most relevant and most influential input variables and the lowest complexity of the learning model. Therefore, in the present study, to predict the index of Tehran Stock Exchange based on financial and economic variables, first prioritize featuresWith MID algorithm, then 4 different neural network models (MLP, SVR, RBF, DNN) are used, which are the most important and innovative prediction models. According to the results of the analysis of the studied models, an algorithm is proposed to select the appropriate features on the index, as ISF-MID, and are compared with several similar methods. The data used in this study were collected daily in the period of 18/01/2014 to 21/08/2018. Evaluation of the models was performed by K-fold cross validation method. The MAE, MSE, and RMSE criteria are also used to evaluate the performanceof the mentionedmodels. The results show that with the proposed method, with 7 selected features, it is possible to achieve high accuracy in predicting the daily index of the Tehran Stock Exchange.

Research has shown that stochastic volatility and jumps play an important role in stock price trends market, and considering these two factors has a significant impact on a better description of assets. Infinite jump Levy processes cover skewness and heavy-tailedness properties but can not present volatility clustering. By time chainging these processes by integrating the Cox-Ingersoll-Ross, it is abtained a stochastic volatility Levy processes model that are applied to determine the conditional value at risk (VaR) in this paper. Applying the fast Fourier transform, a closed form formula of probability density function is derived. Moreover, by applying Hybrid Particle Swarm Optimization algorithm, Grid search method and univariate method algorithm, the parameters are estimated. Based on the introduced model, we estimate the VaR of along total Index of Tehran Stock Exchange in 1388 to 1398 and compare it by historical simulation and Variance-Covariance approaches. The results of Back-test techniques in computing the VaR indicate that the stochastic volatility Levy processes with infinite jumps have better performance than the Variance-Covariance methods.

Investor sentiment about the capital market can play an important role in stock price trends, market transactions, and especially on the stock return synchronicity. The entry of many Individual investors who do not have enough information about investment, the study of this issue has made more important. To this end, in this research we investigate the relationship between investor sentiment and stock return synchronicity in Tehran Stock Exchange, by using the financial data of 167 firms listed on Tehran Stock Exchange from 15 various industries during ten years from March 2010 to March 2020. We use the Baker-Wurgler (2006) sentiment index as our primary measure of investor sentiment that Measured by principal component analysis. Also, three different methods have been used to measure the stock return synchronicity: the Mork model, the Carhart four-factor model and the Fama and French five-factor model. The results show that the Investors sentiment significantly affect on increasing stock return synchronicity. The findings also showed that the coefficients of positive and negative sentiment are not significantly different and as a result, positive and negative sentiment are symmetrically affecting the increase in stock return synchronicity.

The purpose of this study is to investigate the effect of agency problems on relationship between Institutional investor's short-term time horizon and stock price crash risk. This research is applied in terms of purpose and correlation in terms of method. The research period was 2012-2019 and data from two months before 2011 and two months after 2020 were used to measuring the risk of stock price fall. Also, 123 companies were selected as a sample. To analyze the research data, multivariate regression models have been used in Eviews software. The results of this study showed that investor's short-term time horizon has a positive and significant effect on the stock price crash risk. Agency problems (problems due to agency conflict based on managerial ownership) have a negative and significant effect on the relationship between investor's short-term horizon and the stock price crash risk. Agency problems (problems due to agency conflict based on free cash flow) have a positive and significant effect on the relationship between investor's short-term time horizon and the stock price crash risk.

This research has aimed at studying the characteristics and data generation process of TSE index daily return. Applying various tests showed that return data of TSE index follows a chaotic and clustered behavior. Furthermore, beside the condition of efficiency in this market, a novel prediction method is developed. The method introduced in this paper is formed from two consecutive neural networks; a mixture density neural network and a Long short-term memory neural network. It is worthy of note that the proposed method is associated with the inferred statistical structure from the data.  The entire model is compiled in order to predict TSE index considering various number of regimes using daily data December 2008 up to April 2021. Results from various statistical tests rejected the weak form of efficiency and manifested a chaotic behavior in TSE index return. Furthermore, the developed prediction method gained higher accuracy than the same method without considering regimes. Results from Diebold-Mariano test significantly implied the differences of the accuracy between the models with regimes and without regimes. Finally, a back test by considering transaction cost showed that the strategy based on the predicted direction of the model with regimes gains higher return than market benchmark and the model without regimes.

Behavioral finance explains contradictory patterns with market efficiency hypotheses with behavioral biases. One of the most common price patterns in the stock market is the pattern of momentum, which can be driven by investors' adjustment and anchoring bias and disposition effect. In this study, the role of adjustment and anchoring bias and disposition effect on the formation of momentum returns on the Tehran Stock Exchange are examined. Using the portfolio study method and the data of the research period of 2007-2016, it was found that investors are more affected by adjustment and anchoring bias compared to disposition effect and form a pattern of momentum by reversing against the maximum price thresholds with a one-year period as the reference price. Also, among the maximum thresholds, investors are most affected by the maximum price of 26 weeks with a six-month waiting period, and further analysis and analysis using the Fama-Macbeth regression and the Fama-French three-factor model confirm these results.