مطالب

In this research, we use a pair trading strategy to make a profit in an emerging market. This is a statistical arbitrage strategy used for similar assets with dissimilar valuations. In the present study, smooth transition heteroskedastic models are used with the second-order logistic function for producing thresholds as trading entry and exit signals. For generating upper and lower bounds, we apply the rolling window approach and one-step-ahead quantile forecasting. Markov chain Monte Carlo sampling method is used for optimizing the parameters. Also, passive strategy in the out-of-sample period is used to compare the profits. The population consists of 36 daily stock returns in Tehran Stock Exchange. Then, we select ten pairs from these stocks and use Minimum Square Distance method, and five pairs from one industrial sector. Finally, we see strategy1 and 2 have positive returns in the out-of-sample period, and they produce higher returns than passive strategy.

The evaluation of corporate social responsibility has gained significance over the past decade due to the importance of natural and environmental resources. Many studies have been conducted on corporate social responsibility and the presented related models, which add to the importance of this report. However, in addition to its significance, many researchers also believe that corporate social responsibility evaluation models lack the necessary efficiency due to different interpretations, lack of transparency, and abuse of some companies in order to deceive and commit fraud. Therefore, the aim of this research is to present a model to evaluate corporate social responsibility using value added, which can be a suitable criterion in evaluating the social responsibility of commercial entities. In the present study, first, a model was developed based on corporate social responsibility, and then, to test the model, social value added of Nano motor oil was studied as a case study. In this study, data was analyzed through pairwise comparison. The findings of the study conducted on the social value added of Nano motor-oil indicate economic efficiency of 40%, product social efficiency of 8%, and depicts a 38-times increase in social value added compared with its economic added value. Thus, Nano-engine oil producing companies are located on the corporate social responsible category and the corporate social responsibility in this product is on the third level (strong) that indicates the product’s efficiency in the community and can be a suitable incentive for all business organizations to pay more attention to their products’ environmental and social impacts.

Identifying the causal relations between trading volume and stock returns and between trading volume and return volatility plays a vital role in identifying profitable investment opportunities. In this study, the Granger causality test was conducted to analyze the causal relationships between the mentioned variables in Tehran Stock Exchange. Consequently, the Vector Auto Regression (VAR) model was employed to determine the conditional mean equations of returns and volume. Moreover, the bivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model was used to model the conditional variance equation, stating the relationship between volume and return volatility. According to the results, no bilateral causal relationship can be ascertained between returns, volume, and return volatility. In other words, return and return volatility could barely predict volume; therefore, volume cannot be the Granger causality of the other two variables. However, stock returns were found to have an important role in determining the volume. Likewise, return volatility can be used to predict volume accurately. In fact, stock returns and the return volatility were both the Granger causalities of the volume.