Systematic risk

this world; though all the discussions are focused on the causal relationships in allthe scientific arguments. One of the methods to study the designed causal relationshipsobjectively is Granger causality test. This paper aims to investigate the longtermcausal relationship between the stock price and dividends. The statisticalpopulation includes 180 active companies in Stock Exchange of Tehran during2010-2014. In order to analyze the achieved data statistically, the used specifiedmodel has been the regression model using the econometric data panel techniquesand to test the research hypotheses and find the specific relationships among thevariables, the descriptive-inferential statistics and Eviews software were used.Results indicated that the stock price is not due to the dividends; however, thedividends are the Granger causality of stock price. Also, the type of industry, firmgrowth index, and systematic risk index are of impact on the relationships betweenthe stock price and dividends.

The present study aims to model systematic risk using financial and accounting variables. Accordingly, the data for 174 companies in Tehran Stock Exchange are extracted for the period of 2006 to 2016. First, the systematic risk index is estimated using the ARFIMA-FIGARCH model. Then, based on the research background, 35 affective financial and accounting variables are simultaneously used with the help of the backward elimination and forward selection method for modeling. After analyzing and evaluating the variables in Eviews software, the four variables of debt ratio (CL. E), size (SIZE), net profit to sales ratio (NETP. S), and interest rate coverage ratio (ICR) are selected in the backward elimination method. In the forward selection method, in addition to the above variables, operating profit margin (OPM) is also chosen. The estimated model of these variables in both methods shows a low ratio of R2 coefficient that is approximately 7%. In the test case, the model of forward selection method has less error in all four criteria of root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and Tile coefficient (TIC) compared to the backward elimination method.

In this research, we use jump beta and continuous beta as indicators of financial sector companies systematic risk and study their determinants in banking, insurance and investment industry. In result, the value of jump beta is higher than continuous beta. Jump beta of Banking industry and Investment industry is considerably lower than average. We found some negative and positive effects of firm characteristics on jump beta and continuous beta. In insurance companies, the supremacy of jump beta is influenced by firm characteristics. Size has positive effect on aggressiveness of both continuous and jump betas in investment companies. Current ratio has positive effect and debt ratio has negative effect on aggressiveness of insurance companies. Firm characteristic has some positive and negative effects on continuous industry beta deviation, but no effect on jumpy one. Inflation has negative effect on continuous beta but has no considerable effect on jump beta. Inversely, exchange rate has negative effect on jump beta but has no sensible effect on continuous beta. Influence of growth rate is strong positive for all industries of financial sector but weak positive for banking and insurance companies

Investment is an essential factor in a country’s economic development. Meanwhile, return and risk have been effective factors in investment. Today, many financial economists have accepted Risk or Beta as a standard tool for assessing the risk involved in certain actions. This paper has been conducted to find a way to obtain the risk of industries in different timescales included in the short-term and long-term. The statistical population includes a daily index of selected industries (including banks and the food, and car industries) from 2009 to 2014. The present study has measured the risk in different timescales using the wavelet analysis, and consequently, the risk time series have been expressed using a State- Space model. The direct relation between the risk of the selected industries and the market have been eventuated in which, an increase in return of the market would lead to an increase in return of industries and this has also been proven when there is a reduction in return.  

This paper investigates the design of an efficient model so as to anticipate the basic economic market rate of returns. To do so, accepting the relationships, interactions and effectiveness of these markets and exploiting Comonotonic Functions under Probability Function Framework as well as using weekly data for ten years’ period of time(2008-2017) in Iran’s economy we design optimum model and test its capability and estimation power. The results illustrate the efficiency of the achieved model. Furthermore, taking the practical nature of this paper into account, we come up with optimum lag of time and the period of time required to achieve equilibrium in any market and the entire economy as a prototype in the frame of Stock Exchange.

When evaluating companies and investment plans, most analysts use a discount rate that is derived from CAPM models. The beta in these models usually represent risks and opportunities of the relative industry, with almost no attention to the risks that are already included in the beta. This ignorance in risk measurement could ultimately impair shareholders value. What makes things critical is that by adjusting risks and opportunities in beta, the result of investment plans and company valuation could be much different. In this paper we use 1 to 10 years of monthly return data for all industries of Tehran Stock Exchange and Iran Fara Bourse and suggest an adjusted beta for each industry which is stripped of the dazzling effects of the debts and growth opportunities. We separately account for breaking down beta into beta of growth opportunities and beta of existing assets for each industry in various timelines between 1 to 10 years. Our results showed that the beta of growth opportunities is bigger than the beta of assets for almost all industries. The mentioned betas can make a big difference in cost of capital and we suggest using them when evaluating investment plans, development plans, valuation of companies and even start-ups.

Existing models do not adequately capture how changes in the external environment (systematic risk) affect corporate returns. This study addresses this gap by identifying explanatory variables and an experimental model design. The sample includes 16 investment companies over two periods, 2006-1 and 2020-4. We inputted 69 systematic risk variables into the model and identified the 1-12 non-fragile variables affecting investment company weighted averages using a Bayesian model averaging approach. The findings show that the non-official hard currency exchange rate is the most robust variable influencing the Tehran Stock Exchange. Thus, stocks with the highest correlation to the foreign exchange rate should be selected when forming a portfolio. Moreover, fiscal policy variables directly impact investment company weighted average returns. Consequently, portfolios of quasi/semi-government-owned companies will see higher return fluctuations.