stock price Crash risk

Stock price crash risk is a phenomenon in which stock prices are subject to severe negative and sudden adjustments. So far, different approaches have been proposed to model and predict  the  stock price crash risk, which in most cases have been the main emphasis on the factors affecting it, and often traditional methods have been used for prediction. On the other hand, using  Meta Heuristic Algorithms, has led to a lot of research in the field of finance and accounting. Accordingly, the purpose of this research is to model the Stock price crash risk of listed companies in Tehran Stock Exchange using firefly algorithm and compare the results with multivariate regression as a traditional method. Of the companies listed on the stock exchange, 101 companies have been selected as samples. Initially, 19 independent variables were introduced into the model as input property of the particle accumulation algorithm, which was considered as a feature selection method. Finally, in each of the different criteria for calculating the risk Stock price crash risk, some optimal variables were selected, then using firefly algorithm and multivariate regression, the stock price crash risk was  predicted  and results were compared. To quantify the Stock price crash risk, three criteria for negative skewness, high fluctuations and maximum sigma have been used. Two methods of MSE and MAE have been used to compare the methods. The results show that the ability of meta-meta-heuristic methods to predict the risk Stock price crash risk is not  generally higher than the traditional method of multivariate regression, And the research hypothesis was not approved.

The purpose of this study is to examine the relationship between audit fees and stock price crash risk. The study period is from 2013 to 2017 and the selected sample consists of 110 companies listed on Tehran Stock Exchange (TSE). To test the hypotheses of the research, the ordinary least squares regression is used. The findings show that there is a positive and significant relationship between audit fees and stock price crash risk. In other words, for companies with higher audit fees, there is a greater risk of falling stock prices.

The companies with major customers can supply a considerable source of cash flows by selling a large portion of their products to them. Since the lack of purchase, loss, or bankruptcy of major customers can result in a significant reduction in cash flows in the company, thus the risk is the companies with major customers is higher than other companies. Thus, the present study aimed to investigate the effect of customer concentration on company risks. For this purpose, the effect of customer concentration on three criteria of stock price crash risk, bankruptcy risk, and employment risk was studied. The research sample included 127 companies listed in the Tehran Stock Exchange during 2011-2018. Multivariate regression models with panel data were used by the random-effects method to test the research hypotheses. The research findings indicated that customer concentration has a significant positive effect on stock price crash risk, bankruptcy risk, and employment risk. In other words, stock price crash risk, bankruptcy risk, and employment risk are higher in the companies where the concentration of major customers is higher. 

Stock price crash risk has a significant impact on investors, creditors, managers, and shareholders, so the prediction of this phenomenon is a very important issue in investment and risk management decisions. This research investigates the effect of business strategy and stock price synchronicity on stock price crash risk. Following Bentley et al.[2], composite strategy score has been used to proxy for an organization’s business strategy, expanded market model regression following Chen et al. [3] to measure the firm-specific crash risk, and R2 method of Johnstone [14] to calculate the stock price synchronicity. In order to achieve this point, financial information of 171 companies that are listed on Tehran stock exchange have been selected during the time period of 2013 to 2018, and data was analysed using regression model. According to the results, companies with defender (analyser and prospector) business strategy are less (more) prone to future crash risk. Moreover, results show that stock price syn-chronicity has positive effect on stock price crash risk, while in companies with analyser business strategy it can reduce the stock price crash risk. The interactive effect of business strategy and stock price synchronicity on stock price crash risk in companies with prospector and defender business strategy is not significant. Other findings suggest that Institutional ownership has positive, and company’s age has negative effect on stock price crash risk.

The purpose of this study was to examine the effect of uncertainty in economic policy on the stock price crash risk. In this applied descriptive-analytical ex post facto study, the statistical population included the companies listed on the Tehran Stock Exchange. Over a seven-year period, 148 companies are selected using the systematic sampling method (2015–2021). In this study, the negative skewness of return on equity (ROE) and down-to-up volatility were used to assess the stock price crash risk (dependent variable) and uncertainty in economic policy (independent variable). The data was then analyzed using the multivariate regression model. The results of the hypothesis testing indicated that volatility in the interest rate, dollar exchange rate, inflation, and economic growth had a positive significant effect on the stock price crash risk or the negative skewness of ROE and down-to-up volatility. Hence, given the effects of macroeconomic variables on the ROE, in order to achieve economic sustainability, the Government of the Islamic Republic of Iran should pay close attention to the adoption of macroeconomic policies, prevent economic policymakers and planners from implementing hasty unscientific policies, and increase the share of tax revenues from income sources.

This study examines empirically the effect of equity overvaluation on future stock price crash risk in companies with greater information asymmetry. Using the information asymmetry and crash risk indicators, the question that whether overvalued firms are more prone to future crash risk is investigated. Accordingly, the first purpose of this study is to investigate the relation between overvaluation and the future stock price crash risk. With the increase of information asymmetry between a firm and the market, managers have more abilities and opportunities to withhold bad news and accelerating the release of good news. As a result, it is expected that the information asymmetry between managers and investors increases future stock price crash risk. In so doing, we identify one main hypothesis and three subsidiary ones and the data of 111 listed companies of Tehran Stock Exchange for the period between 2009 and 2017 were analyzed and A panel data approach has been used to test of research hypotheses. According to the results, there is a positive and significant relationship between the overvaluation and the future stock price crash risk of companies which are operating in monopolistic markets and this relation intensified by the information asymmetry.