Predict the Stock price crash risk by using firefly algorithm and comparison with regression
| Autor: | Serveh Farzad, Esfandiar Malekian, Hossein Fakhari, Jamal Ghasemi |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | Advances in Mathematical Finance and Applications, Vol 3, Iss 2, Pp 43-58 (2018) |
| ISSN: | 2645-4610 2538-5569 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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