Predict the Stock price crash risk by using firefly algorithm and comparison with regression
Journal Title: Advances in Mathematical Finance and Applications - Year 2018, Vol 3, Issue 2
Abstract
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.
Authors and Affiliations
Serveh Farzad, Esfandiar Malekian, Hossein Fakhari, Jamal Ghasemi
Keywords
Related Articles
- EP ID EP459933
- DOI 10.22034/AMFA.2018.540830
- Views 78
- Downloads 0
Improving the Banks Shareholder Long Term Values by Using Data Envelopment Analysis Model
Given the rapid development of the banking sector, it is reasonable to expect that the performance of banks has become the centre of attention among bank managers, stakeholders, policy makers, and regulators. In order to...
Evaluating the Performance of Forecasting Models for Portfolio Allocation Purposes with Generalized GRACH Method
Portfolio theory assumes that investors accept risk. This means thatin the equal rate of return on the two assets, the assets were chosenthat have a lower risk level. Modern portfolio theory is accepted byinvestors who b...
The Effect of Investment on the Financing of Listed Companies in Tehran Stock Exchange
In the world and in our country, Iran, many researches have been done in the field of financing, but due to the importance of this issue, it needs more thought and contemplation. Therefore, this study assessed “the impac...
Investigating the Relationship between the Facility Interest Rate and the Bank Deposit Interest Rate in Iran
The facility interest rate is one of the most important macroeconomic variables. The bank facility interest rate is associated with other macro-economic variables, one of which is the bank deposit interest rate. Using th...
Confidence Interval for Solutions of the Black-Scholes Model
The forecast is very complex in financial markets. The reasons for this are the fluctuation of financial data, Such as Stock index data over time. The determining a model for forecasting fluctuations, can play a signific...