Predict the Stock price crash risk by using firefly algorithm and comparison with regression
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.
Investigating the Effect of Business Strategy and Stock Price Synchronicity on Stock Price Crash Risk
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., composite strategy score has been used to ...متن کامل
One of the most important methods of opacity accounting information by management is to accelerate the identification of good news versus delaying the identification of bad news on profits, but there is always a final level of accumulation of bad news in the company, and by reaching that its final level, these bad news will be released, which will lead to a Stock Price Crash Risk. In fact, stoc...متن کامل
Forecasting Crash risk using Business Strategy, Equity Overvaluation and Conditional Skewness in Stock Price
A firm is called to have stock price crash risk if the firm has a tendency to experience a sudden drop in its stock price. In this study, the relation between the firm-level of business strategy and future stock price crash risk Is examined, as well as the effect of stock overvaluation on the relationship between business strategy and crash risk investigated. Using the strategy index and crash ...متن کامل
Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box–Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is propo...متن کامل
Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting
Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short and long horizons using multi-output support vector regression (MSVR). Furtherm...متن کامل
دوره 3 شماره 2
صفحات 43- 58
تاریخ انتشار 2018-06-01
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