Forecasting in Financial Data Context - Nayeri, Mahmoud Dehghan; Ghayoumi, Ali Faal; Rostami, Malihe (Asian Journal of Economic Modelling, 2016)
This paper aims to compare the application of most common forecasting techniques within financial data context such as regression analysis and artificial neural network, according to the most popular forecasting efficiency indices. Findings depicted that robust regression has an advantages over the least square regression and ANN, in financial data analysis because of outliers derived from business cycles. To this aim, relationship between earning per share, book value of equity per share and share price as price model and earnings per share, annual change of earning per share and return of stock as return model scrutinized implementing robust and least square regressions as well as ANN. Based on results, it can be concluded that the robust regression can provide better and more reliable analysis owing to eliminating or reducing the contribution of outliers and influential data. Therefore, robust regression outperforms OLS and ANN, and can be recommended reaching more precise analysis in financial data context.