Forecasting bank stock market prices with a hybrid method: the case of Alpha Bank

Abstract

The present study aims at constructing Confidence Intervals (C.I) for the predicted values of a Time Series with the application of a Hybrid method. The presented methodology is complicated and thus is completed in different stages. Initially the Artificial Neural Networks (ANNs) is applied on the raw time series in order to estimate C.I of the forecasts. Then, the Bootstrap method is employed on the residuals generated by the preceded process. On the upper and lower limit of the estimated C.I., two new ANNs are employed in order to make point estimations (of the upper and lower limits) using of Object Oriented Programming. For the empirical analysis daily observations of the closing prices of Alpha Bank stocks have been used. The sample period is extended from 28/01/2004 until 30/11/2005. The nonstationarity of the time series employed in our study is not a forbidding condition for the estimation of the confidence intervals, in our case, since the level of bootstrap still provides a satisfactory approximation for the roots arbitrarily close to unity (Berkowitz, Kilian 1996). The accuracy of the forecasts was surveyed with the use of different criteria and the results were satisfactory.

Article in English.

Vertybinių popierių kainų prognozavimas hibridiniu metodu: Alpha Bank pavyzdys

Santrauka.Šio tyrimo tikslas — nustatyti pasikliautinuosius intervalus (Confidence Intervals, C. I.) prognozuojamam periodui taikant hibridini metodą (Hybrid). Pateikta metodika yra sudėtinga, todėl autoriai jos taikymą suskirstė į kelias fazes. Pradžioje buvo taikyti metodai, pagrįsti dirbtiniais neuroniniais tinklais, kuriu pritaikymas leido atlikti pasikliautinųjų intervalų ribų prognozes. Vėliau autoriai taikė Bootstrap metod. Siekiant nustatyti viršutines ir apatines pasikliautinųjų intervalų ribas, taikant dirbtinių neuroninių tinklų metodą, buvo remtasi į objektą orientuotu programavimu. Empirinei analizei atlikti kasdien autoriai naudojo Alpha Bank pateikiamus duomėnis. Analizuojamas laikotarpis apėmė 2004–01-28–2005–11-30.

Reikšminiai žodžiai: dirbtiniai neuroniniai tinklai, pasikliautinieji intervalai, Bootstraop metodas, programavimas, fondų birža.

Keyword : Artificial Neural Networks, Confidence interval, Bootstrap method, Visual Programming, Stock Markets, Time Series