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.

Santrauka

T. Koutroumanidis, K. Ioannou, E. Zafeiriou

Šio tyrimo tikslas—nustatyti pasikliautinuosius intervalus (Confidence Intervals, C. I.) prognozuojamam periodui taikant hibridini metod (Hybrid). Pateikta metodika yra sudetinga, todel autoriai jos taikym suskirste i kelias fazes. Pradžioje buvo taikyti metodai, pagristi dirbtiniais neuroniniais tinklais, kuriu pritaikymas leido atlikti pasikliautinuju intervalu ribu prognozes. Veliau autoriai taike Bootstrap metod. Siekiant nustatyti viršutines ir apatines pasikliautinuju intervalu ribas, taikant dirbtiniu neuroniniu tinklu metod, buvo remtasi i objekt orientuotu programavimu. Empirinei analizei atlikti kasdien autoriai naudojo Alpha Bank pateikiamus duomenis. Analizuojamas laikotarpis apeme 2004–01-28–2005–11-30.

Keywords:

• Artificial Neural Networks
• Confidence interval
• Bootstrap method
• Visual Programming
• Stock Markets
• Time Series

JEL Classification:

• C15
• C88
• G17

Reikšminiai žodžiai: :

• dirbtiniai neuroniniai tinklai
• pasikliautinieji intervalai
• Bootstraop metodas
• programavimas
• fondu birža