Primerjava uspešnosti metod poslovne inteligence ter metod tehnične analize za napovedovanje gibanja cen vrednostnih papirjev

RIZMAN, KLEMEN (author), Hovelja, Tomaž (mentor)

Diplomska naloga obravnava področje poslovne inteligence (BI). Poslovna inteligenca se ukvarja z orodji in algoritmi, ki se izvajajo nad neobdelanimi podatki, ki jih skušajo spremeniti v uporabne informacije za poslovne analize in napovedi. Tehnologija, ki jo uporabljajo v poslovni inteligenci, je največkrat namenjena obdelavi ogromne količine neurejenih podatkov (big data) ter smiselni in čim bolj enostavni obrazložitvi pridobljenih rezultatov. V svoji nalogi sem uporabil polinomsko regresijo, ki se uporablja v poslovni inteligenci, in jo prilagodil za napovedovanje gibanje cen vrednostnih papirjev. Dobljene rezultate sem primerjal z metodami, ki jih uporabljajo v finančni industriji za napovedovanje cen vrednostnih papirjev.

• Language: Slovenian
• Keywords: poslovna inteligenca, polinomska regresija, tehnična analiza
• Work type: Bachelor thesis/paper
• Organization: FRI - Faculty of Computer and Information Science
• Year: 2017
• PID: 20.500.12556/RUL-97775
• Publication date in RUL: 09.11.2017

Secondary language

• Language: English
• Title: Success comparison between business inteligence methods and technical analysis methods for stock price forecasting

Abstract

The graduation thesis deals with the area of business intelligence (BI). Business intelligence deals with tools and algorithms, which are used on raw data in order to change them into useful information for business analyses and forecasts. Technology used in business intelligence is mostly used for big data processing. The results we get from these methods need to be processed logically and as simply as possible. In my thesis, I used polynomial regression, which is used in business intelligence. I modified it for forecasting of stock’s price movement. I compared the obtained result to different methods used in financial industry for forecasting stock’s price movements.

• Keywords: business intelligence, polynomial regression, technical analysis