Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/239250 
Erscheinungsjahr: 
2020
Quellenangabe: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 13 [Issue:] 8 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-19
Verlag: 
MDPI, Basel
Zusammenfassung: 
Time series analysis of daily stock data and building predictive models are complicated. This paper presents a comparative study for stock price prediction using three different methods, namely autoregressive integrated moving average, artificial neural network, and stochastic process-geometric Brownian motion. Each of the methods is used to build predictive models using historical stock data collected from Yahoo Finance. Finally, output from each of the models is compared to the actual stock price. Empirical results show that the conventional statistical model and the stochastic model provide better approximation for next-day stock price prediction compared to the neural network model.
Schlagwörter: 
artificial neural network
auto-regressive integrated moving average
financial models
stochastic process-geometric Brownian motion
stock price prediction
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article

Datei(en):
Datei
Größe
1.12 MB





Publikationen in EconStor sind urheberrechtlich geschützt.