Artikel
Stock market analysis: A review and taxonomy of prediction techniques
Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that stock markets are essentially a random walk and it is a fool's game to try and predict them. Predicting stock prices is a challenging problem in itself because of the number of variables which are involved. In the short term, the market behaves like a voting machine but in the longer term, it acts like a weighing machine and hence there is scope for predicting the market movements for a longer timeframe. Application of machine learning techniques and other algorithms for stock price analysis and forecasting is an area that shows great promise. In this paper, we first provide a concise review of stock markets and taxonomy of stock market prediction methods. We then focus on some of the research achievements in stock analysis and prediction. We discuss technical, fundamental, short- and long-term approaches used for stock analysis. Finally, we present some challenges and research opportunities in this field
- Sprache
-
Englisch
- Erschienen in
-
Journal: International Journal of Financial Studies ; ISSN: 2227-7072 ; Volume: 7 ; Year: 2019 ; Issue: 2 ; Pages: 1-22 ; Basel: MDPI
- Klassifikation
-
Wirtschaft
General Financial Markets: General (includes Measurement and Data)
International Financial Markets
Financial Forecasting and Simulation
General Equilibrium and Disequilibrium: Financial Markets
- Thema
-
stock exchanges
stock markets
analysis
prediction
statistics
machine learning
pattern recognition
sentiment analysis
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Shah, Dev
Isah, Haruna
Zulkernine, Farhana
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2019
- DOI
-
doi:10.3390/ijfs7020026
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:46 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Shah, Dev
- Isah, Haruna
- Zulkernine, Farhana
- MDPI
Entstanden
- 2019
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