Prediction of highly lucrative companies using annual statements: A Data Mining based approach (PDF)
(Sprache: Englisch)
The intention of this study is to predict one year in advance whether a regarded firm will grow extraordinarily in the next year. This is crucial for private investors and fund managers who need to decide whether they should invest in a certain firm....
sofort als Download lieferbar
Printausgabe 46.30 €
eBook (pdf) -24%
34.99 €
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenloser tolino webreader
Produktdetails
Produktinformationen zu „Prediction of highly lucrative companies using annual statements: A Data Mining based approach (PDF)“
The intention of this study is to predict one year in advance whether a regarded firm will grow extraordinarily in the next year. This is crucial for private investors and fund managers who need to decide whether they should invest in a certain firm. Companies like Apple and Amazon have shown that people who recognized the potential of such companies at the right time earned a lot of money.
The applied prediction models can also be used by politicians to identify companies which are eligible for funding, because growing companies oftentimes hire many employees.
Since annual reports are often publically available for free, it is reasonable to take advantage of them for such a prediction. The prediction models are based on classification trees and forests because they have some very substantial advantages over other methods like neural networks, which are frequently used in literature. For instance, they do not have distributional assumptions, accept both quantitative and qualitative inputs, and are not sensitive with respect to outliers. Furthermore, they are easy to understand by humans and can deal with missing values, which is crucial for practical applications.
The applied prediction models can also be used by politicians to identify companies which are eligible for funding, because growing companies oftentimes hire many employees.
Since annual reports are often publically available for free, it is reasonable to take advantage of them for such a prediction. The prediction models are based on classification trees and forests because they have some very substantial advantages over other methods like neural networks, which are frequently used in literature. For instance, they do not have distributional assumptions, accept both quantitative and qualitative inputs, and are not sensitive with respect to outliers. Furthermore, they are easy to understand by humans and can deal with missing values, which is crucial for practical applications.
Autoren-Porträt von Jurij Weinblat
Jurij Weinblat, M.Sc., was born in Charkov, Ukraine, in 1988 and moved to Germany with his family a few years later. He has studied Information Systems and finished both his undergraduate and graduate studies with distinction at the University of Duisburg
Bibliographische Angaben
- Autor: Jurij Weinblat
- 2014, 1. Auflage, 97 Seiten, Englisch
- Verlag: Diplomica Verlag
- ISBN-10: 3954898047
- ISBN-13: 9783954898046
- Erscheinungsdatum: 01.08.2014
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: PDF
- Größe: 2.36 MB
- Ohne Kopierschutz
Sprache:
Englisch
Kommentar zu "Prediction of highly lucrative companies using annual statements: A Data Mining based approach"
Schreiben Sie einen Kommentar zu "Prediction of highly lucrative companies using annual statements: A Data Mining based approach".
Kommentar verfassen