Stock market forecasting using LASSO linear regression model
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86097016" target="_blank" >RIV/61989100:27240/15:86097016 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-13572-4_31" target="_blank" >http://dx.doi.org/10.1007/978-3-319-13572-4_31</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-13572-4_31" target="_blank" >10.1007/978-3-319-13572-4_31</a>
Alternative languages
Result language
angličtina
Original language name
Stock market forecasting using LASSO linear regression model
Original language description
Predicting stock exchange rates is receiving increasing attention and is a vital financial problem as it contributes to the development of effective strategies for stock exchange transactions. The forecasting of stock price movement in general is considered to be a thought-provoking and essential task for financial time series' exploration. In this paper, a Least Absolute Shrinkage and Selection Operator (LASSO) method based on a linear regression model is proposed as a novel method to predict financialmarket behavior. LASSO method is able to produce sparse solutions and performs very well when the numbers of features are less as compared to the number of observations. Experiments were performed with Goldman Sachs Group Inc. stock to determine the efficiency of the model. The results indicate that the proposed model outperforms the ridge linear regression model. (C) Springer International Publishing Switzerland 2015.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Intelligent Systems and Computing. Volume 334
ISBN
978-3-319-13571-7
ISSN
2194-5357
e-ISSN
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Number of pages
11
Pages from-to
371-381
Publisher name
Springer
Place of publication
Heidelberg
Event location
Addis Ababa
Event date
Nov 17, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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