Stock market forecasting using LASSO linear regression model

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Stock market forecasting using LASSO linear regression model

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Stock market forecasting using LASSO linear regression model

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Intelligent Systems and Computing. Volume 334

ISBN

978-3-319-13571-7

ISSN

2194-5357

e-ISSN

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Počet stran výsledku

11

Strana od-do

371-381

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Addis Ababa

Datum konání akce

17. 11. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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