Fusion of multiple diverse predictors in stock market

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F17%3A10237645" target="_blank" >RIV/61989100:27510/17:10237645 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.researchgate.net/profile/Sasan_Barak/publication/309800923_Fusion_of_Multiple_Diverse_Predictors_in_Stock_Market/links/59d71e25a6fdcc52acabc31e/Fusion-of-Multiple-Diverse-Predictors-in-Stock-Market.pdf" target="_blank" >https://www.researchgate.net/profile/Sasan_Barak/publication/309800923_Fusion_of_Multiple_Diverse_Predictors_in_Stock_Market/links/59d71e25a6fdcc52acabc31e/Fusion-of-Multiple-Diverse-Predictors-in-Stock-Market.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.inffus.2016.11.006" target="_blank" >10.1016/j.inffus.2016.11.006</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fusion of multiple diverse predictors in stock market

Popis výsledku v původním jazyce

Forecasting stock returns and their risk represents one of the most important concerns of market decision makers. Although many studies have examined single classifiers of stock returns and risk methods, fusion methods, which have only recently emerged, require further study in this area. The main aim of this paper is to propose a fusion model based on the use of multiple diverse base classifiers that operate on a common input and a Meta classifier that learns from base classifiers’ outputs to obtain more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes is determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. The results demonstrate that Bagging exhibited superior performance within the fusion scheme and could achieve a maximum of 83.6% accuracy with Decision Tree, LAD Tree and Rep Tree for return prediction and 88.2% accuracy with BF Tree, DTNB and LAD Tree in risk prediction. For feature selection part, a wrapper-GA algorithm is developed and compared with the fusion model. This paper seeks to help researcher select the best individual classifiers and fuse the proper scheme in stock market prediction. To illustrate the approach, we apply it to Tehran Stock Exchange (TSE) data for the period from 2002 to 2012. © 2016 Elsevier B.V.

Název v anglickém jazyce

Fusion of multiple diverse predictors in stock market

Popis výsledku anglicky

Forecasting stock returns and their risk represents one of the most important concerns of market decision makers. Although many studies have examined single classifiers of stock returns and risk methods, fusion methods, which have only recently emerged, require further study in this area. The main aim of this paper is to propose a fusion model based on the use of multiple diverse base classifiers that operate on a common input and a Meta classifier that learns from base classifiers’ outputs to obtain more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes is determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. The results demonstrate that Bagging exhibited superior performance within the fusion scheme and could achieve a maximum of 83.6% accuracy with Decision Tree, LAD Tree and Rep Tree for return prediction and 88.2% accuracy with BF Tree, DTNB and LAD Tree in risk prediction. For feature selection part, a wrapper-GA algorithm is developed and compared with the fusion model. This paper seeks to help researcher select the best individual classifiers and fuse the proper scheme in stock market prediction. To illustrate the approach, we apply it to Tehran Stock Exchange (TSE) data for the period from 2002 to 2012. © 2016 Elsevier B.V.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

<a href="/cs/project/GA15-23699S" target="_blank" >GA15-23699S: RPF a OT aplikovaná na mezinárodních finančních trzích a problému výběru portfolio</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Information Fusion

ISSN

1566-2535

e-ISSN

—

Svazek periodika

36

Číslo periodika v rámci svazku

2017

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

90-102

Kód UT WoS článku

000394070100007

EID výsledku v databázi Scopus

2-s2.0-84995739777