Can machine learning make technical analysis work?

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F24%3A00136248" target="_blank" >RIV/00216224:14560/24:00136248 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s11408-024-00451-8" target="_blank" >https://link.springer.com/article/10.1007/s11408-024-00451-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11408-024-00451-8" target="_blank" >10.1007/s11408-024-00451-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Can machine learning make technical analysis work?

Popis výsledku v původním jazyce

Technical analysis is generally regarded as an ineffective investment strategy. However, with the advent of machine learning in finance, it has been suggested that technical indicators can play a role as features when trying to predict asset returns. One direct application of this approach is portfolio selection and optimization. Technical indicators as predictors represent an attractive choice, as they can easily be obtained. However, although some studies addressed this topic, the literature on this subject is still not well developed. In this study, we apply tree-based methods that use technical indicators as predictors for daily stock returns. We describe the procedures employed for the tuning of the models and we then develop some portfolio strategies that build on the predictions provided by such models. Finally, we conduct a detailed empirical analysis to gauge the profitability of the approach considered in this paper. We find that our machine learning model shows predictive power and that its performance greatly increases when feature selection is performed. While the resulting investing strategies do not consistently beat simpler alternatives after accounting for transaction costs, our results look promising and provide new insights on the use of technical indicators as stock return predictors.

Název v anglickém jazyce

Can machine learning make technical analysis work?

Popis výsledku anglicky

Technical analysis is generally regarded as an ineffective investment strategy. However, with the advent of machine learning in finance, it has been suggested that technical indicators can play a role as features when trying to predict asset returns. One direct application of this approach is portfolio selection and optimization. Technical indicators as predictors represent an attractive choice, as they can easily be obtained. However, although some studies addressed this topic, the literature on this subject is still not well developed. In this study, we apply tree-based methods that use technical indicators as predictors for daily stock returns. We describe the procedures employed for the tuning of the models and we then develop some portfolio strategies that build on the predictions provided by such models. Finally, we conduct a detailed empirical analysis to gauge the profitability of the approach considered in this paper. We find that our machine learning model shows predictive power and that its performance greatly increases when feature selection is performed. While the resulting investing strategies do not consistently beat simpler alternatives after accounting for transaction costs, our results look promising and provide new insights on the use of technical indicators as stock return predictors.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

FINANCIAL MARKETS AND PORTFOLIO MANAGEMENT

ISSN

1934-4554

e-ISSN

2373-8529

Svazek periodika

38

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

399-412

Kód UT WoS článku

001245981800001

EID výsledku v databázi Scopus

2-s2.0-85195836219