<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The main objective of this analysis is to evaluate and compare the various classification algorithms for the automatic identification of favourable days for intraday trading using the Croatian stock index CROBEX data. Intra-day trading refers to the acquisition and sale of financial instruments on the same trading day. If the increase between the opening price and the closing price of the same day is substantial enough to earn a profit by purchasing at the opening price and selling at the closing price, the day is considered to be favourable for intra-day trading. The goal is to discover relation between selected financial indicators on a given day and the market situation on the following day i.e. to determine whether a day is favourable for day trading or not. The problem is modelled as a binary classification problem. The idea is to test different algorithms and to give greater attention to those that are more rarely used than traditional statistical methods. Thus, the following algorithms are used: neural network, support vector machine, random forest, as well as k-nearest neighbours and naïve Bayes classifier as classifiers that are more common. The work is an extension of authors’ previous work in which the algorithms are compared on resamples resulting from tuning the algorithms, while here, each derived model is used to make predictions on new data. The results should add to the increasing corpus of stock market prediction research efforts and try to fill some gaps in this field of research for the Croatian market, in particular by using machine learning algorithms.</p>
</abstract>

The main objective of this analysis is to evaluate and compare the various classification algorithms for the automatic identification of favourable days for intraday trading using the Croatian stock index CROBEX data. Intra-day trading refers to the acquisition and sale of financial instruments on the same trading day. If the increase between the opening price and the closing price of the same day is substantial enough to earn a profit by purchasing at the opening price and selling at the closing price, the day is considered to be favourable for intra-day trading. The goal is to discover relation between selected financial indicators on a given day and the market situation on the following day i.e. to determine whether a day is favourable for day trading or not. The problem is modelled as a binary classification problem. The idea is to test different algorithms and to give greater attention to those that are more rarely used than traditional statistical methods. Thus, the following algorithms are used: neural network, support vector machine, random forest, as well as k-nearest neighbours and naïve Bayes classifier as classifiers that are more common. The work is an extension of authors’ previous work in which the algorithms are compared on resamples resulting from tuning the algorithms, while here, each derived model is used to make predictions on new data. The results should add to the increasing corpus of stock market prediction research efforts and try to fill some gaps in this field of research for the Croatian market, in particular by using machine learning algorithms.

Comparing classification algorithms for prediction on CROBEX data

Faculty of Economics & Business of University of Zagreb

Croatian Review of Economic, Business and Social Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Comparing classification algorithms for prediction on CROBEX data"}

The main objective of this analysis is to evaluate and compare the various classification algorithms for the automatic identification of favourable days for...

Comparing classification algorithms for prediction on CROBEX data

Online veröffentlicht: 05. Jan. 2021

Seitenbereich: 4 - 11

Eingereicht: 31. Okt. 2020

Akzeptiert: 17. Nov. 2020

DOI: https://doi.org/10.2478/crebss-2020-0007

Schlüsselwörter
classification algorithms, CROBEX, day trading, stock market prediction

© 2020 Silvija Vlah Jerić, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Comparing classification algorithms for prediction on CROBEX data

Online veröffentlicht: 05. Jan. 2021

Seitenbereich: 4 - 11

Eingereicht: 31. Okt. 2020

Akzeptiert: 17. Nov. 2020

DOI: https://doi.org/10.2478/crebss-2020-0007

Schlüsselwörterclassification algorithms, CROBEX, day trading, stock market prediction

© 2020 Silvija Vlah Jerić, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
classification algorithms, CROBEX, day trading, stock market prediction