<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>2012</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>100302</pageFirst>
    <pageLast>100302</pageLast>
    <pageNumber/>
    <edition/>
    <issue>6</issue>
    <volume>8</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-04-27</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Forecasting performance of wavelet neural networks and other neural network topologies: A comparative study based on financial market data sets</title>
    <abstract language="eng">In this study, we analyse the advantageous effects of neural networks in combination with wavelet functions on the performance of financial market predictions. We implement different approaches in multiple experiments and test their predictive abilities with different financial time series. We demonstrate experimentally that both wavelet neural networks and neural networks with data pre-processed by wavelets outperform classical network topologies. However, the precision of conducted forecasts implementing neural network algorithms still propose potential for further refinement and enhancement. Hence, we discuss our findings, comparisons with “buy-and-hold” strategies and ethical considerations critically and elaborate on future prospects.</abstract>
    <parentTitle language="eng">Machine Learning with Applications</parentTitle>
    <identifier type="url">https://www.sciencedirect.com/science/article/pii/S2666827022000287</identifier>
    <identifier type="doi">10.1016/j.mlwa.2022.100302</identifier>
    <enrichment key="copyright">0</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</licence>
    <author>Markus Vogl</author>
    <author>Peter Rötzel</author>
    <author>Stefan Homes</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wavelet neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Financial forecasting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural network topology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Intelligent systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Finance</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Wavelet</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Neuronales Netz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Kreditmarkt</value>
    </subject>
    <collection role="institutes" number="">Behavioral Accounting &amp; Finance Lab</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
</export-example>
