Dynamic Multiplier CPPI Strategy with Wavelets and Neural-Fuzzy Systems

Ömer Z. Gürsoy*, Oktay Taş

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Constant Proportional Portfolio Insurance (CPPI) aims to maximize the performance of the portfolio by protecting a determined base value without using any derivative instruments, and by determining the amounts to be invested in risky and risk-free assets with calculations using the risk multiplier and buffer value. Artificial neural networks (ANNs) are mathematical models that are successfully used in studies such as pattern recognition, function estimation, finding the most appropriate value and classifying data by imitating neural networks in the human brain. This study aims to use the advantages of both the decomposition model (Wavelet Transform) and machine learning model (ANN) to predict the future values of stock indices to decide which risk multiplier to use. The dynamic multiplier CPPI yielded better returns than the classic CPPI in all 5 stock market indices analyzed, and both strategies successfully implemented the previously targeted 95% capital protection. It has been observed that predicting future prices of indices using Artificial Intelligence methods and the performance of the dynamic multiplier CPPI strategy applied based on these predictions is more successful than the conventional CPPI strategy with constant multiplier.

Original languageEnglish
Title of host publicationIntelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference
EditorsCengiz Kahraman, Selcuk Cebi, Sezi Cevik Onar, Basar Oztaysi, A. Cagri Tolga, Irem Ucal Sari
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages8
ISBN (Print)9783030855765
Publication statusPublished - 2022
EventInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021 - Istanbul, Turkey
Duration: 24 Aug 202126 Aug 2021

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


ConferenceInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • CPPI
  • Fuzzy neural networks
  • Wavelets


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