Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Switching portfolios

Y Singer1

  • 1AT&T Labs, Florham Park, NJ 07932, USA. singer@research.att.com

International Journal of Neural Systems
|August 1, 1997
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive portfolio selection algorithm that outperforms fixed strategies in non-stationary stock markets. The novel algorithm effectively tracks market changes, even with transaction costs, for superior investment returns.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Acute Kidney Injury: It's not just the 'big' burns.

Injury·2017
Same author

A universal epitope-based influenza vaccine and its efficacy against H5N1.

Vaccine·2009
Same author

Worldwide hospice & palliative care: focus on Africa.

The American journal of hospice & palliative care·2002
Same author

Using mixtures of common ancestors for estimating the probabilities of discrete events in biological sequences.

Bioinformatics (Oxford, England)·2001
Same author

Protein family classification using sparse Markov transducers.

Proceedings. International Conference on Intelligent Systems for Molecular Biology·2000
Same author

[Second degree atrioventricular block in Graves' disease].

Harefuah·2000
Same journal

Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders.

International journal of neural systems·2026
Same journal

Transformer-Based Anomaly Detection for Neurodegenerative Screening in MRI Images.

International journal of neural systems·2026
Same journal

Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.

International journal of neural systems·2026
Same journal

Automatic Seizure Detection using Hierarchical Spectral-Temporal Feature Learning with an Imbalance-Aware Transformer.

International journal of neural systems·2026
Same journal

Pyramid Vision Transformer-Enhanced Conformer Network for Epileptic Seizure Recognition Using MultiChannel EEG Signals.

International journal of neural systems·2026
Same journal

A Time-Frequency Decoupled Contrastive Learning Framework for Electroencephalography-Based Parkinson's Disease Diagnosis.

International journal of neural systems·2026
See all related articles

Area of Science:

  • Quantitative Finance
  • Computational Finance
  • Algorithmic Trading

Background:

  • Constant rebalanced portfolios assume stationary markets, which is often unrealistic.
  • Existing online portfolio selection algorithms struggle with non-stationary market dynamics.
  • Fixed asset allocation can lead to significantly lower wealth compared to adaptive strategies.

Purpose of the Study:

  • To develop an efficient portfolio selection algorithm capable of tracking changing market conditions.
  • To extend the algorithm to incorporate general transaction costs.
  • To analyze the algorithm's competitiveness and performance against existing methods.

Main Methods:

  • Development of an efficient adaptive portfolio selection algorithm.
  • Extension of the algorithm to handle various transaction cost models.

Related Experiment Videos

  • Performance evaluation using 22-year real stock data from the New York Stock Exchange.

    • Main Results:

    • The proposed algorithm demonstrates superior performance compared to constant rebalanced portfolios and other referenced online algorithms.
    • The algorithm's effectiveness is validated both with and without considering transaction costs.
    • Significant outperformance was observed on historical New York Stock Exchange data.

    Conclusions:

    • Adaptive portfolio selection is crucial for navigating non-stationary financial markets.
    • The developed algorithm offers a robust and efficient solution for dynamic asset allocation.
    • This approach provides a competitive advantage over traditional fixed-strategy methods in real-world trading scenarios.