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Portfolio Construction: A Network Approach

Author

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  • Evangelos Ioannidis

    (Economics Department, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece)

  • Iordanis Sarikeisoglou

    (Economics Department, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece)

  • Georgios Angelidis

    (Economics Department, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece)

Abstract

A key parameter when investing is Time Horizon . One of the biggest mistakes investors make is not aligning the timeline of their goals with their investment portfolio. In other words, time horizons determine the investment portfolio you should construct. We examine which portfolios are the best for long-term investing, short-term investing, and intraday trading. This study presents a novel approach for portfolio construction based on Network Science. We use daily returns of stocks that compose the Dow Jones Industrial Average (DJIA) for a 25-year period from 1998 to 2022. Stock networks are estimated from (i) Pearson correlation (undirected linear statistical correlations), as well as (ii) Transfer Entropy (directed non-linear causal relationships). Portfolios are constructed in two main ways: (a) only four stocks are selected, depending on their centrality, with Markowitz investing weights, or (b) all stocks are selected with centrality-based investing weights. Portfolio performance is evaluated in terms of the following indicators: return, risk (total and systematic), and risk-adjusted return (Sharpe ratio and Treynor ratio). Results are compared against two benchmarks: the index DJIA, and the Markowitz portfolio based on Modern Portfolio Theory. The key findings are as follows: (1) Peripheral portfolios of low centrality stocks based on Pearson correlation network are the best in the long-term, achieving an extremely high cumulative return of around 3000% as well as high risk-adjusted return; (2) Markowitz portfolio is the safest in the long-term, while on the contrary, central portfolios of high centrality stocks based on Pearson correlation network are the riskiest; (3) In times of crisis, no portfolio is always the best. However, portfolios based on Transfer Entropy network perform better in most of the crises; (4) Portfolios of all stocks selected with centrality-based investing weights outperform in both short-term investing and intraday trading. A stock brokerage company may utilize the above findings of our work to enhance its portfolio management services.

Suggested Citation

  • Evangelos Ioannidis & Iordanis Sarikeisoglou & Georgios Angelidis, 2023. "Portfolio Construction: A Network Approach," Mathematics, MDPI, vol. 11(22), pages 1-24, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4670-:d:1281781
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    Cited by:

    1. Ardakani, Omid M., 2024. "Portfolio optimization with transfer entropy constraints," International Review of Financial Analysis, Elsevier, vol. 96(PA).

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