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Index tracking using shapley additive explanations and one-dimensional pointwise convolutional autoencoders

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  • Zhang, Yanyi
  • De Smedt, Johannes

Abstract

The aim of index tracking is to mimic the performance of a benchmark index via minimizing the tracking error between the returns of the market index and the tracking portfolio. Lately, various deep learning solutions have been proposed to perform stock prediction or active investment. However, there remains a gap in literature to explore the application of deep learning to index tracking. In this paper, the one-dimensional Pointwise Convolutional Autoencoder is proposed to capture the main market characteristics and the Shapley Additive Explanations feature importance ranking is applied to select stocks to implement the partial replication index tracking with and without Covid-19 data. Moreover, portfolios with different holding periods and with different rebalancing frequency are created on different financial markets to check the effectiveness of the proposed strategy. Compared with different benchmark stock selection strategies, including Pearson correlation, mutual information, and Euclidean distance, the proposed strategy achieves state-of-the-art performance on different financial markets.

Suggested Citation

  • Zhang, Yanyi & De Smedt, Johannes, 2024. "Index tracking using shapley additive explanations and one-dimensional pointwise convolutional autoencoders," International Review of Financial Analysis, Elsevier, vol. 95(PC).
    • Handle: RePEc:eee:finana:v:95:y:2024:i:pc:s1057521924004198
    DOI: 10.1016/j.irfa.2024.103487
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    as
    1. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    2. Pan, Shuiyang & Long, Suwan(Cheng) & Wang, Yiming & Xie, Ying, 2023. "Nonlinear asset pricing in Chinese stock market: A deep learning approach," International Review of Financial Analysis, Elsevier, vol. 87(C).
    3. Vásquez Sáenz, Javier & Quiroga, Facundo Manuel & Bariviera, Aurelio F., 2023. "Data vs. information: Using clustering techniques to enhance stock returns forecasting," International Review of Financial Analysis, Elsevier, vol. 88(C).
    4. Michalski, Lachlan & Low, Rand Kwong Yew, 2024. "Determinants of corporate credit ratings: Does ESG matter?," International Review of Financial Analysis, Elsevier, vol. 94(C).
    5. Ma, Tian & Wang, Wanwan & Chen, Yu, 2023. "Attention is all you need: An interpretable transformer-based asset allocation approach," International Review of Financial Analysis, Elsevier, vol. 90(C).
    6. Juan Díaz & María Cortés & Juan Hernández & Óscar Clavijo & Carlos Ardila & Sergio Cabrales, 2019. "Index fund optimization using a hybrid model: genetic algorithm and mixed-integer nonlinear programming," The Engineering Economist, Taylor & Francis Journals, vol. 64(3), pages 298-309, July.
    7. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    8. Bouteska, Ahmed & Abedin, Mohammad Zoynul & Hajek, Petr & Yuan, Kunpeng, 2024. "Cryptocurrency price forecasting – A comparative analysis of ensemble learning and deep learning methods," International Review of Financial Analysis, Elsevier, vol. 92(C).
    9. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    10. Hakan Yilmazkuday, 2023. "COVID-19 effects on the S&P 500 index," Applied Economics Letters, Taylor & Francis Journals, vol. 30(1), pages 7-13, January.
    11. Wan, Jieru & Yin, Libo & Wu, You, 2024. "Return and volatility connectedness across global ESG stock indexes: Evidence from the time-frequency domain analysis," International Review of Economics & Finance, Elsevier, vol. 89(PB), pages 397-428.
    12. Saejoon Kim & Soong Kim, 2020. "Index tracking through deep latent representation learning," Quantitative Finance, Taylor & Francis Journals, vol. 20(4), pages 639-652, April.
    13. Beasley, J. E. & Meade, N. & Chang, T. -J., 2003. "An evolutionary heuristic for the index tracking problem," European Journal of Operational Research, Elsevier, vol. 148(3), pages 621-643, August.
    14. Curto, José Dias & Serrasqueiro, Pedro, 2022. "The impact of COVID-19 on S&P500 sector indices and FATANG stocks volatility: An expanded APARCH model," Finance Research Letters, Elsevier, vol. 46(PA).
    15. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    16. Rama Malladi & Frank J. Fabozzi, 2017. "Equal-weighted strategy: Why it outperforms value-weighted strategies? Theory and evidence," Journal of Asset Management, Palgrave Macmillan, vol. 18(3), pages 188-208, May.
    17. Sant’Anna, Leonardo R. & Filomena, Tiago P. & Caldeira, João F., 2017. "Index tracking and enhanced indexing using cointegration and correlation with endogenous portfolio selection," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 146-157.
    18. Yang, Cai & Zhang, Hongwei & Weng, Futian, 2024. "Effects of COVID-19 vaccination programs on EU carbon price forecasts: Evidence from explainable machine learning," International Review of Financial Analysis, Elsevier, vol. 91(C).
    19. James B. Heaton & Nicholas Polson & Jan H. Witte, 2017. "Rejoinder to ‘Deep learning for finance: deep portfolios’," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(1), pages 19-21, January.
    20. Eugene F. Fama & Kenneth R. French, 2004. "The Capital Asset Pricing Model: Theory and Evidence," Journal of Economic Perspectives, American Economic Association, vol. 18(3), pages 25-46, Summer.
    21. Malkiel, Burton G, 1995. "Returns from Investing in Equity Mutual Funds 1971 to 1991," Journal of Finance, American Finance Association, vol. 50(2), pages 549-572, June.
    22. Qiu, Zhiguo & Lazar, Emese & Nakata, Keiichi, 2024. "VaR and ES forecasting via recurrent neural network-based stateful models," International Review of Financial Analysis, Elsevier, vol. 92(C).
    23. Rounaghi, Mohammad Mahdi & Nassir Zadeh, Farzaneh, 2016. "Investigation of market efficiency and Financial Stability between S&P 500 and London Stock Exchange: Monthly and yearly Forecasting of Time Series Stock Returns using ARMA model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 10-21.
    24. Goodell, John W. & Ben Jabeur, Sami & Saâdaoui, Foued & Nasir, Muhammad Ali, 2023. "Explainable artificial intelligence modeling to forecast bitcoin prices," International Review of Financial Analysis, Elsevier, vol. 88(C).
    25. J. B. Heaton & N. G. Polson & J. H. Witte, 2017. "Deep learning for finance: deep portfolios," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(1), pages 3-12, January.
    26. Canakgoz, N.A. & Beasley, J.E., 2009. "Mixed-integer programming approaches for index tracking and enhanced indexation," European Journal of Operational Research, Elsevier, vol. 196(1), pages 384-399, July.
    27. Strub, O. & Baumann, P., 2018. "Optimal construction and rebalancing of index-tracking portfolios," European Journal of Operational Research, Elsevier, vol. 264(1), pages 370-387.
    28. Guastaroba, G. & Speranza, M.G., 2012. "Kernel Search: An application to the index tracking problem," European Journal of Operational Research, Elsevier, vol. 217(1), pages 54-68.
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