IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2209.00780.html
   My bibliography  Save this paper

Index Tracking via Learning to Predict Market Sensitivities

Author

Listed:
  • Yoonsik Hong
  • Yanghoon Kim
  • Jeonghun Kim
  • Yongmin Choi

Abstract

Index funds are substantially preferred by investors nowadays, and market sensitivities are instrumental in managing index funds. An index fund is a mutual fund aiming to track the returns of a predefined market index (e.g., the S&P 500). A basic strategy to manage an index fund is replicating the index's constituents and weights identically, which is, however, cost-ineffective and impractical. To address this issue, it is required to replicate the index partially with accurately predicted market sensitivities. Accordingly, we propose a novel partial-replication method via learning to predict market sensitivities. We first examine deep-learning models to predict market sensitivities in a supervised manner with our data-processing methods. Then, we propose a partial-index-tracking optimization model controlling the net predicted market sensitivities of the portfolios and index to be the same. These processes' efficacy is corroborated by our experiments on the Korea Stock Price Index 200. Our experiments show a significant reduction of the prediction errors compared with historical estimations and competitive tracking errors of replicating the index utilizing fewer than half of the entire constituents. Therefore, we show that applying deep learning to predict market sensitivities is promising and that our portfolio construction methods are practically effective. Additionally, to our knowledge, this is the first study addressing market sensitivities focused on deep learning.

Suggested Citation

  • Yoonsik Hong & Yanghoon Kim & Jeonghun Kim & Yongmin Choi, 2022. "Index Tracking via Learning to Predict Market Sensitivities," Papers 2209.00780, arXiv.org, revised Dec 2022.
  • Handle: RePEc:arx:papers:2209.00780
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2209.00780
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pagan, Adrian, 1980. "Some identification and estimation results for regression models with stochastically varying coefficients," Journal of Econometrics, Elsevier, vol. 13(3), pages 341-363, August.
    2. Kempf, Alexander & Korn, Olaf & Saßning, Sven, 2014. "Portfolio optimization using forward-looking information," CFR Working Papers 11-10 [rev.], University of Cologne, Centre for Financial Research (CFR).
    3. David F. Shanno & Roman L. Weil, 1971. "Technical Note—“Linear” Programming with Absolute-Value Functionals," Operations Research, INFORMS, vol. 19(1), pages 120-124, February.
    4. Keim, Donald B., 1999. "An analysis of mutual fund design: the case of investing in small-cap stocks," Journal of Financial Economics, Elsevier, vol. 51(2), pages 173-194, February.
    5. Robert F. Engle, 2016. "Dynamic Conditional Beta," Journal of Financial Econometrics, Oxford University Press, vol. 14(4), pages 643-667.
    6. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    7. Davidson Heath & Daniele Macciocchi & Roni Michaely & Matthew C Ringgenberg, 2022. "Do Index Funds Monitor?," The Review of Financial Studies, Society for Financial Studies, vol. 35(1), pages 91-131.
    8. Adrian Buss & Grigory Vilkov, 2012. "Measuring Equity Risk with Option-implied Correlations," The Review of Financial Studies, Society for Financial Studies, vol. 25(10), pages 3113-3140.
    9. Robert W. Faff & David Hillier & Joseph Hillier, 2000. "Time Varying Beta Risk: An Analysis of Alternative Modelling Techniques," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 27(5‐6), pages 523-554, June.
    10. Vasiliki D. Skintzi & Apostolos‐Paul N. Refenes, 2005. "Implied correlation index: A new measure of diversification," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(2), pages 171-197, February.
    11. Saejoon Kim & Soong Kim, 2020. "Index tracking through deep latent representation learning," Quantitative Finance, Taylor & Francis Journals, vol. 20(4), pages 639-652, April.
    12. Pafka, Szilárd & Kondor, Imre, 2003. "Noisy covariance matrices and portfolio optimization II," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 319(C), pages 487-494.
    13. Luenberger, David, 2009. "Investment Science: International Edition," OUP Catalogue, Oxford University Press, number 9780195391060.
    14. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Kyle J. Kost & Marco C. Sammon & Tasaneeya Viratyosin, 2020. "The Unprecedented Stock Market Impact of COVID-19," NBER Working Papers 26945, National Bureau of Economic Research, Inc.
    15. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    16. Robert W. Faff & David Hillier & Joseph Hillier, 2000. "Time Varying Beta Risk: An Analysis of Alternative Modelling Techniques," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 27(5‐6), pages 523-554, June.
    17. 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.
    18. Andrew F. Siegel, 1995. "Measuring Systematic Risk Using Implicit Beta," Management Science, INFORMS, vol. 41(1), pages 124-128, January.
    19. Ferson, Wayne E & Harvey, Campbell R, 1993. "The Risk and Predictability of International Equity Returns," The Review of Financial Studies, Society for Financial Studies, vol. 6(3), pages 527-566.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Baule, Rainer & Korn, Olaf & Saßning, Sven, 2013. "Which beta is best? On the information content of option-implied betas," CFR Working Papers 13-11, University of Cologne, Centre for Financial Research (CFR).
    2. Rainer Baule & Olaf Korn & Sven Saßning, 2016. "Which Beta Is Best? On the Information Content of Option†implied Betas," European Financial Management, European Financial Management Association, vol. 22(3), pages 450-483, June.
    3. Mehmet Balcilar & Riza Demirer & Festus V. Bekun, 2021. "Flexible Time-Varying Betas in a Novel Mixture Innovation Factor Model with Latent Threshold," Mathematics, MDPI, vol. 9(8), pages 1-20, April.
    4. Härdle, Wolfgang Karl & Hautsch, Nikolaus & Pigorsch, Uta, 2008. "Measuring and modeling risk using high-frequency data," SFB 649 Discussion Papers 2008-045, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    5. Korn, Olaf & Kuntz, Laura-Chloé, 2015. "Low-beta investment strategies," CFR Working Papers 15-17, University of Cologne, Centre for Financial Research (CFR).
    6. Marshall, Andrew & Maulana, Tubagus & Tang, Leilei, 2009. "The estimation and determinants of emerging market country risk and the dynamic conditional correlation GARCH model," International Review of Financial Analysis, Elsevier, vol. 18(5), pages 250-259, December.
    7. Insana, Alessandra, 2022. "Does systematic risk change when markets close? An analysis using stocks’ beta," Economic Modelling, Elsevier, vol. 109(C).
    8. Ortas, E. & Salvador, M. & Moneva, J.M., 2015. "Improved beta modeling and forecasting: An unobserved component approach with conditional heteroscedastic disturbances," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 27-51.
    9. Schadner, Wolfgang, 2021. "Ex-Ante Risk Factors and Required Structures of the Implied Correlation Matrix," Finance Research Letters, Elsevier, vol. 41(C).
    10. Ortas, Eduardo & Moneva, José M. & Salvador, Manuel, 2012. "Does socially responsible investment equity indexes in emerging markets pay off? Evidence from Brazil," Emerging Markets Review, Elsevier, vol. 13(4), pages 581-597.
    11. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Papers 2107.13866, arXiv.org.
    12. Hollstein, Fabian, 2020. "Estimating beta: The international evidence," Journal of Banking & Finance, Elsevier, vol. 121(C).
    13. Yuxin Liu & Jimin Lin & Achintya Gopal, 2024. "NeuralBeta: Estimating Beta Using Deep Learning," Papers 2408.01387, arXiv.org, revised Oct 2024.
    14. Sakemoto, Ryuta, 2023. "The long-run risk premium in the intertemporal CAPM: International evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    15. Асатуров К.Г., 2015. "Динамические Модели Систематического Риска: Сравнение На Примере Индийского Фондового Рынка," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 51(4), pages 59-75, октябрь.
    16. Herrera, Rodrigo & Piña, Marco, 2024. "Market risk modeling with option-implied covariances and score-driven dynamics," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
    17. Wolfgang Schadner, 2021. "Feasible Implied Correlation Matrices from Factor Structures," Papers 2107.00427, arXiv.org.
    18. Yunmi Kim, 2012. "Autoregressive conditional beta," Economics Bulletin, AccessEcon, vol. 32(2), pages 1489-1494.
    19. Kwangmin Jung & Donggyu Kim & Seunghyeon Yu, 2022. "Next generation models for portfolio risk management: An approach using financial big data," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(3), pages 765-787, September.
    20. Charles S. Bos & Phillip Gould, 2007. "Dynamic Correlations and Optimal Hedge Ratios," Tinbergen Institute Discussion Papers 07-025/4, Tinbergen Institute.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2209.00780. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.