IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1512.04916.html
   My bibliography  Save this paper

Deep Learning Stock Volatility with Google Domestic Trends

Author

Listed:
  • Ruoxuan Xiong
  • Eric P. Nichols
  • Yuan Shen

Abstract

We have applied a Long Short-Term Memory neural network to model S&P 500 volatility, incorporating Google domestic trends as indicators of the public mood and macroeconomic factors. In a held-out test set, our Long Short-Term Memory model gives a mean absolute percentage error of 24.2%, outperforming linear Ridge/Lasso and autoregressive GARCH benchmarks by at least 31%. This evaluation is based on an optimal observation and normalization scheme which maximizes the mutual information between domestic trends and daily volatility in the training set. Our preliminary investigation shows strong promise for better predicting stock behavior via deep learning and neural network models.

Suggested Citation

  • Ruoxuan Xiong & Eric P. Nichols & Yuan Shen, 2015. "Deep Learning Stock Volatility with Google Domestic Trends," Papers 1512.04916, arXiv.org, revised Feb 2016.
    • Handle: RePEc:arx:papers:1512.04916
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1512.04916
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. repec:bla:jfinan:v:59:y:2004:i:2:p:755-793 is not listed on IDEAS
    2. Garman, Mark B & Klass, Michael J, 1980. "On the Estimation of Security Price Volatilities from Historical Data," The Journal of Business, University of Chicago Press, vol. 53(1), pages 67-78, January.
    3. Hamid, Shaikh A. & Iqbal, Zahid, 2004. "Using neural networks for forecasting volatility of S&P 500 Index futures prices," Journal of Business Research, Elsevier, vol. 57(10), pages 1116-1125, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
    2. Yuping Song & Xiaolong Tang & Hemin Wang & Zhiren Ma, 2023. "Volatility forecasting for stock market incorporating macroeconomic variables based on GARCH‐MIDAS and deep learning models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 51-59, January.
    3. Andrea Bucci, 2020. "Cholesky–ANN models for predicting multivariate realized volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 865-876, September.
    4. Milan Cibuľa & Michal Tkáč, 2023. "Porovnanie algoritmov strojového učenia pre tvorbu predikčného modelu ceny bitcoinu [Comparison of Machine Learning Algorithms for Creation of a Bitcoin Price Prediction Model]," Politická ekonomie, Prague University of Economics and Business, vol. 2023(5), pages 496-517.
    5. Lucien Boulet, 2021. "Forecasting High-Dimensional Covariance Matrices of Asset Returns with Hybrid GARCH-LSTMs," Papers 2109.01044, arXiv.org.
    6. Chao Liu & Fengfeng Gao & Mengwan Zhang & Yuanrui Li & Cun Qian, 2024. "Reference Vector-Based Multiobjective Clustering Ensemble Approach for Time Series Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 181-210, July.
    7. Shujian Liao & Jian Chen & Hao Ni, 2021. "Forex Trading Volatility Prediction using Neural Network Models," Papers 2112.01166, arXiv.org, revised Dec 2021.
    8. Zhengyong Jiang & Jeyan Thiayagalingam & Jionglong Su & Jinjun Liang, 2023. "CAD: Clustering And Deep Reinforcement Learning Based Multi-Period Portfolio Management Strategy," Papers 2310.01319, arXiv.org.
    9. Nikita Medvedev & Zhiguang Wang, 2022. "Multistep forecast of the implied volatility surface using deep learning," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(4), pages 645-667, April.
    10. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
    11. Milan Cibuľa & Michal Tkáč, . "Porovnanie algoritmov strojového učenia pre tvorbu predikčného modelu ceny bitcoinu [Comparison of Machine Learning Algorithms for Creation of a Bitcoin Price Prediction Model]," Politická ekonomie, Prague University of Economics and Business, vol. 0.
    12. Emilia Fraszka-Sobczyk & Aleksandra Zakrzewska, 2025. "The Impact of Foreign Stock Market Indices on Predictions Volatility of the WIG20 Index Rates of Return Using Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 65(5), pages 2761-2774, May.
    13. Yuping Song & Bolin Lei & Xiaolong Tang & Chen Li, 2024. "Volatility forecasting for stock market index based on complex network and hybrid deep learning model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 544-566, April.
    14. Theodoros Zafeiriou & Dimitris Kalles, 2024. "Comparative analysis of neural network architectures for short-term FOREX forecasting," Papers 2405.08045, arXiv.org.
    15. Manuel Nunes & Enrico Gerding & Frank McGroarty & Mahesan Niranjan, 2020. "Long short-term memory networks and laglasso for bond yield forecasting: Peeping inside the black box," Papers 2005.02217, arXiv.org.

    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. Seiler, Volker, 2024. "The relationship between Chinese and FOB prices of rare earth elements – Evidence in the time and frequency domain," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 160-179.
    2. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    3. Guillermo Llorente & Jiang Wang, 2020. "Trading and information in futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(8), pages 1231-1263, August.
    4. Jonathan Donier & Jean-Philippe Bouchaud, 2015. "Why Do Markets Crash? Bitcoin Data Offers Unprecedented Insights," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-11, October.
    5. Gustavo Peralta, 2016. "The Nature of Volatility Spillovers across the International Capital Markets," CNMV Working Papers CNMV Working Papers no. 6, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
    6. Elsayed, Ahmed H. & Asutay, Mehmet & ElAlaoui, Abdelkader O. & Bin Jusoh, Hashim, 2024. "Volatility spillover across spot and futures markets: Evidence from dual financial system," Research in International Business and Finance, Elsevier, vol. 71(C).
    7. Lafuente, Juan A. & Novales, Alfonso, 2003. "Optimal hedging under departures from the cost-of-carry valuation: Evidence from the Spanish stock index futures market," Journal of Banking & Finance, Elsevier, vol. 27(6), pages 1053-1078, June.
    8. Sapkota, Niranjan, 2022. "News-based sentiment and bitcoin volatility," International Review of Financial Analysis, Elsevier, vol. 82(C).
    9. Olivier Ledoit & Michael Wolf, 2022. "Markowitz portfolios under transaction costs," ECON - Working Papers 420, Department of Economics - University of Zurich, revised Sep 2024.
    10. Kenneth Yung & Yen-Chih Liu, 2009. "Implications of futures trading volume: Hedgers versus speculators," Journal of Asset Management, Palgrave Macmillan, vol. 10(5), pages 318-337, December.
    11. Baur, Dirk G. & Smales, Lee A., 2020. "Hedging geopolitical risk with precious metals," Journal of Banking & Finance, Elsevier, vol. 117(C).
    12. R. Azencott & A. Beri & Y. Gadhyan & N. Joseph & C.-A. Lehalle & M. Rowley, 2014. "Real-time market microstructure analysis: online transaction cost analysis," Quantitative Finance, Taylor & Francis Journals, vol. 14(7), pages 1167-1185, July.
    13. Aslanidis, Nektarios & Bariviera, Aurelio F. & Perez-Laborda, Alejandro, 2021. "Are cryptocurrencies becoming more interconnected?," Economics Letters, Elsevier, vol. 199(C).
    14. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    15. Nikkin L. Beronilla & Dennis S. Mapa, 2008. "Range-based models in estimating value-at-risk (VaR)," Philippine Review of Economics, University of the Philippines School of Economics and Philippine Economic Society, vol. 45(2), pages 87-99, December.
    16. Jonathan Donier & Jean-Philippe Bouchaud, 2015. "Why Do Markets Crash? Bitcoin Data Offers Unprecedented Insights," Post-Print hal-01277584, HAL.
    17. Shi, Yanlin & Ho, Kin-Yip, 2015. "Long memory and regime switching: A simulation study on the Markov regime-switching ARFIMA model," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 189-204.
    18. Paresh Kumar Narayan & Sagarika Mishra & Seema Narayan, 2015. "New empirical evidence on the bid-ask spread," Applied Economics, Taylor & Francis Journals, vol. 47(42), pages 4484-4500, September.
    19. Enrique Ter Horst & Abel Rodriguez & Henryk Gzyl & German Molina, 2012. "Stochastic volatility models including open, close, high and low prices," Quantitative Finance, Taylor & Francis Journals, vol. 12(2), pages 199-212, May.
    20. Gonzalo Cortazar & Alejandro Bernales & Diether Beuermann, 2005. "Methodology and Implementation of Value-at-Risk Measures in Emerging Fixed-Income Markets with Infrequent Trading," Finance 0512030, University Library of Munich, Germany.

    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:1512.04916. 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.