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Hidden Markov Models Applied To Intraday Momentum Trading With Side Information

Author

Listed:
  • Hugh Christensen
  • Simon Godsill
  • Richard E Turner
Abstract
A Hidden Markov Model for intraday momentum trading is presented which specifies a latent momentum state responsible for generating the observed securities' noisy returns. Existing momentum trading models suffer from time-lagging caused by the delayed frequency response of digital filters. Time-lagging results in a momentum signal of the wrong sign, when the market changes trend direction. A key feature of this state space formulation, is no such lagging occurs, allowing for accurate shifts in signal sign at market change points. The number of latent states in the model is estimated using three techniques, cross validation, penalized likelihood criteria and simulation-based model selection for the marginal likelihood. All three techniques suggest either 2 or 3 hidden states. Model parameters are then found using Baum-Welch and Markov Chain Monte Carlo, whilst assuming a single (discretized) univariate Gaussian distribution for the emission matrix. Often a momentum trader will want to condition their trading signals on additional information. To reflect this, learning is also carried out in the presence of side information. Two sets of side information are considered, namely a ratio of realized volatilities and intraday seasonality. It is shown that splines can be used to capture statistically significant relationships from this information, allowing returns to be predicted. An Input Output Hidden Markov Model is used to incorporate these univariate predictive signals into the transition matrix, presenting a possible solution for dealing with the signal combination problem. Bayesian inference is then carried out to predict the securities $t+1$ return using the forward algorithm. Simple modifications to the current framework allow for a fully non-parametric model with asynchronous prediction.

Suggested Citation

  • Hugh Christensen & Simon Godsill & Richard E Turner, 2020. "Hidden Markov Models Applied To Intraday Momentum Trading With Side Information," Papers 2006.08307, arXiv.org.
    • Handle: RePEc:arx:papers:2006.08307
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    as
    1. M. Hashem Pesaran & Bahram Pesaran, 2007. "Volatilities and Conditional Correlations in Futures Markets with a Multivariate t Distribution," CESifo Working Paper Series 2056, CESifo.
    2. Dipeng Chen & Derek Bunn, 2014. "The Forecasting Performance of a Finite Mixture Regime‐Switching Model for Daily Electricity Prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(5), pages 364-375, August.
    3. Tobias Rydén & Timo Teräsvirta & Stefan Åsbrink, 1998. "Stylized facts of daily return series and the hidden Markov model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(3), pages 217-244.
    4. Harrison Hong & Jeremy C. Stein, 1999. "A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets," Journal of Finance, American Finance Association, vol. 54(6), pages 2143-2184, December.
    5. Chib, Siddhartha, 2001. "Markov chain Monte Carlo methods: computation and inference," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 57, pages 3569-3649, Elsevier.
    6. José Juan Cáceres-Hernández & Gloria Martín-Rodríguez, 2007. "Heterogeneous Seasonal Patterns in Agricultural Data and Evolving Splines," The IUP Journal of Agricultural Economics, IUP Publications, vol. 0(3), pages 48-65, July.
    7. Giacomo Giampieri & Mark Davis & Martin Crowder, 2005. "Analysis of default data using hidden Markov models," Quantitative Finance, Taylor & Francis Journals, vol. 5(1), pages 27-34.
    8. Bowsher, Clive G. & Meeks, Roland, 2008. "The Dynamics of Economic Functions: Modeling and Forecasting the Yield Curve," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1419-1437.
    9. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    10. Dufour, Alfonso & Engle, Robert F, 1999. "Time and the Price Impact of a Trade," University of California at San Diego, Economics Working Paper Series qt62c0h04j, Department of Economics, UC San Diego.
    11. Peiro, Amado, 1994. "Daily seasonality in stock returns : Further international evidence," Economics Letters, Elsevier, vol. 45(2), pages 227-232, June.
    12. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    13. Pesaran, B. & Pesaran, M.H., 2007. "Modelling Volatilities and Conditional Correlations in Futures Markets with a Multivariate t Distribution," Cambridge Working Papers in Economics 0734, Faculty of Economics, University of Cambridge.
    14. Taylor, James W., 2010. "Reply to the discussion of: Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles," International Journal of Forecasting, Elsevier, vol. 26(4), pages 658-660, October.
    15. Chopin, Nicolas & Pelgrin, Florian, 2004. "Bayesian inference and state number determination for hidden Markov models: an application to the information content of the yield curve about inflation," Journal of Econometrics, Elsevier, vol. 123(2), pages 327-344, December.
    16. Josef Lakonishok, Seymour Smidt, 1988. "Are Seasonal Anomalies Real? A Ninety-Year Perspective," The Review of Financial Studies, Society for Financial Studies, vol. 1(4), pages 403-425.
    17. Bulla, Jan & Bulla, Ingo, 2006. "Stylized facts of financial time series and hidden semi-Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2192-2209, December.
    18. Peter F. Christoffersen & Francis X. Diebold, 2006. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," Management Science, INFORMS, vol. 52(8), pages 1273-1287, August.
    19. Lesmond, David A. & Schill, Michael J. & Zhou, Chunsheng, 2004. "The illusory nature of momentum profits," Journal of Financial Economics, Elsevier, vol. 71(2), pages 349-380, February.
    20. Levine, Richard A. & Casella, George, 2006. "Optimizing random scan Gibbs samplers," Journal of Multivariate Analysis, Elsevier, vol. 97(10), pages 2071-2100, November.
    21. Richard T. Baillie & Tim Bollerslev, 1991. "Intra-Day and Inter-Market Volatility in Foreign Exchange Rates," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(3), pages 565-585.
    22. J. Austin Murphy, 1987. "The Seasonality of Risk and Return on Agricultural Futures Positions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 69(3), pages 639-646.
    23. Dueker, Michael & Neely, Christopher J., 2007. "Can Markov switching models predict excess foreign exchange returns?," Journal of Banking & Finance, Elsevier, vol. 31(2), pages 279-296, February.
    24. John Buffington & Robert J. Elliott, 2002. "American Options With Regime Switching," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 5(05), pages 497-514.
    25. Ariel, Robert A., 1987. "A monthly effect in stock returns," Journal of Financial Economics, Elsevier, vol. 18(1), pages 161-174, March.
    26. Booth, James R. & Booth, Lena Chua, 2003. "Is presidential cycle in security returns merely a reflection of business conditions?," Review of Financial Economics, Elsevier, vol. 12(2), pages 131-159.
    27. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    28. Michael C. Lovell, 1963. "Seasonal Adjustment of Economic Time Series and Multiple Regression," Cowles Foundation Discussion Papers 151, Cowles Foundation for Research in Economics, Yale University.
    29. C. P. Robert & T. Rydén & D. M. Titterington, 2000. "Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 57-75.
    30. Robb, A Leslie, 1980. "Accounting for Seasonality with Spline Functions," The Review of Economics and Statistics, MIT Press, vol. 62(2), pages 321-323, May.
    31. Timothy C. Johnson, 2002. "Rational Momentum Effects," Journal of Finance, American Finance Association, vol. 57(2), pages 585-608, April.
    32. Robert E. McCulloch & Ruey S. Tsay, 1994. "Statistical Analysis Of Economic Time Series Via Markov Switching Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 15(5), pages 523-539, September.
    33. Bulla, Jan, 2006. "Application of Hidden Markov Models and Hidden Semi-Markov Models to Financial Time Series," MPRA Paper 7675, University Library of Munich, Germany.
    34. Narasimhan Jegadeesh & Sheridan Titman, 1999. "Profitability of Momentum Strategies: An Evaluation of Alternative Explanations," NBER Working Papers 7159, National Bureau of Economic Research, Inc.
    35. Robert J. Elliott & Craig A. Wilson, 2007. "The Term Structure of Interest Rates in a Hidden Markov Setting," International Series in Operations Research & Management Science, in: Rogemar S. Mamon & Robert J. Elliott (ed.), Hidden Markov Models in Finance, chapter 2, pages 15-30, Springer.
    36. Liesenfeld, Roman, 2001. "A generalized bivariate mixture model for stock price volatility and trading volume," Journal of Econometrics, Elsevier, vol. 104(1), pages 141-178, August.
    37. Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, January.
    38. Dueker, Michael J, 1997. "Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 26-34, January.
    39. Hibbert, Ann Marie & Daigler, Robert T. & Dupoyet, Brice, 2008. "A behavioral explanation for the negative asymmetric return-volatility relation," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2254-2266, October.
    40. Paolo Zaffaroni, 2008. "Large‐scale volatility models: theoretical properties of professionals’ practice," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(3), pages 581-599, May.
    41. Pesaran, M. Hashem & Schleicher, Christoph & Zaffaroni, Paolo, 2009. "Model averaging in risk management with an application to futures markets," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 280-305, March.
    42. Rossi, Alessandro & Gallo, Giampiero M., 2006. "Volatility estimation via hidden Markov models," Journal of Empirical Finance, Elsevier, vol. 13(2), pages 203-230, March.
    43. Andersen, Torben G. & Bollerslev, Tim & Meddahi, Nour, 2011. "Realized volatility forecasting and market microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 220-234, January.
    44. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    45. Taylor, James W., 2010. "Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles," International Journal of Forecasting, Elsevier, vol. 26(4), pages 627-646, October.
    46. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
    47. Robert Elliott & Juri Hinz, 2002. "Portfolio Optimization, Hidden Markov Models, And Technical Analysis Of P&F-Charts," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 385-399.
    48. Pafka, Szilárd & Kondor, Imre, 2001. "Evaluating the RiskMetrics methodology in measuring volatility and Value-at-Risk in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 305-310.
    49. Kim, Chang-Jin, 1993. "Unobserved-Component Time Series Models with Markov-Switching Heteroscedasticity: Changes in Regime and the Link between Inflation Rates and Inflation Uncertainty," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(3), pages 341-349, July.
    50. Alfonso Dufour & Robert F. Engle, 2000. "Time and the Price Impact of a Trade," Journal of Finance, American Finance Association, vol. 55(6), pages 2467-2498, December.
    51. Andrew Gelman, 2011. "Induction and Deduction in Baysian Data Analysis," Rationality, Markets and Morals, Frankfurt School Verlag, Frankfurt School of Finance & Management, vol. 2(43), September.
    52. Canova, Fabio, 1993. "Forecasting time series with common seasonal patterns," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 173-200.
    53. Roman Huptas, 2009. "Intraday Seasonality in Analysis of UHF Financial Data: Models and Their Empirical Verification," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 9, pages 128-138.
    54. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
    55. Ang, Andrew & Bekaert, Geert, 2002. "Regime Switches in Interest Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 163-182, April.
    56. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    57. Stephen J. Taylor, 2007. "Introduction to Asset Price Dynamics, Volatility, and Prediction," Introductory Chapters, in: Asset Price Dynamics, Volatility, and Prediction, Princeton University Press.
    58. Scott S. L., 2002. "Bayesian Methods for Hidden Markov Models: Recursive Computing in the 21st Century," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 337-351, March.
    59. Francesco Audrino & Peter Bühlmann, 2009. "Splines for financial volatility," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 655-670, June.
    60. Philip Hans Franses & Richard Paap, 2000. "Modelling day-of-the-week seasonality in the S&P 500 index," Applied Financial Economics, Taylor & Francis Journals, vol. 10(5), pages 483-488.
    61. Thomas, Lyn C. & Allen, David E. & Morkel-Kingsbury, Nigel, 2002. "A hidden Markov chain model for the term structure of bond credit risk spreads," International Review of Financial Analysis, Elsevier, vol. 11(3), pages 311-329.
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