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Assessing multivariate predictors of financial market movements: A latent factor framework for ordinal data


  • Philippe HUBER

    (University of Geneva)

  • Olivier SCAILLET

    (University of Geneva, HEC and Swiss Finance Institute)


    (University of Geneva)


Much of the trading activity in Equity markets is directed to brokerage houses. In exchange they provide so-called “soft dollars” which basically are amounts spent in “research” for identifying profitable trading opportunities. Soft dollars represent about USD 1 out of every USD 10 paid in commissions. Obviously they are costly, and it is interesting for an institutional investor to determine whether soft dollar inputs are worth being used (and indirectly paid for) or not, from a statistical point of view. To address this question, we develop association measures between what broker-dealers predict and what markets realize. Our data are ordinal predictions by two broker-dealers and realized values on several markets, on the same ordinal scale. We develop a structural equation model with latent variables in an ordinal setting which allows us to test broker-dealer predictive ability of financial market movements. We use a multivariate logit model in a latent factor framework, develop a tractable estimator based on a Laplace approximation, and show its consistency and asymptotic normality. Monte Carlo experiments reveal that both the estimation method and the testing procedure perform well in small samples. The method is then used to analyze our dataset.

Suggested Citation

  • Philippe HUBER & Olivier SCAILLET & Maria-Pia VICTORIA-FESER, "undated". "Assessing multivariate predictors of financial market movements: A latent factor framework for ordinal data," Swiss Finance Institute Research Paper Series 08-45, Swiss Finance Institute.
  • Handle: RePEc:chf:rpseri:rp0845

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    References listed on IDEAS

    1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
    2. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654, March.
    3. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-430, March.
    4. Andersson, Michael K. & Eklund, Bruno & Lyhagen, Johan, 1999. "A simple linear time series model with misleading nonlinear properties," Economics Letters, Elsevier, vol. 65(3), pages 281-284, December.
    5. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
    6. Hansen, Bruce E., 2000. "Testing for structural change in conditional models," Journal of Econometrics, Elsevier, vol. 97(1), pages 93-115, July.
    7. Anas, Jacques & Ferrara, Laurent, 2002. "Un indicateur d'entrée et sortie de récession: application aux Etats-Unis
      [A start-end recession index: Application for United-States]
      ," MPRA Paper 4043, University Library of Munich, Germany.
    8. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    9. Benoit Bellone & David Saint-Martin, 2004. "Detecting Turning Points with Many Predictors through Hidden Markov Models," Econometrics 0407001, EconWPA.
    10. van Dijk, Dick & Franses, Philip Hans & Paap, Richard, 2002. "A nonlinear long memory model, with an application to US unemployment," Journal of Econometrics, Elsevier, vol. 110(2), pages 135-165, October.
    11. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    12. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
    13. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    14. Hidalgo, Javier & Robinson, Peter M., 1996. "Testing for structural change in a long-memory environment," Journal of Econometrics, Elsevier, vol. 70(1), pages 159-174, January.
    15. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    16. Guay, Alain & Scaillet, Olivier, 2003. "Indirect Inference, Nuisance Parameter, and Threshold Moving Average Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 122-132, January.
    17. Philip Rothman, 1998. "Forecasting Asymmetric Unemployment Rates," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 164-168, February.
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    Cited by:

    1. Cai, Jing-Heng & Song, Xin-Yuan & Lam, Kwok-Hap & Ip, Edward Hak-Sing, 2011. "A mixture of generalized latent variable models for mixed mode and heterogeneous data," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2889-2907, November.

    More about this item


    latent variable; generalized linear model; factor analysis; multinomial logit; forecasts; LAMLE; Laplace approximation.;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)


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