Original Contribution: On learning the derivatives of an unknown mapping with multilayer feedforward networks

By A. Gallant, Halbert White. 1992

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Citations in the corpus

Deep Learning for Predicting Asset Returns (2018, background): “While the use of (artiﬁcial) neural networks is not novel in economics and ﬁnance, see Gal-lant and White (1988a,b); Hornik et al. (1989); Gallant and White (1992); Kuan and White (1994); Hutchinson et al. (1994); Lo (1994); Qi (1999); Jones (2006); Sirignano et al. (2016) and Heaton et al. (2017),…”
Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules (1999, background): “Many authors have investigated the universal approximation properties of neural networks (Gallant and White, 1988, 1992; Cybenko, 1989; Funanhashi, 1989; Hecht-Nielsen, 1989; Hornik et al., 1989, 1990).”
Assessing nonlinear structures in real exchange rates using recurrence plot strategies (2002, methodology): “Once estimated, we can use the partial derivatives of the fitted model (Gallant and White (1992)) to estimate the dominant Lyapunov exponent: 12 ( )N N 12 1 λλ ˆlogˆ* = where ( )N1λ̂ is the largest eigenvalue of the matrix NTN TT ˆˆ , and where5 11 JJJT NNN ˆˆˆˆ L−= .”
Martingales, Nonlinearity, and Chaos (2000, methodology): “As shown by Gallant and White (1992), we can use b Jt = @ b F (Xt)=@X 0 as a nonparametric estimator of Jt when (L;m; q) are selected to minimize BIC.”
Sobolev Training for Neural Networks (2017, background): “by pushing Jacobian norm to 0 [19]), or to encode additional, hand crafted invariances to some transformations (for instance, as in Tangentprop [23]), or estimated derivatives for dynamical systems [6] and very recently to provide additional learning signal during attention distillation [31]1.”
Forecasting Exchange Rates: A Robust Regression Approach (2007, background): “Funanhashi (1989) Hornik et al. (1989), White (1990) and Gallant and White (1992) ).”