no code implementations • 30 Jun 2022 • Derek O. Hoare, David S. Matteson, Martin T. Wells
We then apply our method first with an AR($p$) clustering example and show how the clustering algorithm can be made robust to outliers using a least-absolute deviations criteria.
no code implementations • 3 Mar 2022 • Haoxuan Wu, David S. Matteson, Martin T. Wells
We introduce a new version of deep state-space models (DSSMs) that combines a recurrent neural network with a state-space framework to forecast time series data.
no code implementations • 5 Jul 2021 • Liao Zhu, Ningning Sun, Martin T. Wells
This paper builds the clustering model of measures of market microstructure features which are popular in predicting stock returns.
no code implementations • 13 Jun 2021 • Liao Zhu, Haoxuan Wu, Martin T. Wells
The paper proposes a new asset pricing model -- the News Embedding UMAP Selection (NEUS) model, to explain and predict the stock returns based on the financial news.
no code implementations • 9 Nov 2020 • Liao Zhu, Robert A. Jarrow, Martin T. Wells
We show that for nearly all time periods with length less than 6 years, the beta coefficients are time-invariant for the AMF model, but not for the FF5 model.
1 code implementation • 24 Aug 2020 • Skyler Seto, Martin T. Wells, Wenyu Zhang
Deep neural networks achieve state-of-the-art performance in a variety of tasks by extracting a rich set of features from unstructured data, however this performance is closely tied to model size.
no code implementations • 25 May 2020 • Daqian Sun, Martin T. Wells
Vast majority of the solutions have proposed computationally feasible estimators with strong statistical guarantees for the case where the underlying distribution of data in the matrix is continuous.
no code implementations • 16 Mar 2020 • Robert A. Jarrow, Rinald Murataj, Martin T. Wells, Liao Zhu
The paper provides a new explanation of the low-volatility anomaly.
no code implementations • 26 Jun 2019 • Benjamin R. Baer, Daniel E. Gilbert, Martin T. Wells
A substantial portion of the literature on fairness in algorithms proposes, analyzes, and operationalizes simple formulaic criteria for assessing fairness.
2 code implementations • 23 Apr 2018 • Liao Zhu, Sumanta Basu, Robert A. Jarrow, Martin T. Wells
The paper proposes a new algorithm for the high-dimensional financial data -- the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small.
no code implementations • 19 Nov 2017 • Skyler Seto, Sarah Tan, Giles Hooker, Martin T. Wells
Non-negative matrix factorization (NMF) is a technique for finding latent representations of data.
2 code implementations • 22 Nov 2016 • Sarah Tan, Matvey Soloviev, Giles Hooker, Martin T. Wells
Ensembles of decision trees perform well on many problems, but are not interpretable.
no code implementations • 22 Oct 2014 • Irina Gaynanova, James Booth, Martin T. Wells
We investigate the difference between using an $\ell_1$ penalty versus an $\ell_1$ constraint in generalized eigenvalue problems, such as principal component analysis and discriminant analysis.
no code implementations • 24 Mar 2014 • Irina Gaynanova, James G. Booth, Martin T. Wells
Secondly, we propose an extension of this form to the $p\gg N$ setting and achieve feature selection by using a group penalty.
no code implementations • 21 Jan 2013 • Irina Gaynanova, James G. Booth, Martin T. Wells
We apply a lasso-type penalty to the discriminant vector to ensure sparsity of the solution and use a shrinkage type estimator for the covariance matrix.