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Found 9 papers, 5 papers with code
Learning Financial Networks with High-frequency Trade Data
The edges in our network are determined by using microstructure measures of one firm to forecast the sign of the change in a market measure (either realized volatility or returns kurtosis) of another firm.
Exploring Financial Networks Using Quantile Regression and Granger Causality
We propose statistical methods that measure connectivity in the financial sector using system-wide tail-based analysis and is able to distinguish between connectivity in lower and upper tails of the return distribution.
Random Forests for dependent data
The key to this extension is the equivalent representation of the local decision-making in a regression tree as a global OLS optimization which is then replaced with a GLS loss to create a GLS-style regression tree.
A Debiased MDI Feature Importance Measure for Random Forests
Based on the original definition of MDI by Breiman et al. for a single tree, we derive a tight non-asymptotic bound on the expected bias of MDI importance of noisy features, showing that deep trees have higher (expected) feature selection bias than shallow ones.
Large Spectral Density Matrix Estimation by Thresholding
Spectral density matrix estimation of multivariate time series is a classical problem in time series and signal processing.
Signed iterative random forests to identify enhancer-associated transcription factor binding
Standard ChIP-seq peak calling pipelines seek to differentiate biochemically reproducible signals of individual genomic elements from background noise.
High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model
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.
Interpretable Vector AutoRegressions with Exogenous Time Series
The Vector AutoRegressive (VAR) model is fundamental to the study of multivariate time series.
Iterative Random Forests to detect predictive and stable high-order interactions
Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes.
