A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New models reduce discrimination in machine learning without sacrificing explanatory bias.
Style Miner generates stable and significant style factors for time series analysis.
PROD method improves high-dimensional regression by handling strong correlations.
Enhances patient failure prediction using dynamic survival models.
A new framework for time series analysis using state-space learning.
Paper uses interbank contagion to predict U.S. bank defaults, finding it highly explanatory.
This paper compares two stock factor models in China's A-share market.
A new PCR method using SVD with sparse regularization.
Bayesian framework explains diverse explanatory values.
FFRK automatically extracts features for spatial interpolation without external variables.
Interactive EMA combines multiple explainability methods to improve model understanding.
The paper develops a method to model high-dimensional data with many variables and weak signals.
Improved neural network model for predicting latent budgets in compositional data.
XGL uses global explanations to guide human supervision in machine learning.
Realized moments of higher order computed from intraday returns are introduced in recent years. The literature indicates that realized skewness is an important factor in explaining future asset returns. However, the literature mainly focuses on the whole market and on the monthly or weekly scale. In this paper, we cond…
A measure of relative importance of variables is often desired by researchers when the explanatory aspects of econometric methods are of interest. To this end, the author briefly reviews the limitations of conventional econometrics in constructing a reliable measure of variable importance. The author highlights the rel…
Paper improves deep learning convergence rates for low-dimensional data.
Probabilistic learning for binary classification with categorical variables.
We propose a procedure for assigning a relevance measure to each explanatory variable in a complex predictive model. We assume that we have a training set to fit the model and a test set to check the out of sample performance. First, the individual relevance of each variable is computed by comparing the predictions in …
RelatIF selects more intuitive training examples for explaining model predictions.
Root Cause Analysis for Anomalies is challenging because of the trade-off between the accuracy and its explanatory friendliness, required for industrial applications. In this paper we propose a framework for simple and friendly RCA within the Bayesian regime under certain restrictions (that Hessian at the mode is diago…
Multimodal sensory data resembles the form of information perceived by humans for learning, and are easy to obtain in large quantities. Compared to unimodal data, synchronization of concepts between modalities in such data provides supervision for disentangling the underlying explanatory factors of each modality. Previ…
Study predicts customer data sharing in Open Banking and explains key factors.
Interpretable representations improve explainable AI by translating complex data into understandable concepts.
Proposes counterfactual explainability for causal attribution, extending variance analysis methods.
Study tests if equity factors explain Bitcoin's risk and returns.
This text discusses several popular explanatory methods that go beyond the error measurements and plots traditionally used to assess machine learning models. Some of the explanatory methods are accepted tools of the trade while others are rigorously derived and backed by long-standing theory. The methods, decision tree…
Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of the causes that lie behind a poor trading performance. It also gives theoretical foundations to a generic framework for real-time tra…
We present explicit formulas - that are also computer code - for 101 real-life quantitative trading alphas. Their average holding period approximately ranges 0.6-6.4 days. The average pair-wise correlation of these alphas is low, 15.9%. The returns are strongly correlated with volatility, but have no significant depend…
Study uses geometric algebra to analyze credit cycles, revealing dangerous feedback loops.
A new baseline for Shapley values in MLPs considers model use.
Knockoffs method selects financial factors, controlling false discoveries.
Proposes models to better represent ordinal data with non-unimodal distributions.
We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By co…
Statistical detection of a rare class of objects in a two-class classification problem can pose several challenges. Because the class of interest is rare in the training data, there is relatively little information in the known class response labels for model building. At the same time the available explanatory variabl…
Develops method to assess feature importance in black-box models for unconditional distribution.
Principal component regression (PCR) is a widely used two-stage procedure: principal component analysis (PCA), followed by regression in which the selected principal components are regarded as new explanatory variables in the model. Note that PCA is based only on the explanatory variables, so the principal components a…
There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, p…
Although interactive learning puts the user into the loop, the learner remains mostly a black box for the user. Understanding the reasons behind queries and predictions is important when assessing how the learner works and, in turn, trust. Consequently, we propose the novel framework of explanatory interactive learning…
SAR evaluates ML-based linear regression models for statistical significance.
CD-RCA method identifies causal relationships in prediction errors without predefined graphs.
Principal component regression (PCR) is a two-stage procedure that selects some principal components and then constructs a regression model regarding them as new explanatory variables. Note that the principal components are obtained from only explanatory variables and not considered with the response variable. To addre…
Study improves understanding of network degree distributions using non-linear ERGs.
Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight. In this paper, we propose a novel method of inducing spatial aggregations as a component of the machine lea…
Nonlinear kernel regression models are often used in statistics and machine learning because they are more accurate than linear models. Variable selection for kernel regression models is a challenge partly because, unlike the linear regression setting, there is no clear concept of an effect size for regression coeffici…
Global Sensitivity Analysis improves feature importance ranking in Random Forests.
The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines. Recent work exploits stability of regression coefficients or invariance properties of models across different experimental conditions for reconstructi…
We investigate entropy as a financial risk measure. Entropy explains the equity premium of securities and portfolios in a simpler way and, at the same time, with higher explanatory power than the beta parameter of the capital asset pricing model. For asset pricing we define the continuous entropy as an alternative meas…