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.
Style Miner generates stable and significant style factors for time series analysis.
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…
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…
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…
Paper uses interbank contagion to predict U.S. bank defaults, finding it highly explanatory.
Develops method to assess feature importance in black-box models for unconditional distribution.
Bayesian framework explains diverse explanatory values.
Deep learning extracts terrain texture covariates for geostatistical modeling.
Interpretable machine learning uncovers ESG's explanatory power on equity returns across sectors and capitalizations.
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…
The paper models intraday power prices using fundamental drivers.
PiNets provide faithful explanations for neural networks.
This paper evaluates the impact of the power extent on price in the electricity market. The competitiveness extent of the electricity market during specific times in a day is considered to achieve this. Then, the effect of competitiveness extent on the forecasting precision of the daily power price is assessed. A price…
Method identifies causal interactions between time series using extreme eigenvalue variability.
Subset selection in multiple linear regression aims to choose a subset of candidate explanatory variables that tradeoff fitting error (explanatory power) and model complexity (number of variables selected). We build mathematical programming models for regression subset selection based on mean square and absolute errors…
Recent years have seen much research on fairness in machine learning. Here, mean difference (MD) or demographic parity is one of the most popular measures of fairness. However, MD quantifies not only discrimination but also explanatory bias which is the difference of outcomes justified by explanatory features. In this …
Study tests if equity factors explain Bitcoin's risk and returns.
Interpretable representations improve explainable AI by translating complex data into understandable concepts.
PROD method improves high-dimensional regression by handling strong correlations.
FFRK automatically extracts features for spatial interpolation without external variables.
Skewness dispersion predicts future stock market returns, especially in months with monetary policy announcements.
In light of the power problems of statistical tests and undisciplined use of alpha-based statistics to compare models, this paper proposes a unified set of distance-based performance metrics, derived as the square root of the sum of squared alphas and squared standard errors. The Bayesian investor views model performan…
We statistically investigate the distribution of share price and the distributions of three common financial indicators using data from approximately 8,000 companies publicly listed worldwide for the period 2004-2013. We find that the distribution of share price follows Zipf's law; that is, it can be approximated by a …
XGL uses global explanations to guide human supervision in machine learning.
Subset selection for multiple linear regression aims to construct a regression model that minimizes errors by selecting a small number of explanatory variables. Once a model is built, various statistical tests and diagnostics are conducted to validate the model and to determine whether the regression assumptions are me…
Defines explainability as reasoning under background knowledge.
New framework predicts earnings announcements using press release content, surpassing earnings surprises.
Knockoffs method selects financial factors, controlling false discoveries.
Principal component regression (PCR) is a two-stage procedure: the first stage performs principal component analysis (PCA) and the second stage constructs a regression model whose explanatory variables are replaced by principal components obtained by the first stage. Since PCA is performed by using only explanatory var…
Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine-learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely fle…
A new baseline for Shapley values in MLPs considers model use.
Machine learning predicts CO2 emissions in power grids, reducing uncertainty.
New research shows input-gradients can be manipulated without changing model's core function, challenging their use for model interpretation.
Study finds it hard to establish common factor pricing in corporate bonds.
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…
RelatIF selects more intuitive training examples for explaining model predictions.
Estimates self- and cross-impact concavity and decay patterns in financial markets.
Survey examines deep neural networks' ability to approximate functions.
We find that factors explaining bank loan recovery rates vary depending on the state of the economic cycle. Our modeling approach incorporates a two-state Markov switching mechanism as a proxy for the latent credit cycle, helping to explain differences in observed recovery rates over time. We are able to demonstrate ho…
This paper investigates the risk-return relationship in determination of housing asset pricing. In so doing, the paper evaluates behavioral hypotheses advanced by Case and Shiller (1988, 2002, 2009) in studies of boom and post-boom housing markets. The paper specifies and tests a multi-factor housing asset pricing mode…
A new estimator learns sparse linear models with context-dependent coefficients.
Study shows cryptocurrency market impact on DeFi returns stronger than other drivers.
The paper predicts and explains the decay of stock anomaly performance over time.
New statistical factors improve portfolio risk estimation.
Improved disability insurance model with collective health claims.
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…
This paper examines the possibility of using derivative-implied risk premia to explain stock returns. The rapid development of derivative markets has led to the possibility of trading various kinds of risks, such as credit and interest rate risk, separately from each other. This paper uses credit default swaps and equi…
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…