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.
This paper introduces compositional data analysis for financial ratios, improving industry-level analysis.
Narrative disclosures in 10-K filings improve bankruptcy prediction beyond accounting ratios.
New PU ratio predicts long-term Bitcoin returns better than other methods.
EB improves asset pricing by mining large strategies without lookahead bias.
The study uses CoDa to analyze family business financial ratios, highlighting methodological issues.
Edgeworth Accountant calculates privacy loss under differential privacy compositions efficiently.
Simple bounds show most cross-sectional predictability findings are likely true.
New financial ratios using compositional data improve analysis of firm health.
Paper introduces Market-adaptive Ratio for better portfolio management.
The paper tests if optimal hedge ratios for Bitcoin are position-dependent.
The Sharpe ratio, which is defined as the ratio of the excess expected return of an investment to its standard deviation, has been widely cited in the financial literature by researchers and practitioners. However, very little attention has been paid to the statistical properties of the estimation of the ratio. Lo (200…
A new method selects regions of interest in GC-MS data without prior target selection.
Bayesian optimization improves by focusing on outputs with the likelihood ratio method.
The paper aims to explore the impacts of bi-demographic structure on the current account and growth. Using a SVAR modeling, we track the dynamic impacts between these underlying variables. New insights have been developed about the dynamic interrelation between population growth, current account and economic growth. Th…
Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution, but the resulting estimate can be highly variable when the importance ratios have a heavy right tail. This routinely occurs when there are aspects of the target distribution that are not well captur…
This article extends, in a stochastic environment, the Yagil (1987) model which establishes, in a deterministic dividend discount model, a range for the exchange ratio in a stock-for-stock merger agreement. Here, we generalize Yagil's work letting both pre- and post-merger dividends grow randomly over time. If Yagil fo…
Contingent Convertible bonds (CoCos) are debt instruments that convert into equity or are written down in times of distress. Existing pricing models assume conversion triggers based on market prices and on the assumption that markets can always observe all relevant firm information. But all Cocos issued so far have tri…
Paper uses Transformers to predict intraday volume ratio with high accuracy.
Turnover-adjusted IR is always lower than classic IR, suggesting managers can improve performance by limiting turnover.
A new method optimizes a generalized Kullback-Leibler divergence for better simulation-based inference.
Study finds CNNs perform better with financial ratio data than fundamental data.
We introduce a new non parametric method that allows for a direct, fast and efficient estimation of the matrix of kernel norms of a multivariate Hawkes process, also called branching ratio matrix. We demonstrate the capabilities of this method by applying it to high-frequency order book data from the EUREX exchange. We…
FF algorithm uses goodness as a likelihood-ratio test for scalar normalization.
The existence of asymmetric information has always been a major concern for financial institutions. Financial intermediaries such as commercial banks need to study the quality of potential borrowers in order to make their decision on corporate loans. Classical methods model the default probability by financial ratios u…
FF algorithm uses goodness as a measure of input quality, derived from likelihood-ratio tests.
New model considers unfairness complaints to ensure multiple fairness criteria.
This paper deals with the stability properties of a closed market, where capital and labour force are acting like a predator-prey system in population-dynamics. The spatial movement of the capital and labour force are taken into account by cross-diffusion effect. First, we are showing two possible ways for modeling thi…
In this paper we propose an improved method for transfer learning that takes into account the balance between target and source data. This method builds on the state-of-the-art Multisource Tradaboost, but weighs the importance of each datapoint taking into account the amount of target and source data available. A compa…
We estimate Radon-Nikodym derivatives using regularization in reproducing kernel Hilbert spaces.
Researchers develop methods for inference in hierarchical models using neural simulations.
Graph-based LRE estimates likelihood-ratios collaboratively for nodes.
We employ stochastic dynamic microsimulations to analyse and forecast the pension cost dependency ratio for England and Wales from 1991 to 2061, evaluating the impact of the ongoing state pension reforms and changes in international migration patterns under different Brexit scenarios. To fully account for the recently …
Credit estimation and bankruptcy prediction methods have been utilizing Altman's score method for the last several years. It is reported in many studies that score is sensitive to changes in accounting figures. Researches have proposed different variations to conventional score that can improve the predicti…
Unified framework for counterfactual survival analysis improves treatment effect estimation.
The purpose of this paper is to introduce a new growth adjusted price-earnings measure (GA-P/E) and assess its efficacy as measure of value and predictor of future stock returns. Taking inspiration from the interpretation of the traditional price-earnings ratio as a period of time, the new measure computes the requisit…
We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. The key innovations are to use the fundamental no-arbitrage condition as criterion funct…
Study uses VC correlation to uncover directional financial relationships.
We present a new method for the separation of superimposed, independent, auto-correlated components from noisy multi-channel measurement. The presented method simultaneously reconstructs and separates the components, taking all channels into account and thereby increases the effective signal-to-noise ratio considerably…
The role of Network Theory in the study of the financial crisis has been widely spotted in the latest years. It has been shown how the network topology and the dynamics running on top of it can trigger the outbreak of large systemic crisis. Following this methodological perspective we introduce here the Accounting Netw…
This paper examines how investors mislearn factor risk premia under structural breaks in a misspecified Bayesian framework.
This paper presents new deviation inequalities that are valid uniformly in time under adaptive sampling in a multi-armed bandit model. The deviations are measured using the Kullback-Leibler divergence in a given one-dimensional exponential family, and may take into account several arms at a time. They are obtained by c…
The paper analyzes optimal overbetting strategies for a satellite investment account.
We study hedging and pricing of unattainable contingent claims in a non-Markovian regime-switching financial model. Our financial market consists of a bank account and a risky asset whose dynamics are driven by a Brownian motion and a multivariate counting process with stochastic intensities. The interest rate, drift, …
The intrinsic entropy model accurately estimates stock market volatility.
The informational context is regularly questioned in a transitional economic regime like the one implemented in China or Vietnam. This article investigates this issue and the predictive power of fundamental analysis in such context and more precisely in a Chinese context with an analysis of 3 different industries (medi…
The paper improves competitive and dynamic regret bounds for smoothed online learning.
Paper develops a robust hedging framework to reduce market risk and uncertainty.
We present a detailed study of the performance of a trading rule that uses moving average of past returns to predict future returns on stock indexes. Our main goal is to link performance and the stochastic process of the traded asset. Our study reports short, medium and long term effects by looking at the Sharpe ratio …