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.
Improved stock volume prediction using Kalman Filters with various hidden states.
Research compares ML and Time Series methods for generating trading signals.
Enhances pairs trading with neural networks and Kalman Filters.
This paper analyzes the skewness of momentum trading strategies.
We devise a USDCHF trading strategy using the dynamics of gold as a filter. Our strategy involves modelling both USDCHF and gold using a coupled hidden Markov model (CHMM). The observations will be indicators, RSI and CCI, which will be used as triggers for our trading signals. Upon decoding the model in each iteration…
The paper studies derivative asset analysis in structural credit risk models where the asset value of the firm is not fully observable. It is shown that in order to compute the price dynamics of traded securities one needs to solve a stochastic filtering problem for the asset value. We transform this problem to a filte…
The paper audits trading filters, finding a high save-to-miss ratio.
CNN predicts stock fluctuations using company news headlines.
The Kalman filter and Heston model are used to estimate asset prices and trading performance.
Study optimal trading strategies with expert signals in a hidden Gaussian drift market.
RED-2400 is a public benchmark of trading events from a Solana exchange, labeled by algorithmic rejection.
New method improves nonlinear filtering accuracy with reduced computation.
Matched filters reveal optimal normalization methods for different market participants.
The paper uses machine learning to predict cryptocurrency price changes.
Hybrid method uses LLM to filter lead-lag relationships in prediction markets.
This paper investigates optimal trading strategies in a financial market with multidimensional stock returns where the drift is an unobservable multivariate Ornstein-Uhlenbeck process. Information about the drift is obtained by observing stock returns and expert opinions. The latter provide unbiased estimates on the cu…
We investigate the planar maximally filtered graphs of the portfolio of the 300 most capitalized stocks traded at the New York Stock Exchange during the time period 2001-2003. Topological properties such as the average length of shortest paths, the betweenness and the degree are computed on different planar maximally f…
New Gibbs sampling reduces GLMB filtering complexity to linear time.
Model predicts trading strategies based on latent demand and price impact.
Optimizes portfolio with two controls to minimize trades and maintain signal integrity.
Proposes a new deep learning framework for financial stock trading.
This paper presents a fast Bayesian filtering technique for state estimation.
This paper presents a novel adaptive-filter approach for predicting assets on the stock markets. Concepts are introduced here, which allow understanding this method and computing of the corresponding forecast. This approach is applied, as an example, through the prediction over the actual valuation of the PETR3 shares …
Hour-Aware Adaptive Risk Management for Autonomous Memecoin Trading
QFIL improves offline RL by filtering data to reduce bias and variance.
In recent years, state-of-the-art methods for supervised learning have exploited increasingly gradient boosting techniques, with mainstream efficient implementations such as xgboost or lightgbm. One of the key points in generating proficient methods is Feature Selection (FS). It consists in selecting the right valuable…
In this paper we develop a methodology to analyze and compare multiple global networks. We focus our analysis on the relation between human migration and trade. First, we identify the subset of products for which the presence of a community of migrants significantly increases trade intensity. To assure comparability ac…
Structured prediction tasks pose a fundamental trade-off between the need for model complexity to increase predictive power and the limited computational resources for inference in the exponentially-sized output spaces such models require. We formulate and develop the Structured Prediction Cascade architecture: a seque…
Bayesian investor learns unknown asset drift, trades mean-variance optimal portfolio, but policy is robust to observation model distortion.
The large-scale organization of the world economies is exhibiting increasingly levels of local heterogeneity and global interdependency. Understanding the relation between local and global features calls for analytical tools able to uncover the global emerging organization of the international trade network. Here we an…
Have you ever felt miserable because of a sudden whipsaw in the price that triggered an unfortunate trade? In an attempt to remove this noise, technical analysts have used various types of moving averages (simple, exponential, adaptive one or using Nyquist criterion). These tools may have performed decently but we show…
Filters on order flow improve short-term market directionality.
New method improves Kalman filtering and smoothing for large state spaces.
This paper introduces a new perspective on multi-class ensemble classification that considers training an ensemble as a state estimation problem. The new perspective considers the final ensemble classifier model as a static state, which can be estimated using a Kalman filter that combines noisy estimates made by indivi…
We propose a new heavy-tailed distribution --- Gaussian-Chain (GC) distribution, which is inspirited by the hierarchical structures prevailing in social organizations. We determine the mean, variance and kurtosis of the Gaussian-Chain distribution to show its heavy-tailed property, and compute the tail distribution tab…
Study optimal liquidation strategies under partial information in high-frequency trading.
Unified approach solves Kyle model with dynamic information.
Although deep neural networks (NNs) have achievedstate-of-the-art accuracy in many visual recognition tasks,the growing computational complexity and energy con-sumption of networks remains an issue, especially for ap-plications on platforms with limited resources and requir-ing real-time processing. Filter pruning tech…
DeepFair improves fairness in recommender systems without sacrificing accuracy.
Particle MCMC involves using a particle filter within an MCMC algorithm. For inference of a model which involves an unobserved stochastic process, the standard implementation uses the particle filter to propose new values for the stochastic process, and MCMC moves to propose new values for the parameters. We show how p…
We perform wavelet decomposition of high frequency financial time series into large and small time scale components. Taking the FTSE100 index as a case study, and working with the Haar basis, it turns out that the small scale component defined by most ( 99.6%) of the wavelet coefficients can be neglected for th…
In this paper, we model the dependencies among the items that are recommended to a user in a collaborative-filtering problem via a Gaussian Markov Random Field (MRF). We build upon Besag's auto-normal parameterization and pseudo-likelihood, which not only enables computationally efficient learning, but also connects th…
A new method corrects bias in machine learning for trading by filtering out non-executable prices.
We propose a novel method for maximum likelihood-based parameter inference in nonlinear and/or non-Gaussian state space models. The method is an iterative procedure with three steps. At each iteration a particle filter is used to estimate the value of the log-likelihood function at the current parameter iterate. Using …
The paper analyzes statistical arbitrage using a factor model of equity returns.
Metaheuristics optimize portfolios with pre-assignment and margin trading for better risk-adjusted returns.
Strategic brokers exploit private information in broker-mediated markets, affecting informed traders' performance.
There is resurging interest, in statistics and machine learning, in solvers for ordinary differential equations (ODEs) that return probability measures instead of point estimates. Recently, Conrad et al. introduced a sampling-based class of methods that are 'well-calibrated' in a specific sense. But the computational c…