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.
Deep RL ensemble strategy outperforms individual algorithms in stock trading.
An ensemble method enhances cryptocurrency trading strategies using deep reinforcement learning.
We study the price dynamics of stocks traded in the NASDAQ market by considering the statistical properties of an ensemble of stocks traded simultaneously. For each trading day of our database, we study the ensemble return distribution by extracting its first two central moments. According to previous results obtained …
SharpBalance improves deep ensemble performance by balancing sharpness and diversity.
Paper improves financial trading models using GPU parallelism.
We study the price dynamics of stocks traded in a financial market by considering the statistical properties both of a single time series and of an ensemble of stocks traded simultaneously. We use the stocks traded in the New York Stock Exchange to form a statistical ensemble of daily stock returns. For each tradin…
Dynamic sentiment analysis improves stock trading strategies.
Paper combines RL with classifiers to improve financial trading strategies.
We select the stocks traded in the New York Stock Exchange and we form a statistical ensemble of daily stock returns for each of the trading days of our database from the stock price time series. We study the ensemble return distribution for each trading day and we find that the symmetry properties of the ensem…
Paper optimizes trading profits by predicting price direction using ensemble models.
In this paper we quantitatively investigate the statistical properties of an ensemble of {\it stock prices}. We selected 1200 stocks traded in the Tokyo Stock Exchange and formed a statistical ensemble of daily stock prices for each trading day in the 5 year period from January 4, 1988 to December 30, 1992. We found th…
Deep RL strategy improves natural gas trading performance.
We select n stocks traded in the New York Stock Exchange and we form a statistical ensemble of daily stock returns for each of the k trading days of our database from the stock price time series. We analyze each ensemble of stock returns by extracting its first four central moments. We observe that these moments are fl…
New framework learns interpretable rule ensembles without sacrificing accuracy.
Improved stock trading model using feature selection and ensemble learning.
It is assumed that under suitable economic and information-theoretic conditions, market exchange rates are free from arbitrage. Commodity markets in which trades occur over a complete graph are shown to be trivial. We therefore examine the vector space of no-arbitrage exchange rate ensembles over an arbitrary connected…
Hybrid ML ensemble predicts market risk and generates alpha.
XEM improves multivariate time series classification with explainable models.
We consider different levels of complexity which are observed in the empirical investigation of financial time series. We discuss recent empirical and theoretical work showing that statistical properties of financial time series are rather complex under several ways. Specifically, they are complex with respect to their…
Paper studies ensemble probabilistic regression trees for smooth approximations.
Randomized gradient-based ensemble improves prediction accuracy.
This paper considers generalized linear models using rule-based features, also referred to as rule ensembles, for regression and probabilistic classification. Rules facilitate model interpretation while also capturing nonlinear dependences and interactions. Our problem formulation accordingly trades off rule set comple…
New method creates diverse neural ensembles for better uncertainty estimation and robustness.
Fast estimates of model uncertainty are required for many robust robotics applications. Deep Ensembles provides state of the art uncertainty without requiring Bayesian methods, but still it is computationally expensive. In this paper we propose deep sub-ensembles, an approximation to deep ensembles where the core idea …
Research compares ML and Time Series methods for generating trading signals.
Jointly tuning ensemble models improves performance and uncertainty calibration.
Paper proposes ECOC for deep neural network ensembles to improve performance.
Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context…
An Ensemble Anomaly Detection Framework for Risk Calculation Integrity
The study forecasts hourly intraday electricity prices using ensemble methods.
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 study dynamical behavior of the Chinese stock markets by investigating the statistical properties of daily ensemble returns and varieties defined respectively as the mean and the standard deviation of the ensemble daily price returns of a portfolio of stocks traded in China's stock markets on a given day. The distri…
A new batch ensemble method reduces regret in stochastic bandits.
Theory and method for reducing prediction variance in noisy feature-subsampled ridge ensembles.
The paper classifies market states to predict trading strategies, outperforming traditional methods.
In this paper, we quantitatively investigate the statistical properties of a statistical ensemble of stock prices. We selected 1200 stocks traded on the Tokyo Stock Exchange, and formed a statistical ensemble of daily stock prices for each trading day in the 3-year period from January 4, 1999 to December 28, 2001, corr…
A hybrid strategy forecasts short-term loads using Warm-start Gradient Tree Boosting.
Shallow trees in ensemble models make models more interpretable and sometimes better.
We consider the problem of learning decision rules for prediction with feature budget constraint. In particular, we are interested in pruning an ensemble of decision trees to reduce expected feature cost while maintaining high prediction accuracy for any test example. We propose a novel 0-1 integer program formulation …
Deep ensembles don't necessarily improve calibration in low data regimes.
In the context of variable selection, ensemble learning has gained increasing interest due to its great potential to improve selection accuracy and to reduce false discovery rate. A novel ordering-based selective ensemble learning strategy is designed in this paper to obtain smaller but more accurate ensembles. In part…
Ensembles of random-feature models can't outperform a single large model.
New metrics improve quantum ensemble learning efficiency and power.
Deep learning models show bias and variance are aligned, not in trade-off.
High-capacity neural network ensembles often benefit more from high-capacity models than from increased diversity.
In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC) that improves classification accuracy and minimizes the number of classifiers is proposed. Using pre-trained classifiers, an ensemble in which base classifiers votes according to assigned weights is formed. These assigned weights directly affec…
Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion. To obtain an edge in a highly competitive environment, the analyst needs to proper fine-tune its strategy, or discover how to combine weak signals in novel alpha creating manners. Both aspec…
In this paper, we quantitatively investigate the properties of a statistical ensemble of stock prices. We focus attention on the relative price defined as , where is the initial price. We selected approximately 3200 stocks traded on the Japanese Stock Exchange and formed a statistical ensem…