arXiv research

A new method detects outliers in dirty data using a leave-out strategy.

As deep learning methods form a critical part in commercially important applications such as autonomous driving and medical diagnostics, it is important to reliably detect out-of-distribution (OOD) inputs while employing these algorithms. In this work, we propose an OOD detection algorithm which comprises of an ensembl…

New method R-LOCO improves local feature importance analysis.

In this article, we briefly review the different aspects and applications of kinetic exchange models in economics and sociology. Our main aim is to show in what manner the kinetic exchange models for closed economic systems were inspired by the kinetic theory of gas molecules. The simple yet powerful framework of kinet…

Proposes a new method to estimate variable importance in black box models, mitigating correlation effects.

We address the problem of finding influential training samples for a particular case of tree ensemble-based models, e.g., Random Forest (RF) or Gradient Boosted Decision Trees (GBDT). A natural way of formalizing this problem is studying how the model's predictions change upon leave-one-out retraining, leaving out each…

A new nonparametric test measures dependence between variables using decision trees.

Develops methods to find most probable paths on complex manifolds.

Associated to any Coxeter system $(W,S)$, there is a labeled simplicial complex $L$ and a contractible CW-complex $Σ_L$ (the Davis complex) on which $W$ acts properly and cocompactly. $Σ_L$ admits a cellulation under which the nerve of each vertex is $L$. It follows that if $L$ is a triangulation of $\mathbb{S}^{n-1}$,…

The paper compares LOCO and Shapley values for feature importance, highlighting their limitations and suggesting improvements.

NAMLSS models provide interpretable neural regression for location, scale, and shape.

This work introduces adversarial sparsity to measure robustness beyond adversarial accuracy.

ACV improves structured data CV, handling dependence and noisy initial fits.

An uncollateralized swap hedged back-to-back by a CCP swap is used to introduce FVA. The open IR01 of FVA, however, is a sure sign of risk not being fully hedged, a theoretical no-arbitrage pricing concern, and a bait to lure market risk capital, a practical business concern. By dynamically trading the CCP swap, with t…

This paper provides estimation and inference methods for a conditional average treatment effects (CATE) characterized by a high-dimensional parameter in both homogeneous cross-sectional and unit-heterogeneous dynamic panel data settings. In our leading example, we model CATE by interacting the base treatment variable w…

This study analyzes mutual influence on investment strategies of financial market agents.

Paper proposes a new framework for combining investment strategies without market-specific assumptions.

This paper introduces strategies to maximize arbitrage profits in decentralized exchanges.

In this paper we propose an investing strategy based on neural network models combined with ideas from game-theoretic probability of Shafer and Vovk. Our proposed strategy uses parameter values of a neural network with the best performance until the previous round (trading day) for deciding the investment in the curren…

Recent studies have shown that online portfolio selection strategies that exploit the mean reversion property can achieve excess return from equity markets. This paper empirically investigates the performance of state-of-the-art mean reversion strategies on real market data. The aims of the study are twofold. The first…

Stratify unifies and improves multi-step forecasting strategies.

Study examines volatility-based strategy for Chinese ETF options, improving returns in volatile markets.

Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually…

Model shows how heterogeneity in strategies and risk tolerance affects financial market stability.

Optimal order execution strategies for brokers under reference benchmarks.

Whether you trade futures for yourself or a hedge fund, your strategy is counted. Long and short position limits make the number of unique strategies finite. Formulas of the numbers of strategies, transactions, do nothing actions are derived. A discrete distribution of actions, corresponding probability mass, cumulativ…

New trading strategy beats traditional grid in crypto markets.

A new approach to continuous-time universal portfolios using pathwise Itô calculus.

This paper deals with the explicit design of strategy formulations to make the best strategic choices from a conventional matrix form of representing strategic choices. The explicit strategy formulation is an analytical model which is targeted to provide a mathematical strategy framework to find the best moment for str…

New method solves continuous time mean-variance model for consistent investment strategy.

Paper introduces dynamic strategies for multi-period investment models.

We introduce a new general framework for constructing the best trading strategy for a given historical indicator. We construct the unique trading strategy with the highest expected return. This optimal strategy may be implemented directly, or its expected return may be used as a benchmark to evaluate how far away from …

We consider a scenario where an agent has multiple available strategies to explore an unknown environment. For each new interaction with the environment, the agent must select which exploration strategy to use. We provide a new strategy-agnostic method that treat the situation as a Multi-Armed Bandits problem where the…

A game theory study on optimal hiding and searching strategies in discrete locations.

Global optimization in Bayesian inference yields little additional benefit.

The author proposes a finance trading strategy named Entropy Oriented Trading and apply thermodynamics on the strategy. The state variables are chosen so that the strategy satisfies the second law of thermodynamics. Using the law, the author proves that the rate of investment (ROI) of the strategy is equal to or more t…

Generalized statistical arbitrage concepts are introduced corresponding to trading strategies which yield positive gains on average in a class of scenarios rather than almost surely. The relevant scenarios or market states are specified via an information system given by a $σ$-algebra and so this notion contains classi…

Study optimal growth strategies in a continuous-time asset market.

Survival strategies in a market with self-determined prices are closely tied to log-optimal investment.

The aim of this paper is to compare the performances of the optimal strategy under parameters mis-specification and of a technical analysis trading strategy. The setting we consider is that of a stochastic asset price model where the trend follows an unobservable Ornstein-Uhlenbeck process. For both strategies, we prov…

Investigates optimal portfolio strategies in markets with latent side information.

We consider the problem of high-level strategy selection in the adversarial setting of real-time strategy games from a reinforcement learning perspective, where taking an action corresponds to switching to the respective strategy. Here, a good strategy successfully counters the opponent's current and possible future st…

The paper examines how trading strategies lose value due to stock turnover.

We consider a stochastic game-theoretic model of an investment market in continuous time with short-lived assets and study strategies, called survival, which guarantee that the relative wealth of an investor who uses such a strategy remains bounded away from zero. The main results consist in obtaining a sufficient cond…

Explains classic quantitative strategies and their workings.

Deep RL ensemble strategy outperforms individual algorithms in stock trading.

A deep learning strategy outperforms traditional methods in stocks portfolio management.

Study Figgie card game strategies using agent-based simulation.