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 RL in BMDPs reduces regret to O(sqrt(T)+n).
To analyze high-dimensional and complex data in the real world, deep generative models, such as variational autoencoder (VAE) embed data in a low-dimensional space (latent space) and learn a probabilistic model in the latent space. However, they struggle to accurately reproduce the probability distribution function (PD…
Olympus benchmarks optimization algorithms for noisy experiments.
Bayesian optimization speeds up bioprocess development across scales.
Probabilistic programming languages (PPLs) are a powerful modeling tool, able to represent any computable probability distribution. Unfortunately, probabilistic program inference is often intractable, and existing PPLs mostly rely on expensive, approximate sampling-based methods. To alleviate this problem, one could tr…
MLtuner automatically tunes settings for training tunables (such as the learning rate, the momentum, the mini-batch size, and the data staleness bound) that have a significant impact on large-scale machine learning (ML) performance. Traditionally, these tunables are set manually, which is unsurprisingly error-prone and…
We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as p…
P-BO reduces black-box adversarial attacks by 10x with Bayesian optimization and function prior.
Bayesian optimization guided by experimenter intuition and beliefs.
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 -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…
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.
A deep learning strategy outperforms traditional methods in stocks portfolio management.
Deep RL ensemble strategy outperforms individual algorithms in stock trading.
Study Figgie card game strategies using agent-based simulation.
Calibrated strategies can be obtained by performing strategies that have no internal regret in some auxiliary game. Such strategies can be constructed explicitly with the use of Blackwell's approachability theorem, in an other auxiliary game. We establish the converse: a strategy that approaches a convex -set can be…
The paper analyzes optimal dealer strategies in agent-based market models.
Backtests of structured strategies lose much of their predictive power in live trading.
New method detects heuristics in complex game strategies.
We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive historical back-testing as well a…
Improved active output selection reduces calibration time by 10% or more.
This paper optimizes periodic dividend strategies for Lévy processes with transaction costs.