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.
New insights into convergence and accuracy trade-offs in federated and meta-learning.
New proof shows local wealth condensation in economic models with biases.
Study stability of trading strategy under market perturbations.
Optimal trading strategy with unobservable pricing errors for co-integrated assets.
We construct explicitly a bridge process whose distribution, in its own filtration, is the same as the difference of two independent Poisson processes with the same intensity and its time 1 value satisfies a specific constraint. This construction allows us to show the existence of Glosten-Milgrom equilibrium and its as…
The overall performance or expected excess risk of an iterative machine learning algorithm can be decomposed into training error and generalization error. While the former is controlled by its convergence analysis, the latter can be tightly handled by algorithmic stability. The machine learning community has a rich his…
Optimal trading strategy derived for nonlinear price impact models.
We study Nash equilibria for inventory-averse high-frequency traders (HFTs), who trade to exploit information about future price changes. For discrete trading rounds, the HFTs' optimal trading strategies and their equilibrium price impact are described by a system of nonlinear equations; explicit solutions obtain aroun…
Optimal trading strategy for multiple futures contracts with stochastic bases.
Asynchronous SGD can speed up training with a trade-off of gradient staleness.
Study utility maximization with costs, proving convergence and strategies.
A number of optimization approaches have been proposed for optimizing nonconvex objectives (e.g. deep learning models), such as batch gradient descent, stochastic gradient descent and stochastic variance reduced gradient descent. Theory shows these optimization methods can converge by using an unbiased gradient estimat…
A simple learning agent learns to trade in an agent-based market model.
Short proof shows wealth condensation in trading model.
Study shows exponential error reduction in multiclass classification without bias-variance trade-off.
For Portugal there are few or none works about the international trade of fruits between Portugal and the other countries. In this work it aims to analyze the more recent data for the Portuguese international trade of fruits. They were used data for the years from 2006 to 2010, available by the INE (Statistics Portugal…
There are few papers about the international trade of flowers, so it is believed that this paper, with this topic, could be an important contribution to the international scientific community. It is intended to analyze if the international trade flowers tendencies and policies are adapted to the actual world global con…
This article examines arbitrage investment in a mispriced asset when the mispricing follows the Ornstein-Uhlenbeck process and a credit-constrained investor maximizes a generalization of the Kelly criterion. The optimal differentiable and threshold policies are derived. The optimal differentiable policy is linear with …
Paper develops Gaussian approximations and bootstrap for federated LSA with trade-off bounds.
We consider a setup in which confidential i.i.d. samples from an unknown finite-support distribution are passed through copies of a discrete privatization channel (a.k.a. mechanism) producing outputs . The channel law guarantees a local differential privacy of . …
In this article we show that the payment flow of a linear tax on trading gains from a security with a semimartingale price process can be constructed for all càglàd and adapted trading strategies. It is characterized as the unique continuous extension of the tax payments for elementary strategies w.r.t. the convergence…
Insiders camouflage trading to balance wealth and stealth, avoiding legal penalties.
We construct a general stochastic process and prove weak convergence results. It is scaled in space and through the parameters of its distribution. We show that our simplified scaling is equivalent to time scaling used frequently. The process is constructed as an integral with respect to a Poisson random measure which …
We study the problem of dynamically trading a futures contract and its underlying asset under a stochastic basis model. The basis evolution is modeled by a stopped scaled Brownian bridge to account for non-convergence of the basis at maturity. The optimal trading strategies are determined from a utility maximization pr…
Evolutions of the trading landscape lead to the capability to exchange the same financial instrument on different venues. Because of liquidity issues, the trading firms split large orders across several trading destinations to optimize their execution. To solve this problem we devised two stochastic recursive learning …
Paper proposes MS-k-NN for improved convergence rate in k-NN classification.
New algorithms ensure reproducibility and optimal convergence in convex optimization.
Study improves MACD trading strategy with volume and price adjustments.
Stochastic gradient descent is the method of choice for large-scale machine learning problems, by virtue of its light complexity per iteration. However, it lags behind its non-stochastic counterparts with respect to the convergence rate, due to high variance introduced by the stochastic updates. The popular Stochastic …
Study optimal pairs trading with transaction costs using stochastic control.
Unified analysis of DLNs using DMFT reveals dynamics of loss convergence and generalization trade-offs.
We present a convergence rate analysis for biased stochastic gradient descent (SGD), where individual gradient updates are corrupted by computation errors. We develop stochastic quadratic constraints to formulate a small linear matrix inequality (LMI) whose feasible points lead to convergence bounds of biased SGD. Base…
Distributed Stochastic Gradient Descent (SGD) when run in a synchronous manner, suffers from delays in waiting for the slowest learners (stragglers). Asynchronous methods can alleviate stragglers, but cause gradient staleness that can adversely affect convergence. In this work we present a novel theoretical characteriz…
Newton-LESS sparsifies Gaussian sketching for faster optimization.
A protocol reduces transaction costs for portfolio managers.
Study high-frequency trading game with price impact, finding unique equilibrium.
Game theory models how agents trade in a risky asset considering price impact and a common signal.
We study the problem of optimal portfolio selection in an illiquid market with discrete order flow. In this market, bids and offers are not available at any time but trading occurs more frequently near a terminal horizon. The investor can observe and trade the risky asset only at exogenous random times corresponding to…
Study proves existence and convergence of discrete-time Kyle models with multiple insiders.
Optimal trading strategy under market resistance and concave price impact model.
Study speculative trading using RL with exploratory framework.
Machine learning models outperform traditional technical analysis in Bitcoin trading.
We consider statistical estimation of superhedging prices using historical stock returns in a frictionless market with d traded assets. We introduce a plugin estimator based on empirical measures and show it is consistent but lacks suitable robustness. To address this we propose novel estimators which use a larger set …
Discrete time hedging in a complete diffusion market is considered. The hedge portfolio is rebalanced when the absolute difference between delta of the hedge portfolio and the derivative contract reaches a threshold level. The rate of convergence of the expected squared hedging error as the threshold level approaches z…
Dealing with the shear size and complexity of today's massive data sets requires computational platforms that can analyze data in a parallelized and distributed fashion. A major bottleneck that arises in such modern distributed computing environments is that some of the worker nodes may run slow. These nodes a.k.a.~str…
With this work it is analyzed the import and export of horticultural products between Portugal and the other world countries. It is used data about Portuguese international trade of vegetables from 2006 to 2010. The data were obtained from the INE (Statistics Portugal), gently given by the AICEP (Trade & Investment Age…
Paper tackles liquidating stocks using reinforcement learning.
We consider a limit order book, where buyers and sellers register to trade a security at specific prices. The largest price buyers on the book are willing to offer is called the market bid price, and the smallest price sellers on the book are willing to accept is called the market ask price. Market ask price is always …