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.
Paper introduces TAP-Vid, a benchmark for tracking any point in videos.
The paper extends Merton's problem by adding benchmark tracking, finding optimal strategies.
The paper solves a control problem using reflections to track a benchmark process.
Optimal portfolio tracking with dynamic capital injection into a ratcheting benchmark.
Study optimal consumption with relaxed benchmarks and drawdown constraints.
A new method tracks market performance without active management.
Portfolio management problems are often divided into two types: active and passive, where the objective is to outperform and track a preselected benchmark, respectively. Here, we formulate and solve a dynamic asset allocation problem that combines these two objectives in a unified framework. We look to maximize the exp…
This paper studies the empirical tracking performance of leveraged ETFs on gold, and their price relationships with gold spot and futures. For tracking the gold spot, we find that our optimized portfolios with short-term gold futures are highly effective in replicating prices. The market-traded gold ETF (GLD) also exhi…
Commodity exchange-traded funds (ETFs) are a significant part of the rapidly growing ETF market. They have become popular in recent years as they provide investors access to a great variety of commodities, ranging from precious metals to building materials, and from oil and gas to agricultural products. In this article…
This paper optimizes tracking portfolios in incomplete markets using reinforcement learning.
We propose a long term portfolio management method which takes into account a liability. Our approach is based on the LQG (Linear, Quadratic cost, Gaussian) control problem framework and then the optimal portfolio strategy hedges the liability by directly tracking a benchmark process which represents the liability. Two…
This paper presents GRASTA (Grassmannian Robust Adaptive Subspace Tracking Algorithm), an efficient and robust online algorithm for tracking subspaces from highly incomplete information. The algorithm uses a robust -norm cost function in order to estimate and track non-stationary subspaces when the streaming data …
This review analyzes recent advances in solving index tracking problems.
Partial (replication) index tracking is a popular passive investment strategy. It aims to replicate the performance of a given index by constructing a tracking portfolio which contains some constituents of the index. The tracking error optimisation is quadratic and NP-hard when taking the L0 constraint into account so …
We address the problem of partial index tracking, replicating a benchmark index using a small number of assets. Accurate tracking with a sparse portfolio is extensively studied as a classic finance problem. However in practice, a tracking portfolio must also be diverse in order to minimise risk -- a requirement which h…
Paper studies optimal tracking portfolio in mean field game of large fund competition.
New RL method improves financial index tracking accuracy.
Recent advances in visual tracking are based on siamese feature extractors and template matching. For this category of trackers, latest research focuses on better feature embeddings and similarity measures. In this work, we focus on building holistic object representations for tracking. We propose a framework that is d…
Hybrid quantum-classical method optimizes financial index tracking.
The task of dialog management is commonly decomposed into two sequential subtasks: dialog state tracking and dialog policy learning. In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate the true dialog state from noisy observations produced by the speech recognition and the natural…
BootsTAP uses real-world data to improve TAP tracking performance.
The paper analyzes constrained optimal portfolios in high dimensions using novel statistical learning techniques.
This paper proposes a technique for the unsupervised detection and tracking of arbitrary objects in videos. It is intended to reduce the need for detection and localization methods tailored to specific object types and serve as a general framework applicable to videos with varied objects, backgrounds, and image qualiti…
Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combinatio…
This paper optimizes portfolio selection by penalizing tracking error, improving Sharpe ratio.
We study the competition of two strategic agents for liquidity in the benchmark portfolio tracking setup of Bank, Soner, Voß (2017). Specifically, both agents track their own stochastic running trading targets while interacting through common aggregated temporary and permanent price impact à la Almgren and Chriss (2001…
PiVoT improves real-time multi-object detection and tracking in clutter.
Study optimizes portfolio to minimize relative drawdown duration, penalizing unfavorable performance states.
The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving. We here provide an easy-to-use benchmark to assess how object detection models perform when image quality degrades. The three resulting benchmark datasets,…
We present a simple quantile regression-based forecasting method that was applied in a probabilistic load forecasting framework of the Global Energy Forecasting Competition 2017 (GEFCom2017). The hourly load data is log transformed and split into a long-term trend component and a remainder term. The key forecasting ele…
EviTrack improves sequential prediction in delayed disambiguation scenarios.
Paper compares machine learning methods for predicting target motion.
GAMA is a user-friendly AutoML system for machine learning pipeline optimization.
For many modern applications in science and engineering, data are collected in a streaming fashion carrying time-varying information, and practitioners need to process them with a limited amount of memory and computational resources in a timely manner for decision making. This often is coupled with the missing data pro…
Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and public benchmarks. Recent availability of large clinical datasets has enabl…
This paper solves the normalizability crisis in sequential inference by introducing bounded information geometry.
New method finds Lie group representations without explicit groups, enabling new neural network architectures.
Performance of investment managers are evaluated in comparison with benchmarks, such as financial indices. Due to the operational constraint that most professional databases do not track the change of constitution of benchmark portfolios, standard tests of performance suffer from the "look-ahead benchmark bias," when t…
RL approach for target tracking with unknown dynamics and sensor control.
This work uses SVM to identify track component failures in AC Track Circuits.
New train tracks for complex homeomorphisms found.
Improved vehicle motion prediction with uncertainty estimation.
New method uses cluster shapes to improve track finding in particle collisions.
MMD-Flagger detects hallucinations in LLMs by tracking MMD between outputs and temperature-generated counterparts.
We provide a method for approximating Bayesian inference using rejection sampling. We not only make the process efficient, but also dramatically reduce the memory required relative to conventional methods by combining rejection sampling with particle filtering. We also provide an approximate form of rejection sampling …
Backtests of structured strategies lose much of their predictive power in live trading.
FSD50K provides an open dataset of over 51k audio clips for sound event recognition.
This paper optimizes object tracking on edge devices with small matrices.