The paper optimizes asset selection for index trackers and enhanced trackers with varying cardinality constraints.
problem Optimizing asset selection for index trackers and enhanced trackers with cardinality constraints.
method Divided into two steps: asset pre-selection and asset weight estimation. Used eight pre-selection procedures with different combinations of selection methods and regression types.
result Out-of-sample tracking errors are roughly proportional to 1/sqrt(cardinality). OLS is more effective than LAD, BE marginally more effective than FS, and (n) marginally more effective than (c).
Quantum computing tackles non-convex portfolio optimization with cardinality constraints.
problem Non-convex portfolio optimization problems in asset management.
method Application of quantum annealing with non-linear cardinality constraints.
result Quantum portfolio optimization yields smaller, more profitable portfolios.
A new method tracks index using topological data analysis for sparse portfolios.
problem Sparse index tracking with robust risk management.
method Topological learning via Vietoris-Rips filtration for sparse regularization.
result The method outperforms state-of-the-art techniques in various market conditions.
Paper improves Lasso for S&P500 index tracking with post-selection inference.
problem Index tracking for S&P500 with many applications.
method Used Lasso for dimension reduction and post-selection inference.
result Lasso method for S&P500 index tracking shows high performance.
This paper reviews and analyzes various modeling approaches for financial index tracking.
problem Efficient replication of market index performance in financial markets.
method Categorization into three frameworks: optimization, statistical, and machine learning; empirical study on S&P 500 dataset.
result Optimization-based models deliver the most precise index tracking, statistical-based models achieve the strongest return-risk balance, and data-driven models provide competitive performance.
This review analyzes recent advances in solving index tracking problems.
problem Creating a portfolio that closely follows a specific index with lower costs.
method Systematic review of mathematical approaches and metaheuristics.
result Metaheuristics have been extensively applied and improved in solving index tracking problems.
Bayesian approach for constructing and rebalancing sparse index-tracking portfolios.
problem Sparse tracking of a reference index with uncertainty quantification.
method Sparse linear regression with Laplace prior, empirical-Bayes calibration, Langevin-type MCMC, threshold-based rules.
result Posterior uncertainty on tracking error, portfolio composition, and rebalancing moves.
Paper introduces Arte-Blue Chip Index for diversifying portfolios with art investments.
problem Evaluating blue-chip art as a viable asset class for diversification.
method Developed Arte-Blue Chip Index tracking top-performing artists over 24 years.
result 20% allocation of blue-chip art in a diversified portfolio increases risk-adjusted returns by 20%.
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 …
Index tracking is a popular form of asset management. Typically, a quadratic function is used to define the tracking error of a portfolio and the look back approach is applied to solve the index tracking problem. We argue that a forward looking approach is more suitable, whereby the tracking error is expressed as expec…
Improved FDR control for sparse financial index tracking.
problem Maintaining FDR control in high-dimensional financial data with strong variable dependencies.
method Expanding T-Rex framework to handle overlapping groups of correlated variables with nearest neighbors penalization.
result Accurately tracks the S&P 500 index using only a small number of stocks.
Deep learning predicts market sensitivities for cost-effective index tracking.
problem Costly and impractical replication of index funds.
method Learning to predict market sensitivities using deep learning models.
result Significant reduction in prediction errors compared to historical methods.
Proposes an efficient method for sparse index tracking with ℓ0-norm constraints.
problem Constructing a sparse portfolio to track a financial index.
method Formulates a new problem using ℓ0-norm constraints, develops an efficient algorithm based on primal-dual splitting. result Demonstrates effectiveness through experiments on S&P500 and Russell3000 datasets.
This paper identifies and analyzes biases in risk-adjusted index weighting methods, affecting social welfare and market fairness.
problem Biases in risk-adjusted index weighting methods lead to tracking errors and fraud in indices and ETFs.
method Characterizes and analyzes the biases and adverse effects of risk-adjusted index weighting methods.
result These biases reduce social welfare and can enable harmful arbitrage activities.
Hybrid SA algorithm optimizes index tracking for large indices.
problem Optimizing index tracking for large indices with financial constraints.
method Hybrid simulated annealing algorithm.
result Algorithm finds optimal solutions for past and future returns.
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…
We develop a methodology for index tracking and risk exposure control using financial derivatives. Under a continuous-time diffusion framework for price evolution, we present a pathwise approach to construct dynamic portfolios of derivatives in order to gain exposure to an index and/or market factors that may be not di…
New RL method improves financial index tracking accuracy.
problem Optimizing financial index tracking with dynamic market information.
method Discrete-time dynamic model, Banach fixed point iteration, deep reinforcement learning.
result Proposed RL method outperforms benchmarks in tracking accuracy.
THRML uses energy-based models for index tracking, reducing portfolio tracking error and improving returns.
problem NP-hard combinatorial optimization in portfolio optimization under cardinality constraints.
method THRML reformulates index tracking as probabilistic inference on an Ising Hamiltonian, using GPU-accelerated block Gibbs sampling.
result THRML achieves 4.31 percent annualized tracking error compared to 5.66-6.30 percent for baselines, with 128.63 percent total return.
Hybrid quantum-classical method optimizes financial index tracking.
problem Optimizing asset weights for financial index replication.
method Hybrid quantum-classical optimization with pruning algorithm.
result Improved performance through quantum and classical optimization.
PLS-Lasso integrates dimension reduction into regression for financial index tracking.
problem Dimension reduction and regression are traditionally treated separately in multivariate data analysis.
method PLS-Lasso integrates dimension reduction directly into the regression process, presenting two formulations: PLS-Lasso-v1 and PLS-Lasso-v2.
result PLS-Lasso-v1 and PLS-Lasso-v2 outperform Lasso in financial index tracking.
A new model tracks indices without rebalancing, solving NP-hard problems.
problem Tracking indices without rebalancing and minimizing deviations.
method Metaheuristic algorithms and local branching for solving mixed integer linear programming.
result The heuristic generates portfolios that outperform commercial solvers in both in-sample and out-of-sample data.
A new index CRIX for cryptocurrencies is proposed to track market changes.
problem Tracking the volatile and frequently changing cryptocurrency market.
method AIC-based method to select index constituents and assign optimal weights to altcoins.
result CRIX index improves tracking performance of cryptocurrency portfolios.
Enhanced indexation uses equity and index options for better performance.
problem Improving portfolio performance through enhanced indexation.
method Integrating index options into an enhanced indexation strategy based on second-order stochastic dominance.
result Introducing option strategies in enhanced indexation leads to improved out-of-sample performance.
This paper optimizes decarbonized indices for financial tracking, balancing risk and environmental impact.
problem Balancing financial performance with environmental responsibilities in the context of climate risks.
method Develops decarbonized indices using mean-VaR and mean-ES optimization methods.
result Optimized indices reduce financial risk and carbon footprint, providing a balanced investment option.
Method improves volatility targeting for index construction.
problem High turnover, leverage spikes, and sensitivity to estimation error in existing volatility-targeting strategies.
method Proportional-control approach for setting index weights that corrects tracking error through feedback.
result The proportional-control approach achieves the target volatility more effectively than open-loop alternatives.
The paper uses TDA to select stocks for a sparse portfolio, improving performance across market scenarios.
problem Sparse portfolio selection in financial markets.
method Topological data analysis (TDA) for clustering stock price movements.
result The TDA-based clustering strategy significantly enhances sparse portfolio performance.
Enhances particle filters with neural augmentation for multi-sub-state tracking.
problem Particle filters struggle with complex or approximated models and low latency requirements.
method Learning Flock (LF) uses a neural network to correct particle weights based on sub-particle relationships.
result LF improves performance, robustness, and latency in radar multi-target tracking.
The tracking method based on the extreme learning machine (ELM) is efficient and effective. ELM randomly generates input weights and biases in the hidden layer, and then calculates and computes the output weights by reducing the iterative solution to the problem of linear equations. Therefore, ELM offers the satisfying…
A new method tracks market performance without active management.
problem Active portfolio management does not outperform benchmarks.
method Developed a hybrid PCA-based tracking portfolio strategy.
result The hybrid PCA strategy outperforms optimization-based approaches.
We study a series of static and dynamic portfolios of VIX futures and their effectiveness to track the VIX index. We derive each portfolio using optimization methods, and evaluate its tracking performance from both empirical and theoretical perspectives. Among our results, we show that static portfolios of different VI…
Optimal portfolio tracking with dynamic capital injection into a ratcheting benchmark.
problem Optimizing a portfolio's performance by dynamically adding capital to a non-decreasing benchmark.
method Formulated as an unconstrained control problem with a running maximum cost, transformed into an auxiliary problem with a nonlinear HJB equation, solved using probabilistic representation and stochastic flow analysis.
result Established the existence of a unique classical solution to the HJB equation, providing feedback optimal portfolio strategies.
Several known results, by Rivin, Calegari-Maher and Sisto, show that an element φn∈Out(Fr), obtained after n steps of a simple random walk on Out(Fr), is fully irreducible with probability tending to 1 as n→∞. In this paper we construct a natural "train-track directed" random walk W on $…
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2019. This index is not complete, as some accepted abstracts chose to opt-out of inclusion.
We propose a new NFT price index to track the digital art market.
problem Lack of a comprehensive NFT price index.
method Developed a new methodology to create a NFT Price Index.
result Demonstrated the dynamics and performances of NFT markets.
Enhances geodesic fiber tracking in white matter using modified metrics and tensor data.
problem Improving the accuracy and robustness of geodesic fiber tracking in white matter.
method Modification of geodesic ray-tracing method using rescaled metrics and fourth-order tensor data.
result More satisfactory results in the construction of white matter tracts as geodesics.
In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate a compact representation of the current dialog status from a sequence of noisy observations produced by the speech recognition and the natural language understanding modules. This paper introduces a novel method of dialog state t…
This paper proposes a DGP approach with UCBs for point target tracking over WSNs.
problem Uncertainty quantification in distributed machine learning-based tracking over WSNs.
method Distributed Gaussian process (DGP) approach with upper confidence bounds (UCBs).
result UCBs provide 88% and 42% higher probability of encompassing true target states in X and Y coordinates, respectively.
Passive investing can incur hidden costs due to market timing inefficiencies.
problem Hidden costs in passive investing due to market timing inefficiencies.
method Analysis of passive investing strategies, including gradual share acquisition and post-announcement trading.
result Post-announcement trading can earn significant profits, often exceeding 1%.
In this paper we propose a computationally efficient algorithm for on-line variable selection in multivariate regression problems involving high dimensional data streams. The algorithm recursively extracts all the latent factors of a partial least squares solution and selects the most important variables for each facto…
SOOTT framework optimizes target tracking with robust and learning-augmented algorithms.
problem Optimizing target tracking in dynamic environments with adversarial perturbations.
method Integrates robust and learning-augmented algorithms for online decision-making.
result CoRT learning-augmented algorithm strictly improves over robust BEST when predictions are accurate.
A new clustering algorithm tracks satellite hotspot data for bushfire tracking.
problem Tracking bushfires using satellite data.
method Spatiotemporal clustering algorithm with adjustable parameters.
result The algorithm effectively clusters satellite hotspot data for tracking bushfires.
The paper analyzes constrained optimal portfolios in high dimensions using novel statistical learning techniques.
problem Forming optimal portfolios with constraints in high-dimensional asset spaces.
method CROWN method integrating factor models with nodewise regression for estimation in large dimensions.
result Demonstrates estimation consistency and convergence rates for constrained portfolio weights, risk, and Sharpe Ratio.
We study the problem of defining maps on link Floer homology induced by unoriented link cobordisms. We provide a natural notion of link cobordism, disoriented link cobordism, which tracks the motion of index zero and index three critical points. Then we construct a map on unoriented link Floer homology associated to a …
Optimal text-based indices track VIX and inflation.
problem Maximizing contemporaneous relation or predictive performance with target variables.
method Optimizing text-based indices focusing on VIX and inflation expectations.
result Superior performance compared to existing indices.
Improved Kalman filtering with hierarchical variational approach.
problem Inconsistent process covariance estimation and slow convergence speed in traditional variational Kalman filtering.
method Introducing a surrogate variable for process-noise-free state, reformulating CAVI, and sliding-window hyperparameter estimation.
result Enhanced convergence speed and superior estimation accuracy compared to existing methods.
Deep neural networks predict CVCM track circuit failures early.
problem Subtle anomalies in CVCM track circuits lead to failures, causing disruptions.
method Deep neural networks classify anomalies before they escalate.
result Deep neural networks achieve 99.31% overall accuracy in detecting CVCM failures.
The progress of deep convolutional neural networks has been successfully exploited in various real-time computer vision tasks such as image classification and segmentation. Owing to the development of computational units, availability of digital datasets, and improved performance of deep learning models, fully automati…