arXiv research

Quantum SVM improves financial data classification.

Investigates the relationship between US money supply and asset indices over 2001-2019.

We develop a simple stock selection model to explain why active equity managers tend to underperform a benchmark index. We motivate our model with the empirical observation that the best performing stocks in a broad market index often perform much better than the other stocks in the index. Randomly selecting a subset o…

This paper reviews and analyzes various modeling approaches for financial index tracking.

Real Estate Investment Trusts (REITs) are the only truly liquid assets related to real estate investments. We study the behavior of U.S. REITs over the past three decades and document their return characteristics. REITs have somewhat less market risk than equity; their betas against a broad market index average about .…

In this paper, we explore the limitations of PCA as a dimension reduction technique and study its extension, projection pursuit (PP), which is a broad class of linear dimension reduction methods. We first discuss the relevant concepts and theorems and then apply PCA and PP (with negative standardized Shannon's entropy …

SGD shows distinct phases in learning single-index models, achieving optimal sample complexity and regret.

Efficiently learns Single-Index Models with constant factor approximation.

Noise Sensitivity Exponent controls statistical-computational gaps in learning.

MallowsPO enhances LLM fine-tuning with a dispersion index of human preferences.

We establish multiplicity results for geometrically distinct contractible closed Reeb orbits of non-degenerate contact forms on a broad class of prequantization bundles. The results hold under certain index requirements on the contact form and are sharp for unit cotangent bundles of CROSS's. In particular, we generaliz…

New framework limits SGD for multi-index models, addressing SQ framework shortcomings.

Post-estimation smoothing improves prediction accuracy with structural indices.

Recently we reported on an application of the Tsallis non-extensive statistics to the S&P500 stock index. There we argued that the statistics are applicable to a broad range of markets and exchanges where anamolous (super) diffusion and 'heavy' tails of the distribution are present, as they are in the S&P500. We have c…

Proposes a transfer learning framework for sparse SIMs without raw source data.

Study shows computational and statistical gaps in Gaussian Single-Index Models.

Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short an…

Despite the advances of deep learning in specific tasks using images, the principled assessment of image fidelity and similarity is still a critical ability to develop. As it has been shown that Mean Squared Error (MSE) is insufficient for this task, other measures have been developed with one of the most effective bei…

We recently showed that the S&P500 stock market index is well described by Tsallis non-extensive statistics and nonlinear Fokker-Planck time evolution. We argued that these results should be applicable to a broad range of markets and exchanges where anomalous diffusion and `heavy' tails of the distribution are present.…

New algorithms learn multi-index models via harmonic analysis, achieving statistical and computational trade-offs.

Unified framework for analyzing neural networks in high dimensions.

Investigate using LETFs to outperform benchmarks, finding them more likely to succeed.

Many introductory courses in quantum mechanics include Feynman's time-slicing definition of the path integral, with a complete derivation of the propagator in the simplest of cases. However, attempts to generalize this, for instance to non-quadratic potentials, encounter formidable analytic issues in showing the succes…

Study forecasts U.S. bond index using deep learning, finding persistence is key.

Neural networks can achieve optimal sample complexity for learning single-index models.

Commodity ETFs' portfolio optimization under heavy-tailed returns.

New algorithms for generalized linear bandits with unknown reward functions.

Study evaluates three position sizing methods for put-writing on S&P 500 Index options.

A well-interpretable measure of information has been recently proposed based on a partition obtained by intersecting a random sequence with its moving average. The partition yields disjoint sets of the sequence, which are then ranked according to their size to form a probability distribution function and finally fed in…

We develop a pricing model for Sovereign Contingent Convertible bonds (S-CoCo) with payment standstills triggered by a sovereign's Credit Default Swap (CDS) spread. We model CDS spread regime switching, which is prevalent during crises, as a hidden Markov process, coupled with a mean-reverting stochastic process of spr…

tsflex speeds up time series processing and feature extraction.

Market dynamic is quantified in terms of the entropy $S(τ,n)$ of the clusters formed by the intersections between the series of the prices $p_t$ and the moving average $\widetilde{p}_{t,n}$. The entropy $S(τ,n)$ is defined according to Shannon as $\sum P(τ,n)\log P(τ,n),$ with $P(τ,n)$ the probability for the cluster t…

Paper proposes a new daily benchmark for post-GFC government bond CIP deviations.

Optimal control problems on Riemannian manifolds are solved by penalizing constraint violations.

The proximal inertial gradient descent is efficient for the composite minimization and applicable for broad of machine learning problems. In this paper, we revisit the computational complexity of this algorithm and present other novel results, especially on the convergence rates of the objective function values. The no…

Unified algebraic framework for virtual braid structures with strong structural consequences.

SurvFD and SurvSHAP-IQ provide interpretable survival models by analyzing feature interactions.

Benchmarking deep time series models for equity portfolios

The paper models US inflation and hyperinflation using monetary and GDP data.

Investigates if adding cryptocurrencies to German portfolios diversifies better, finding mixed results.

In this paper, we propose Broad Neural Architecture Search (BNAS) where we elaborately design broad scalable architecture dubbed Broad Convolutional Neural Network (BCNN) to solve the above issue. On one hand, the proposed broad scalable architecture has fast training speed due to its shallow topology. Moreover, we als…

We consider in this paper some structured financial products, known as reverse convertible notes, that resulted in substantial losses to certain buyers of these notes in recent years. We shall focus on specific reverse convertible notes known as "Autocallable Optimization Securities with Contingent Protection Linked to…

Three-layer networks learn complex hierarchical polynomials of multiple nonlinear features.

Exponential growth of stable subgroups in Morse geodesics.

Estimates crypto risk premia using hidden factors and finds significant integration with traditional markets.

A semi-parametric, non-linear regression model in the presence of latent variables is introduced. These latent variables can correspond to unmodeled phenomena or unmeasured agents in a complex networked system. This new formulation allows joint estimation of certain non-linearities in the system, the direct interaction…

IndexGAN predicts stock trends using GAN with expert knowledge and news context.

skscope simplifies sparsity-constrained optimization in Python.