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.
DiffEqFlux.jl integrates neural networks with differential equations.
Sparse JL with higher sparsity improves feature hashing accuracy.
Scorio.jl ranks systems from repeated tasks using various methods.
The Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles with finite-time singular crash hazard rates has been developed to describe the dynamics of financial bubbles and crashes. It has been applied successfully to a large variety of financial bubbles in many different markets. Having been developed fo…
Unified analysis simplifies Johnson-Lindenstrauss lemma for data reduction.
New faster, space-saving methods for subspace embeddings in tensors.
NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia
There are two major streams of literature on the modeling of financial bubbles: the strict local martingale framework and the Johansen-Ledoit-Sornette (JLS) financial bubble model. Based on a class of models that embeds the JLS model and can exhibit strict local martingale behavior, we clarify the connection between th…
In this paper, we study a fast approximation method for {\it large-scale high-dimensional} sparse least-squares regression problem by exploiting the Johnson-Lindenstrauss (JL) transforms, which embed a set of high-dimensional vectors into a low-dimensional space. In particular, we propose to apply the JL transforms to …
Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources. Key to applying Gaussian process models is the availability of well-developed open source software, which is available in many programming languages. In this …
Optimally sketches tensors with minimal rows for preserving norms.
We introduce the concept of "negative bubbles" as the mirror image of standard financial bubbles, in which positive feedback mechanisms may lead to transient accelerating price falls. To model these negative bubbles, we adapt the Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles with a hazard rate de…
We study the Johansen-Ledoit-Sornette (JLS) model of financial market crashes (Johansen, Ledoit, and Sornette [2000] "Crashes as Critical Points." Int. J. Theor. Appl. Finan. 3(2) 219-255). On our view, the JLS model is a curious case from the perspective of the recent philosophy of science literature, as it is natural…
DynamicPPL speeds up probabilistic modeling in Julia.
Financial markets are well known for their dramatic dynamics and consequences that affect much of the world's population. Consequently, much research has aimed at understanding, identifying and forecasting crashes and rebounds in financial markets. The Johansen-Ledoit-Sornette (JLS) model provides an operational framew…
We applied the Johansen-Ledoit-Sornette (JLS) model to detect possible bubbles and crashes related to the Brexit/Bremain referendum scheduled for 23rd June 2016. Our implementation includes an enhanced model calibration using Genetic Algorithms. We selected a few historical financial series sensitive to the Brexit/Brem…
This paper surveys various methods for dimensionality reduction and nearest neighbor search.
Automates feature extraction from JSON data for machine learning.
Identifying unambiguously the presence of a bubble in an asset price remains an unsolved problem in standard econometric and financial economic approaches. A large part of the problem is that the fundamental value of an asset is, in general, not directly observable and it is poorly constrained to calculate. Further, it…
This work improves tensor decomposition methods, especially for large datasets.
Leverage is strongly related to liquidity in a market and lack of liquidity is considered a cause and/or consequence of the recent financial crisis. A repurchase agreement is a financial instrument where a security is sold simultaneously with an agreement to buy it back at a later date. Repurchase agreements (repos) ma…
We present an extension of the Johansen-Ledoit-Sornette (JLS) model to include an additional pricing factor called the "Zipf factor", which describes the diversification risk of the stock market portfolio. Keeping all the dynamical characteristics of a bubble described in the JLS model, the new model provides additiona…
Optimization can learn Johnson-Lindenstrauss embeddings without randomization.
New algorithm trains neural networks in near-linear time, overcoming slow convergence issues.
New method preserves distances in time series data.
A new evolutionary algorithm improves k-means clustering by recombining the entire population.
Cryptocurrencies like Bitcoin and Ether show signs of financial bubbles, leading to market crashes.
Algorithm approximates functions into manifolds with curvature bounds.
A new method solves convex optimization on curved spaces.
Neural networks approximate high-dimensional functions better than theory predicts.
We develop a probabilistic framework for sequential random projection.
Paper uses random projection to preserve subspace structure for efficient data analysis.
AP-Perf integrates custom metrics into neural networks.
We present POLO --- a C++ library for large-scale parallel optimization research that emphasizes ease-of-use, flexibility and efficiency in algorithm design. It uses multiple inheritance and template programming to decompose algorithms into essential policies and facilitate code reuse. With its clear separation between…
PNN-smoothing improves -means clustering by merging subsets' clusterings.
A new optimization method handles Euclidean bounds efficiently.
The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increasingly recognized by the machine learning community. As a result, interest in probabilistic programming frameworks has much increased over the past few years. This paper explores a specific probabilistic programming para…
Bayesian framework improves minority class performance in class-imbalanced data.
Paper proposes a new method for robust speaker verification.
WENDy now estimates nonlinear ODEs with noisy data.
A genome-wide association study (GWAS) correlates marker variation with trait variation in a sample of individuals. Each study subject is genotyped at a multitude of SNPs (single nucleotide polymorphisms) spanning the genome. Here we assume that subjects are unrelated and collected at random and that trait values are n…
QMME balances cost and speed in convex optimization.
High throughput biomedical measurements normally capture multiple overlaid biologically relevant signals and often also signals representing different types of technical artefacts like e.g. batch effects. Signal identification and decomposition are accordingly main objectives in statistical biomedical modeling and data…
Extends DCP framework to Hadamard manifolds for geodesically convex functions.
Paper introduces probabilistic approach to CO layers in ML.
We present a plausible micro-founded model for the previously postulated power law finite time singular form of the crash hazard rate in the Johansen-Ledoit-Sornette model of rational expectation bubbles. The model is based on a percolation picture of the network of traders and the concept that clusters of connected tr…
A new framework for time series analysis using state-space learning.