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.
This study optimizes trading strategy parameters using walk-forward techniques and finds robust performance.
Study refines trend-following strategy to improve adaptability.
AAMDRL uses DRL to manage assets in noisy, changing environments.
Random walks on free groups reveal asymmetric expansion factors.
A framework for computing holonomy groups of hybrid systems to achieve forward motion.
XGBoost predicts NEPSE Index log returns with low error and high directional accuracy.
Audit financial machine learning workflows to detect spurious predictability.
Machine learning predicts Bitcoin returns but trading performance drops with costs.
Develops a validated trading framework for market microstructure signals.
Combines model-based and model-free RL for better financial market performance.
LSTM and gradient boosting models fail to outperform random chance in predicting MNQ futures.
Study examines how different time series cross-validation methods affect anomaly detection in multivariate time series.
The study analyzes convergence of random-walk embeddings in graph theory.
Developed a random walk analog of geodesic flow on hyperbolic groups.
This paper addresses the problem of neighborhood selection for Gaussian graphical models. We present two heuristic algorithms: a forward-backward greedy algorithm for general Gaussian graphical models based on mutual information test, and a threshold-based algorithm for walk summable Gaussian graphical models. Both alg…
Quantum walk model captures asymmetry and bimodality in long-term financial returns.
Training very deep networks is an important open problem in machine learning. One of many difficulties is that the norm of the back-propagated error gradient can grow or decay exponentially. Here we show that training very deep feed-forward networks (FFNs) is not as difficult as previously thought. Unlike when back-pro…
RGRR allocates between QQQ and DIA based on relative states, improving Sharpe and CAGR.
We introduce the geodesic walk for sampling Riemannian manifolds and apply it to the problem of generating uniform random points from polytopes in R^n specified by m inequalities. The walk is a discrete-time simulation of a stochastic differential equation (SDE) on the Riemannian manifold equipped with the metric induc…
New method improves blockchain analysis by handling temporal changes and scalability.
A novel approach learns goal-conditioned policies for locomotion using batch RL.
Predicting the direction of assets have been an active area of study and a difficult task. Machine learning models have been used to build robust models to model the above task. Ensemble methods is one of them showing results better than a single supervised method. In this paper, we have used generative and discriminat…
New method improves sampling from logconcave distributions truncated on polytopes.
Reinforcement learning algorithms struggle when the reward signal is very sparse. In these cases, naive random exploration methods essentially rely on a random walk to stumble onto a rewarding state. Recent works utilize intrinsic motivation to guide the exploration via generative models, predictive forward models, or …
We provide a direct proof of Cramér's theorem for geodesic random walks in a complete Riemannian manifold . We show how to exploit the vector space structure of the tangent spaces to study large deviation properties of geodesic random walks in . Furthermore, we reveal the geometric obstructions one runs into …
The purpose of this research paper it is to present a new approach in the framework of a biased roulette wheel. It is used the approach of a quantitative trading strategy, commonly used in quantitative finance, in order to assess the profitability of the strategy in the short term. The tools of backtesting and walk-for…
New random walk results on rank one symmetric spaces.
We introduce a novel harmonic analysis for functions defined on the vertices of a strongly connected directed graph of which the random walk operator is the cornerstone. As a first step, we consider the set of eigenvectors of the random walk operator as a non-orthogonal Fourier-type basis for functions over directed gr…
Algorithms find second and third shortest non-trivial closed walks on surfaces.
We relate some basic constructions of stochastic analysis to differential geometry, via random walk approximations. We consider walks on both Riemannian and sub-Riemannian manifolds in which the steps consist of travel along either geodesics or integral curves associated to orthonormal frames, and we give particular at…
GT-Score reduces overfitting in trading strategies by integrating multiple criteria.
As a model of market price, we introduce a new type of random walk in a moving potential which is approximated by a quadratic function with its center given by the moving average of its own trace. The properties of resulting random walks are similar to those of ordinary random walks for large time scales; however, thei…
We analyze the time series of overnight returns for the bund and btp futures exchanged at LIFFE (London). The overnight returns of both assets are mapped onto a one-dimensional symbolic-dynamics random walk: The `bond walk'. During the considered period (October 1991 - January 1994) the bund-future market opened earlie…
In this paper, we introduce a new gait segmentation method based on accelerometer data and develop a new distance function between two time series, showing novel and effectiveness in simultaneously identifying user and adversary. Comparing with the normally used Neural Network methods, our approaches use geometric feat…
New method explains GNN predictions using walks.
The weights of a neural network are typically initialized at random, and one can think of the functions produced by such a network as having been generated by a prior over some function space. Studying random networks, then, is useful for a Bayesian understanding of the network evolution in early stages of training. In…
Transformers learn random walks optimally with gradient descent.
Deep learning predicts Bitcoin spot price movements from order books.
Principal Component Analysis (PCA) is the most common nonparametric method for estimating the volatility structure of Gaussian interest rate models. One major difficulty in the estimation of these models is the fact that forward rate curves are not directly observable from the market so that non-trivial observational e…
In learning with noisy labels, for every instance, its label can randomly walk to other classes following a transition distribution which is named a noise model. Well-studied noise models are all instance-independent, namely, the transition depends only on the original label but not the instance itself, and thus they a…
We construct a new type of quantum walks on simplicial complexes as a natural extension of the well-known Szegedy walk on graphs. One can numerically observe that our proposing quantum walks possess linear spreading and localization as in the case of the Grover walk on lattices. Moreover, our numerical simulation sugge…
Hybrid classical-quantum framework optimizes portfolio rebalancing with reduced transaction costs.
NodeSig efficiently computes binary node embeddings for scalable graph analysis.
The Cannon-Thurston map's measures become singular with respect to sphere measures.
We consider a generalization of the Heath Jarrow Morton model for the term structure of interest rates where the forward rate is driven by Paretian fluctuations. We derive a generalization of Itô's lemma for the calculation of a differential of a Paretian stochastic variable and use it to derive a Stochastic Differenti…
Paper improves ETF tail-risk monitoring reliability.
A modular cash-overlay rule for allocating between a fixed growth-defensive risky sleeve and interest-bearing cash.
For the pedestrian observer, financial markets look completely random with erratic and uncontrollable behavior. To a large extend, this is correct. At first approximation the difference between real price changes and the random walk model is too small to be detected using traditional time series analysis. However, we s…