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.
Predicts the age of astronomical transients from real-time data.
Detects anomalies in astronomical time series data.
We present RAPID (Real-time Automated Photometric IDentification), a novel time-series classification tool capable of automatically identifying transients from within a day of the initial alert, to the full lifetime of a light curve. Using a deep recurrent neural network with Gated Recurrent Units (GRUs), we present th…
First-passage times in random walks have a vast number of diverse applications in physics, chemistry, biology, and finance. In general, environmental conditions for a stochastic process are not constant on the time scale of the average first-passage time, or control might be applied to reduce noise. We investigate mome…
Study solves utility maximization in a transient price impact market.
Improves inverse uncertainty quantification for time-dependent data using PCA and deep neural networks.
Unsupervised learning classifies transient noise in gravitational wave detectors.
Study examines the training process of an unsupervised learning model for detecting gravitational-wave transient noise.
Random matrix theory explains transient signal detectability in early-stopped gradient flow.
Study non-rectangular robust MDPs for average-reward, finding optimal policies and transient values.
Novel method uses information theory to measure causal influences during transient neural events.
Study optimal execution in a transient price impact model with multiple traders.
A new kernel improves statistical surrogates for stochastic manifolds with diverse data.
We study a multiplicative transient price impact model for an illiquid financial market, where trading causes price impact which is multiplicative in relation to the current price, transient over time with finite rate of resilience, and non-linear in the order size. We construct explicit solutions for the optimal contr…
Bayesian BIC for multi-trial data improves VAR model order selection.
New Roman pipeline detects astronomical transients.
Sharp pseudospectral bounds prevent transient amplification in coupled gradient descent.
Calculates the transient number of knots using homology groups.
Recently, Ross showed that it is possible to recover an objective measure from a risk-neutral measure. His model assumes that there is a finite-state Markov process X that drives the economy in discrete time. Many authors extended his model to a continuous-time setting with a Markov diffusion process X with state space…
Transient market impact explained via Nash equilibrium in a game.
We prove a scaling limit theorem for the super-replication cost of options in a Cox--Ross--Rubinstein binomial model with transient price impact. The correct scaling turns out to keep the market depth parameter constant while resilience over fixed periods of time grows in inverse proportion with the duration between tr…
New method decomposes Markov chain rewards into persistent and transient components.
We solve the superhedging problem for European options in an illiquid extension of the Black-Scholes model, in which transactions have transient price impact and the costs and the strategies for hedging are affected by physical or cash settlement requirements at maturity. Our analysis is based on a convenient choice of…
Optimal energy trading strategy for intraday markets using Hawkes processes.
We consider a market impact game for risk-averse agents that are competing in a market model with linear transient price impact and additional transaction costs. For both finite and infinite time horizons, the agents aim to minimize a mean-variance functional of their costs or to maximize the expected exponential u…
Deep learning dynamics and NTK evolution studied through diverse measures.
The paper uses Frenet frame to unify electrical and geometric quantities.
We solve the problem of optimal liquidation with volume weighted average price (VWAP) benchmark when the market impact is linear and transient. Our setting is indeed more general as it considers the case when the trading interval is not necessarily coincident with the benchmark interval: Implementation Shortfall and Ta…
We study optimal liquidation in the presence of linear temporary and transient price impact along with taking into account a general price predicting finite-variation signal. We formulate this problem as minimization of a cost-risk functional over a class of absolutely continuous and signal-adaptive strategies. The sto…
This paper presents a spatiotemporal unsupervised feature learning method for cause identification of electromagnetic transient events (EMTE) in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurement, and using the convolutional neural network (CNN) as t…
Modeling poverty transitions in India over 54 years, showing rising but persistent poverty.
We discuss an "extrinsic" property of knots in a 3-subspace of the 3-sphere to characterize how the subspace is embedded in . Specifically, we show that every knot in a subspace of the 3-sphere is transient if and only if the exterior of the subspace is a disjoint union of handlebodies, i.e. regular neighbor…
CNNs improve transient detection in DES-SN images.
This paper presents a study on power grid disturbance classification by Deep Learning (DL). A real synchrophasor set composing of three different types of disturbance events from the Frequency Monitoring Network (FNET) is used. An image embedding technique called Gramian Angular Field is applied to transform each time …
Paper uses deep reinforcement learning for better control of rocket engines during start-up phases.
New method uses machine learning to analyze catalyst reactions.
Generating eye diagrams by using a circuit simulator can be very computationally intensive, especially in the presence of nonlinearities. It often involves multiple Newton-like iterations at every time step when a SPICE-like circuit simulator handles a nonlinear system in the transient regime. In this paper, we leverag…
New algorithm improves understanding of decentralized SBO transient iteration complexity.
Regularization is typically understood as improving generalization by altering the landscape of local extrema to which the model eventually converges. Deep neural networks (DNNs), however, challenge this view: We show that removing regularization after an initial transient period has little effect on generalization, ev…
SMC analysis reveals key transient effects in macroeconomic ABM.
Study high-frequency trading game with price impact, finding unique equilibrium.
We present a sparse and invariant representation with low asymptotic complexity for robust unsupervised transient and onset zone detection in noisy environments. This unsupervised approach is based on wavelet transforms and leverages the scattering network from Mallat et al. by deriving frequency invariance. This frequ…
Study optimal liquidation strategies on Uniswap v2/v3 considering price impact.
Many iterative procedures in stochastic optimization exhibit a transient phase followed by a stationary phase. During the transient phase the procedure converges towards a region of interest, and during the stationary phase the procedure oscillates in that region, commonly around a single point. In this paper, we devel…
A new ML method speeds up PDE simulations without needing classical training.
This study examines how market makers balance risk and impact in foreign exchange markets.
We propose two rational expectation models of transient financial bubbles with heterogeneous arbitrageurs and positive feedbacks leading to self-reinforcing transient stochastic faster-than-exponential price dynamics. As a result of the nonlinear feedbacks, the termination of a bubble is found to be characterized by a …
We study the problem of the optimal execution of a large trade in the presence of nonlinear transient impact. We propose an approach based on homotopy analysis, whereby a well behaved initial strategy is continuously deformed to lower the expected execution cost. We find that the optimal solution is front loaded for co…