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.
ElasTST improves time-series forecasting across varying horizons.
This paper studies the utility maximization problem with changing time horizons in the incomplete Brownian setting. We first show that the primal value function and the optimal terminal wealth are continuous with respect to the time horizon . Secondly, we exemplify that the expected utility stemming from applying th…
Optimizes investment under uncertain time horizons with non-concave utility.
The paper finds the shortest time to exploit arbitrage in multi-stock markets.
Study on BSDEs with random time horizon, focusing on existence and properties.
We aim to construct the optimal solutions to the undiscounted continuous-time infinite horizon optimization problems, the objective functionals of which may be unbounded. We identify the condition under which the limit of the solutions to the finite horizon problems is optimal for the infinite horizon problems under th…
ForecastGAN improves multi-horizon time series forecasting by integrating numerical and categorical features.
We show that the supersymmetric near horizon black hole geometries of 6-dimensional supergravity coupled to any number of scalar and tensor multiplets are either locally , where Σ^3 is a homology 3-sphere, or $\bR^{1,1}\times {\cal S}^4$, where is a 4-manifold whose geometry depends on the…
Careful tuning of the learning rate, or even schedules thereof, can be crucial to effective neural net training. There has been much recent interest in gradient-based meta-optimization, where one tunes hyperparameters, or even learns an optimizer, in order to minimize the expected loss when the training procedure is un…
A correct description of the past and the future event horizons for timelike geodesics in de Sitter space-time of the first kind is given.
We investigate the growth optimal strategy over a finite time horizon for a stock and bond portfolio in an analytically solvable multiplicative Markovian market model. We show that the optimal strategy consists in holding the amount of capital invested in stocks within an interval around an ideal optimal investment. Th…
Study optimal portfolios in a non-Markovian regime-switching model with random time horizon.
Study of isometries in spacetimes without observer horizons.
The paper clarifies long-horizon investment and DCA, showing no risk reduction but different exposure profiles.
The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and increased cost of operation. This research paper proposes…
Proposes a model for multi-horizon probabilistic forecasting of time series influenced by asynchronous events.
A new Bayesian method optimizes time-dependent expensive functions with lookahead.
We study the Laplace spectra of the intrinsic instantaneous metrics on the event and cosmological horizons of a Kerr-Newman de Sitter space-time and prove that the spectral data from these horizons uniquely determine the space-time. This is accomplished by exhibiting formulae relating the parameters of the space-time m…
State-of-the-art forecasting methods using Recurrent Neural Net- works (RNN) based on Long-Short Term Memory (LSTM) cells have shown exceptional performance targeting short-horizon forecasts, e.g given a set of predictor features, forecast a target value for the next few time steps in the future. However, in many appli…
We consider the problem of portfolio optimization in a simple incomplete market and under a general utility function. By working with the associated Hamilton-Jacobi-Bellman partial differential equation (HJB PDE), we obtain a closed-form formula for a trading strategy which approximates the optimal trading strategy whe…
This paper uses recent results on continuous-time finite-horizon optimal switching problems with negative switching costs to prove the existence of a saddle point in an optimal stopping (Dynkin) game. Sufficient conditions for the game's value to be continuous with respect to the time horizon are obtained using recent …
Study optimal stopping problems with finite-time horizon and proves continuity and strict monotonicity of the boundary.
We investigate the planar maximally filtered graphs of the portfolio of the 300 most capitalized stocks traded at the New York Stock Exchange during the time period 2001-2003. Topological properties such as the average length of shortest paths, the betweenness and the degree are computed on different planar maximally f…
Reservoir computing predicts chaotic systems for long horizons with sparse updates.
Extends RL to random stopping times, improving optimization.
New concepts of barriers and black regions defined for Lorentzian manifolds.
We review a resent {\em time-dependent} performance measure for economical time series -- the (optimal) investment horizon approach. For stock indices, the approach shows a pronounced gain-loss asymmetry that is {\em not} observed for the individual stocks that comprise the index. This difference may hint towards an sy…
Improved algorithm for optimal stopping problems reduces runtime.
Online learning rbfnet improves multi-horizon returns forecasts for financial time series.
Proves intrinsic rigidity of extremal horizons, classifying their geometry.
We compare some methods recently used in the literature to detect the existence of a certain degree of common behavior of stock returns belonging to the same economic sector. Specifically, we discuss methods based on random matrix theory and hierarchical clustering techniques. We apply these methods to a portfolio of s…
We consider axisymmetric stationary dirty black holes with regular non-extremal or extremal horizons, and compute their on-horizon Petrov types. The Petrov type (PT) in the frame of the observer crossing the horizon can be different from that formally obtained in the usual (but singular in the horizon limit) frame of a…
LLapDiff models irregular multivariate time series without step-by-step integration.
We show that axisymmetric extremal horizons are unstable under linear scalar perturbations. Specifically, we show that translation invariant derivatives of generic solutions to the wave equation do not decay along such horizons as advanced time tends to infinity, and in fact, higher order derivatives blow up. This resu…
Study long-term asset liquidation behavior with external flows.
This paper solves the consumption-investment problem under Epstein-Zin preferences on a random horizon. In an incomplete market, we take the random horizon to be a stopping time adapted to the market filtration, generated by all observable, but not necessarily tradable, state processes. Contrary to prior studies, we do…
Derives time-averaged active inference from control principles.
New RL method learns K-step lookahead Q-functions for fixed-horizon MDPs.
Deep neural nets approximate random dynamical system trajectories uniformly in time.
No time-periodic Majorana fermions found in Kerr-Newman spacetimes with nontrivial charge.
The paper proves the existence of a horizon for certain spacetimes.
This review tackles long horizon forecasting in time series analysis using deep learning.
The article discusses new horizons in black hole physics.
Consider a compact, orientable, three dimensional Riemannian manifold with boundary with nonnegative scalar curvature. Suppose its boundary is the disjoint union of two pieces: the horizon boundary and the outer boundary, where the horizon boundary consists of the unique closed minimal surfaces in the manifold and the …
We present a continuous-time maximum likelihood estimation methodology for credit rating transition probabilities, taking into account the presence of censored data. We perform rolling estimates of the transition matrices with exponential time weighting with varying horizons and discuss the underlying dynamics of trans…
Using high frequency data, we have studied empirically the change of volatility, also called volatility derivative, for various time horizons. In particular, the correlation between the volatility derivative and the volatility realized in the next time period is a measure of the response function of the market particip…
A new ML algorithm solves complex economic control problems.
This paper completes the analysis of Choulli et al. Non-Arbitrage up to Random Horizons and after Honest Times for Semimartingale Models and contains two principal contributions. The first contribution consists in providing and analysing many practical examples of market models that admit classical arbitrages while the…