arXiv research

ElasTST improves time-series forecasting across varying horizons.

Predicts long-term return distributions with time-varying volatility.

NVMDP framework tackles non-stationary MDPs with varying discount rates.

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…

Multi-step-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multi-step-ahead…

A pairs trading model with time-varying volatility using stochastic control.

Derives time-averaged active inference from control principles.

The paper examines how loss aversion impacts multi-armed bandit decisions over long periods.

Over the past three decades, black holes have played an important role in quantum gravity, mathematical physics, numerical relativity and gravitational wave phenomenology. However, conceptual settings and mathematical models used to discuss them have varied considerably from one area to another. Over the last five year…

New RL method learns K-step lookahead Q-functions for fixed-horizon MDPs.

ModelRadar evaluates forecasting models across multiple aspects.

This paper introduces new risk measures for evaluating losses with varying time horizons.

Online L2D algorithm for multiclass classification with varying experts.

Behavioral theories posit that investor sentiment exhibits predictive power for stock returns, whereas there is little study have investigated the relationship between the time horizon of the predictive effect of investor sentiment and the firm characteristics. To this end, by using a Granger causality analysis in the …

This paper proposes a multi-scale Markov-Switching GARCH model for EUR/USD volatility.

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…

Foundation models improve on econometric benchmarks for forecasting volatility, but vary widely across models.

A new approach optimizes weights in DLP for better risk-adjusted performance.

The problem of portfolio optimization is one of the most important issues in asset management. This paper proposes a new dynamic portfolio strategy based on the time-varying structures of MST networks in Chinese stock markets, where the market condition is further considered when using the optimal portfolios for invest…

We consider the sequential Bayesian optimization problem with bandit feedback, adopting a formulation that allows for the reward function to vary with time. We model the reward function using a Gaussian process whose evolution obeys a simple Markov model. We introduce two natural extensions of the classical Gaussian pr…

We study the optimal stopping of an American call option in a random time-horizon under exponential spectrally negative Lévy models. The random time-horizon is modeled as the so-called Omega default clock in insurance, which is the first time when the occupation time of the underlying Lévy process below a level $y$, ex…

We consider a finite-horizon multi-armed bandit (MAB) problem in a Bayesian setting, for which we propose an information relaxation sampling framework. With this framework, we define an intuitive family of control policies that include Thompson sampling (TS) and the Bayesian optimal policy as endpoints. Analogous to TS…

Study online control of unknown time-varying systems with negative and positive results.

Paper fine-tunes a language model to predict long-term stock buy signals.

The paper models exchange rate risk premium using mean-reverting dynamics.

New model predicts energy prices volatility by smoothing time variation and persistence.

Paper tackles utility maximization with job-switching and retirement constraints.

New model predicts blood glucose in diabetics with improved accuracy.

Scaling laws found for reinforcement learning performance with model size and compute.

Study uses zero-shot models to forecast mortality rates globally.

For a long investment time horizon, it is preferable to rebalance the portfolio weights at intermediate times. This necessitates a multi-period market model in which portfolio optimization is usually done through dynamic programming. However, this assumes a known distribution for the parameters of the financial time se…

Deep learning reveals lagged correlations in stock markets, showing accuracy decreases with shorter prediction horizons.

Agents compose pre-trained policies for complex tasks, improving zero-shot performance.

Dynamic Black-Litterman integrates expert views with portfolio optimization over varying time horizons.

Motivated by the many real-world applications of reinforcement learning (RL) that require safe-policy iterations, we consider the problem of off-policy evaluation (OPE) -- the problem of evaluating a new policy using the historical data obtained by different behavior policies -- under the model of nonstationary episodi…

A new framework improves volatility forecasting for financial markets.

New method estimates robust multi-period portfolios using entropy.

We consider a stochastic linear bandit model in which the available actions correspond to arbitrary context vectors whose associated rewards follow a non-stationary linear regression model. In this setting, the unknown regression parameter is allowed to vary in time. To address this problem, we propose D-LinUCB, a nove…

Unified framework infers time-varying graphs from incomplete signals.

Bayesian model predicts interest rates with short-term accuracy and long-term stability.

Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling. The increasing amounts of generated traffic data have been used to train machine learning predictors for traffic, however this is a challenging task due to inter-dependencies of…

Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games. We identify three key challenges that any algorithm needs to master in order to perform well on all games: process…

SurvFM-RMST converts survival outcomes into pseudo-observation targets for tabular models.

Study cost-driven state representation learning for control from partial observations.

Proves rigidity of extremal Kerr-Newman horizons.

Develops a formalism for studying general horizons and derives a near-horizon equation.

Proposes a deep spectral Q-learning for mobile health data.

We measure the influence of different time-scales on the dynamics of financial market data. This is obtained by decomposing financial time series into simple oscillations associated with distinct time-scales. We propose two new time-varying measures: 1) an amplitude scaling exponent and 2) an entropy-like measure. We a…