3 papers
q-fin.TR2026
How do standard time series forecasting benchmarks translate to finance? A broad study of risk adjusted performance
Adir Saly-Kaufmann, Kieran Wood, Jan-Peter Calliess +2
Forecasting financial asset returns is challenging due to low signal-to-noise ratios, non-stationarity and heavy-tailed distributions. We benchmark 16 deep learning architectures f…
q-fin.ST2023
Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers
Alvaro Arroyo, Alvaro Cartea, Fernando Moreno-Pino +1
One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order…
q-fin.RM2022
DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions
Fernando Moreno-Pino, Stefan Zohren
Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques based on machine learning can be employed…