Forecasting
Machine learning in oil market volatility forecasting: the role of feature selection and forecast horizon
This paper investigates oil market volatility prediction, showing financial variables to dominate short-horizon forecasting, while macroeconomic and sentiment factors increase in importance at longer horizons
Repricing risk in a global rate reset
Firms are reallocating risk exposure, evolving forecasting models and leveraging deep macroeconomic data to uncover patterns and challenge assumptions
Deep learning alpha signals from limit order books
An analysis on network architectures applied to limit order book data is presented
Bank FX market-makers ramp up AI usage
Barclays applies tech to predictions, while HSBC and ING look at pricing accuracy
The fate of zombie firms: prediction, determinants and exit paths
This paper examines how machine learning and statistical methods may be used to predict whether or not zombie firms will escape their fate as zombies.
One to watch: Dexter Energy
Energy Risk Awards 2025: Tech firm uses AI and weather tools to support power trading amid rise in renewables
Quantum cognition machine learning: financial forecasting
A new paradigm for training machine learning algorithms based on quantum cognition is presented
Forecasting India’s foreign trade dynamics: evaluation of alternative forecasting models in the post-pandemic period
The authors aim to determine how India's foreign trade will change following Covid-19 and the Russia-Ukraine conflict, comparing several forecasting models and identifying that which performs best.
An entropy-based class of moving averages
The author proposes a family of maximum-entropy-based moving averages with a framework of a moving average corresponding to a risk-neutral valuation scheme for financial time series applied to generalized forms of entropy.
The impact of economic sentiment on financial portfolios during the recent turmoil
The authors investigate the influence of economic sentiment on financial portfolios during Covid-19 and the Russia-Ukraine conflict before conducting a portfolio management analysis on their data.
Energy50/Energy pricing systems 2024: Hitachi Energy
How ETRM, modelling and pricing tools are evolving, shining a light on Hitachi Energy, which performed strongly in the Chartis Energy50 rankings
Execs can game sentiment engines, but can they fool LLMs?
Quants are firing up large language models to cut through corporate blather
AOCI worsens across the board at US banks in Q1
JP Morgan, Wells Fargo and Citi hit hardest in trend reversal
The cost of mis-specifying price impact
Expected returns can be significantly affected by the wrong use of impact models
Conditional and unconditional intraday value-at-risk models: an application to high-frequency tick-by-tick exchange-traded fund data
The authors consider conditional and unconditional intraday value-at-risk models for high-frequency exchange-traded funds, providing results useful to practitioners of high-frequency trading.
Can machine learning help predict recessions? Not really
Artificial intelligence models stumble on noisy data and lack of interpretability
The Fed’s stress-test models are inaccurate. Something has to change
First step for US regulator to improve its bank loss forecasts would be to open up its models to public scrutiny, argue two banking industry advocates
Using a skewed exponential power mixture for value-at-risk and conditional value-at-risk forecasts to comply with market risk regulation
The authors investigate a method that combines two skewed exponential power distributions and models the conditional forecasting of VaR and CVaR and is in compliance with the recent Basel framework for market risk.
Default forecasting based on a novel group feature selection method for imbalanced data
The authors construct a group feature selection method which combines optimal instance selection with weighted comprehensive precision in an effort to improve the performance of prediction models in relation to defaulting firms.
Time-varying higher moments, economic policy uncertainty and renminbi exchange rate volatility
The authors investigate how time-varying higher moments and economic policy uncertainty may be used for predicting the renminbi exchange rate volatility.
Allocating and forecasting changes in risk
This paper considers time-dependent portfolios and discuss the allocation of changes in the risk of a portfolio to changes in the portfolio’s components.