Technical paper/Forecasting
Geopolitical shocks and market memory: evidence from crypto and energy assets during the Russo-Ukrainian war
Focussing on energy stocks and cryptocurrencies prior to and during the Russo-Ukrainian conflict, this paper examines the impact of geopolitical conflict on the informational efficiency of financial markets
Forecasting Chinese crude oil futures’ volatility: a heterogeneous volatility spillover-conditional autoregressive range model
This paper offers a new model with which to model and forecast the volatility of Chinese crude oil futures.
Stock trend forecasting with graph neural networks
The authors put forward a new method for short-term stock trend prediction based on graph neural networks.
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
Incorporating economic outlook into exposure at default models
This paper outlines a new means to include macroeconomic variables in exposure at default models while satisfying all IFRS 9 expectations.
Deep learning alpha signals from limit order books
An analysis on network architectures applied to limit order book data is presented
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.
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.
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.
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.
Forecasting the realized volatility of stock markets with financial stress
This paper investigates the impact of financial stress on the predictability of the realized volatility of five stock markets
Forecasting the European Monetary Union equity risk premium with regression trees
The authors use EMU data from the period between 2000 to 2020 to forecast equity risk premium and investigate Classification and Regression Trees.
Application of the moving Lyapunov exponent to the S&P 500 index to predict major declines
The authors suggest an innovative method based in econophysics that provides early warning signs for major declines in the S&P 500 Index