Technical indicator selection and trading signal forecasting: varying input window length and forecast horizon for the Pakistan Stock Exchange
This paper investigates how input window length and forecast horizon affect the predictive performance of a trading signal prediction system.
This paper presents a novel and direct approach to solving boundary- and final-value problems, corresponding to barrier options, using forward pathwise deep learning and forward–backward stochastic differential equations.
Predicting financial distress of Chinese listed companies using a novel hybrid model framework with an imbalanced-data perspective
In this paper a novel hybrid model framework is constructed to solve the problem of predicting the financial distress of Chinese listed companies using imbalanced data.
Embryonic work on quantum neural networks raises hope of faster, more accurate models
Risk Awards 2022: Big bet on AI is delivering results
Customer churn prediction for commercial banks using customer-value-weighted machine learning models
In this paper the authors propose a framework to address the issue of customer churn prediction, and they quantify customer values with the use of an improved customer value model.
This paper presents a stochastic optimization framework for integrating time-varying factor covariance models in a risk-based portfolio optimization setting.
A multi-step path is forecast using deep learning and parallel computing
Machine learning method edges regression techniques in linking nonlinearities among delinquent borrowers
In this paper the universal approximation theorem of artificial neural networks (ANNs) is applied to the stochastic alpha beta rho (SABR) stochastic volatility model in order to construct highly efficient representations.
Quants use neural networks to upgrade classic options pricing model
Superior computational grunt of neural networks is attractive to lenders. Lack of explainability is the downside
The P&L distribution of a complex derivatives portfolio is computed via deep learning
Comprehensive Capital Analysis and Review consistent yield curve stress testing: from Nelson–Siegel to machine learning
This paper develops different techniques for interpreting yield curve scenarios generated from the FRB’s annual CCAR review.
Differential PCA is introduced to reduce the dimensionality in derivative pricing problems
This paper presents several algorithms based on machine learning to solve hedging problems in incomplete markets.
An interpretable Comprehensive Capital Analysis and Review (CCAR) neural network model for portfolio loss forecasting and stress testing
This paper proposes an interpretable nonlinear neural network model that translates business regulatory requirements into model constraints.
Novel interpretability method could spur greater use of ReLU neural networks for credit scoring
Machine learning could help with loan decisions – but only if banks can explain how it works. And that’s not easy
Data gaps and potential biases must be accounted for in approaches to tackling money laundering
Initial scepticism about using neural networks for derivatives pricing is giving way to enthusiasm
Buy-siders look to machine learning for clues on the effect of rising prices on portfolios
Intelligent robots can value complex derivatives in minutes rather than hours
Dealers say agencies’ request for info could prompt new rules that stifle model innovation