Technical paper/Data
A comprehensive explainable approach for imbalanced financial distress prediction
The authors suggest an explainable machine learning method for imbalanced financial distress prediction which uses extreme gradient boosting.
Assessing the efficiency of pure-play internet banks in South Korea, Japan and China with data envelopment analysis
The authors investigate the efficiency of pure-play internet banks in China, Japan and South Korea, recommending they focus on the management of noninterest expenses and income to ensure stable profts.
A method of classifying imbalanced credit data based on the AC-CTGAN hybrid sampling algorithm
The authors put forward a novel method with which to identify risk in consumer credit data and demonstrate its enhanced generalization ability compared to commonly used methods.
Default prediction based on a locally weighted dynamic ensemble model for imbalanced data
The authors put forward a locally weighted dynamic ensemble model which can predict financial institutions' default statues five years ahed.
Does the term structure of the at-the-money skew really follow a power law?
A power law can fit the ATM skew, but struggles with short maturities
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.
Sculpting implied volatility surfaces of illiquid assets
From the stock cumulative distribution function an arbitrage-free volatility surface is derived
Semi-analytic conditional expectations
A data-driven approach to computing expectations for the pricing and hedging of exotics
A structural credit risk model based on purchase order information
This paper proposes a credit risk model based on purchase order information to address the deficiencies of monitoring methods that use only financial statements.
Dynamically controlled kernel estimation
An accurate data-driven and model-agnostic method to compute conditional expectations is presented
Nonlinear risk decomposition for any type of fund
A risk decomposition by fund manager, factor or instrument is proposed
Covariance estimation for risk-based portfolio optimization: an integrated approach
This paper presents a stochastic optimization framework for integrating time-varying factor covariance models in a risk-based portfolio optimization setting.
Bayesian nonparametric covariance estimation with noisy and nonsynchronous asset prices
This paper introduces a Bayesian nonparametric method to estimate the ex post covariance matrix from high-frequency data.
Modeling realized volatility with implied volatility for the EUR/GBP exchange rate
This paper concerns the application of implied volatility in modeling realized volatility in the daily, weekly and monthly horizon using high-frequency data for the EUR/GBP exchange rate.
Efficient representation of supply and demand curves on day-ahead electricity markets
The authors model the supply and demand curves of electricity day-ahead auctions in a parsimonious way by building an appropriate algorithm to present the information about electricity prices and demand with far fewer parameters than the existing…
Nowcasting networks
The authors devise a neural network-based compression/completion methodology for financial nowcasting.
Research on listed companies’ credit ratings, considering classification performance and interpretability
This study uses the correlation coefficient and F-test to select the initial features of a credit evaluation system, and then a validity index for a second selection to ensure that the feature system has the optimum ability to discriminate in determining…
The impact of energy costs on industrial performance: identifying price and quantity effects in the aluminum industry using a data envelopment analysis approach
The authors build a frontier function model with technical and cost efficiency measures to assess the impact of energy costs on competitiveness in the aluminum industry, a heavy energy consumer, by identifying what may be attributed to price and quantity…
Zooming in on equity factor crowding
A measure for crowding in trades is derived from supply and demand imbalances
Neural networks for option pricing and hedging: a literature review
This paper provides a comprehensive review of the field of neural networks, comparing articles in terms of input features, output variables, benchmark models, performance measures, data partition methods and underlying assets. Related work and…
Quantifying systemic risk using Bayesian networks
Creditworthiness of individual entities may offer an insight into systemic risk of financial markets