Technical paper/Regression analysis
Expectile risk quadrangles and applications
The authors study the expectile risk measure within the fundamental risk quadrangle framework, constructing a new quadrangle where the expectile is both a statistic and a risk measure.
Alternatives to deep neural networks in finance
Two methods to approximate complex functions in an explainable way are presented
A factor-based risk model for multifactor investment strategies
This paper presents a novel, practical approach to risk management for multifactor equity investment strategies.
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.
Preventing the unpleasant: fraudulent financial statement detection using financial ratios
In this paper, the authors investigate financial fraud in companies listed on the Athens Stock Exchange during the period 2008–18 and propose a model to detect fraudulent financial statements.
Revisiting the linkage between internal audit function characteristics and internal control quality
This paper revisits the linkage between internal audit function characteristics and internal control quality and proposes a random polynomial model for assessing ICQ.
Are there multiple independent risk anomalies in the cross section of stock returns?
Using multivariate portfolio sorts, firm-level cross-sectional regressions and spanning tests, this paper shows that, in the cross section of stock returns, most commonly used risk measures in academia and in practice are separate return predictors with…
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.
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.
Incorporating small-sample defaults history in loss given default models
This paper proposes a methodology for estimating loss given default (LGD) that accounts for small default sample sizes.
A review of tree-based approaches to solving forward–backward stochastic differential equations
This paper looks at ways of solving (decoupled) forward–backward stochastic differential equations numerically using regression trees.
Fast pricing of American options under variance gamma
This research develops a new fast and accurate approximation method, inspired by the quadratic approximation, to get rid of the time steps required in finite-difference and simulation methods, while reducing error by making use of a machine learning…
Zone-wide prediction of generating unit-specific power outputs for electricity grid congestion forecasts
This paper explores various statistical and statistical learning methods, with the goal of adequately predicting the on/off status and power output levels of all power plants within a control zone.
Sign prediction and sign regression
This paper proposes an approach whereby the loss function regularizes the errors in prediction in different ways.
Gradient boosting for quantitative finance
In this paper, the authors discuss how tree-based machine learning techniques can be used in the context of derivatives pricing.
The effects of customer segmentation, borrower behaviors and analytical methods on the performance of credit scoring models in the agribusiness sector
The main aim of this study is to analyze the joint effects of customer segmentation, borrower characteristics and modeling techniques on the classification accuracy of a scoring model for agribusinesses.
Pricing path-dependent Bermudan options using Wiener chaos expansion: an embarrassingly parallel approach
In this work, the authors propose a new policy iteration algorithm for pricing Bermudan options when the payoff process cannot be written as a function of a lifted Markov process.
The data anonymiser
Non-parametric approaches anonymise datasets while reproducing their statistical properties
Economic policy uncertainty, investors’ attention and US real estate investment trusts’ herding behaviors
Using a quantile regression model, this study examines economic policy uncertainty and investors’ attention for policy risk on US real estate investment trusts’ (REITs’) herding behaviors.
Estimating marginal effects of key factors that influence wholesale electricity demand and price distributions in Texas via quantile variable selection methods
Using a large data set from the Electric Reliability Council of Texas, this study uses quantile regressions and attendant variable selection methods to choose the most important factors that influence demand and price distributions; subsequently, the…