We study the effects of non-systematic and systematic mortality risks on the required initial capital in a pension plan, in the presence of financial risks. We discover that for a pension plan with few members the impact of pooling on the required capital per person is strong, but non-systematic risk diminishes rapidly…
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48 results for “systematic”
Detects systematic anomalies in consumer complaints using NLP.
problem Detecting small, frequent anomalies in consumer complaints.
method NLP conversion of narratives, followed by anomaly detection algorithm.
result Demonstrates effectiveness of NLP for detecting systematic anomalies.
Neuro-symbolic agent learns systematic generalisation from formal instructions.
problem Achieving zero-shot generalisation of formally specified tasks.
method Combines deep reinforcement learning with temporal logic.
result Systematic learning emerges with convolutional layers and abstract operators.
Machine learning models predict brain age with systematic bias, corrected in this study.
problem Systematic bias in machine learning regression models for brain age prediction.
method General constrained optimization approach to correct bias.
result Our method effectively eliminates the bias from brain age predictions.
This short note provides a systematic construction of market models without unbounded profits but with arbitrage opportunities.
CLSVAE repairs systematic errors in images with minimal labeled data.
problem Repairing systematic errors in data, especially in images.
method CLSVAE models inliers as a smaller latent space representation, separating inlier and outlier patterns.
result CLSVAE achieves superior repairs with less than 2% labeled data, outperforming other methods.
The paper models and prices cyber insurance risks, distinguishing idiosyncratic, systematic, and systemic risks.
problem Modeling and pricing cyber insurance policies, especially for systemic risks.
method Distinguishes three types of cyber risks and proposes methods for their valuation.
result Complex methods are needed for systemic cyber risks, including risk-neutral valuation and monetary risk measures.
A new framework for systematic graph neural network data augmentation.
problem Diversity and difficulty in choosing graph neural network data augmentation techniques.
method Comprehensive framework capturing all previous RDAs, formal universality proof, automatic training method.
result Improved state of the art through new RDAs and impartial comparison.
Profiling systematic uncertainties in Simulation-Based Inference with Factorizable Normalizing Flowshep-ph
A method for profiling systematic uncertainties in SBI using Factorizable Normalizing Flows.
problem Computational cost and limited applicability of current SBI methods for realistic analyses.
method Simulation-Based Inference with Factorizable Normalizing Flows to model systematic variations.
result Efficient profiling of nuisance parameters and multivariate DoI in complex analyses.
Study shows short exposure and systematic risk exposure affect disposition effect asymmetries.
problem Understanding disposition effect in short vs long exposure positions and systematic risk.
method Generalized Odean measures, introduced Value metric, implemented dispositionEffect R package.
result Short positions exhibit weaker disposition effect than long positions under narrow framing, reversing in integrated framing.
Systematic and multifactor risk models are revisited via methods which were already successfully developed in signal processing and in automatic control. The results, which bypass the usual criticisms on those risk modeling, are illustrated by several successful computer experiments.
Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively samples variables from their conditional distributions. There are two common scan orders for the variables: random scan and systematic scan. Due to the benefits of locality in hardware, systematic scan is commonly used, even though most st…
Paper develops an AI-driven framework for systematic investing.
problem Manual prompts limit model adaptability and data snooping biases.
method Closed-loop system with self-evolving AI, out-of-sample validation, and economic rationale.
result Long-short portfolios on factor signals outperform with Sharpe ratio 3.11 and return 59.53%.
Analytical, free of time consuming Monte Carlo simulations, framework for credit portfolio systematic risk metrics calculations is presented. Techniques are described that allow calculation of portfolio-level systematic risk measures (standard deviation, VaR and Expected Shortfall) as well as allocation of risk down to…
In this paper we generate and systematically classify all prime planar knotoids with up to 5 crossings. We also extend the existing list of knotoids in and add all knotoids with 6 crossings.
Spotlight method finds hidden errors in deep learning models.
problem Systematic errors in deep learning models on rare data subsets.
method Shining a spotlight on hidden layer representations to find poor performance areas.
result Identifies semantically meaningful areas of weakness in various models.
Tuning into Climate Risks: Extracting Innovation from Television News for Clean Energy Firmsq-fin.ST
Study uses TV news to measure climate risks affecting clean energy firms.
problem Understanding how climate risks impact clean energy firms' financial stability.
method Developed climate risk measures from TV news coverage and analyzed their effects on clean energy firms' risks.
result Increased TV news coverage of climate risks correlates with higher systematic risk and lower idiosyncratic risk for clean energy firms.
Extends ASRF model for green and brown loans, accounting for systematic and idiosyncratic risks.
problem Credit risk assessment for portfolios of green and brown loans.
method Two-factor copula structure, skewed distributions for systematic risk, Gaussian for idiosyncratic risk, non-uniform exposure setting.
result Portfolio loss convergence to a limit reflecting green and brown loan characteristics.
Paper analyzes systematic jump risk around the clock using news narratives.
problem Identifying and managing priced risks in real-time market conditions.
method Combining high-frequency market data with news narratives classified by an LLM.
result Significant heterogeneity in risk premia, with macroeconomic news commanding the largest premium.
Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on Factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, w…
This paper reviews digital transformation research from 2011-2024, focusing on corporate finance.
problem Lack of systematic review in digital transformation from corporate finance perspective.
method Combines bibliometric and content analysis methods.
result Emerging and rapidly growing focus on digital transformation, particularly in developed countries.
Study on NNs for forecasting time series with novel control variable combinations.
problem Forecast future time series with novel combinations of control variables.
method Modular NN architecture with inductive bias for independence of control variables.
result Modular NN architecture improves forecasting of dependent variables up to large horizons.
Study examines how measurement errors impact clustering algorithms.
problem Impact of measurement errors on clustering algorithms.
method Monte Carlo study of two clustering algorithms: GMM with merging and DBSCAN.
result Systematic measurement errors are more problematic for clustering than random errors.
Modelling China's Credit System with Complex Network Theory for Systematic Credit Risk Controlq-fin.RM
The insufficient understanding of the credit network structure was recognized as a key factor for regulators' underestimation of the destructive systematic risk during the financial crisis that started in 2007. The existing credit network research either took a macro perspective to clarify the topological properties of…
Equity options are known to be notoriously difficult to price accurately, and even with the development of established mathematical models there are many assumptions that must be made about the underlying processes driving market movements. As such, the theoretical prices outputted by these models are often slightly di…
Systematic review of conformal inference for treatment effect estimation.
problem Uncertainty quantification in treatment effect estimation.
method Conformal prediction methods for treatment effect estimation.
result Current state-of-the-art conformal prediction methods identified and described.
Predictive Modeling: An Optimized and Dynamic Solution Framework for Systematic Value Investingq-fin.PM
This paper defines systematic value investing as an empirical optimization problem. Predictive modeling is introduced as a systematic value investing methodology with dynamic and optimization features. A predictive modeling process is demonstrated using financial metrics from Gray & Carlisle and Buffett & Clark. A 31-y…
Systematic prolongation for Killing two-tensors in symmetric spaces.
problem Understanding Killing two-tensors in symmetric spaces.
method Systematic prolongation procedure for Killing two-tensors, focusing on locally symmetric spaces.
result Natural quadratic mapping from Killing fields to Killing two-tensors on irreducible locally symmetric spaces of compact type.
Unified neural network model for astro-particle physics predictions with coverage, systematics, and goodness-of-fit.
problem Lack of statistical uncertainties, coverage, systematic uncertainties, and goodness-of-fit in neural network predictions.
method KL-divergence objective for joint distribution of data and labels, conditional normalizing flows, amortized with neural networks.
result Unified supervised learning and VAEs under stochastic variational inference for event property predictions.
Systematic review of ML explainability in process mining.
problem Understanding the black-box nature of ML models in process mining.
method Systematic literature review using PRISMA framework.
result Identification of key trends and challenges in interpretability.
Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument, and random measurement errors. Several novel biological technologies, such as ma…
Proposes a method to select features for subgroup datasets with systematic missing data.
problem Feature selection for datasets with subgroup structure and systematic missing data.
method Develops a heterogeneous graph neural network to propagate information between feature-subgroup-target variable connections.
result Demonstrates improved feature selection performance and scalability.
LR-Robot accelerates SLRs by combining expert oversight and AI, revealing trends and patterns in financial research.
problem Manual SLRs are impractical due to the scale and complexity of modern financial research.
method Domain experts define taxonomies and constraints, LLMs execute classification, and human evaluation ensures reliability.
result AI can understand and synthesize literature, revealing trends and core research directions.
The aim of this paper is to propose a realistic and operational model to quantify the systematic risk of mortality included in an engagement of retirement. The model presented is built on the basis of model of Lee-Carter. The stochastic prospective tables thus built make it possible to project the evolution of the rand…
This review examines deep learning in financial fraud detection over 5 years.
problem Improving deep learning techniques for financial fraud detection.
method Systematic literature review of 57 studies using performance metrics.
result Deep learning models enhance fraud detection across various financial domains.
Study shows non-systematic bias in customer satisfaction surveys limits data value.
problem Non-systematic bias in customer satisfaction surveys limits data value.
method Used real customer satisfaction survey data of a large retail bank to show the irreducible error and suggest thoughtful survey design methods.
result A thoughtful survey design can reduce non-systematic error in customer satisfaction surveys.
This paper introduces a simple and efficient density estimator that enables fast systematic search. To show its advantage over commonly used kernel density estimator, we apply it to outlying aspects mining. Outlying aspects mining discovers feature subsets (or subspaces) that describe how a query stand out from a given…
Measuring Strategy-Decay Risk: Minimum Regime Performance and the Durability of Systematic Investingq-fin.RM
Measures strategy durability through minimum regime performance, revealing trade-offs between efficiency and resilience.
problem Systematic investing strategies are vulnerable to regime changes, affecting their effectiveness and performance.
method Introduces minimum regime performance (MRP) to quantify the durability of systematic strategies, capturing how performance deteriorates under changing market conditions.
result Higher long-term Sharpe ratios do not always correlate with higher MRP, highlighting a new dimension of portfolio fragility.
Hybrid ML ensemble predicts market risk and generates alpha.
problem Forecasting short-horizon market risk and generating alpha.
method Neural networks and tree-based voting models integrated for trading signal.
result Sharpe ratio of 2.51 and annualized CAPM alpha of +0.28.
Study compares machine learning methods for improving wind gust forecasts.
problem Improving accuracy of wind gust forecasts from ensemble models.
method Comprehensive comparison of 8 statistical and machine learning methods.
result Locally adaptive neural networks significantly outperform other methods.
The effect of proportional transaction costs on systematically generated portfolios is studied empirically. The performance of several portfolios (the index tracking portfolio, the equally-weighted portfolio, the entropy-weighted portfolio, and the diversity-weighted portfolio) in the presence of dividends and transact…
In this paper, we measure systematic risk with a new nonparametric factor model, the neural network factor model. The suitable factors for systematic risk can be naturally found by inserting daily returns on a wide range of assets into the bottleneck network. The network-based model does not stick to a probabilistic st…
The aim of this paper is to propose a realistic and operational model to quantify the systematic risk of mortality included in an engagement of retirement. The model presented is built on the basis of model of Lee-Carter. The stochastic prospective tables thus built make it possible to project the evolution of the rand…
Machine vision is critical to robotics due to a wide range of applications which rely on input from visual sensors such as autonomous mobile robots and smart production systems. To create the smart homes and systems of tomorrow, an overview about current challenges in the research field would be of use to identify furt…
Method for creating synthetic multi-fidelity data sets.
problem Lack of representative synthetic datasets for multifidelity optimisation benchmarks.
method Systematic generation of synthetic fidelities from preexisting datasets.
result Allows systematic investigation of lower fidelity proxies' influence.
Systematic reviews, which summarize and synthesize all the current research in a specific topic, are a crucial component to academia. They are especially important in the biomedical and health sciences, where they synthesize the state of medical evidence and conclude the best course of action for various diseases, path…
In Part I of this series of papers, we made Riley's definition of Heckoid groups for 2-bridge links explicit, and gave a systematic construction of epimorphisms from 2-bridge link groups onto Heckoid groups, generalizing Riley's construction. In this paper, we give a complete characterization of upper-meridian-pair-pre…
Review of uncertainty representation methods in risk management.
problem Inadequate consideration of uncertainty in risk management.
method Systematic literature review of 370 publications.
result Probabilistic methods are predominant, but fuzzy and evidence-based approaches are also useful.