pwc-archive/papers-with-abstracts huggingface dataset:
ajdmom: A Python Package for Deriving Moment Formulas of Affine Jump Diffusion Processes
Authors: Yan-Feng Wu, Jian-Qiang Hu
Date: 2024-11-10 00:00:00
Tasks: Sensitivity

We introduce ajdmom, a Python package designed for automatically deriving moment formulae for the well-established affine jump diffusion processes with state-independent jump intensities. ajdmom can produce explicit closed-form expressions for conditional and unconditional moments of any order, significantly enhancing the usability of these models. Additionally, ajdmom can compute partial derivatives of these moments with respect to the model parameters, offering a valuable tool for sensitivity analysis. The package's modular architecture makes it easy for adaptation and extension by researchers. ajdmom is open-source and readily available for installation from GitHub or the Python package index (PyPI).
Schur Complementary Allocation: A Unification of Hierarchical Risk Parity and Minimum Variance Portfolios
Authors: Peter Cotton
Date: 2024-10-29 00:00:00

Despite many attempts to make optimization-based portfolio construction in the spirit of Markowitz robust and approachable, it is far from universally adopted. Meanwhile, the collection of more heuristic divide-and-conquer approaches was revitalized by Lopez de Prado where Hierarchical Risk Parity (HRP) was introduced. This paper reveals the hidden connection between these seemingly disparate approaches.
The lexical ratio: A new perspective on portfolio diversification
Authors: Sayyed Faraz Mohseni, Hamid R. Arian, Jean-François Bégin
Date: 2024-11-09 00:00:00

Portfolio diversification, traditionally measured through asset correlations and volatilitybased metrics, is fundamental to managing financial risk. However, existing diversification metrics often overlook non-numerical relationships between assets that can impact portfolio stability, particularly during market stresses. This paper introduces the lexical ratio (LR), a novel metric that leverages textual data to capture diversification dimensions absent in standard approaches. By treating each asset as a unique document composed of sectorspecific and financial keywords, the LR evaluates portfolio diversification by distributing these terms across assets, incorporating entropy-based insights from information theory. We thoroughly analyze LR's properties, including scale invariance, concavity, and maximality, demonstrating its theoretical robustness and ability to enhance risk-adjusted portfolio returns. Using empirical tests on S&P 500 portfolios, we compare LR's performance to established metrics such as Markowitz's volatility-based measures and diversification ratios. Our tests reveal LR's superiority in optimizing portfolio returns, especially under varied market conditions. Our findings show that LR aligns with conventional metrics and captures unique diversification aspects, suggesting it is a viable tool for portfolio managers.
Asymptotic Properties of Generalized Shortfall Risk Measures for Heavy-tailed Risks
Authors: Tiantian Mao, Gilles Stupfler, Fan Yang
Date: 2024-11-11 00:00:00

We study a general risk measure called the generalized shortfall risk measure, which was first introduced in Mao and Cai (2018). It is proposed under the rank-dependent expected utility framework, or equivalently induced from the cumulative prospect theory. This risk measure can be flexibly designed to capture the decision maker's behavior toward risks and wealth when measuring risk. In this paper, we derive the first- and second-order asymptotic expansions for the generalized shortfall risk measure. Our asymptotic results can be viewed as unifying theory for, among others, distortion risk measures and utility-based shortfall risk measures. They also provide a blueprint for the estimation of these measures at extreme levels, and we illustrate this principle by constructing and studying a quantile-based estimator in a special case. The accuracy of the asymptotic expansions and of the estimator is assessed on several numerical examples.
Portfolio credit risk with Archimedean copulas: asymptotic analysis and efficient simulation
Authors: Hengxin Cui, Ken Seng Tan, Fan Yang
Date: 2024-11-11 00:00:00

In this paper, we study large losses arising from defaults of a credit portfolio. We assume that the portfolio dependence structure is modelled by the Archimedean copula family as opposed to the widely used Gaussian copula. The resulting model is new, and it has the capability of capturing extremal dependence among obligors. We first derive sharp asymptotics for the tail probability of portfolio losses and the expected shortfall. Then we demonstrate how to utilize these asymptotic results to produce two variance reduction algorithms that significantly enhance the classical Monte Carlo methods. Moreover, we show that the estimator based on the proposed two-step importance sampling method is logarithmically efficient while the estimator based on the conditional Monte Carlo method has bounded relative error as the number of obligors tends to infinity. Extensive simulation studies are conducted to highlight the efficiency of our proposed algorithms for estimating portfolio credit risk. In particular, the variance reduction achieved by the proposed conditional Monte Carlo method, relative to the crude Monte Carlo method, is in the order of millions.
Graph Neural Networks for Financial Fraud Detection: A Review
Authors: Dawei Cheng, Yao Zou, Sheng Xiang, Changjun Jiang
Date: 2024-11-01 00:00:00
Tasks: Fraud Detection

The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology. This complexity poses greater challenges in detecting and managing financial fraud. This review explores the role of Graph Neural Networks (GNNs) in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection. Specifically, by examining a series of detailed research questions, this review delves into the suitability of GNNs for financial fraud detection, their deployment in real-world scenarios, and the design considerations that enhance their effectiveness. This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks, significantly outperforming traditional fraud detection methods. Unlike previous surveys that often overlook the specific potentials of GNNs or address them only superficially, our review provides a comprehensive, structured analysis, distinctly focusing on the multifaceted applications and deployments of GNNs in financial fraud detection. This review not only highlights the potential of GNNs to improve fraud detection mechanisms but also identifies current gaps and outlines future research directions to enhance their deployment in financial systems. Through a structured review of over 100 studies, this review paper contributes to the understanding of GNN applications in financial fraud detection, offering insights into their adaptability and potential integration strategies.
Comparative Analysis of LSTM, GRU, and Transformer Models for Stock Price Prediction
Authors: Jue Xiao, Tingting Deng, Shuochen Bi
Date: 2024-10-20 00:00:00
Tasks: Stock Price Prediction, Stock Trend Prediction

In recent fast-paced financial markets, investors constantly seek ways to gain an edge and make informed decisions. Although achieving perfect accuracy in stock price predictions remains elusive, artificial intelligence (AI) advancements have significantly enhanced our ability to analyze historical data and identify potential trends. This paper takes AI driven stock price trend prediction as the core research, makes a model training data set of famous Tesla cars from 2015 to 2024, and compares LSTM, GRU, and Transformer Models. The analysis is more consistent with the model of stock trend prediction, and the experimental results show that the accuracy of the LSTM model is 94%. These methods ultimately allow investors to make more informed decisions and gain a clearer insight into market behaviors.
News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models
Authors: Kaushal Attaluri, Mukesh Tripathi, Srinithi Reddy, Shivendra
Date: 2024-10-14 00:00:00
Tasks: Sentiment Analysis, Stock Price Prediction

Forecasting stock market prices remains a complex challenge for traders, analysts, and engineers due to the multitude of factors that influence price movements. Recent advancements in artificial intelligence (AI) and natural language processing (NLP) have significantly enhanced stock price prediction capabilities. AI's ability to process vast and intricate data sets has led to more sophisticated forecasts. However, achieving consistently high accuracy in stock price forecasting remains elusive. In this paper, we leverage 30 years of historical data from national banks in India, sourced from the National Stock Exchange, to forecast stock prices. Our approach utilizes state-of-the-art deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM optimized through Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). We further integrate sentiment analysis from tweets and reliable financial sources such as Business Standard and Reuters, acknowledging their crucial influence on stock price fluctuations.
Ozone level forecasting in Mexico City with temporal features and interactions
Authors: J. M. Sánchez Cerritos, J. A. Martínez-Cadena, A. Marín-López, J. Delgado-Fernández
Date: 2024-11-04 00:00:00
Tasks: regression

Tropospheric ozone is an atmospheric pollutant that negatively impacts human health and the environment. Precise estimation of ozone levels is essential for preventive measures and mitigating its effects. This work compares the accuracy of multiple regression models in forecasting ozone levels in Mexico City, first without adding temporal features and interactions, and then with these features included. Our findings show that incorporating temporal features and interactions improves the accuracy of the models.
Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning
Authors: Chenqing Hua
Date: 2024-11-09 00:00:00
Tasks: Graph Representation Learning, Knowledge Graphs, Representation Learning

Graphs serve as fundamental descriptors for systems composed of interacting elements, capturing a wide array of data types, from molecular interactions to social networks and knowledge graphs. In this paper, we present an exhaustive review of the latest advancements in graph representation learning and Graph Neural Networks (GNNs). GNNs, tailored to handle graph-structured data, excel in deriving insights and predictions from intricate relational information, making them invaluable for tasks involving such data. Graph representation learning, a pivotal approach in analyzing graph-structured data, facilitates numerous downstream tasks and applications across machine learning, data mining, biomedicine, and healthcare. Our work delves into the capabilities of GNNs, examining their foundational designs and their application in addressing real-world challenges. We introduce a GNN equipped with an advanced high-order pooling function, adept at capturing complex node interactions within graph-structured data. This pooling function significantly enhances the GNN's efficacy in both node- and graph-level tasks. Additionally, we propose a molecular graph generative model with a GNN as its core framework. This GNN backbone is proficient in learning invariant and equivariant molecular characteristics. Employing these features, the molecular graph generative model is capable of simultaneously learning and generating molecular graphs with atom-bond structures and precise atom positions. Our models undergo thorough experimental evaluations and comparisons with established methods, showcasing their superior performance in addressing diverse real-world challenges with various datasets.
SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEG
Authors: Shanglin Li, Motoaki Kawanabe, Reinmar J. Kobler
Date: 2024-10-26 00:00:00
Tasks: Brain Computer Interface, Domain Adaptation, EEG, EEG based sleep staging, Sleep Staging, Unsupervised Domain Adaptation

The non-stationary nature of electroencephalography (EEG) introduces distribution shifts across domains (e.g., days and subjects), posing a significant challenge to EEG-based neurotechnology generalization. Without labeled calibration data for target domains, the problem is a source-free unsupervised domain adaptation (SFUDA) problem. For scenarios with constant label distribution, Riemannian geometry-aware statistical alignment frameworks on the symmetric positive definite (SPD) manifold are considered state-of-the-art. However, many practical scenarios, including EEG-based sleep staging, exhibit label shifts. Here, we propose a geometric deep learning framework for SFUDA problems under specific distribution shifts, including label shifts. We introduce a novel, realistic generative model and show that prior Riemannian statistical alignment methods on the SPD manifold can compensate for specific marginal and conditional distribution shifts but hurt generalization under label shifts. As a remedy, we propose a parameter-efficient manifold optimization strategy termed SPDIM. SPDIM uses the information maximization principle to learn a single SPD-manifold-constrained parameter per target domain. In simulations, we demonstrate that SPDIM can compensate for the shifts under our generative model. Moreover, using public EEG-based brain-computer interface and sleep staging datasets, we show that SPDIM outperforms prior approaches.
High quality ECG dataset based on MIT-BIH recordings for improved heartbeats classification
Authors: Ahmed. S Benmessaoud, Farida Medjani, Yahia Bousseloub, Khalid Bouaita, Dhia Benrahem, Tahar Kezai
Date: 2024-10-27 00:00:00

Electrocardiogram (ECG) is a reliable tool for medical professionals to detect and diagnose abnormal heart waves that may cause cardiovascular diseases. This paper proposes a methodology to create a new high-quality heartbeat dataset from all 48 of the MIT-BIH recordings. The proposed approach computes an optimal heartbeat size, by eliminating outliers and calculating the mean value over 10-second windows. This results in independent QRS-centered heartbeats avoiding the mixing of successive heartbeats problem. The quality of the newly constructed dataset has been evaluated and compared with existing datasets. To this end, we built and trained a PyTorch 1-D Resnet architecture model that achieved 99.24\% accuracy with a 5.7\% improvement compared to other methods. Additionally, downsampling the dataset has improved the model's execution time by 33\% and reduced 3x memory usage.
A Tutorial on Teaching Data Analytics with Generative AI
Authors: Robert L. Bray
Date: 2024-10-25 00:00:00

This tutorial addresses the challenge of incorporating large language models (LLMs), such as ChatGPT, in a data analytics class. It details several new in-class and out-of-class teaching techniques enabled by AI. For example, instructors can parallelize instruction by having students interact with different custom-made GPTs to learn different parts of an analysis and then teach each other what they learned from their AIs. For another example, instructors can turn problem sets into AI tutoring sessions, whereby a custom-made GPT guides a student through the problems, and the student uploads the chatlog for their homework submission. For a third example, you can assign different labs to each section of your class and have each section create AI assistants to help the other sections work through their labs. This tutorial advocates the programming in the English paradigm, in which students express the desired data transformations in prose and then use AI to generate the corresponding code. Students can wrangle data more effectively by programming in English than by manipulating in Excel. However, some students will program in English better than others, so you will still derive a robust grade distribution (at least with current LLMs).
Navigating AI in Social Work and Beyond: A Multidisciplinary Review
Authors: Matt Victor Dalziel, Krystal Schaffer, Neil Martin
Date: 2024-10-25 00:00:00
Tasks: Articles, Navigate, Philosophy, Sociology

This review began with the modest goal of drafting a brief commentary on how the social work profession engages with and is impacted by artificial intelligence (AI). However, it quickly became apparent that a deeper exploration was required to adequately capture the profound influence of AI, one of the most transformative and debated innovations in modern history. As a result, this review evolved into an interdisciplinary endeavour, gathering seminal texts, critical articles, and influential voices from across industries and academia. This review aims to provide a comprehensive yet accessible overview, situating AI within broader societal and academic conversations as 2025 dawns. We explore perspectives from leading tech entrepreneurs, cultural icons, CEOs, and politicians alongside the pioneering contributions of AI engineers, innovators, and academics from fields as diverse as mathematics, sociology, philosophy, economics, and more. This review also briefly analyses AI's real-world impacts, ethical challenges, and implications for social work. It presents a vision for AI-facilitated simulations that could transform social work education through Advanced Personalised Simulation Training (APST). This tool uses AI to tailor high-fidelity simulations to individual student needs, providing real-time feedback and preparing them for the complexities of their future practice environments. We maintain a critical tone throughout, balancing our awe of AI's remarkable advancements with necessary caution. As AI continues to permeate every professional realm, understanding its subtleties, challenges, and opportunities becomes essential. Those who fully grasp the intricacies of this technology will be best positioned to navigate the impending AI Era.
Mukhtasir-Khail-Net: An Ultra-Efficient Convolutional Neural Network for Sports Activity Recognition with Wearable Inertial Sensors
Authors: Hamza Ali Imran, Shaida Muhammad, Saad Wazir, Ataul Aziz Ikram
Date: 2024-10-22 00:00:00
Proceeding: 4th International Conference on Digital Futures and Transformative Technologies (ICoDT2) 2024 10
Tasks: Activity Recognition, Human Activity Recognition, Sports Activity Recognition

The current prevalent approach of the Internet of Health and Medical Things entails proactively preventing disease onset through routine monitoring of individuals’ physical activities, making Human Activity Recognition (HAR) and Behaviour Analysis an important field of study. Sports Activity Recognition (SAcR) is a subset of HAR that focuses on identifying athletic movements. Computer Vision, Environmental Sensors, and Wearable Sensors are the three main methods for monitoring such activities. When the merits and limitations of these methods are considered, Wearable Sensors emerge as the most practical option. This study introduces the Mukhtasir-Khail-Net, a model with only 651 parameters that efficiently uses addition layers. When applied to six sports activities, the model achieves an impressive average accuracy of 98.865% using inertial sensor data generated by wearable sensors. For benchmark purposes, the presented model is also evaluated on the WISDM 11 dataset which is a Human Activity Recognition dataset from Fordham University and has achieved an accuracy of 94.24%.
Generative Pre-Training from Molecules
Authors: Sanjar Adilov
Date: 2021-09-16 00:00:00
Proceeding: ChemRxiv 2021 9
Tasks: Feature Engineering, General Knowledge, Language Modelling, Molecular Property Prediction, Property Prediction

SMILES is a line notation for entering and representing molecules. Being inherently a language construct, it allows estimating molecular data in a self-supervised fashion by employing machine learning methods for natural language processing (NLP). The recent success of attention-based neural networks in NLP has made large-corpora transformer pretraining a de facto standard for learning representations and transferring knowledge to downstream tasks. In this work, we attempt to adapt transformer capabilities to a large SMILES corpus by constructing a GPT-2-like language model. We experimentally show that a pretrained causal transformer captures general knowledge that can be successfully transferred to such downstream tasks as focused molecule generation and single-/multi-output molecular-property prediction. For each task, we freeze model parameters and attach trainable lightweight networks between attention blocks—adapters—as alternative to fine-tuning. With a relatively modest setup, our transformer outperforms the recently proposed ChemBERTa transformer and approaches state-of-the-art MoleculeNet and Chemprop results. Overall, transformers pretrained on SMILES corpora are promising alternatives that do not require handcrafted feature engineering, make few assumptions about structure of data, and scale well with the pretraining data size.
A Comprehensive Survey of Time Series Forecasting: Architectural Diversity and Open Challenges
Authors: Jongseon Kim, Hyungjoon Kim, HyunGi Kim, Dongjun Lee, Sungroh Yoon
Date: 2024-10-24 00:00:00
Tasks: Diversity, Mamba, Time Series, Time Series Forecasting

Time series forecasting is a critical task that provides key information for decision-making across various fields. Recently, various fundamental deep learning architectures such as MLPs, CNNs, RNNs, and GNNs have been developed and applied to solve time series forecasting problems. However, the structural limitations caused by the inductive biases of each deep learning architecture constrained their performance. Transformer models, which excel at handling long-term dependencies, have become significant architectural components for time series forecasting. However, recent research has shown that alternatives such as simple linear layers can outperform Transformers. These findings have opened up new possibilities for using diverse architectures. In this context of exploration into various models, the architectural modeling of time series forecasting has now entered a renaissance. This survey not only provides a historical context for time series forecasting but also offers comprehensive and timely analysis of the movement toward architectural diversification. By comparing and re-examining various deep learning models, we uncover new perspectives and presents the latest trends in time series forecasting, including the emergence of hybrid models, diffusion models, Mamba models, and foundation models. By focusing on the inherent characteristics of time series data, we also address open challenges that have gained attention in time series forecasting, such as channel dependency, distribution shift, causality, and feature extraction. This survey explores vital elements that can enhance forecasting performance through diverse approaches. These contributions lead to lowering the entry barriers for newcomers to the field of time series forecasting, while also offering seasoned researchers broad perspectives, new opportunities, and deep insights.