pwc-archive/papers-with-abstracts huggingface dataset:
Robust Asset-Liability Management
Authors: Tjeerd de Vries, Alexis Akira Toda
Date: 2023-10-01 00:00:00
Tasks: Management

How should financial institutions hedge their balance sheets against interest rate risk when managing long-term assets and liabilities? We address this question by proposing a bond portfolio solution based on ambiguity-averse preferences, which generalizes classical immunization and accommodates arbitrary liability structures, portfolio constraints, and interest rate perturbations. In a further extension, we show that the optimal portfolio can be computed as a simple generalized least squares problem, making the solution both transparent and computationally efficient. The resulting portfolio also reduces leverage by implicitly regularizing the portfolio weights, which enhances out-of-sample performance. Numerical evaluations using both empirical and simulated yield curves from a no-arbitrage term structure model support the feasibility and accuracy of our approach relative to existing methods.
Navigating Uncertainty in ESG Investing
Authors: Jiayue Zhang, Ken Seng Tan, Tony S. Wirjanto, Lysa Porth
Date: 2023-10-03 00:00:00
Tasks: Navigate, Reinforcement Learning (RL)

The widespread confusion among investors regarding Environmental, Social, and Governance (ESG) rankings assigned by rating agencies has underscored a critical issue in sustainable investing. To address this uncertainty, our research has devised methods that not only recognize this ambiguity but also offer tailored investment strategies for different investor profiles. By developing ESG ensemble strategies and integrating ESG scores into a Reinforcement Learning (RL) model, we aim to optimize portfolios that cater to both financial returns and ESG-focused outcomes. Additionally, by proposing the Double-Mean-Variance model, we classify three types of investors based on their risk preferences. We also introduce ESG-adjusted Capital Asset Pricing Models (CAPMs) to assess the performance of these optimized portfolios. Ultimately, our comprehensive approach provides investors with tools to navigate the inherent ambiguities of ESG ratings, facilitating more informed investment decisions.
CAD: Clustering And Deep Reinforcement Learning Based Multi-Period Portfolio Management Strategy
Authors: Zhengyong Jiang, Jeyan Thiayagalingam, Jionglong Su, Jinjun Liang
Date: 2023-10-02 00:00:00
Tasks: Clustering, Deep Reinforcement Learning, Management, reinforcement-learning

In this paper, we present a novel trading strategy that integrates reinforcement learning methods with clustering techniques for portfolio management in multi-period trading. Specifically, we leverage the clustering method to categorize stocks into various clusters based on their financial indices. Subsequently, we utilize the algorithm Asynchronous Advantage Actor-Critic to determine the trading actions for stocks within each cluster. Finally, we employ the algorithm DDPG to generate the portfolio weight vector, which decides the amount of stocks to buy, sell, or hold according to the trading actions of different clusters. To the best of our knowledge, our approach is the first to combine clustering methods and reinforcement learning methods for portfolio management in the context of multi-period trading. Our proposed strategy is evaluated using a series of back-tests on four datasets, comprising a of 800 stocks, obtained from the Shanghai Stock Exchange and National Association of Securities Deal Automated Quotations sources. Our results demonstrate that our approach outperforms conventional portfolio management techniques, such as the Robust Median Reversion strategy, Passive Aggressive Median Reversion Strategy, and several machine learning methods, across various metrics. In our back-test experiments, our proposed strategy yields an average return of 151% over 360 trading periods with 800 stocks, compared to the highest return of 124% achieved by other techniques over identical trading periods and stocks.
Actuarial Implications and Modeling of Yellow Virus on Sugar Beet After the EU's Ban on Neonicotinoids and Climate Change
Authors: Martial Phélippé-Guinvarc, Jean Cordier
Date: 2023-10-03 00:00:00

Following the EU's decision to ban neonicotinoids, this article investigates the impacts of yellow virus on sugar beet yields under the ban and under current and future climates. Using a model that factors in key variables such as sowing dates, phenological stages, first aphid flight and aphid abundance, simulations are performed using long-period climate datasets as inputs. Coupled with incidence and sugar yield loss assumptions, this model allows to reconstruct the impact of yellow virus on sugar beet yields using a so called 'as if' approach. By simulating the effects of viruses over a longer period of time, as if neonicotinoids weren't used in the past, this methodology allows an accurate assessment of risks associated with yellow viruses, as well as impact of future agroecological mesures. The study eventually provides an actuarial rating for an insurance policy that compensates the losses triggered by those viruses.
Resolving a Clearing Member's Default, A Radner Equilibrium Approach
Authors: Dorinel Bastide, Stéphane Crépey, Samuel Drapeau, Mekonnen Tadese
Date: 2023-10-04 00:00:00

For vanilla derivatives that constitute the bulk of investment banks' hedging portfolios, central clearing through central counterparties (CCPs) has become hegemonic. A key mandate of a CCP is to provide an efficient and proper clearing member default resolution procedure. When a clearing member defaults, the CCP can hedge and auction or liquidate its positions. The counterparty credit risk cost of auctioning has been analyzed in terms of XVA metrics in Bastide, Cr{\'e}pey, Drapeau, and Tadese (2023). In this work we assess the costs of hedging or liquidating. This is done by comparing pre- and post-default market equilibria, using a Radner equilibrium approach for portfolio allocation and price discovery in each case. We show that the Radner equilibria uniquely exist and we provide both analytical and numerical solutions for the latter in elliptically distributed markets. Using such tools, a CCP could decide rationally on which market to hedge and auction or liquidate defaulted portfolios.
Bitcoin versus S&P 500 Index: Return and Risk Analysis
Authors: A. H. Nzokem
Date: 2023-10-03 00:00:00

The S&P 500 index is considered the most popular trading instrument in financial markets. With the rise of cryptocurrencies over the past years, Bitcoin has also grown in popularity and adoption. The paper aims to analyze the daily return distribution of the Bitcoin and S&P 500 index and assess their tail probabilities through two financial risk measures. As a methodology, We use Bitcoin and S&P 500 Index daily return data to fit The seven-parameter General Tempered Stable (GTS) distribution using the advanced Fast Fractional Fourier transform (FRFT) scheme developed by combining the Fast Fractional Fourier (FRFT) algorithm and the 12-point rule Composite Newton-Cotes Quadrature. The findings show that peakedness is the main characteristic of the S&P 500 return distribution, whereas heavy-tailedness is the main characteristic of the Bitcoin return distribution. The GTS distribution shows that $80.05\%$ of S&P 500 returns are within $-1.06\%$ and $1.23\%$ against only $40.32\%$ of Bitcoin returns. At a risk level ($\alpha$), the severity of the loss ($AVaR_{\alpha}(X)$) on the left side of the distribution is larger than the severity of the profit ($AVaR_{1-\alpha}(X)$) on the right side of the distribution. Compared to the S&P 500 index, Bitcoin has $39.73\%$ more prevalence to produce high daily returns (more than $1.23\%$ or less than $-1.06\%$). The severity analysis shows that at a risk level ($\alpha$) the average value-at-risk ($AVaR(X)$) of the bitcoin returns at one significant figure is four times larger than that of the S&P 500 index returns at the same risk.
Study of Stylized Facts in Stock Market Data
Authors: Vaibhav Sherkar, Rituparna Sen
Date: 2023-10-01 00:00:00
Tasks: Time Series

A property of data which is common across a wide range of instruments, markets and time periods is known as stylized empirical fact in the financial statistics literature. This paper first presents a wide range of stylized facts studied in literature which include some univariate distributional properties, multivariate properties and time series related properties of the financial time series data. In the next part of the paper, price data from several stocks listed on 10 stock exchanges spread across different continents has been analysed and data analysis has been presented.
Axiomatic Aggregations of Abductive Explanations
Authors: Gagan Biradar, Yacine Izza, Elita Lobo, Vignesh Viswanathan, Yair Zick
Date: 2023-09-29 00:00:00
Tasks: Feature Importance, valid

The recent criticisms of the robustness of post hoc model approximation explanation methods (like LIME and SHAP) have led to the rise of model-precise abductive explanations. For each data point, abductive explanations provide a minimal subset of features that are sufficient to generate the outcome. While theoretically sound and rigorous, abductive explanations suffer from a major issue -- there can be several valid abductive explanations for the same data point. In such cases, providing a single abductive explanation can be insufficient; on the other hand, providing all valid abductive explanations can be incomprehensible due to their size. In this work, we solve this issue by aggregating the many possible abductive explanations into feature importance scores. We propose three aggregation methods: two based on power indices from cooperative game theory and a third based on a well-known measure of causal strength. We characterize these three methods axiomatically, showing that each of them uniquely satisfies a set of desirable properties. We also evaluate them on multiple datasets and show that these explanations are robust to the attacks that fool SHAP and LIME.
Synergistic Fusion of Graph and Transformer Features for Enhanced Molecular Property Prediction
Authors: M V Sai Prakash, Siddartha Reddy N, Ganesh Parab, Varun V, Vishal Vaddina, Saisubramaniam Gopalakrishnan
Date: 2023-08-25 00:00:00
Tasks: Drug Discovery, Molecular Property Prediction, molecular representation, Property Prediction

Molecular property prediction is a critical task in computational drug discovery. While recent advances in Graph Neural Networks (GNNs) and Transformers have shown to be effective and promising, they face the following limitations: Transformer self-attention does not explicitly consider the underlying molecule structure while GNN feature representation alone is not sufficient to capture granular and hidden interactions and characteristics that distinguish similar molecules. To address these limitations, we propose SYN- FUSION, a novel approach that synergistically combines pre-trained features from GNNs and Transformers. This approach provides a comprehensive molecular representation, capturing both the global molecule structure and the individual atom characteristics. Experimental results on MoleculeNet benchmarks demonstrate superior performance, surpassing previous models in 5 out of 7 classification datasets and 4 out of 6 regression datasets. The performance of SYN-FUSION has been compared with other Graph-Transformer models that have been jointly trained using a combination of transformer and graph features, and it is found that our approach is on par with those models in terms of performance. Extensive analysis of the learned fusion model across aspects such as loss, latent space, and weight distribution further validates the effectiveness of SYN-FUSION. Finally, an ablation study unequivocally demonstrates that the synergy achieved by SYN-FUSION surpasses the performance of its individual model components and their ensemble, offering a substantial improvement in predicting molecular properties.
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning
Authors: Yang Liu, Chen Chen, Can Wang, Xulin King, Mengyuan Liu
Date: 2023-09-25 00:00:00
Tasks: 3D Point Cloud Classification, Decoder, Few-Shot 3D Point Cloud Classification, Representation Learning, Self-Supervised Learning

Masked Autoencoders (MAE) have demonstrated promising performance in self-supervised learning for both 2D and 3D computer vision. Nevertheless, existing MAE-based methods still have certain drawbacks. Firstly, the functional decoupling between the encoder and decoder is incomplete, which limits the encoder's representation learning ability. Secondly, downstream tasks solely utilize the encoder, failing to fully leverage the knowledge acquired through the encoder-decoder architecture in the pre-text task. In this paper, we propose Point Regress AutoEncoder (Point-RAE), a new scheme for regressive autoencoders for point cloud self-supervised learning. The proposed method decouples functions between the decoder and the encoder by introducing a mask regressor, which predicts the masked patch representation from the visible patch representation encoded by the encoder and the decoder reconstructs the target from the predicted masked patch representation. By doing so, we minimize the impact of decoder updates on the representation space of the encoder. Moreover, we introduce an alignment constraint to ensure that the representations for masked patches, predicted from the encoded representations of visible patches, are aligned with the masked patch presentations computed from the encoder. To make full use of the knowledge learned in the pre-training stage, we design a new finetune mode for the proposed Point-RAE. Extensive experiments demonstrate that our approach is efficient during pre-training and generalizes well on various downstream tasks. Specifically, our pre-trained models achieve a high accuracy of \textbf{90.28\%} on the ScanObjectNN hardest split and \textbf{94.1\%} accuracy on ModelNet40, surpassing all the other self-supervised learning methods. Our code and pretrained model are public available at: \url{https://github.com/liuyyy111/Point-RAE}.
Non-Invasive Detection of Anaemia Using Digital Photographs of the Conjunctiva
Authors: Shaun Collings, Oliver Thompson, Evan Hirst, Louise Goossens, Anup George, Robert Weinkove
Date: 2016-04-12 00:00:00
Proceeding: PLOS ONE 2016 4
Tasks: Specificity

Background and Aims
Anaemia is a major health burden worldwide. Although the finding of conjunctival pallor on clinical examination is associated with anaemia, inter-observer variability is high, and definitive diagnosis of anaemia requires a blood sample. We aimed to detect anaemia by quantifying conjunctival pallor using digital photographs taken with a consumer camera and a popular smartphone. Our goal was to develop a non-invasive screening test for anaemia.

Patients and Methods
The conjunctivae of haemato-oncology in- and outpatients were photographed in ambient lighting using a digital camera (Panasonic DMC-LX5), and the internal rear-facing camera of a smartphone (Apple iPhone 5S) alongside an in-frame calibration card. Following image calibration, conjunctival erythema index (EI) was calculated and correlated with laboratory-measured haemoglobin concentration. Three clinicians independently evaluated each image for conjunctival pallor.

Results
Conjunctival EI was reproducible between images (average coefficient of variation 2.96%). EI of the palpebral conjunctiva correlated more strongly with haemoglobin concentration than that of the forniceal conjunctiva. Using the compact camera, palpebral conjunctival EI had a sensitivity of 93% and 57% and specificity of 78% and 83% for detection of anaemia (haemoglobin < 110 g/L) in training and internal validation sets, respectively. Similar results were found using the iPhone camera, though the EI cut-off value differed. Conjunctival EI analysis compared favourably with clinician assessment, with a higher positive likelihood ratio for prediction of anaemia.

Conclusions
Erythema index of the palpebral conjunctiva calculated from images taken with a compact camera or mobile phone correlates with haemoglobin and compares favourably to clinician assessment for prediction of anaemia. If confirmed in further series, this technique may be useful for the non-invasive screening for anaemia.
Oceanic Eddy Identification Using an AI Scheme
Authors: Guangjun Xu, Cheng Cheng, Wenxian Yang, Wenhong Xie, Lingmei Kong, Renlong Hang, Furong Ma, Changming Dong, Jingsong Yang
Date: 2019-06-05 00:00:00
Proceeding: Remote Sensing 2019 6
Tasks: Scene Parsing

Oceanic eddies play an important role in global energyand material transport, and contribute
greatly to nutrient and phytoplankton distribution. Deep learning is employed to identify oceanic
eddies from sea surface height anomalies data. In order to adapt to segmentation problems for
multi-scale oceanic eddies, the pyramid scene parsing network (PSPNet), which is able to satisfy
the fusion of semantics and details, is applied as the core algorithm in the eddy detection methods.
The results of eddies identified from this artificial intelligence (AI) method are well compared with
those from a traditional vector geometry-based (VG) method. More oceanic eddies are detected by
the AI algorithm than the VG method, especially for small-scale eddies. Therefore, the present study
demonstrates that the AI algorithm is applicable of oceanic eddy detection. It is one of the first few of
efforts to bridge AI techniques and oceanography research.
Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution
Authors: Irshad Ullah, Syed Muhammad Hasanat, Khursheed Aurangzeb, Musaed Alhussein, Muhammad Rizwan, Muhammad Shahid Anwar
Date: 2023-08-15 00:00:00
Proceeding: PeerJ Computer Science 2023 8
Tasks: Data Ablation, Load Forecasting, Missing Elements, Multivariate Time Series Forecasting, Outlier Interpretation, Time Series Forecasting, Univariate Time Series Forecasting

Precise short-term load forecasting (STLF) plays a crucial role in the smooth
 operation of power systems, future capacity planning, unit commitment, and
 demand response. However, due to its non-stationary and its dependency on
multiple cyclic and non-cyclic calendric features and non-linear highly correlated
 metrological features, an accurate load forecasting with already existing techniques is
 challenging. To overcome this challenge, a novel hybrid technique based on long
short-term memory (LSTM) and a modified split-convolution (SC) neural network
 (LSTM-SC) is proposed for single-step and multi-step STLF. The concatenating
order of LSTM and SC in the proposed hybrid network provides an excellent
 capability of extraction of sequence-dependent features and other hierarchical spatial
features. The model is evaluated by the Pakistan National Grid load dataset recorded
 by the National Transmission and Dispatch Company (NTDC). The load data is preprocessed and multiple other correlated features are incorporated into the data for
performance enhancement. For generalization capability, the performance of LSTM-SC is evaluated on publicly available datasets of American Electric Power (AEP) and
 Independent System Operator New England (ISO-NE). The effect of temperature, a
 highly correlated input feature, on load forecasting is investigated either by removing
the temperature or adding a Gaussian random noise into it. The performance
 evaluation in terms of RMSE, MAE, and MAPE of the proposed model on the NTDC
dataset are 500.98, 372.62, and 3.72% for multi-step while 322.90, 244.22, and 2.38%
 for single-step load forecasting. The result shows that the proposed method has less
forecasting error, strong generalization capability, and satisfactory performance on
multi-horizon.
Synthesizing Aspect-Driven Recommendation Explanations from Reviews
Authors: Trung-Hoang Le, Hady W. Lauw
Date: 2020-07-11 00:00:00
Proceeding: International Joint Conference on Artificial Intelligence 2020 7
Tasks: Explainable Recommendation, Text Generation

Explanations help to make sense of recommendations, increasing the likelihood of adoption. However, existing approaches to explainable recommendations tend to rely on rigid, standardized templates, customized only via fill-in-the-blank aspect sentiments. For more flexible, literate, and varied explanations covering various aspects of interest, we synthesize an explanation by selecting snippets from reviews, while optimizing for representativeness and coherence. To fit target users’ aspect preferences, we contextualize the opinions based on a compatible explainable recommendation model. Experiments on datasets of several product categories showcase the efficacies of our method as compared to baselines based on templates, review summarization, selection, and text generation.
Explainable Recommendation with Comparative Constraints on Product Aspects
Authors: Trung-Hoang Le, Hady W. Lauw
Date: 2021-03-08 00:00:00
Proceeding: International Conference on Web Search and Data Mining 2021 3
Tasks: Explainable Recommendation

To aid users in choice-making, explainable recommendation models seek to provide not only accurate recommendations but also accompanying explanations that help to make sense of those recommendations. Most of the previous approaches rely on evaluative explanations, assessing the quality of an individual item along some aspects of interest to the user. In this work, we are interested in comparative explanations, the less studied problem of assessing a recommended item in comparison to another reference item. 
In particular, we propose to anchor reference items on the previously adopted items in a user’s history. Not only do we aim at providing comparative explanations involving such items, but we also formulate comparative constraints involving aspect-level comparisons between the target item and the reference items. The framework allows us to incorporate these constraints and integrate them with recommendation objectives involving both types of subjective and objective aspect-level quality assumptions. Experiments on public datasets of several product categories showcase the efficacies of our methodology as compared to baselines at attaining better recommendation accuracies and intuitive explanations.
Question-Attentive Review-Level Recommendation Explanation
Authors: Trung-Hoang Le, Hady W. Lauw
Date: 2022-12-17 00:00:00
Proceeding: IEEE International Conference on Big Data (Big Data) 2022 12
Tasks: Explainable Recommendation, Form

Recommendation explanations help to improve their acceptance by end users. The form of explanation of interest here is presenting an existing review of the recommended item. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance of each review to the recommendation objective. Our focus is on improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution jointly to the recommendation objective. The benefits are two-fold. For one, QA aids in selecting more useful reviews. For another, QA itself could accompany a well-aligned review in an expanded form of explanation. Experiments showcase the efficacies of our method as compared to baselines in identifying useful reviews and QAs, while maintaining parity in recommendation performance.