pwc-archive/papers-with-abstracts huggingface dataset:
Quantifying Seasonal Weather Risk in Indian Markets: Stochastic Model for Risk-Averse State-Specific Temperature Derivative Pricing
Authors: Soumil Hooda, Shubham Sharma, Kunal Bansal
Date: 2024-09-06 00:00:00
Tasks: Management

This technical report presents a stochastic model for pricing weather derivatives and devising hedging strategies tailored to Indian markets. We model temperature dynamics using a modified Ornstein-Uhlenbeck process with jumps to account for sudden shocks, such as heatwaves and coldwaves. Historical data from 12 Indian states (1951-2023) is used for calibration, and Monte Carlo simulations are employed under the risk-neutral measure to price Heating Degree Days (HDD), Cooling Degree Days (CDD), and extreme event options. Sensitivity analysis reveals that a 20% increase in volatility leads to an approximate 4.2% increase in option prices, highlighting the critical impact of volatility on derivative pricing. Results show that HDD options in colder states like Himachal Pradesh are significantly more expensive, with prices reaching up to INR 684,693, while CDD options in hotter states like Gujarat are priced higher, up to INR 262,986. A comprehensive portfolio analysis indicates that investing INR 120,000 in HDD put options in Uttar Pradesh yields an expected payoff of INR 132,369, resulting in a return on investment (ROI) of 10.3%. Conversely, a similar investment in Karnataka yields a negative ROI of -66.7% due to its milder climate. Hedging strategies are tailored to each state's climatic risk, with recommendations to buy 90.66 HDD put options at a strike of 90.89 in Uttar Pradesh and invest in CDD call options in Gujarat. These insights offer practical solutions for managing temperature-related financial risk in energy and agriculture, providing actionable, state-specific hedging strategies for diverse climatic scenarios in India.
Lapse-supported life insurance and adverse selection
Authors: Oytun Haçarız, Torsten Kleinow, Angus S. Macdonald
Date: 2024-09-03 00:00:00

If individuals at the highest mortality risk are also least likely to lapse a life insurance policy, then lapse-supported premiums magnify adverse selection costs. As an example, we model 'Term to 100' contracts, and risk as revealed by genetic test results. We identify three methods of managing lapse surplus: eliminating it by design; disposing of it retrospectively (through participation); or disposing of it prospectively (through lapse-supported premiums). We then assume a heterogeneous population in which: (a) insurers cannot identify individuals at high mortality risk; (b) a secondary market exists that prevents high-risk policies from lapsing; (c) financial underwriting is lax or absent; and (d) life insurance policies may even be initiated by third parties as a financial investment (STOLI). Adverse selection losses under (a) are typically very small, but under (b) can be increased by multiples, and under (c) and (d) increased almost without limit. We note that the different approaches to modeling lapses used in studies of adverse selection and genetic testing appear to be broadly equivalent and robust.
Predicting Foreign Exchange EUR/USD direction using machine learning
Authors: Kevin Cedric Guyard, Michel Deriaz
Date: 2024-09-04 00:00:00

The Foreign Exchange market is a significant market for speculators, characterized by substantial transaction volumes and high volatility. Accurately predicting the directional movement of currency pairs is essential for formulating a sound financial investment strategy. This paper conducts a comparative analysis of various machine learning models for predicting the daily directional movement of the EUR/USD currency pair in the Foreign Exchange market. The analysis includes both decorrelated and non-decorrelated feature sets using Principal Component Analysis. Additionally, this study explores meta-estimators, which involve stacking multiple estimators as input for another estimator, aiming to achieve improved predictive performance. Ultimately, our approach yielded a prediction accuracy of 58.52% for one-day ahead forecasts, coupled with an annual return of 32.48% for the year 2022.
Fundamental properties of linear factor models
Authors: Damir Filipovic, Paul Schneider
Date: 2024-09-04 00:00:00

We study conditional linear factor models in the context of asset pricing panels. Our analysis focuses on conditional means and covariances to characterize the cross-sectional and inter-temporal properties of returns and factors as well as their interrelationships. We also review the conditions outlined in Kozak and Nagel (2024) and show how the conditional mean-variance efficient portfolio of an unbalanced panel can be spanned by low-dimensional factor portfolios, even without assuming invertibility of the conditional covariance matrices. Our analysis provides a comprehensive foundation for the specification and estimation of conditional linear factor models.
Review of the EU ETS Literature: A Bibliometric Perspective
Authors: Cristiano Salvagnin
Date: 2024-09-03 00:00:00
Tasks: Articles, Financial Analysis

This study conducts a bibliometric review of scientific literature on the European Union Emissions Trading System (EU ETS) from 2004 to 2024, using research articles from the Scopus database. Using the Bibliometrix R package, we analyze publication trends, key themes, influential authors, and prominent journals related to the EU ETS. Our results indicate a notable increase in research activity over the past two decades, particularly during significant policy changes and economic events affecting carbon markets. Key research focuses include carbon pricing, market volatility, and economic impacts, highlighting a shift toward financial analysis and policy implications. Thematic mapping shows cap-and-trade systems, and carbon leakage as central topics linking various research areas. Additionally, we observe key areas where further research could be beneficial, such as expanding non-parametric methodologies, deepening the exploration of macroeconomic factors, and enhancing the examination of financial market connections. Moreover, we highlight recent and innovative papers that contribute new insights, showcasing emerging trends and cutting-edge approaches within the field. This review provides insights for researchers and policymakers, highlighting the evolving landscape of EU ETS research and its relevance to global climate strategies.
State-Space Dynamic Functional Regression for Multicurve Fixed Income Spread Analysis and Stress Testing
Authors: Peilun He, Gareth W. Peters, Nino Kordzakhia, Pavel V. Shevchenko
Date: 2024-08-31 00:00:00
Tasks: regression

The Nelson-Siegel model is widely used in fixed income markets to produce yield curve dynamics. The multiple time-dependent parameter model conveniently addresses the level, slope, and curvature dynamics of the yield curves. In this study, we present a novel state-space functional regression model that incorporates a dynamic Nelson-Siegel model and functional regression formulations applied to multi-economy setting. This framework offers distinct advantages in explaining the relative spreads in yields between a reference economy and a response economy. To address the inherent challenges of model calibration, a kernel principal component analysis is employed to transform the representation of functional regression into a finite-dimensional, tractable estimation problem. A comprehensive empirical analysis is conducted to assess the efficacy of the functional regression approach, including an in-sample performance comparison with the dynamic Nelson-Siegel model. We conducted the stress testing analysis of yield curves term-structure within a dual economy framework. The bond ladder portfolio was examined through a case study focused on spread modelling using historical data for US Treasury and UK bonds.
Leveraging RNNs and LSTMs for Synchronization Analysis in the Indian Stock Market: A Threshold-Based Classification Approach
Authors: Sanjay Sathish, Charu C Sharma
Date: 2024-08-27 00:00:00
Tasks: Management, Time Series, Time Series Analysis

Our research presents a new approach for forecasting the synchronization of stock prices using machine learning and non-linear time-series analysis. To capture the complex non-linear relationships between stock prices, we utilize recurrence plots (RP) and cross-recurrence quantification analysis (CRQA). By transforming Cross Recurrence Plot (CRP) data into a time-series format, we enable the use of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks for predicting stock price synchronization through both regression and classification. We apply this methodology to a dataset of 20 highly capitalized stocks from the Indian market over a 21-year period. The findings reveal that our approach can predict stock price synchronization, with an accuracy of 0.98 and F1 score of 0.83 offering valuable insights for developing effective trading strategies and risk management tools.
Combining supervised and unsupervised learning methods to predict financial market movements
Authors: Gabriel Rodrigues Palma, Mariusz Skoczeń, Phil Maguire
Date: 2024-08-19 00:00:00

The decisions traders make to buy or sell an asset depend on various analyses, with expertise required to identify patterns that can be exploited for profit. In this paper we identify novel features extracted from emergent and well-established financial markets using linear models and Gaussian Mixture Models (GMM) with the aim of finding profitable opportunities. We used approximately six months of data consisting of minute candles from the Bitcoin, Pepecoin, and Nasdaq markets to derive and compare the proposed novel features with commonly used ones. These features were extracted based on the previous 59 minutes for each market and used to identify predictions for the hour ahead. We explored the performance of various machine learning strategies, such as Random Forests (RF) and K-Nearest Neighbours (KNN) to classify market movements. A naive random approach to selecting trading decisions was used as a benchmark, with outcomes assumed to be equally likely. We used a temporal cross-validation approach using test sets of 40%, 30% and 20% of total hours to evaluate the learning algorithms' performances. Our results showed that filtering the time series facilitates algorithms' generalisation. The GMM filtering approach revealed that the KNN and RF algorithms produced higher average returns than the random algorithm.
Bitcoin ETF: Opportunities and risk
Authors: Di wu
Date: 2024-08-30 00:00:00

The year 2024 witnessed a major development in the cryptocurrency industry with the long-awaited approval of spot Bitcoin exchange-traded funds (ETFs). This innovation provides investors with a new, regulated path to gain exposure to Bitcoin through a familiar investment vehicle (Kumar et al., 2024). However, unlike traditional ETFs that directly hold underlying assets, Bitcoin ETFs rely on a creation and redemption process managed by authorized participants (APs). This unique structure introduces distinct characteristics in terms of premium/discount behavior compared to traditional ETFs. This paper investigates the premium and discount patterns observed in Bitcoin ETFs during first four-month period (January 11th, 2024, to May 17th, 2024). Our analysis reveals that these patterns differ significantly from those observed in traditional index ETFs, potentially exposing investors to additional risk factors. By identifying and analyzing these risk factors associated with Bitcoin ETF premiums/discounts, this paper aims to achieve two key objectives: Enhance market understanding: Equip and market and investors with a deeper comprehension of the unique liquidity risks inherent in Bitcoin ETFs. Provide a clearer risk management frameworks: Offer a clearer perspective on the risk-return profile of digital asset ETFs, specifically focusing on Bitcoin ETFs. Through a thorough analysis of premium/discount behavior and the underlying factors contributing to it, this paper strives to contribute valuable insights for investors navigating the evolving landscape of digital asset investments
LSST: Learned Single-Shot Trajectory and Reconstruction Network for MR Imaging
Authors: Hemant Kumar Aggarwal, Sudhanya Chatterjee, Dattesh Shanbhag, Uday Patil, K. V. S. Hari
Date: 2024-08-08 00:00:00

Single-shot magnetic resonance (MR) imaging acquires the entire k-space data in a single shot and it has various applications in whole-body imaging. However, the long acquisition time for the entire k-space in single-shot fast spin echo (SSFSE) MR imaging poses a challenge, as it introduces T2-blur in the acquired images. This study aims to enhance the reconstruction quality of SSFSE MR images by (a) optimizing the trajectory for measuring the k-space, (b) acquiring fewer samples to speed up the acquisition process, and (c) reducing the impact of T2-blur. The proposed method adheres to physics constraints due to maximum gradient strength and slew-rate available while optimizing the trajectory within an end-to-end learning framework. Experiments were conducted on publicly available fastMRI multichannel dataset with 8-fold and 16-fold acceleration factors. An experienced radiologist's evaluation on a five-point Likert scale indicates improvements in the reconstruction quality as the ACL fibers are sharper than comparative methods.
Reflective Human-Machine Co-adaptation for Enhanced Text-to-Image Generation Dialogue System
Authors: Yuheng Feng, Yangfan He, Yinghui Xia, Tianyu Shi, Jun Wang, Jinsong Yang
Date: 2024-08-27 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation

Today's image generation systems are capable of producing realistic and high-quality images. However, user prompts often contain ambiguities, making it difficult for these systems to interpret users' potential intentions. Consequently, machines need to interact with users multiple rounds to better understand users' intents. The unpredictable costs of using or learning image generation models through multiple feedback interactions hinder their widespread adoption and full performance potential, especially for non-expert users. In this research, we aim to enhance the user-friendliness of our image generation system. To achieve this, we propose a reflective human-machine co-adaptation strategy, named RHM-CAS. Externally, the Agent engages in meaningful language interactions with users to reflect on and refine the generated images. Internally, the Agent tries to optimize the policy based on user preferences, ensuring that the final outcomes closely align with user preferences. Various experiments on different tasks demonstrate the effectiveness of the proposed method.
Small Object Detection for Indoor Assistance to the Blind using YOLO NAS Small and Super Gradients
Authors: Rashmi BN, R. Guru, Anusuya M A
Date: 2024-08-28 00:00:00
Tasks: Object, object-detection, Object Detection, Small Object Detection

Advancements in object detection algorithms have opened new avenues for assistive technologies that cater to the needs of visually impaired individuals. This paper presents a novel approach for indoor assistance to the blind by addressing the challenge of small object detection. We propose a technique YOLO NAS Small architecture, a lightweight and efficient object detection model, optimized using the Super Gradients training framework. This combination enables real-time detection of small objects crucial for assisting the blind in navigating indoor environments, such as furniture, appliances, and household items. Proposed method emphasizes low latency and high accuracy, enabling timely and informative voice-based guidance to enhance the user's spatial awareness and interaction with their surroundings. The paper details the implementation, experimental results, and discusses the system's effectiveness in providing a practical solution for indoor assistance to the visually impaired.
Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis
Authors: Sakhinana Sagar Srinivas, Geethan Sannidhi, Venkataramana Runkana
Date: 2024-08-27 00:00:00
Tasks: Instruction Following, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We present a novel framework for analyzing and interpreting electron microscopy images in semiconductor manufacturing using vision-language instruction tuning. The framework employs a unique teacher-student approach, leveraging pre-trained multimodal large language models such as GPT-4 to generate instruction-following data for zero-shot visual question answering (VQA) and classification tasks, customizing smaller multimodal models (SMMs) for microscopy image analysis, resulting in an instruction-tuned language-and-vision assistant. Our framework merges knowledge engineering with machine learning to integrate domain-specific expertise from larger to smaller multimodal models within this specialized field, greatly reducing the need for extensive human labeling. Our study presents a secure, cost-effective, and customizable approach for analyzing microscopy images, addressing the challenges of adopting proprietary models in semiconductor manufacturing.
A proof of contribution in blockchain using game theoretical deep learning model
Authors: Jin Wang
Date: 2024-08-25 00:00:00
Tasks: Edge-computing, Graph Neural Network, Scheduling

Building elastic and scalable edge resources is an inevitable prerequisite for providing platform-based smart city services. Smart city services are delivered through edge computing to provide low-latency applications. However, edge computing has always faced the challenge of limited resources. A single edge device cannot undertake the various intelligent computations in a smart city, and the large-scale deployment of edge devices from different service providers to build an edge resource platform has become a necessity. Selecting computing power from different service providers is a game-theoretic problem. To incentivize service providers to actively contribute their valuable resources and provide low-latency collaborative computing power, we introduce a game-theoretic deep learning model to reach a consensus among service providers on task scheduling and resource provisioning. Traditional centralized resource management approaches are inefficient and lack credibility, while the introduction of blockchain technology can enable decentralized resource trading and scheduling. We propose a contribution-based proof mechanism to provide the low-latency service of edge computing. The deep learning model consists of dual encoders and a single decoder, where the GNN (Graph Neural Network) encoder processes structured decision action data, and the RNN (Recurrent Neural Network) encoder handles time-series task scheduling data. Extensive experiments have demonstrated that our model reduces latency by 584% compared to the state-of-the-art.
Respect the model: Fine-grained and Robust Explanation with Sharing Ratio Decomposition
Authors: Sangyu Han, Yearim Kim, Nojun Kwak
Date: 2024-01-25 00:00:00
Tasks: Decision Making

The truthfulness of existing explanation methods in authentically elucidating the underlying model's decision-making process has been questioned. Existing methods have deviated from faithfully representing the model, thus susceptible to adversarial attacks. To address this, we propose a novel eXplainable AI (XAI) method called SRD (Sharing Ratio Decomposition), which sincerely reflects the model's inference process, resulting in significantly enhanced robustness in our explanations. Different from the conventional emphasis on the neuronal level, we adopt a vector perspective to consider the intricate nonlinear interactions between filters. We also introduce an interesting observation termed Activation-Pattern-Only Prediction (APOP), letting us emphasize the importance of inactive neurons and redefine relevance encapsulating all relevant information including both active and inactive neurons. Our method, SRD, allows for the recursive decomposition of a Pointwise Feature Vector (PFV), providing a high-resolution Effective Receptive Field (ERF) at any layer.
DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models
Authors: Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood, Karthik Nandakumar
Date: 2024-04-05 00:00:00
Proceeding: CVPR 2024 1
Tasks: Adversarial Robustness, Data Augmentation, General Classification

Recently, a number of image-mixing-based augmentation techniques have been introduced to improve the generalization of deep neural networks. In these techniques, two or more randomly selected natural images are mixed together to generate an augmented image. Such methods may not only omit important portions of the input images but also introduce label ambiguities by mixing images across labels resulting in misleading supervisory signals. To address these limitations, we propose DiffuseMix, a novel data augmentation technique that leverages a diffusion model to reshape training images, supervised by our bespoke conditional prompts. First, concatenation of a partial natural image and its generated counterpart is obtained which helps in avoiding the generation of unrealistic images or label ambiguities. Then, to enhance resilience against adversarial attacks and improves safety measures, a randomly selected structural pattern from a set of fractal images is blended into the concatenated image to form the final augmented image for training. Our empirical results on seven different datasets reveal that DiffuseMix achieves superior performance compared to existing state-of the-art methods on tasks including general classification,fine-grained classification, fine-tuning, data scarcity, and adversarial robustness. Augmented datasets and codes are available here: https://diffusemix.github.io/
A Hybrid Transformer-Mamba Network for Single Image Deraining
Authors: Shangquan Sun, Wenqi Ren, Juxiang Zhou, Jianhou Gan, Rui Wang, Xiaochun Cao
Date: 2024-08-31 00:00:00
Tasks: Mamba, Rain Removal, Single Image Deraining

Existing deraining Transformers employ self-attention mechanisms with fixed-range windows or along channel dimensions, limiting the exploitation of non-local receptive fields. In response to this issue, we introduce a novel dual-branch hybrid Transformer-Mamba network, denoted as TransMamba, aimed at effectively capturing long-range rain-related dependencies. Based on the prior of distinct spectral-domain features of rain degradation and background, we design a spectral-banded Transformer blocks on the first branch. Self-attention is executed within the combination of the spectral-domain channel dimension to improve the ability of modeling long-range dependencies. To enhance frequency-specific information, we present a spectral enhanced feed-forward module that aggregates features in the spectral domain. In the second branch, Mamba layers are equipped with cascaded bidirectional state space model modules to additionally capture the modeling of both local and global information. At each stage of both the encoder and decoder, we perform channel-wise concatenation of dual-branch features and achieve feature fusion through channel reduction, enabling more effective integration of the multi-scale information from the Transformer and Mamba branches. To better reconstruct innate signal-level relations within clean images, we also develop a spectral coherence loss. Extensive experiments on diverse datasets and real-world images demonstrate the superiority of our method compared against the state-of-the-art approaches.