pwc-archive/papers-with-abstracts huggingface dataset:
Volatility Parametrizations with Random Coefficients: Analytic Flexibility for Implied Volatility Surfaces
Authors: Nicola F. Zaugg, Leonardo Perotti, Lech A. Grzelak
Date: 2024-11-06 00:00:00
Tasks: Form

It is a market practice to express market-implied volatilities in some parametric form. The most popular parametrizations are based on or inspired by an underlying stochastic model, like the Heston model (SVI method) or the SABR model (SABR parametrization). Their popularity is often driven by a closed-form representation enabling efficient calibration. However, these representations indirectly impose a model-specific volatility structure on observable market quotes. When the market's volatility does not follow the parametric model regime, the calibration procedure will fail or lead to extreme parameters, indicating inconsistency. This article addresses this critical limitation - we propose an arbitrage-free framework for letting the parameters from the parametric implied volatility formula be random. The method enhances the existing parametrizations and enables a significant widening of the spectrum of permissible shapes of implied volatilities while preserving analyticity and, therefore, computation efficiency. We demonstrate the effectiveness of the novel method on real data from short-term index and equity options, where the standard parametrizations fail to capture market dynamics. Our results show that the proposed method is particularly powerful in modeling the implied volatility curves of short expiry options preceding an earnings announcement, when the risk-neutral probability density function exhibits a bimodal form.
Short-maturity options on realized variance in local-stochastic volatility models
Authors: Dan Pirjol, Xiaoyu Wang, Lingjiong Zhu
Date: 2024-11-04 00:00:00

We derive the short-maturity asymptotics for prices of options on realized variance in local-stochastic volatility models. We consider separately the short-maturity asymptotics for out-of-the-money and in-the-money options cases. The analysis for the out-of-the-money case uses large deviations theory and the solution for the rate function involves solving a two-dimensional variational problem. In the special case when the Brownian noises in the asset price dynamics and the volatility process are uncorrelated, we solve this variational problem explicitly. For the correlated case, we obtain upper and lower bounds for the rate function, as well as an expansion around the at-the-money point. Numerical simulations of the prices of variance options in a local-stochastic volatility model with bounded local volatility are in good agreement with the asymptotic results for sufficiently small maturity. The leading-order asymptotics for at-the-money options on realized variance is dominated by fluctuations of the asset price around the spot value, and is computed in closed form.
Efficient Nested Estimation of CoVaR: A Decoupled Approach
Authors: Nifei Lin, Yingda Song, L. Jeff Hong
Date: 2024-11-02 00:00:00

This paper addresses the estimation of the systemic risk measure known as CoVaR, which quantifies the risk of a financial portfolio conditional on another portfolio being at risk. We identify two principal challenges: conditioning on a zero-probability event and the repricing of portfolios. To tackle these issues, we propose a decoupled approach utilizing smoothing techniques and develop a model-independent theoretical framework grounded in a functional perspective. We demonstrate that the rate of convergence of the decoupled estimator can achieve approximately $O_{\rm P}(\Gamma^{-1/2})$, where $\Gamma$ represents the computational budget. Additionally, we establish the smoothness of the portfolio loss functions, highlighting its crucial role in enhancing sample efficiency. Our numerical results confirm the effectiveness of the decoupled estimators and provide practical insights for the selection of appropriate smoothing techniques.
Corporate Fundamentals and Stock Price Co-Movement
Authors: Lyuhong Wang, Jiawei Jiang, Yang Zhao
Date: 2024-11-06 00:00:00

We introduce an innovative framework that leverages advanced big data techniques to analyze dynamic co-movement between stocks and their underlying fundamentals using high-frequency stock market data. Our method identifies leading co-movement stocks through four distinct regression models: Forecast Error Variance Decomposition, transaction volume-normalized FEVD, Granger causality test frequency, and Granger causality test days. Validated using Chinese banking sector stocks, our framework uncovers complex relationships between stock price co-movements and fundamental characteristics, demonstrating its robustness and wide applicability across various sectors and markets. This approach not only enhances our understanding of market dynamics but also provides actionable insights for investors and policymakers, helping to mitigate broader market volatilities and improve financial stability. Our model indicates that banks' influence on their peers is significantly affected by their wealth management business, interbank activities, equity multiplier, non-performing loans, regulatory requirements, and reserve requirement ratios. This aids in mitigating the impact of broader market volatilities and provides deep insights into the unique influence of banks within the financial ecosystem.
TradExpert: Revolutionizing Trading with Mixture of Expert LLMs
Authors: Qianggang Ding, Haochen Shi, Jiadong Guo, Bang Liu
Date: 2024-10-16 00:00:00
Tasks: Articles, Prediction

The integration of Artificial Intelligence (AI) in the financial domain has opened new avenues for quantitative trading, particularly through the use of Large Language Models (LLMs). However, the challenge of effectively synthesizing insights from diverse data sources and integrating both structured and unstructured data persists. This paper presents TradeExpert, a novel framework that employs a mix of experts (MoE) approach, using four specialized LLMs, each analyzing distinct sources of financial data, including news articles, market data, alpha factors, and fundamental data. The insights of these expert LLMs are further synthesized by a General Expert LLM to make a final prediction or decision. With specific prompts, TradeExpert can be switched between the prediction mode and the ranking mode for stock movement prediction and quantitative stock trading, respectively. In addition to existing benchmarks, we also release a large-scale financial dataset to comprehensively evaluate TradeExpert's effectiveness. Our experimental results demonstrate TradeExpert's superior performance across all trading scenarios.
Advancing DeFi Analytics: Efficiency Analysis with Decentralized Exchanges Comparison Service
Authors: Evgenii Onishchuk, Maksim Dubovitskii, Eduard Horch
Date: 2024-11-04 00:00:00
Tasks: Decision Making

This empirical study presents the Decentralized Exchanges Comparison Service (DECS), a novel tool developed by 1inch Analytics to assess exchange efficiency in decentralized finance. The DECS utilizes swap transaction monitoring and simulation techniques to provide unbiased comparisons of swap rates across various DEXes and aggregators. Analysis of almost 1.2 million transactions across multiple blockchain networks demonstrates that both 1inch Classic and 1inch Fusion consistently outperform competitors. These findings not only validate 1inch's superior rates but also provide valuable insights for continuous protocol optimization and underscore the critical role of data-driven decision-making in advancing DeFi infrastructure.
Sentiment Analysis Based on RoBERTa for Amazon Review: An Empirical Study on Decision Making
Authors: Xinli Guo
Date: 2024-10-18 00:00:00
Tasks: Decision Making, Marketing, Sentiment Analysis

In this study, we leverage state-of-the-art Natural Language Processing (NLP) techniques to perform sentiment analysis on Amazon product reviews. By employing transformer-based models, RoBERTa, we analyze a vast dataset to derive sentiment scores that accurately reflect the emotional tones of the reviews. We provide an in-depth explanation of the underlying principles of these models and evaluate their performance in generating sentiment scores. Further, we conduct comprehensive data analysis and visualization to identify patterns and trends in sentiment scores, examining their alignment with behavioral economics principles such as electronic word of mouth (eWOM), consumer emotional reactions, and the confirmation bias. Our findings demonstrate the efficacy of advanced NLP models in sentiment analysis and offer valuable insights into consumer behavior, with implications for strategic decision-making and marketing practices.
Dirichlet process mixtures of block $g$ priors for model selection and prediction in linear models
Authors: Anupreet Porwal, Abel Rodriguez
Date: 2024-11-01 00:00:00
Tasks: Model Selection

This paper introduces Dirichlet process mixtures of block $g$ priors for model selection and prediction in linear models. These priors are extensions of traditional mixtures of $g$ priors that allow for differential shrinkage for various (data-selected) blocks of parameters while fully accounting for the predictors' correlation structure, providing a bridge between the literatures on model selection and continuous shrinkage priors. We show that Dirichlet process mixtures of block $g$ priors are consistent in various senses and, in particular, that they avoid the conditional Lindley ``paradox'' highlighted by Som et al.(2016). Further, we develop a Markov chain Monte Carlo algorithm for posterior inference that requires only minimal ad-hoc tuning. Finally, we investigate the empirical performance of the prior in various real and simulated datasets. In the presence of a small number of very large effects, Dirichlet process mixtures of block $g$ priors lead to higher power for detecting smaller but significant effects without only a minimal increase in the number of false discoveries.
Towards More Accurate US Presidential Election via Multi-step Reasoning with Large Language Models
Authors: Chenxiao Yu, Zhaotian Weng, Yuangang Li, Zheng Li, Xiyang Hu, Yue Zhao
Date: 2024-10-21 00:00:00

Can Large Language Models (LLMs) accurately predict election outcomes? While LLMs have demonstrated impressive performance in various domains, including healthcare, legal analysis, and creative tasks, their ability to forecast elections remains unknown. Election prediction poses unique challenges, such as limited voter-level data, rapidly changing political landscapes, and the need to model complex human behavior. To address these challenges, we introduce a multi-step reasoning framework designed for political analysis. Our approach is validated on real-world data from the American National Election Studies (ANES) 2016 and 2020, as well as synthetic personas generated by the leading machine learning framework, offering scalable datasets for voter behavior modeling. To capture temporal dynamics, we incorporate candidates' policy positions and biographical details, ensuring that the model adapts to evolving political contexts. Drawing on Chain of Thought prompting, our multi-step reasoning pipeline systematically integrates demographic, ideological, and time-dependent factors, enhancing the model's predictive power.
RuAG: Learned-rule-augmented Generation for Large Language Models
Authors: Yudi Zhang, Pei Xiao, Lu Wang, Chaoyun Zhang, Meng Fang, Yali Du, Yevgeniy Puzyrev, Randolph Yao, Si Qin, QIngwei Lin, Mykola Pechenizkiy, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
Date: 2024-11-04 00:00:00
Tasks: Decision Making, In-Context Learning, RAG, Retrieval-augmented Generation

In-context learning (ICL) and Retrieval-Augmented Generation (RAG) have gained attention for their ability to enhance LLMs' reasoning by incorporating external knowledge but suffer from limited contextual window size, leading to insufficient information injection. To this end, we propose a novel framework, RuAG, to automatically distill large volumes of offline data into interpretable first-order logic rules, which are injected into LLMs to boost their reasoning capabilities. Our method begins by formulating the search process relying on LLMs' commonsense, where LLMs automatically define head and body predicates. Then, RuAG applies Monte Carlo Tree Search (MCTS) to address the combinational searching space and efficiently discover logic rules from data. The resulting logic rules are translated into natural language, allowing targeted knowledge injection and seamless integration into LLM prompts for LLM's downstream task reasoning. We evaluate our framework on public and private industrial tasks, including natural language processing, time-series, decision-making, and industrial tasks, demonstrating its effectiveness in enhancing LLM's capability over diverse tasks.
A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness
Authors: Fali Wang, Zhiwei Zhang, Xianren Zhang, Zongyu Wu, Tzuhao Mo, Qiuhao Lu, Wanjing Wang, Rui Li, Junjie Xu, Xianfeng Tang, Qi He, Yao Ma, Ming Huang, Suhang Wang
Date: 2024-11-04 00:00:00
Tasks: Question Answering, Text Generation

Large language models (LLMs) have demonstrated emergent abilities in text generation, question answering, and reasoning, facilitating various tasks and domains. Despite their proficiency in various tasks, LLMs like PaLM 540B and Llama-3.1 405B face limitations due to large parameter sizes and computational demands, often requiring cloud API use which raises privacy concerns, limits real-time applications on edge devices, and increases fine-tuning costs. Additionally, LLMs often underperform in specialized domains such as healthcare and law due to insufficient domain-specific knowledge, necessitating specialized models. Therefore, Small Language Models (SLMs) are increasingly favored for their low inference latency, cost-effectiveness, efficient development, and easy customization and adaptability. These models are particularly well-suited for resource-limited environments and domain knowledge acquisition, addressing LLMs' challenges and proving ideal for applications that require localized data handling for privacy, minimal inference latency for efficiency, and domain knowledge acquisition through lightweight fine-tuning. The rising demand for SLMs has spurred extensive research and development. However, a comprehensive survey investigating issues related to the definition, acquisition, application, enhancement, and reliability of SLM remains lacking, prompting us to conduct a detailed survey on these topics. The definition of SLMs varies widely, thus to standardize, we propose defining SLMs by their capability to perform specialized tasks and suitability for resource-constrained settings, setting boundaries based on the minimal size for emergent abilities and the maximum size sustainable under resource constraints. For other aspects, we provide a taxonomy of relevant models/methods and develop general frameworks for each category to enhance and utilize SLMs effectively.
Learning Force Distribution Estimation for the GelSight Mini Optical Tactile Sensor Based on Finite Element Analysis
Authors: Erik Helmut, Luca Dziarski, Niklas Funk, Boris Belousov, Jan Peters
Date: 2024-10-08 00:00:00
Tasks: Contact-rich Manipulation

Contact-rich manipulation remains a major challenge in robotics. Optical tactile sensors like GelSight Mini offer a low-cost solution for contact sensing by capturing soft-body deformations of the silicone gel. However, accurately inferring shear and normal force distributions from these gel deformations has yet to be fully addressed. In this work, we propose a machine learning approach using a U-net architecture to predict force distributions directly from the sensor's raw images. Our model, trained on force distributions inferred from Finite Element Analysis (FEA), demonstrates promising accuracy in predicting normal and shear force distributions for the commercially available GelSight Mini sensor. It also shows potential for generalization across indenters, sensors of the same type, and for enabling real-time application. The codebase, dataset and models are open-sourced and available at https://feats-ai.github.io .
Foundation Models for Rapid Autonomy Validation
Authors: Alec Farid, Peter Schleede, Aaron Huang, Christoffer Heckman
Date: 2024-10-22 00:00:00

We are motivated by the problem of autonomous vehicle performance validation. A key challenge is that an autonomous vehicle requires testing in every kind of driving scenario it could encounter, including rare events, to provide a strong case for safety and show there is no edge-case pathological behavior. Autonomous vehicle companies rely on potentially millions of miles driven in realistic simulation to expose the driving stack to enough miles to estimate rates and severity of collisions. To address scalability and coverage, we propose the use of a behavior foundation model, specifically a masked autoencoder (MAE), trained to reconstruct driving scenarios. We leverage the foundation model in two complementary ways: we (i) use the learned embedding space to group qualitatively similar scenarios together and (ii) fine-tune the model to label scenario difficulty based on the likelihood of a collision upon simulation. We use the difficulty scoring as importance weighting for the groups of scenarios. The result is an approach which can more rapidly estimate the rates and severity of collisions by prioritizing hard scenarios while ensuring exposure to every kind of driving scenario.
MARTA: a model for the automatic phonemic grouping of the parkinsonian speech
Authors: Alejandro Guerrero-López, Julián D. Arias-Londoño, Stefanie Shattuck-Hufnagel, Juan I. Godino-Llorente
Date: 2024-03-19 00:00:00
Proceeding: techrxiv 2024 3
Tasks: Benchmarking, Classification, Metric Learning

Parkinson's disease significantly impacts speech, particularly affecting phonemic groups like stop-plosives, fricatives, and affricates. However, its objective impact on the different phonemic groups has been briefly addressed in the past.
This study introduces a new model, called MARTA, built upon a Gaussian Mixture Variational AutoEncoder with metric learning to measure the disease's impact on the phonemic grouping automatically and objectively. MARTA was trained on normophonic speech before adapting it to parkinsonian speech. The model effectively clusters phonemic groups unsupervised and demonstrates enhanced discriminative power when supervised using forced-aligned labels. Our findings reveal that beyond the traditionally affected phonemes, Parkinson's disease not only affects stop-plosives, voiced-plosives, and nasals, but also significantly impacts liquids, vowels, and fricatives, with the model achieving a benchmarking 91% ± 9 discrimination capability. An in-depth evaluation of the impact of the disease on the different phonemic groups represents an advance in the current knowledge of its effects on the speech, and has clear implications in the speech therapy of people with Parkinson's disease.
Moreover, regardless of the specific application domain presented, the model introduced has potential downstream utility in assessing the manner of articulation, whether influenced by other medical conditions or certain dialectal variations.
Leaving Some Facial Features Behind
Authors: Cheng Qiu
Date: 2024-10-29 00:00:00
Tasks: Classification, Emotion Classification, Emotion Recognition

Facial expressions are crucial to human communication, offering insights into emotional states. This study examines how specific facial features influence emotion classification, using facial perturbations on the Fer2013 dataset. As expected, models trained on data with the removal of some important facial feature experienced up to an 85% accuracy drop when compared to baseline for emotions like happy and surprise. Surprisingly, for the emotion disgust, there seem to be slight improvement in accuracy for classifier after mask have been applied. Building on top of this observation, we applied a training scheme to mask out facial features during training, motivating our proposed Perturb Scheme. This scheme, with three phases-attention-based classification, pixel clustering, and feature-focused training, demonstrates improvements in classification accuracy. The experimental results obtained suggests there are some benefits to removing individual facial features in emotion recognition tasks.
A voice and speech corpus of patients who underwent upper airway surgery in pre- and post-operative states
Authors: Estefanía Hernández-García, Alejandro Guerrero-López, Julián D. Arias-Londoño, Juan I. Godino-Llorente
Date: 2024-07-09 00:00:00
Proceeding: Scientific Data 2024 7
Tasks: Articles, Classification, Speaker Recognition, Speech Recognition

Many research articles have explored the impact of surgical interventions on voice and speech evaluations, but advances are limited by the lack of publicly accessible datasets. To address this, a comprehensive corpus of 107 Spanish Castilian speakers was recorded, including control speakers and patients who underwent upper airway surgeries such as Tonsillectomy, Functional Endoscopic Sinus Surgery, and Septoplasty. The dataset contains 3,800 audio files, averaging 35.51 ± 5.91 recordings per patient. This resource enables systematic investigation of the effects of upper respiratory tract surgery on voice and speech. Previous studies using this corpus have shown no relevant changes in key acoustic parameters for sustained vowel phonation, consistent with initial hypotheses. However, the analysis of speech recordings, particularly nasalised segments, remains open for further research. Additionally, this dataset facilitates the study of the impact of upper airway surgery on speaker recognition and identification methods, and testing of anti-spoofing methodologies for improved robustness.
Moreover, regardless of the specific application domain presented, the model introduced has potential downstream utility in assessing the manner of articulation, whether influenced by other medical conditions or certain dialectal variations.
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
Authors: Quan Zhou, Changhua Pei, Fei Sun, Jing Han, Zhengwei Gao, Dan Pei, Haiming Zhang, Gaogang Xie, Jianhui Li
Date: 2024-11-01 00:00:00
Tasks: Anomaly Detection, Kolmogorov-Arnold Networks, Temporal Sequences, Time Series, Time Series Anomaly Detection

Time series anomaly detection (TSAD) has become an essential component of large-scale cloud services and web systems because it can promptly identify anomalies, providing early warnings to prevent greater losses. Deep learning-based forecasting methods have become very popular in TSAD due to their powerful learning capabilities. However, accurate predictions don't necessarily lead to better anomaly detection. Due to the common occurrence of noise, i.e., local peaks and drops in time series, existing black-box learning methods can easily learn these unintended patterns, significantly affecting anomaly detection performance. Kolmogorov-Arnold Networks (KAN) offers a potential solution by decomposing complex temporal sequences into a combination of multiple univariate functions, making the training process more controllable. However, KAN optimizes univariate functions using spline functions, which are also susceptible to the influence of local anomalies. To address this issue, we present KAN-AD, which leverages the Fourier series to emphasize global temporal patterns, thereby mitigating the influence of local peaks and drops. KAN-AD improves both effectiveness and efficiency by transforming the existing black-box learning approach into learning the weights preceding univariate functions. Experimental results show that, compared to the current state-of-the-art, we achieved an accuracy increase of 15% while boosting inference speed by 55 times.