pwc-archive/papers-with-abstracts huggingface dataset:
Diceplot: A package for high dimensional categorical data visualization
Authors: Matthias Flotho, Philipp Flotho, Andreas Keller
Date: 2024-10-30 00:00:00
Tasks: Data Visualization

Visualization of multidimensional, categorical data is a common challenge across scientific areas and, in particular, the life sciences. The goal is to create a comprehensive overview of the underlying data which allows to assess multiple variables intuitively. One application where such visualizations are particularly useful is pathway analysis, where we check for dysregulation in known biological regulatory mechanisms and functions across multiple conditions. Here, we propose a new visualization approach that codes such data in a comprehensive and intuitive representation: Dice plots visualize up to four distinct categorical classes in a single view that consist of multiple elements resembling the faces of dice, whereas domino plots add an additional layer of information for binary comparison. The code is available as the diceplot R package, as pydiceplot on pip and at https://github.com/maflot.
Beyond Current Boundaries: Integrating Deep Learning and AlphaFold for Enhanced Protein Structure Prediction from Low-Resolution Cryo-EM Maps
Authors: Xin, MA, Dong Si
Date: 2024-10-30 00:00:00
Tasks: Protein Structure Prediction

Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and graph neural networks (GNNs), have spurred the development of sophisticated map-to-model tools like DeepTracer and ModelAngelo, their efficacy notably diminishes with low-resolution maps beyond 4 {\AA}. To address this shortfall, our research introduces DeepTracer-LowResEnhance, an innovative framework that synergizes a deep learning-enhanced map refinement technique with the power of AlphaFold. This methodology is designed to markedly improve the construction of models from low-resolution cryo-EM maps. DeepTracer-LowResEnhance was rigorously tested on a set of 37 protein cryo-EM maps, with resolutions ranging between 2.5 to 8.4 {\AA}, including 22 maps with resolutions lower than 4 {\AA}. The outcomes were compelling, demonstrating that 95.5\% of the low-resolution maps exhibited a significant uptick in the count of total predicted residues. This denotes a pronounced improvement in atomic model building for low-resolution maps. Additionally, a comparative analysis alongside Phenix's auto-sharpening functionality delineates DeepTracer-LowResEnhance's superior capability in rendering more detailed and precise atomic models, thereby pushing the boundaries of current computational structural biology methodologies.
Discovering Multi-omic Biomarkers for Prostate Cancer Severity Using Machine Learning
Authors: Jefferson Zhou, Kahn Rhrissorrakrai
Date: 2024-10-29 00:00:00
Tasks: Prognosis

Prostate cancer is the second most common form of cancer, though most patients have a positive prognosis with many experiencing long-term survival with current treatment options. Yet, each treatment carries varying levels of intensity and side effects, therefore determining the severity of prostate cancer is an important criteria in selecting the most appropriate treatment. The Gleason score is the most common grading system used to judge the severity of prostate cancer, but much of the grading process can be affected by human error or subjectivity. Finding biomarkers for prostate cancer Gleason scores in a quantitative, machine-driven approach could enable pathologists to validate their assessment of a patient cancer sample by examining such biomarkers. In our study, we identified biomarkers from multi-omics data using machine learning, statistical tools, and deep learning to train models against the Gleason score and capture the most important features that could potentially serve as biomarkers for the Gleason score. Through this process, multiple genes, such as COL1A1 and SFRP4, and cell cycle pathways, such as G2M checkpoint, E2F targets, and the PLK1 pathways, were found to be important predictive features for particular Gleason scores. The combination of these analytical methods shows potential for more accurate grading of prostate cancer, and greater understanding of biological processes behind prostate cancer severity that could provide additional therapeutic targets.
Continuous Risk Factor Models: Analyzing Asset Correlations through Energy Distance
Authors: Marcus Gawronsky, Chun-Sung Huang
Date: 2024-10-30 00:00:00
Tasks: Management

This paper introduces a novel approach to financial risk analysis that does not rely on traditional price and market data, instead using market news to model assets as distributions over a metric space of risk factors. By representing asset returns as integrals over the scalar field of these risk factors, we derive the covariance structure between asset returns. Utilizing encoder-only language models to embed this news data, we explore the relationships between asset return distributions through the concept of Energy Distance, establishing connections between distributional differences and excess returns co-movements. This data-agnostic approach provides new insights into portfolio diversification, risk management, and the construction of hedging strategies. Our findings have significant implications for both theoretical finance and practical risk management, offering a more robust framework for modelling complex financial systems without depending on conventional market data.
Emerging countries' counter-currency cycles in the face of crises and dominant currencies
Authors: Hugo Spring-Ragain
Date: 2024-10-30 00:00:00

This article examines how emerging economies use countercyclical monetary policies to manage economic crises and fluctuations in dominant currencies, such as the US dollar and the euro. Global economic cycles are marked by phases of expansion and recession, often exacerbated by major financial crises. These crises, such as those of 1997, 2008 and the disruption caused by the COVID-19 pandemic, have a particular impact on emerging economies due to their heightened vulnerability to foreign capital flows and exports.Counter-cyclical monetary policies, including interest rate adjustments, foreign exchange interventions and capital controls, are essential to stabilize these economies. These measures aim to mitigate the effects of economic shocks, maintain price stability and promote sustainable growth. This article presents a theoretical analysis of economic cycles and financial crises, highlighting the role of dominant currencies in global economic stability. Currencies such as the dollar and the euro strongly influence emerging economies, notably through exchange rate variations and international capital movements. Analysis of the monetary strategies of emerging economies, through case studies of Brazil, India and Nigeria, reveals how these countries use tools such as interest rates, foreign exchange interventions and capital controls to manage the impacts of crises and fluctuations in dominant currencies. The article also highlights the challenges and limitations faced by these countries, including structural and institutional constraints and the reactions of international financial markets.Finally, an econometric analysis using a Vector AutoRegression (VAR) model illustrates the impact of monetary policies on key economic variables, such as GDP, interest rates, inflation and exchange rates. The results show that emerging economies, although sensitive to external shocks, can adjust their policies to stabilize economic growth in the medium and long term.
Exploiting Risk-Aversion and Size-dependent fees in FX Trading with Fitted Natural Actor-Critic
Authors: Vito Alessandro Monaco, Antonio Riva, Luca Sabbioni, Lorenzo Bisi, Edoardo Vittori, Marco Pinciroli, Michele Trapletti, Marcello Restelli
Date: 2024-10-15 00:00:00

In recent years, the popularity of artificial intelligence has surged due to its widespread application in various fields. The financial sector has harnessed its advantages for multiple purposes, including the development of automated trading systems designed to interact autonomously with markets to pursue different aims. In this work, we focus on the possibility of recognizing and leveraging intraday price patterns in the Foreign Exchange market, known for its extensive liquidity and flexibility. Our approach involves the implementation of a Reinforcement Learning algorithm called Fitted Natural Actor-Critic. This algorithm allows the training of an agent capable of effectively trading by means of continuous actions, which enable the possibility of executing orders with variable trading sizes. This feature is instrumental to realistically model transaction costs, as they typically depend on the order size. Furthermore, it facilitates the integration of risk-averse approaches to induce the agent to adopt more conservative behavior. The proposed approaches have been empirically validated on EUR-USD historical data.
Graph Signal Processing for Global Stock Market Realized Volatility Forecasting
Authors: Zhengyang Chi, Junbin Gao, Chao Wang
Date: 2024-10-30 00:00:00
Tasks: Graph Neural Network

This paper introduces an innovative realized volatility (RV) forecasting framework that extends the conventional Heterogeneous Auto-Regressive (HAR) model via integrating the Graph Signal Processing (GSP) technique. The volatility spillover effect is embedded and modeled in the proposed framework, which employs the graph Fourier transformation method to effectively analyze the global stock market dynamics in the spectral domain. In addition, convolution filters with learnable weights are applied to capture the historical mid-term and long-term volatility patterns. The empirical study is conducted with RV data of $24$ global stock market indices with around $3500$ common trading days from May 2002 to June 2022. The proposed model's short-term, middle-term and long-term RV forecasting performance is compared with various HAR type models and the graph neural network based HAR model. The results show that the proposed model consistently outperforms all other models considered in the study, demonstrating the effectiveness of integrating the GSP technique into the HAR model for RV forecasting.
Applications of the Second-Order Esscher Pricing in Risk Management
Authors: Tahir Choulli, Ella Elazkany, Mich`ele Vanmaele
Date: 2024-10-29 00:00:00
Tasks: Management

This paper explores the application and significance of the second-order Esscher pricing model in option pricing and risk management. We split the study into two main parts. First, we focus on the constant jump diffusion (CJD) case, analyzing the behavior of option prices as a function of the second-order parameter and the resulting pricing intervals. Using real data, we perform a dynamic delta hedging strategy, illustrating how risk managers can determine an interval of value-at-risks (VaR) and expected shortfalls (ES), granting flexibility in pricing based on additional information. We compare our pricing interval to other jump-diffusion models, showing its comprehensive risk factor incorporation. The second part extends the second-order Esscher pricing to more complex models, including the Merton jump-diffusion, Kou's Double Exponential jump-diffusion, and the Variance Gamma model. We derive option prices using the fast Fourier transform (FFT) method and provide practical formulas for European call and put options under these models.
Clustering Digital Assets Using Path Signatures: Application to Portfolio Construction
Authors: Hugo Inzirillo
Date: 2024-10-15 00:00:00
Tasks: Clustering

We propose a new way of building portfolios of cryptocurrencies that provide good diversification properties to investors. First, we seek to filter these digital assets by creating some clusters based on their path signature. The goal is to identify similar patterns in the behavior of these highly volatile assets. Once such clusters have been built, we propose "optimal" portfolios by comparing the performances of such portfolios to a universe of unfiltered digital assets. Our intuition is that clustering based on path signatures will make it easier to capture the main trends and features of a group of cryptocurrencies, and allow parsimonious portfolios that reduce excessive transaction fees. Empirically, our assumptions seem to be satisfied.
The VIX as Stochastic Volatility for Corporate Bonds
Authors: Jihyun Park, Andrey Sarantsev
Date: 2024-10-29 00:00:00
Tasks: Time Series

Classic stochastic volatility models assume volatility is unobservable. We use the Volatility Index: S&P 500 VIX to observe it, to easier fit the model. We apply it to corporate bonds. We fit autoregression for corporate rates and for risk spreads between these rates and Treasury rates. Next, we divide residuals by VIX. Our main idea is such division makes residuals closer to the ideal case of a Gaussian white noise. This is remarkable, since these residuals and VIX come from separate market segments. Similarly, we model corporate bond returns as a linear function of rates and rate changes. Our article has two main parts: Moody's AAA and BAA spreads; Bank of America investment-grade and high-yield rates, spreads, and returns. We analyze long-term stability of these models.
Log Heston Model for Monthly Average VIX
Authors: Jihyun Park, Andrey Sarantsev
Date: 2024-10-29 00:00:00
Tasks: model, Time Series

We model time series of VIX (monthly average) and monthly stock index returns. We use log-Heston model: logarithm of VIX is modeled as an autoregression of order 1. Our main insight is that normalizing monthly stock index returns (dividing them by VIX) makes them much closer to independent identically distributed Gaussian. The resulting model is mean-reverting, and the innovations are non-Gaussian. The combined stochastic volatility model fits well, and captures Pareto-like tails of real-world stock market returns. This works for small and large stock indices, for both price and total returns.
Generalized Distribution Prediction for Asset Returns
Authors: Ísak Pétursson, María Óskarsdóttir
Date: 2024-10-15 00:00:00
Tasks: Density Estimation, Prediction, quantile regression

We present a novel approach for predicting the distribution of asset returns using a quantile-based method with Long Short-Term Memory (LSTM) networks. Our model is designed in two stages: the first focuses on predicting the quantiles of normalized asset returns using asset-specific features, while the second stage incorporates market data to adjust these predictions for broader economic conditions. This results in a generalized model that can be applied across various asset classes, including commodities, cryptocurrencies, as well as synthetic datasets. The predicted quantiles are then converted into full probability distributions through kernel density estimation, allowing for more precise return distribution predictions and inferencing. The LSTM model significantly outperforms a linear quantile regression baseline by 98% and a dense neural network model by over 50%, showcasing its ability to capture complex patterns in financial return distributions across both synthetic and real-world data. By using exclusively asset-class-neutral features, our model achieves robust, generalizable results.
Representation Learning for Regime detection in Block Hierarchical Financial Markets
Authors: Alexa Orton, Tim Gebbie
Date: 2024-10-14 00:00:00
Tasks: Representation Learning

We consider financial market regime detection from the perspective of deep representation learning of the causal information geometry underpinning traded asset systems using a hierarchical correlation structure to characterise market evolution. We assess the robustness of three toy models: SPDNet, SPD-NetBN and U-SPDNet whose architectures respect the underlying Riemannian manifold of input block hierarchical SPD correlation matrices. Market phase detection for each model is carried out using three data configurations: randomised JSE Top 60 data, synthetically-generated block hierarchical SPD matrices and block-resampled chronology-preserving JSE Top 60 data. We show that using a singular performance metric is misleading in our financial market investment use cases where deep learning models overfit in learning spatio-temporal correlation dynamics.
Rebalancing-versus-Rebalancing: Improving the fidelity of Loss-versus-Rebalancing
Authors: Matthew Willetts, Christian Harrington
Date: 2024-10-30 00:00:00
Tasks: Asset Management, Management

Automated Market Makers (AMMs) hold assets and are constantly being rebalanced by external arbitrageurs to match external market prices. Loss-versus-rebalancing (LVR) is a pivotal metric for measuring how an AMM pool performs for its liquidity providers (LPs) relative to an idealised benchmark where rebalancing is done not via the action of arbitrageurs but instead by trading with a perfect centralised exchange with no fees, spread or slippage. This renders it an imperfect tool for judging rebalancing efficiency between execution platforms. We introduce Rebalancing-versus-rebalancing (RVR), a higher-fidelity model that better captures the frictions present in centralised rebalancing. We perform a battery of experiments comparing managing a portfolio on AMMs vs this new and more realistic centralised exchange benchmark-RVR. We are also particularly interested in dynamic AMMs that run strategies beyond fixed weight allocations-Temporal Function Market Makers. This is particularly important for asset managers evaluating execution management systems. In this paper we simulate more than 1000 different strategies settings as well as testing hundreds of different variations in centralised exchange (CEX) fees, AMM fees & gas costs. We find that, under this modeling approach, AMM pools (even with no retail/noise traders) often offer superior execution and rebalancing efficiency compared to centralised rebalancing, for all but the lowest CEX fee levels. We also take a simple approach to model noise traders & find that even a small amount of noise volume increases modeled AMM performance such that CEX rebalancing finds it hard to compete. This indicates that decentralised AMM-based asset management can offer superior performance and execution management for asset managers looking to rebalance portfolios, offering an alternative use case for dynamic AMMs beyond core liquidity providing.
TraderTalk: An LLM Behavioural ABM applied to Simulating Human Bilateral Trading Interactions
Authors: Alicia Vidler, Toby Walsh
Date: 2024-10-10 00:00:00

We introduce a novel hybrid approach that augments Agent-Based Models (ABMs) with behaviors generated by Large Language Models (LLMs) to simulate human trading interactions. We call our model TraderTalk. Leveraging LLMs trained on extensive human-authored text, we capture detailed and nuanced representations of bilateral conversations in financial trading. Applying this Generative Agent-Based Model (GABM) to government bond markets, we replicate trading decisions between two stylised virtual humans. Our method addresses both structural challenges, such as coordinating turn-taking between realistic LLM-based agents, and design challenges, including the interpretation of LLM outputs by the agent model. By exploring prompt design opportunistically rather than systematically, we enhance the realism of agent interactions without exhaustive overfitting or model reliance. Our approach successfully replicates trade-to-order volume ratios observed in related asset markets, demonstrating the potential of LLM-augmented ABMs in financial simulations
FVEval: Understanding Language Model Capabilities in Formal Verification of Digital Hardware
Authors: Minwoo Kang, Mingjie Liu, Ghaith Bany Hamad, Syed Suhaib, Haoxing Ren
Date: 2024-10-15 00:00:00
Tasks: Code Generation, Language Modeling, Language Modelling

The remarkable reasoning and code generation capabilities of large language models (LLMs) have spurred significant interest in applying LLMs to enable task automation in digital chip design. In particular, recent work has investigated early ideas of applying these models to formal verification (FV), an approach to verifying hardware implementations that can provide strong guarantees of confidence but demands significant amounts of human effort. While the value of LLM-driven automation is evident, our understanding of model performance, however, has been hindered by the lack of holistic evaluation. In response, we present FVEval, the first comprehensive benchmark and evaluation framework for characterizing LLM performance in tasks pertaining to FV. The benchmark consists of three sub-tasks that measure LLM capabilities at different levels: from the generation of SystemVerilog assertions (SVAs) given natural language descriptions to reasoning about the design RTL and suggesting assertions directly without additional human input. As test instances, we present both collections of expert-written verification collateral and methodologies to scalably generate synthetic examples aligned with industrial FV workflows. A wide range of existing LLMs, both proprietary and open-source, are evaluated against FVEval, based on which we investigate where today's LLMs stand and how we might further enable their application toward improving productivity in digital FV. Our benchmark and evaluation code is available at \url{https://github.com/NVlabs/FVEval}.
Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank
Authors: Donald Loveland, Xinyi Wu, Tong Zhao, Danai Koutra, Neil Shah, Mingxuan Ju
Date: 2024-10-15 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Collaborative Filtering (CF) methods dominate real-world recommender systems given their ability to learn high-quality, sparse ID-embedding tables that effectively capture user preferences. These tables scale linearly with the number of users and items, and are trained to ensure high similarity between embeddings of interacted user-item pairs, while maintaining low similarity for non-interacted pairs. Despite their high performance, encouraging dispersion for non-interacted pairs necessitates expensive regularization (e.g., negative sampling), hurting runtime and scalability. Existing research tends to address these challenges by simplifying the learning process, either by reducing model complexity or sampling data, trading performance for runtime. In this work, we move beyond model-level modifications and study the properties of the embedding tables under different learning strategies. Through theoretical analysis, we find that the singular values of the embedding tables are intrinsically linked to different CF loss functions. These findings are empirically validated on real-world datasets, demonstrating the practical benefits of higher stable rank, a continuous version of matrix rank which encodes the distribution of singular values. Based on these insights, we propose an efficient warm-start strategy that regularizes the stable rank of the user and item embeddings. We show that stable rank regularization during early training phases can promote higher-quality embeddings, resulting in training speed improvements of up to 66%. Additionally, stable rank regularization can act as a proxy for negative sampling, allowing for performance gains of up to 21% over loss functions with small negative sampling ratios. Overall, our analysis unifies current CF methods under a new perspective, their optimization of stable rank, motivating a flexible regularization method.