pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Scale Fiber Remodeling in HCM Using a Stress-Based Fiber Reorientation Law
Authors: Mohammad Mehri, Hossein Sharifi, Kenneth S. Campbell, Jonathan F. Wenk
Date: 2024-09-23 00:00:00

Quantifying fiber disarray, which is a prominent maladaptation associated with hypertrophic cardiomyopathy, remains critical to understanding the disease's complex pathophysiology. This study investigates the role of heterogeneous impairment of fiber contractility and fibrosis in the induction of disarray and their subsequent impact on cardiac pumping function. Fiber disarray is modeled via a stress-based fiber reorientation law within a multiscale finite element cardiac modeling framework called MyoFE. Using multiscale modeling capabilities, this study quantifies the distinct impacts of hypocontractility, hypercontractility and fibrosis on the development of fiber disarray and quantifies how their contributions affect the functional characteristics of the heart.
A prospectus on the surface metrology of seborrheic keratoses
Authors: Nicole Werpachowski, Juliette Nutovits, Therese Limbana, John Goncalves, Alina Bridges, Brian Lee Beatty
Date: 2024-09-21 00:00:00
Tasks: Diagnostic

Background: Human skin texture has yet to be quantified for diagnostic purposes. Here, the surface metrology of seborrheic keratoses is investigated with an optical profiler. Materials and Methods: Dermatologic specimens of 7 cadavers were prepared. Specimens were molded with polyvinyl siloxane and casts prepared with resin, which were scanned using a 3D white light optical profiler. Each scan produced 48 variables, categorized into 3 groups for each location: control, lesion center, and lesion edge. Images of the histopathology slides for suspected seborrheic keratoses were reviewed by a dermatopathologist. Results: The parameters under investigation included border versus center of keratoses, age, sex, lesion location, degree of sun exposure, and cause of death. Although some parameters differ between individuals and age groups, the majority of differences identified between the roughness parameters measured are a result of sex, sun exposure, and histological diagnosis, listed in order of increasing importance. Histological diagnosis provided the most significant, definitive number of individual measurements of areas of roughness in seborrheic keratoses in comparison to other parts of the skin (regardless if those were controls or a different pigmented lesion aside from seborrheic keratoses). Conclusions: This study demonstrates that there are quantifiable patterns of surface textures that can be compared between keratotic and standard normal skin surfaces. These findings suggest this method has the potential to be applied as a noninvasive adjunct to current methods of dermatological diagnosis.
Simulating and analyzing a sparse order book: an application to intraday electricity markets
Authors: Philippe Bergault, Enzo Cognéville
Date: 2024-10-09 00:00:00

This paper presents a novel model for simulating and analyzing sparse limit order books (LOBs), with a specific application to the European intraday electricity market. In illiquid markets, characterized by significant gaps between order levels due to sparse trading volumes, traditional LOB models often fall short. Our approach utilizes an inhomogeneous Poisson process to accurately capture the sporadic nature of order arrivals and cancellations on both the bid and ask sides of the book. By applying this model to the intraday electricity market, we gain insights into the unique microstructural behaviors and challenges of this dynamic trading environment. The results offer valuable implications for market participants, enhancing their understanding of LOB dynamics in illiquid markets. This work contributes to the broader field of market microstructure by providing a robust framework adaptable to various illiquid market settings beyond electricity trading.
Numerical analysis of American option pricing in a two-asset jump-diffusion model
Authors: Hao Zhou, Duy-Minh Dang
Date: 2024-10-07 00:00:00

This paper addresses an important gap in rigorous numerical treatments for pricing American options under correlated two-asset jump-diffusion models using the viscosity solution framework, with a particular focus on the Merton model. The pricing of these options is governed by complex two-dimensional (2-D) variational inequalities that incorporate cross-derivative terms and nonlocal integro-differential terms due to the presence of jumps. Existing numerical methods, primarily based on finite differences, often struggle with preserving monotonicity in the approximation of cross-derivatives, a key requirement for ensuring convergence to the viscosity solution. In addition, these methods face challenges in accurately discretizing 2-D jump integrals. We introduce a novel approach to effectively tackle the aforementioned variational inequalities while seamlessly handling cross-derivative terms and nonlocal integro-differential terms through an efficient and straightforward-to-implement monotone integration scheme. Within each timestep, our approach explicitly enforces the inequality constraint, resulting in a 2-D Partial Integro-Differential Equation (PIDE) to solve. Its solution is expressed as a 2-D convolution integral involving the Green's function of the PIDE. We derive an infinite series representation of this Green's function, where each term is non-negative and computable. This facilitates the numerical approximation of the PIDE solution through a monotone integration method. To enhance efficiency, we develop an implementation of this monotone scheme via FFTs, exploiting the Toeplitz matrix structure. The proposed method is proved to be both $\ell_{\infty} $-stable and consistent in the viscosity sense, ensuring its convergence to the viscosity solution of the variational inequality. Extensive numerical results validate the effectiveness and robustness of our approach.
American Call Options Pricing With Modular Neural Networks
Authors: Ananya Unnikrishnan
Date: 2024-09-29 00:00:00
Tasks: Decision Making

An accurate valuation of American call options is critical in most financial decision making environments. However, traditional models like the Barone-Adesi Whaley (B-AW) and Binomial Option Pricing (BOP) methods fall short in handling the complexities of early exercise and market dynamics present in American options. This paper proposes a Modular Neural Network (MNN) model which aims to capture the key aspects of American options pricing. By dividing the prediction process into specialized modules, the MNN effectively models the non-linear interactions that drive American call options pricing. Experimental results indicate that the MNN model outperform both traditional models as well as a simpler Feed-forward Neural Network (FNN) across multiple stocks (AAPL, NVDA, QQQ), with significantly lower RMSE and nRMSE (by mean). These findings highlight the potential of MNNs as a powerful tool to improve the accuracy of predicting option prices.
ChatGPT and Corporate Policies
Authors: Manish Jha, Jialin Qian, Michael Weber, Baozhong Yang
Date: 2024-09-26 00:00:00

We create a firm-level ChatGPT investment score, based on conference calls, that measures managers' anticipated changes in capital expenditures. We validate the score with interpretable textual content and its strong correlation with CFO survey responses. The investment score predicts future capital expenditure for up to nine quarters, controlling for Tobin's $q$ and other determinants, implying the investment score provides incremental information about firms' future investment opportunities. The investment score also separately forecasts future total, intangible, and R\&D investments. Consistent with theoretical predictions, high-investment-score firms experience significant positive short-term returns upon disclosure, and negative long-run future abnormal returns. We demonstrate ChatGPT's applicability to measure other policies, such as dividends and employment.
Economic effects on households of an augmentation of the cash back duration of real estate loan
Authors: Hugo Spring-Ragain
Date: 2024-09-23 00:00:00

This article examines the economic effects of an increase in the duration of home loans on households, focusing on the French real estate market. It highlights trends in the property market, existing loan systems in other countries (such as bullet loans in Sweden and Japanese home loans), the current state of the property market in France, the potential effects of an increase in the amortization period of home loans, and the financial implications for households.The article points out that increasing the repayment period on home loans could reduce the amount of monthly instalments to be repaid, thereby facilitating access to credit for the most modest households. However, this measure also raises concerns about overall credit costs, financial stability and the impact on property prices. In addition, it highlights the differences between existing lending systems in other countries, such as the bullet loan in Sweden and Japanese home loans, and the current characteristics of home loans in France, notably interest rates and house price trends. The article proposes a model of the potential effects of an increase in the amortization period of home loans on housing demand, housing supply, property prices and the associated financial risks.In conclusion, the article highlights the crucial importance of household debt for individual and economic financial stability. It highlights the distortion between supply and demand for home loans as amortization periods increase, and the significant rise in overall loan costs for households. It also underlines the need to address structural issues such as the sustainable reduction in interest rates, the stabilization of banks' equity capital and the development of a regulatory framework for intergenerational lending to ensure a properly functioning market.
Deep Gamma Hedging
Authors: John Armstrong, George Tatlow
Date: 2024-09-20 00:00:00

We train neural networks to learn optimal replication strategies for an option when two replicating instruments are available, namely the underlying and a hedging option. If the price of the hedging option matches that of the Black--Scholes model then we find the network will successfully learn the Black-Scholes gamma hedging strategy, even if the dynamics of the underlying do not match the Black--Scholes model, so long as we choose a loss function that rewards coping with model uncertainty. Our results suggest that the reason gamma hedging is used in practice is to account for model uncertainty rather than to reduce the impact of transaction costs.
Concentrated Liquidity with Leverage
Authors: Atis Elsts, Krešimir Klas
Date: 2024-09-19 00:00:00

Concentrated liquidity (CL) provisioning is a way how to improve the capital efficiency of Automated Market Makers (AMM). Allowing liquidity providers to use leverage is a step towards even higher capital efficiency. A number of Decentralized Finance (DeFi) protocols implement this technique in conjunction with overcollateralized lending. However, the properties of leveraged CL positions have not been formalized and are poorly understood in practice. This article describes the principles of a leveraged CL provisioning protocol, formally models the notions of margin level, assets, and debt, and proves that within this model, leveraged LP positions possess several properties that make them safe to use.
Market Simulation under Adverse Selection
Authors: Luca Lalor, Anatoliy Swishchuk
Date: 2024-09-19 00:00:00

In this paper, we study the effects of fill probabilities and adverse fills on the trading strategy simulation process. We specifically focus on a stochastic optimal control market-making problem and test the strategy on ES (E-mini S\&P 500), NQ (E-mini Nasdaq 100), CL (Crude Oil) and ZN (10-Year Treasury Note), which are some of the most liquid futures contracts listed on the CME (Chicago Mercantile Exchange). We provide empirical evidence that shows how fill probabilities and adverse fills can significantly affect performance and propose a more prudent simulation framework to deal with this. Many previous works aim to measure different types of adverse selection in the limit order book (LOB), however, they often simulate price processes and market orders independently. This has the ability to largely inflate the performance of a short-term style trading strategy. Our studies show that using more realistic fill probabilities and tracking adverse fills in the strategy simulation process more accurately shows how these types of trading strategies would perform in reality.
Interpool: a liquidity pool designed for interoperability that mints, exchanges, and burns
Authors: Henrique de Carvalho Videira
Date: 2024-09-13 00:00:00

The lack of proper interoperability poses a significant challenge in leveraging use cases within the blockchain industry. Unlike typical solutions that rely on third parties such as oracles and witnesses, the interpool design operates as a standalone solution that mints, exchanges, and burns (MEB) within the same liquidity pool. This MEB approach ensures that minting is backed by the locked capital supplied by liquidity providers. During the exchange process, the order of transactions in the mempool is optimized to maximize returns, effectively transforming the front-running issue into a solution that forges an external blockchain hash. This forged hash enables a novel protocol, Listrack (Listen and Track), which ensures that ultimate liquidity is always enforced through a solid burning procedure, strengthening a trustless design. Supported by Listrack, atomic swaps become feasible even outside the interpool, thereby enhancing the current design into a comprehensive interoperability solution
Portfolio Stress Testing and Value at Risk (VaR) Incorporating Current Market Conditions
Authors: Krishan Mohan Nagpal
Date: 2024-09-12 00:00:00
Tasks: Variational Inference

Value at Risk (VaR) and stress testing are two of the most widely used approaches in portfolio risk management to estimate potential market value losses under adverse market moves. VaR quantifies potential loss in value over a specified horizon (such as one day or ten days) at a desired confidence level (such as 95'th percentile). In scenario design and stress testing, the goal is to construct extreme market scenarios such as those involving severe recession or a specific event of concern (such as a rapid increase in rates or a geopolitical event), and quantify potential impact of such scenarios on the portfolio. The goal of this paper is to propose an approach for incorporating prevailing market conditions in stress scenario design and estimation of VaR so that they provide more accurate and realistic insights about portfolio risk over the near term. The proposed approach is based on historical data where historical observations of market changes are given more weight if a certain period in history is "more similar" to the prevailing market conditions. Clusters of market conditions are identified using a Machine Learning approach called Variational Inference (VI) where for each cluster future changes in portfolio value are similar. VI based algorithm uses optimization techniques to obtain analytical approximations of the posterior probability density of cluster assignments (market regimes) and probabilities of different outcomes for changes in portfolio value. Covid related volatile period around the year 2020 is used to illustrate the performance of the proposed approach and in particular show how VaR and stress scenarios adapt quickly to changing market conditions. Another advantage of the proposed approach is that classification of market conditions into clusters can provide useful insights about portfolio performance under different market conditions.
Crisis Alpha: A High-Performance Trading Algorithm Tested in Market Downturns
Authors: Maysam Khodayari Gharanchaei, Reza Babazadeh
Date: 2024-08-18 00:00:00
Tasks: Decision Making, Management

Forming quantitative portfolios using statistical risk models presents a significant challenge for hedge funds and portfolio managers. This research investigates three distinct statistical risk models to construct quantitative portfolios of 1,000 floating stocks in the US market. Utilizing five different investment strategies, these models are tested across four periods, encompassing the last three major financial crises: The Dot Com Bubble, Global Financial Crisis, and Covid-19 market downturn. Backtests leverage the CRSP dataset from January 1990 through December 2023. The results demonstrate that the proposed models consistently outperformed market excess returns across all periods. These findings suggest that the developed risk models can serve as valuable tools for asset managers, aiding in strategic decision-making and risk management in various economic conditions.
The Impact of Implicit Government Guarantee on Credit Rating of Municipal Investment Bonds
Authors: Yan Zhang, Yixiang Tian, Lin Chen
Date: 2024-09-21 00:00:00

One type of bond with the most implicit government guarantee is municipal investment bonds. In recent years, there have been an increasing number of downgrades in the credit ratings of municipal bonds, which has led some people to question whether the implicit government guarantee may affect the objectivity of the bond ratings? This paper uses text mining methods to mine relevant policy documents related to municipal investment bond issuance, and calculates the implicit guarantee strength of municipal investment bonds based on the PMC index model. It further analyzes the impact of the implicit guarantee strength of municipal bonds on their credit evaluation. The study found that the implicit government guarantee on municipal investment bonds does indeed help to raise the credit ratings assigned by credit rating agencies. The study found that, moreover, the government's implicit guarantee has a more pronounced effect in boosting credit ratings in less developed western regions.
Implicit Government Guarantee Measurement Based on PMC Index Model
Authors: Yan Zhang, Yixiang Tian, Lin Chen, Qi Wang
Date: 2024-09-19 00:00:00
Tasks: Management, Specificity

The implicit government guarantee hampers the recognition and management of risks by all stakeholders in the bond market, and it has led to excessive debt for local governments or state-owned enterprises. To prevent the risk of local government debt defaults and reduce investors' expectations of implicit government guarantees, various regulatory departments have issued a series of policy documents related to municipal investment bonds. By employing text mining techniques on policy documents related to municipal investment bond, and utilizing the PMC index model to assess the effectiveness of policy documents. This paper proposes a novel method for quantifying the intensity of implicit governmental guarantees based on PMC index model. The intensity of implicit governmental guarantees is inversely correlated with the PMC index of policies aimed at de-implicitizing governmental guarantees. Then as these policies become more effective, the intensity of implicit governmental guarantees diminishes correspondingly. These findings indicate that recent policies related to municipal investment bond have indeed succeeded in reducing implicit governmental guarantee intensity, and these policies have achieved the goal of risk management. Furthermore, it was showed that the intensity of implicit governmental guarantee affected by diverse aspects of these policies such as effectiveness, clarity, and specificity, as well as incentive and assurance mechanisms.
Evaluating Investment Risks in LATAM AI Startups: Ranking of Investment Potential and Framework for Valuation
Authors: Abraham Ramos-Torres, Laura N. Montoya
Date: 2024-09-17 00:00:00

The growth of the tech startup ecosystem in Latin America (LATAM) is driven by innovative entrepreneurs addressing market needs across various sectors. However, these startups encounter unique challenges and risks that require specific management approaches. This paper explores a case study with the Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) metrics within the context of the online food delivery industry in LATAM, serving as a model for valuing startups using the Discounted Cash Flow (DCF) method. By analyzing key emerging powers such as Argentina, Colombia, Uruguay, Costa Rica, Panama, and Ecuador, the study highlights the potential and profitability of AI-driven startups in the region through the development of a ranking of emerging powers in Latin America for tech startup investment. The paper also examines the political, economic, and competitive risks faced by startups and offers strategic insights on mitigating these risks to maximize investment returns. Furthermore, the research underscores the value of diversifying investment portfolios with startups in emerging markets, emphasizing the opportunities for substantial growth and returns despite inherent risks.
Efficient calibration of the shifted square-root diffusion model to credit default swap spreads using asymptotic approximations
Authors: Ankush Agarwal, Ying Liao
Date: 2024-10-03 00:00:00

We derive a closed-form approximation for the credit default swap (CDS) spread in the two-dimensional shifted square-root diffusion (SSRD) model using asymptotic coefficient expansion technique to approximate solutions of nonlinear partial differential equations. Specifically, we identify the Cauchy problems associated with two terms in the CDS spread formula that lack analytical solutions and derive asymptotic approximations for these terms. Our approximation does not require the assumption of uncorrelated interest rate and default intensity processes as typically required for calibration in the SSRD model. Through several calibration studies using market data on CDS spread, we demonstrate the accuracy and efficiency of our proposed formula.
Robust forward investment and consumption under drift and volatility uncertainties: A randomization approach
Authors: Wing Fung Chong, Gechun Liang
Date: 2024-10-02 00:00:00

This paper studies robust forward investment and consumption preferences and optimal strategies for a risk-averse and ambiguity-averse agent in an incomplete financial market with drift and volatility uncertainties. We focus on non-zero volatility and constant relative risk aversion (CRRA) forward preferences. Given the non-convexity of the Hamiltonian with respect to uncertain volatilities, we first construct robust randomized forward preferences through endogenous randomization in an auxiliary market. We derive the corresponding optimal and robust investment and consumption strategies. Furthermore, we show that such forward preferences and strategies, developed in the auxiliary market, remain optimal and robust in the physical market, offering a comprehensive framework for forward investment and consumption under model uncertainty.