pwc-archive/papers-with-abstracts huggingface dataset:
Fibroblast mediated dynamics in diffusively uncoupled myocytes -- a simulation study using 2-cell motifs
Authors: S. Sridhar, Richard. H. Clayton
Date: 2023-11-13 00:00:00

In healthy hearts myocytes are typically coupled to nearest neighbours through gap junctions. Under pathological conditions such as fibrosis, or in scar tissue, or across ablation lines myocytes can uncouple from their neighbours. Electrical conduction may still occur via fibroblasts that not only couple proximal myocytes but can also couple otherwise unconnected regions. We hypothesise that such coupling can alter conduction between myocytes via introduction of delays or by initiation of premature stimuli that can potentially result in reentry or conduction blocks. To test this hypothesis we have developed several $2$-cell motifs and investigated the effect of fibroblast mediated electrical coupling between uncoupled myocytes. We have identified various regimes of myocyte behaviour that depend on the strength of gap-junctional conductance, connection topology, and parameters of the myocyte and fibroblast models. These motifs are useful in developing a mechanistic understanding of long-distance coupling on myocyte dynamics and enable the characterisation of interaction between different features such as myocyte and fibroblast properties, coupling strengths and pacing period. They are computationally inexpensive and allow for incorporation of spatial effects such as conduction velocity. They provide a framework for constructing scar tissue boundaries and enable linking of cellular level interactions with scar induced arrhythmia.
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options
Authors: Jirong Zhuang, Deng Ding, Weiguo Lu, Xuan Wu, Gangnan Yuan
Date: 2023-11-13 00:00:00
Tasks: Gaussian Processes, GPR, regression, Variational Inference

In this work, we present a novel machine learning approach for pricing high-dimensional American options based on the modified Gaussian process regression (GPR). We incorporate deep kernel learning and sparse variational Gaussian processes to address the challenges traditionally associated with GPR. These challenges include its diminished reliability in high-dimensional scenarios and the excessive computational costs associated with processing extensive numbers of simulated paths Our findings indicate that the proposed method surpasses the performance of the least squares Monte Carlo method in high-dimensional scenarios, particularly when the underlying assets are modeled by Merton's jump diffusion model. Moreover, our approach does not exhibit a significant increase in computational time as the number of dimensions grows. Consequently, this method emerges as a potential tool for alleviating the challenges posed by the curse of dimensionality.
From Deep Filtering to Deep Econometrics
Authors: Robert Stok, Paul Bilokon
Date: 2023-09-13 00:00:00
Tasks: Econometrics, Management

Calculating true volatility is an essential task for option pricing and risk management. However, it is made difficult by market microstructure noise. Particle filtering has been proposed to solve this problem as it favorable statistical properties, but relies on assumptions about underlying market dynamics. Machine learning methods have also been proposed but lack interpretability, and often lag in performance. In this paper we implement the SV-PF-RNN: a hybrid neural network and particle filter architecture. Our SV-PF-RNN is designed specifically with stochastic volatility estimation in mind. We then show that it can improve on the performance of a basic particle filter.
AI for Investment: A Platform Disruption
Authors: Mohammad Rasouli, Ravi Chiruvolu, Ali Risheh
Date: 2023-09-06 00:00:00

With the investment landscape becoming more competitive, efficiently scaling deal sourcing and improving deal insights have become a dominant strategy for funds. While funds are already spending significant efforts on these two tasks, they cannot be scaled with traditional approaches; hence, there is a surge in automating them. Many third party software providers have emerged recently to address this need with productivity solutions, but they fail due to a lack of personalization for the fund, privacy constraints, and natural limits of software use cases. Therefore, most major funds and many smaller funds have started developing their in-house AI platforms: a game changer for the industry. These platforms grow smarter by direct interactions with the fund and can be used to provide personalized use cases. Recent developments in large language models, e.g. ChatGPT, have provided an opportunity for other funds to also develop their own AI platforms. While not having an AI platform now is not a competitive disadvantage, it will be in two years. Funds require a practical plan and corresponding risk assessments for such AI platforms.
The QLBS Model within the presence of feedback loops through the impacts of a large trader
Authors: Ahmet Umur Özsoy, Ömür Uğur
Date: 2023-11-12 00:00:00

We extend the QLBS model by reformulating via considering a large trader whose transactions leave a permanent impact on the evolution of the exchange rate process and therefore affect the price of contingent claims on such processes. Through a hypothetical limit order book we quantify the exchange rate altered by such transactions. We therefore define the quoted exchange rate process, for which we assume the existence of a postulated hedging strategy. Given the quoted exchange rate and postulated hedging strategy, we find an optimal hedging strategy through batch-mode reinforcement learning given the trader alters the course of the exchange rate process. We assume that the trader has its own concept of fair price and we define our problem as finding the hedging strategy with much lower transaction costs yet delivering a price that well converges to the fair price of the trader. We show our contribution results in an optimal hedging strategy with much lower transaction costs and convergence to the fair price is obtained assuming sensible parameters.
Dynamic portfolio selection for nonlinear law-dependent preferences
Authors: Zongxia Liang, Jianming Xia, Fengyi Yuan
Date: 2023-11-12 00:00:00

This paper addresses the portfolio selection problem for nonlinear law-dependent preferences in continuous time, which inherently exhibit time inconsistency. Employing the method of stochastic maximum principle, we establish verification theorems for equilibrium strategies, accommodating both random market coefficients and incomplete markets. We derive the first-order condition (FOC) for the equilibrium strategies, using a notion of functional derivatives with respect to probability distributions. Then, with the help of the FOC we obtain the equilibrium strategies in closed form for two classes of implicitly defined preferences: CRRA and CARA betweenness preferences, with deterministic market coefficients. Finally, to show applications of our theoretical results to problems with random market coefficients, we examine the weighted utility. We reveal that the equilibrium strategy can be described by a coupled system of Quadratic Backward Stochastic Differential Equations (QBSDEs). The well-posedness of this system is generally open but is established under the special structures of our problem.
Quantum Computing for Financial Mathematics
Authors: Antoine Jacquier, Oleksiy Kondratyev, Gordon Lee, Mugad Oumgari
Date: 2023-11-11 00:00:00

Quantum computing has recently appeared in the headlines of many scientific and popular publications. In the context of quantitative finance, we provide here an overview of its potential.
Optimal portfolio allocation with uncertain covariance matrix
Authors: Maxime Markov, Vladimir Markov
Date: 2023-11-13 00:00:00

In this paper, we explore the portfolio allocation problem involving an uncertain covariance matrix. We calculate the expected value of the Constant Absolute Risk Aversion (CARA) utility function, marginalized over a distribution of covariance matrices. We show that marginalization introduces a logarithmic dependence on risk, as opposed to the linear dependence assumed in the mean-variance approach. Additionally, it leads to a decrease in the allocation level for higher uncertainties. Our proposed method extends the mean-variance approach by considering the uncertainty associated with future covariance matrices and expected returns, which is important for practical applications.
Withdrawal Success Optimization
Authors: Hayden Brown
Date: 2023-11-11 00:00:00

For $n$ assets and discrete-time rebalancing, the probability to complete a given schedule of investments and withdrawals is maximized over progressively measurable portfolio weight functions. Applications consider two assets, namely the S&P Composite Index and an inflation-protected bond. The maximum probability and optimal portfolio weight functions are computed for annually rebalanced schedules involving an arbitrary initial investment and then equal annual withdrawals over the remainder of the time period. Applications also consider annually rebalanced schedules that start with dollar cost averaging (equal annual investments) and then shift to equal annual withdrawals. Results indicate noticeable improvements in the probability to complete a given schedule when optimal portfolio weights are used instead of constant portfolio weights like the standard of keeping 90% in the S&P Composite Index and 10% in inflation-protected bonds.
Portfolio diversification with varying investor abilities
Authors: Nick James, Max Menzies
Date: 2023-11-11 00:00:00

We introduce new mathematical methods to study the optimal portfolio size of investment portfolios over time, considering investors with varying skill levels. First, we explore the benefit of portfolio diversification on an annual basis for poor, average and strong investors defined by the 10th, 50th and 90th percentiles of risk-adjusted returns, respectively. Second, we conduct a thorough regression experiment examining quantiles of risk-adjusted returns as a function of portfolio size across investor ability, testing for trends and curvature within these functions. Finally, we study the optimal portfolio size for poor, average and strong investors in a continuously temporal manner using more than 20 years of data. We show that strong investors should hold concentrated portfolios, poor investors should hold diversified portfolios; average investors have a less obvious distribution with the optimal number varying materially over time.
Markov Decision Processes with Risk-Sensitive Criteria: An Overview
Authors: Nicole Bäuerle, Anna Jaśkiewicz
Date: 2023-11-12 00:00:00

The paper provides an overview of the theory and applications of risk-sensitive Markov decision processes. The term 'risk-sensitive' refers here to the use of the Optimized Certainty Equivalent as a means to measure expectation and risk. This comprises the well-known entropic risk measure and Conditional Value-at-Risk. We restrict our considerations to stationary problems with an infinite time horizon. Conditions are given under which optimal policies exist and solution procedures are explained. We present both the theory when the Optimized Certainty Equivalent is applied recursively as well as the case where it is applied to the cumulated reward. Discounted as well as non-discounted models are reviewed
Towards a data-driven debt collection strategy based on an advanced machine learning framework
Authors: Abel Sancarlos, Edgar Bahilo, Pablo Mozo, Lukas Norman, Obaid Ur Rehma, Mihails Anufrijevs
Date: 2023-11-03 00:00:00

The European debt purchase market as measured by the total book value of purchased debt approached 25bn euros in 2020 and it was growing at double-digit rates. This is an example of how big the debt collection and debt purchase industry has grown and the important impact it has in the financial sector. However, in order to ensure an adequate return during the debt collection process, a good estimation of the propensity to pay and/or the expected cashflow is crucial. These estimations can be employed, for instance, to create different strategies during the amicable collection to maximize quality standards and revenues. And not only that, but also to prioritize the cases in which a legal process is necessary when debtors are unreachable for an amicable negotiation. This work offers a solution for these estimations. Specifically, a new machine learning modelling pipeline is presented showing how outperforms current strategies employed in the sector. The solution contains a pre-processing pipeline and a model selector based on the best model calibration. Performance is validated with real historical data of the debt industry.
A Modeling Approach of Return and Volatility of Structured Investment Products with Caps and Floors
Authors: Jiaer He, Roberto Rivera
Date: 2023-10-28 00:00:00

Popular investment structured products in Puerto Rico are stock market tied Individual Retirement Accounts (IRA), which offer some stock market growth while protecting the principal. The performance of these retirement strategies has not been studied. This work examines the expected return and risk of Puerto Rico stock market IRA (PRIRAs) and compares their statistical properties with other investment instruments before and after tax. We propose a parametric modeling approach for structured products and apply it to PRIRAs. Our method first estimates the conditional expected return (and variance) of PRIRA assets from which we extract marginal moments through the Law of Iterated Expectation. Our results indicate that PRIRAs underperform against investing directly in the stock market while still carrying substantial risk. The expected return of the stock market IRA from Popular Bank (PRIRA1) after tax is slightly greater than that of investing in U.S. bonds, while PRIRA1 has almost two times the risk. The stock market IRA from Universal (PRIRA2) performs similarly to PRIRA1, while PRIRA2 has a lower risk than PRIRA1. PRIRAs may be reasonable for some risk-averse investors due to their principal protection and tax deferral.
A Data-driven Deep Learning Approach for Bitcoin Price Forecasting
Authors: Parth Daxesh Modi, Kamyar Arshi, Pertami J. Kunz, Abdelhak M. Zoubir
Date: 2023-10-27 00:00:00
Tasks: Deep Learning, Feature Engineering

Bitcoin as a cryptocurrency has been one of the most important digital coins and the first decentralized digital currency. Deep neural networks, on the other hand, has shown promising results recently; however, we require huge amount of high-quality data to leverage their power. There are some techniques such as augmentation that can help us with increasing the dataset size, but we cannot exploit them on historical bitcoin data. As a result, we propose a shallow Bidirectional-LSTM (Bi-LSTM) model, fed with feature engineered data using our proposed method to forecast bitcoin closing prices in a daily time frame. We compare the performance with that of other forecasting methods, and show that with the help of the proposed feature engineering method, a shallow deep neural network outperforms other popular price forecasting models.
Boosting Stock Price Prediction with Anticipated Macro Policy Changes
Authors: Md Sabbirul Haque, Md Shahedul Amin, Jonayet Miah, Duc Minh Cao, Ashiqul Haque Ahmed
Date: 2023-10-27 00:00:00
Tasks: Decision Making, Prediction, Stock Price Prediction

Prediction of stock prices plays a significant role in aiding the decision-making of investors. Considering its importance, a growing literature has emerged trying to forecast stock prices with improved accuracy. In this study, we introduce an innovative approach for forecasting stock prices with greater accuracy. We incorporate external economic environment-related information along with stock prices. In our novel approach, we improve the performance of stock price prediction by taking into account variations due to future expected macroeconomic policy changes as investors adjust their current behavior ahead of time based on expected future macroeconomic policy changes. Furthermore, we incorporate macroeconomic variables along with historical stock prices to make predictions. Results from this strongly support the inclusion of future economic policy changes along with current macroeconomic information. We confirm the supremacy of our method over the conventional approach using several tree-based machine-learning algorithms. Results are strongly conclusive across various machine learning models. Our preferred model outperforms the conventional approach with an RMSE value of 1.61 compared to an RMSE value of 1.75 from the conventional approach.
Potential of ChatGPT in predicting stock market trends based on Twitter Sentiment Analysis
Authors: Ummara Mumtaz, Summaya Mumtaz
Date: 2023-10-13 00:00:00
Tasks: Sentiment Analysis, Twitter Sentiment Analysis

The rise of ChatGPT has brought a notable shift to the AI sector, with its exceptional conversational skills and deep grasp of language. Recognizing its value across different areas, our study investigates ChatGPT's capacity to predict stock market movements using only social media tweets and sentiment analysis. We aim to see if ChatGPT can tap into the vast sentiment data on platforms like Twitter to offer insightful predictions about stock trends. We focus on determining if a tweet has a positive, negative, or neutral effect on two big tech giants Microsoft and Google's stock value. Our findings highlight a positive link between ChatGPT's evaluations and the following days stock results for both tech companies. This research enriches our view on ChatGPT's adaptability and emphasizes the growing importance of AI in shaping financial market forecasts.
Algorithmic Robustness
Authors: David Jensen, Brian LaMacchia, Ufuk Topcu, Pamela Wisniewski
Date: 2023-10-17 00:00:00
Tasks: Fairness

Algorithmic robustness refers to the sustained performance of a computational system in the face of change in the nature of the environment in which that system operates or in the task that the system is meant to perform. Below, we motivate the importance of algorithmic robustness, present a conceptual framework, and highlight the relevant areas of research for which algorithmic robustness is relevant. Why robustness? Robustness is an important enabler of other goals that are frequently cited in the context of public policy decisions about computational systems, including trustworthiness, accountability, fairness, and safety. Despite this dependence, it tends to be under-recognized compared to these other concepts. This is unfortunate, because robustness is often more immediately achievable than these other ultimate goals, which can be more subjective and exacting. Thus, we highlight robustness as an important goal for researchers, engineers, regulators, and policymakers when considering the design, implementation, and deployment of computational systems. We urge researchers and practitioners to elevate the attention paid to robustness when designing and evaluating computational systems. For many key systems, the immediate question after any demonstration of high performance should be: "How robust is that performance to realistic changes in the task or environment?" Greater robustness will set the stage for systems that are more trustworthy, accountable, fair, and safe. Toward that end, this document provides a brief roadmap to some of the concepts and existing research around the idea of algorithmic robustness.
Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning
Authors: Parham Gohari, Matthew Hale, Ufuk Topcu
Date: 2023-09-13 00:00:00
Tasks: Multi-agent Reinforcement Learning, Privacy Preserving, reinforcement-learning, SMAC, SMAC+, Starcraft

In cooperative multi-agent reinforcement learning (Co-MARL), a team of agents must jointly optimize the team's long-term rewards to learn a designated task. Optimizing rewards as a team often requires inter-agent communication and data sharing, leading to potential privacy implications. We assume privacy considerations prohibit the agents from sharing their environment interaction data. Accordingly, we propose Privacy-Engineered Value Decomposition Networks (PE-VDN), a Co-MARL algorithm that models multi-agent coordination while provably safeguarding the confidentiality of the agents' environment interaction data. We integrate three privacy-engineering techniques to redesign the data flows of the VDN algorithm, an existing Co-MARL algorithm that consolidates the agents' environment interaction data to train a central controller that models multi-agent coordination, and develop PE-VDN. In the first technique, we design a distributed computation scheme that eliminates Vanilla VDN's dependency on sharing environment interaction data. Then, we utilize a privacy-preserving multi-party computation protocol to guarantee that the data flows of the distributed computation scheme do not pose new privacy risks. Finally, we enforce differential privacy to preempt inference threats against the agents' training data, past environment interactions, when they take actions based on their neural network predictions. We implement PE-VDN in StarCraft Multi-Agent Competition (SMAC) and show that it achieves 80% of Vanilla VDN's win rate while maintaining differential privacy levels that provide meaningful privacy guarantees. The results demonstrate that PE-VDN can safeguard the confidentiality of agents' environment interaction data without sacrificing multi-agent coordination.
Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19
Authors: Yingying Fang, Xiaodan Xing, Shiyi Wang, Simon Walsh, Guang Yang
Date: 2023-09-24 00:00:00
Tasks: Diagnostic

Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools. These tools were intended to alleviate the burden on healthcare systems, control the rapid spread of the virus, and enhance intervention outcomes, all in response to this unprecedented global crisis. As we transition into a post-COVID era, we retrospectively evaluate these proposed studies and offer a review of the techniques employed in AI diagnostic models, with a focus on the solutions proposed for different challenges. This review endeavors to provide insights into the diverse solutions designed to address the multifaceted challenges that arose during the pandemic. By doing so, we aim to prepare the AI community for the development of AI tools tailored to address public health emergencies effectively.