pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Asset Pricing in a Unified Bachelier-Black-Scholes-Merton Model
Authors: W. Brent Lindquist, Svetlozar T. Rachev, Jagdish Gnawali, Frank J. Fabozzi
Date: 2024-05-21 00:00:00

We present a unified, market-complete model that integrates both the Bachelier and Black-Scholes-Merton frameworks for asset pricing. The model allows for the study, within a unified framework, of asset pricing in a natural world that experiences the possibility of negative security prices or riskless rates. In contrast to classical Black-Scholes-Merton, we show that option pricing in the unified model displays a difference depending on whether the replicating, self-financing portfolio uses riskless bonds or a single riskless bank account. We derive option price formulas and extend our analysis to the term structure of interest rates by deriving the pricing of zero-coupon bonds, forward contracts, and futures contracts. We identify a necessary condition for the unified model to support a perpetual derivative. Discrete binomial pricing under the unified model is also developed. In every scenario analyzed, we show that the unified model simplifies to the standard Black-Scholes-Merton pricing under specific limits and provides pricing in the Bachelier model limit. We note that the Bachelier limit within the unified model allows for positive riskless rates. The unified model prompts us to speculate on the possibility of a mixed multiplicative and additive deflator model for risk-neutral option pricing.
Risk, utility and sensitivity to large losses
Authors: Martin Herdegen, Nazem Khan, Cosimo Munari
Date: 2024-05-20 00:00:00
Tasks: Sensitivity

Risk and utility functionals are fundamental building blocks in economics and finance. In this paper we investigate under which conditions a risk or utility functional is sensitive to the accumulation of losses in the sense that any sufficiently large multiple of a position that exposes an agent to future losses has positive risk or negative utility. We call this property sensitivity to large losses and provide necessary and sufficient conditions thereof that are easy to check for a very large class of risk and utility functionals. In particular, our results do not rely on convexity and can therefore also be applied to most examples discussed in the recent literature, including (non-convex) star-shaped risk measures or S-shaped utility functions encountered in prospect theory. As expected, Value at Risk generally fails to be sensitive to large losses. More surprisingly, this is also true of Expected Shortfall. By contrast, expected utility functionals as well as (optimized) certainty equivalents are proved to be sensitive to large losses for many standard choices of concave and nonconcave utility functions, including $S$-shaped utility functions. We also show that Value at Risk and Expected Shortfall become sensitive to large losses if they are either properly adjusted or if the property is suitably localized.
Risk-neutral valuation of options under arithmetic Brownian motions
Authors: Qiang Liu, Shuxin Guo
Date: 2024-05-18 00:00:00

On April 22, 2020, the CME Group switched to Bachelier pricing for a group of oil futures options. The Bachelier model, or more generally the arithmetic Brownian motion (ABM), is not so widely used in finance, though. This paper provides the first comprehensive survey of options pricing under ABM. Using the risk-neutral valuation, we derive formulas for European options for three underlying types, namely an underlying that does not pay dividends, an underlying that pays a continuous dividend yield, and futures. Further, we derive Black-Scholes-Merton-like partial differential equations, which can in principle be utilized to price American options numerically via finite difference.
Resilience Analysis of Multi-modal Logistics Service Network Through Robust Optimization with Budget-of-Uncertainty
Authors: Yaxin Pang, Shenle Pan, Eric Ballot
Date: 2024-05-21 00:00:00

Supply chain resilience analysis aims to identify the critical elements in the supply chain, measure its reliability, and analyze solutions for improving vulnerabilities. While extensive methods like stochastic approaches have been dominant, robust optimization-widely applied in robust planning under uncertainties without specific probability distributions-remains relatively underexplored for this research problem. This paper employs robust optimization with budget-of-uncertainty as a tool to analyze the resilience of multi-modal logistics service networks under time uncertainty. We examine the interactive effects of three critical factors: network size, disruption scale, disruption degree. The computational experiments offer valuable managerial insights for practitioners and researchers.
A note on continuity and asymptotic consistency of measures of risk and variability
Authors: Niushan Gao, Foivos Xanthos
Date: 2024-05-16 00:00:00

In this short note, we show that every convex, order bounded above functional on a Frechet lattice is automatically norm continuous. This improves a result in \cite{RS06} and applies to many deviation and variability measures. We also show that an order-continuous, law-invariant functional on an Orlicz space is strongly consistent everywhere, extending a result in \cite{KSZ14}.
"Microstructure Modes" -- Disentangling the Joint Dynamics of Prices & Order Flow
Authors: Salma Elomari-Kessab, Guillaume Maitrier, Julius Bonart, Jean-Philippe Bouchaud
Date: 2024-05-17 00:00:00

Understanding the micro-dynamics of asset prices in modern electronic order books is crucial for investors and regulators. In this paper, we use an order by order Eurostoxx database spanning over 3 years to analyze the joint dynamics of prices and order flow. In order to alleviate various problems caused by high-frequency noise, we propose a double coarse-graining procedure that allows us to extract meaningful information at the minute time scale. We use Principal Component Analysis to construct "microstructure modes" that describe the most common flow/return patterns and allow one to separate them into bid-ask symmetric and bid-ask anti-symmetric. We define and calibrate a Vector Auto-Regressive (VAR) model that encodes the dynamical evolution of these modes. The parameters of the VAR model are found to be extremely stable in time, and lead to relatively high $R^2$ prediction scores, especially for symmetric liquidity modes. The VAR model becomes marginally unstable as more lags are included, reflecting the long-memory nature of flows and giving some further credence to the possibility of "endogenous liquidity crises". Although very satisfactory on several counts, we show that our VAR framework does not account for the well known square-root law of price impact.
The $κ$-generalised Distribution for Stock Returns
Authors: Samuel Forbes
Date: 2024-05-16 00:00:00

Empirical evidence shows stock returns are often heavy-tailed rather than normally distributed. The $\kappa$-generalised distribution, originated in the context of statistical physics by Kaniadakis, is characterised by the $\kappa$-exponential function that is asymptotically exponential for small values and asymptotically power law for large values. This proves to be a useful property and makes it a good candidate distribution for many types of quantities. In this paper we focus on fitting historic daily stock returns for the FTSE 100 and the top 100 Nasdaq stocks. Using a Monte-Carlo goodness of fit test there is evidence that the $\kappa$-generalised distribution is a good fit for a significant proportion of the 200 stock returns analysed.
Adaptive Optimal Market Making Strategies with Inventory Liquidation Cos
Authors: Jonathan Chávez-Casillas, José E. Figueroa-López, Chuyi Yu, Yi Zhang
Date: 2024-05-19 00:00:00

A novel high-frequency market-making approach in discrete time is proposed that admits closed-form solutions. By taking advantage of demand functions that are linear in the quoted bid and ask spreads with random coefficients, we model the variability of the partial filling of limit orders posted in a limit order book (LOB). As a result, we uncover new patterns as to how the demand's randomness affects the optimal placement strategy. We also allow the price process to follow general dynamics without any Brownian or martingale assumption as is commonly adopted in the literature. The most important feature of our optimal placement strategy is that it can react or adapt to the behavior of market orders online. Using LOB data, we train our model and reproduce the anticipated final profit and loss of the optimal strategy on a given testing date using the actual flow of orders in the LOB. Our adaptive optimal strategies outperform the non-adaptive strategy and those that quote limit orders at a fixed distance from the midprice.
Causal inference approach to appraise long-term effects of maintenance policy on functional performance of asphalt pavements
Authors: Lingyun You, Nanning Guo, Zhengwu Long, Fusong Wang, Chundi Si, Aboelkasim Diab
Date: 2024-05-06 00:00:00
Tasks: Causal Inference

Asphalt pavements as the most prevalent transportation infrastructure, are prone to serious traffic safety problems due to functional or structural damage caused by stresses or strains imposed through repeated traffic loads and continuous climatic cycles. The good quality or high serviceability of infrastructure networks is vital to the urbanization and industrial development of nations. In order to maintain good functional pavement performance and extend the service life of asphalt pavements, the long-term performance of pavements under maintenance policies needs to be evaluated and favorable options selected based on the condition of the pavement. A major challenge in evaluating maintenance policies is to produce valid treatments for the outcome assessment under the control of uncertainty of vehicle loads and the disturbance of freeze-thaw cycles in the climatic environment. In this study, a novel causal inference approach combining a classical causal structural model and a potential outcome model framework is proposed to appraise the long-term effects of four preventive maintenance treatments for longitudinal cracking over a 5-year period of upkeep. Three fundamental issues were brought to our attention: 1) detection of causal relationships prior to variables under environmental loading (identification of causal structure); 2) obtaining direct causal effects of treatment on outcomes excluding covariates (identification of causal effects); and 3) sensitivity analysis of causal relationships. The results show that the method can accurately evaluate the effect of preventive maintenance treatments and assess the maintenance time to cater well for the functional performance of different preventive maintenance approaches. This framework could help policymakers to develop appropriate maintenance strategies for pavements.
Fast Stochastic Policy Gradient: Negative Momentum for Reinforcement Learning
Authors: Haobin Zhang, Zhuang Yang
Date: 2024-05-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Stochastic Optimization

Stochastic optimization algorithms, particularly stochastic policy gradient (SPG), report significant success in reinforcement learning (RL). Nevertheless, up to now, that how to speedily acquire an optimal solution for RL is still a challenge. To tackle this issue, this work develops a fast SPG algorithm from the perspective of utilizing a momentum, coined SPG-NM. Specifically, in SPG-NM, a novel type of the negative momentum (NM) technique is applied into the classical SPG algorithm. Different from the existing NM techniques, we have adopted a few hyper-parameters in our SPG-NM algorithm. Moreover, the computational complexity is nearly same as the modern SPG-type algorithms, e.g., accelerated policy gradient (APG), which equips SPG with Nesterov's accelerated gradient (NAG). We evaluate the resulting algorithm on two classical tasks, bandit setting and Markov decision process (MDP). Numerical results in different tasks demonstrate faster convergence rate of the resulting algorithm by comparing state-of-the-art algorithms, which confirm the positive impact of NM in accelerating SPG for RL. Also, numerical experiments under different settings confirm the robustness of our SPG-NM algorithm for some certain crucial hyper-parameters, which ride the user feel free in practice.
Influence based explainability of brain tumors segmentation in multimodal Magnetic Resonance Imaging
Authors: Tommaso Torda, Andrea Ciardiello, Simona Gargiulo, Greta Grillo, Simone Scardapane, Cecilia Voena, Stefano Giagu
Date: 2024-04-05 00:00:00
Tasks: Segmentation, Semantic Segmentation

In recent years Artificial Intelligence has emerged as a fundamental tool in medical applications. Despite this rapid development, deep neural networks remain black boxes that are difficult to explain, and this represents a major limitation for their use in clinical practice. We focus on the segmentation of medical images task, where most explainability methods proposed so far provide a visual explanation in terms of an input saliency map. The aim of this work is to extend, implement and test instead an influence-based explainability algorithm, TracIn, proposed originally for classification tasks, in a challenging clinical problem, i.e., multiclass segmentation of tumor brains in multimodal Magnetic Resonance Imaging. We verify the faithfulness of the proposed algorithm linking the similarities of the latent representation of the network to the TracIn output. We further test the capacity of the algorithm to provide local and global explanations, and we suggest that it can be adopted as a tool to select the most relevant features used in the decision process. The method is generalizable for all semantic segmentation tasks where classes are mutually exclusive, which is the standard framework in these cases.
Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation
Authors: Yinchi Zhou, Tianqi Chen, Jun Hou, Huidong Xie, Nicha C. Dvornek, S. Kevin Zhou, David L. Wilson, James S. Duncan, Chi Liu, Bo Zhou
Date: 2024-04-06 00:00:00
Tasks: Image-to-Image Translation, Translation

Image-to-image translation is a vital component in medical imaging processing, with many uses in a wide range of imaging modalities and clinical scenarios. Previous methods include Generative Adversarial Networks (GANs) and Diffusion Models (DMs), which offer realism but suffer from instability and lack uncertainty estimation. Even though both GAN and DM methods have individually exhibited their capability in medical image translation tasks, the potential of combining a GAN and DM to further improve translation performance and to enable uncertainty estimation remains largely unexplored. In this work, we address these challenges by proposing a Cascade Multi-path Shortcut Diffusion Model (CMDM) for high-quality medical image translation and uncertainty estimation. To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step. Additionally, a multi-path shortcut diffusion strategy is employed to refine translation results and estimate uncertainty. A cascaded pipeline further enhances translation quality, incorporating residual averaging between cascades. We collected three different medical image datasets with two sub-tasks for each dataset to test the generalizability of our approach. Our experimental results found that CMDM can produce high-quality translations comparable to state-of-the-art methods while providing reasonable uncertainty estimations that correlate well with the translation error.
Estimation of FFR in coronary arteries with deep learning
Authors: Patryk Rygiel
Date: 2024-04-06 00:00:00
Tasks: Deep Learning, Diagnostic

Coronary artery disease (CAD) is one of the most common causes of death in the European Union and the USA. The crucial biomarker in its diagnosis is called Fractional Flow Reserve (FFR) and its in-vivo measurement is obtained via an invasive diagnostic technique in the form of coronagraphy. In order to address the invasive drawbacks associated with a procedure, a new approach virtual FFR (vFFR) measurement has emerged in recent years. This technique involves using computed tomography angiography (CTA) to obtain virtual measurements of FFR. By utilizing Computational Fluid Dynamics (CFD), vFFR estimates can be derived from CTA data, providing a promising in-silico alternative to traditional methods. However, the widespread adoption of vFFR from CTA as a diagnostic technique is hindered by two main challenges: time and computational requirements. In this work, we explore the usage of deep learning techniques as surrogate CFD engine models in the task of vFFR estimation in coronary arteries to drastically limit the required time and computational costs without a major drop in quality. We propose a novel approach to vFFR estimation by representing the input vessel geometry as a point cloud and utilizing the hybrid neural network that learns geometry representation based on both explicitly and implicitly given features. We evaluate the method from the clinical point of view and showcase that it can serve as a compelling replacement for commonly utilized CFD-based approaches.
Stable Diffusion is Unstable
Authors: Chengbin Du, Yanxi Li, Zhongwei Qiu, Chang Xu
Date: 2023-06-05 00:00:00
Proceeding: NeurIPS 2023 11

Recently, text-to-image models have been thriving. Despite their powerful generative capacity, our research has uncovered a lack of robustness in this generation process. Specifically, the introduction of small perturbations to the text prompts can result in the blending of primary subjects with other categories or their complete disappearance in the generated images. In this paper, we propose Auto-attack on Text-to-image Models (ATM), a gradient-based approach, to effectively and efficiently generate such perturbations. By learning a Gumbel Softmax distribution, we can make the discrete process of word replacement or extension continuous, thus ensuring the differentiability of the perturbation generation. Once the distribution is learned, ATM can sample multiple attack samples simultaneously. These attack samples can prevent the generative model from generating the desired subjects without compromising image quality. ATM has achieved a 91.1% success rate in short-text attacks and an 81.2% success rate in long-text attacks. Further empirical analysis revealed four attack patterns based on: 1) the variability in generation speed, 2) the similarity of coarse-grained characteristics, 3) the polysemy of words, and 4) the positioning of words.
Learning Transformer Programs
Authors: Dan Friedman, Alexander Wettig, Danqi Chen
Date: 2023-06-01 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: In-Context Learning, Interpretable Machine Learning, named-entity-recognition, Named Entity Recognition, text-classification, Text Classification

Recent research in mechanistic interpretability has attempted to reverse-engineer Transformer models by carefully inspecting network weights and activations. However, these approaches require considerable manual effort and still fall short of providing complete, faithful descriptions of the underlying algorithms. In this work, we introduce a procedure for training Transformers that are mechanistically interpretable by design. We build on RASP [Weiss et al., 2021], a programming language that can be compiled into Transformer weights. Instead of compiling human-written programs into Transformers, we design a modified Transformer that can be trained using gradient-based optimization and then automatically converted into a discrete, human-readable program. We refer to these models as Transformer Programs. To validate our approach, we learn Transformer Programs for a variety of problems, including an in-context learning task, a suite of algorithmic problems (e.g. sorting, recognizing Dyck languages), and NLP tasks including named entity recognition and text classification. The Transformer Programs can automatically find reasonable solutions, performing on par with standard Transformers of comparable size; and, more importantly, they are easy to interpret. To demonstrate these advantages, we convert Transformers into Python programs and use off-the-shelf code analysis tools to debug model errors and identify the "circuits" used to solve different sub-problems. We hope that Transformer Programs open a new path toward the goal of intrinsically interpretable machine learning.
Exploring Naive Approaches to Tell Apart LLMs Productions from Human-written Text
Authors: Oliver Giudice, Alessandro Maggi, Matteo Nardelli
Date: 2024-03-05 00:00:00
Proceeding: NLPIR '23: Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval 2024 3
Tasks: LLM-generated Text Detection, Sentence, Text Generation

Powerful Large Language Models (large LMs or LLMs) such as BERT and GPT are making the task of detecting machine-generated text more and more prominent and crucial to minimize threats posed by text generation models misuse. Nonetheless, only a limited number of efforts exist so far, which can be classified into simple classifiers, zero-shot approaches, and fine-tuned LMs. These approaches usually rely on LMs whose discrimination accuracy decreases as the size difference in favor of the generator model increases (hence, a detector should always employ a LM with at least the same number of parameters of the source LM). Also, most of these approaches do not explicitly investigate whether the sentence syntactic structure can provide additional information that helps to build better detectors. All these considerations make the generalizing ability of detection methods into question. While generation techniques become more and more capable of producing human-like text, are the detection techniques capable of keeping up if not properly trained?

In this paper, we evaluate the most effective (and reproducible) detection method available in the state of the art in order to figure out the limits in its robustness. We complement this analysis by discussing results obtained using a novel naive approach that demonstrably achieves comparable results in terms of robustness with respect to much more advanced and sophisticated state-of-the-art methods. Code with details on experiments are available at: https://github.com/bancaditalia/gen-text-detect.
Joining up the scattered anticancer knowledge on auraptene and umbelliprenin: a meta-analysis
Authors: Shakiba, M. and Rassouli, F.B.
Date: 2024-05-23 00:00:00
Proceeding: Scientific Reports 2024 5
Tasks: Classification, regression

Auraptene (AUR) and umbelliprenin (UMB) are naturally occurring prenylated coumarins that have demonstrated promising anticancer effects across various human cancer cell lines. This meta-analysis aimed to systematically assess, compare, and quantify the anticancer efficacy of AUR and UMB by synthesizing evidence from in vitro studies. A comprehensive literature search identified 27 eligible studies investigating AUR or UMB against cancer cells. Mixed-effects models revealed significant negative associations between coumarin dose and viability for AUR (est. = − 2.27) and UMB (est. = − 3.990), underscoring their dose-dependent cytotoxicity. Meta-regression indicated slightly higher potency for UMB over AUR, potentially due to increased lipophilicity imparted by additional isoprenyl units. Machine learning approaches identified coumarin dose and cancer type as the most influential determinants of toxicity, while treatment duration and the specific coumarin displayed weaker effects. Moderate (AUR) to substantial (UMB) between-study heterogeneity was detected, although the findings proved robust. In summary, this meta-analysis establishes AUR and UMB as promising natural anticancer candidates with clear dose-toxicity relationships across diverse malignancies. The structural insights and quantifications of anticancer efficacy can inform forthcoming efforts assessing therapeutic potential in pre-clinical models and human trials.