pwc-archive/papers-with-abstracts huggingface dataset:
A Unifying Theory of Aging and Mortality
Authors: Valentin Flietner, Bernd Heidergott, Frank den Hollander, Ines Lindner, Azadeh Parvaneh, Holger Strulik
Date: 2024-12-17 00:00:00

In this paper, we advance the network theory of aging and mortality by developing a causal mathematical model for the mortality rate. First, we show that in large networks, where health deficits accumulate at nodes representing health indicators, the modeling of network evolution with Poisson processes is universal and can be derived from fundamental principles. Second, with the help of two simplifying approximations, which we refer to as mean-field assumption and homogeneity assumption, we provide an analytical derivation of Gompertz law under generic and biologically relevant conditions. We identify the parameters in Gompertz law as a function of the parameters driving the evolution of the network, and illustrate our computations with simulations and analytic approximations.
Solving the Inverse Problem of Magnetic Induction Tomography Using Gauss-Newton Iterative Method and Zoning Technique to Reduce Unknown Coefficients
Authors: Mohammad Reza Yousefi, Amin Dehghani, Ali Asghar Amini, S. M. Mehdi Mirtalaei
Date: 2024-12-01 00:00:00

Magnetic Induction Tomography (MIT) is a promising modality for noninvasive imaging due to its contactless and nonionizing technology. In this imaging method, a primary magnetic field is applied by excitation coils to induce eddy currents in the material to be studied, and a secondary magnetic field is detected from these eddy currents using sensing coils. The image (spatial distribution of electrical conductivity) is then reconstructed using measurement data, the initial estimation of electrical conductivity, and the iterative solution of forward and inverse problems. The inverse problem can be solved using one-step linear, iterative nonlinear, and special methods. In general, the MIT inverse problem can be solved by Gauss- Newton iterative method with acceptable accuracy. In this paper, this algorithm is extended and the zoning technique is employed for the reduction of unknown coefficients. The simulation results obtained by the proposed method are compared with the real conductivity coefficients and the mean relative error rate is reduced to 24.22%. On the other hand, Gauss-Newton iterative method is extended for solving the inverse problem of the MIT, and sensitivity measurement matrices are extracted in different experimental and normalization conditions.
Re-visiting the intracellular pathway of transferrin on board of a mathematical simulation
Authors: Franco Nieto, Luis S. Mayorga
Date: 2024-12-18 00:00:00

Modeling and simulation are transforming all fields of biology. Tools like AlphaFold have revolutionized structural biology, while molecular dynamics simulations provide invaluable insights into the behavior of macromolecules in solution or on membranes. In contrast, we lack effective tools to represent the dynamic behavior of the endomembrane system. Static diagrams that connect organelles with arrows are used to depict transport across space and time but fail to specify the underlying mechanisms. This static representation obscures the dynamism of intracellular traffic, freezing it in an immobilized framework. The intracellular transport of transferrin, a key process for cellular iron delivery, is among the best-characterized trafficking pathways. In this commentary, we revisit this process using a mathematical simulation of the endomembrane system. Our model reproduces many experimental observations and highlights the strong contrast between dynamic simulations and static illustrations. This work underscores the urgent need for a consensus-based minimal functional working model for the endomembrane system and emphasizes the importance of generating more quantitative experimental data -- including precise measurements of organelle size, volume, and transport kinetics -- practices that were more common among cell biologists in past decades.
Stochastic modeling of cyclic cancer treatments under common noise
Authors: Jason Sonith
Date: 2024-12-02 00:00:00
Tasks: Diversity

Path integral control is an effective method in cancer drug treatment, providing a structured approach to handle the complexities and unpredictability of tumor behavior. Utilizing mathematical principles from physics, this technique optimizes drug delivery in environments influenced by randomness. It takes into account the intricate interactions between cancer cells, healthy tissues, and the immune system, as well as factors such as patient-specific characteristics and tumor diversity. Path integral control offers tailored solutions to these issues, enabling the design of drug dosing regimens that enhance therapeutic effectiveness while minimizing side effects. Its flexibility makes it a valuable tool in creating personalized, precision-driven therapies, ultimately improving patient outcomes in cancer treatment. In this paper we give a review about the current status of path integral control in cancer research.
Volatility-Volume Order Slicing via Statistical Analysis
Authors: Ritwika Chattopadhyay, Abhishek Malichkar, Zhixuan Ren, Xinyue Zhang
Date: 2024-12-17 00:00:00

This paper addresses the challenges faced in large-volume trading, where executing substantial orders can result in significant market impact and slippage. To mitigate these effects, this study proposes a volatility-volume-based order slicing strategy that leverages Exponential Weighted Moving Average and Markov Chain Monte Carlo simulations. These methods are used to dynamically estimate future trading volumes and price ranges, enabling traders to adapt their strategies by segmenting order execution sizes based on these predictions. Results show that the proposed approach improves trade execution efficiency, reduces market impact, and offers a more adaptive solution for volatile market conditions. The findings have practical implications for large-volume trading, providing a foundation for further research into adaptive execution strategies.
Option Pricing with a Compound CARMA(p,q)-Hawkes
Authors: Lorenzo Mercuri, Andrea Perchiazzo, Edit Rroji
Date: 2024-12-19 00:00:00

A self-exciting point process with a continuous-time autoregressive moving average intensity process, named CARMA(p,q)-Hawkes model, has recently been introduced. The model generalizes the Hawkes process by substituting the Ornstein-Uhlenbeck intensity with a CARMA(p,q) model where the associated state process is driven by the counting process itself. The proposed model preserves the same degree of tractability as the Hawkes process, but it can reproduce more complex time-dependent structures observed in several market data. The paper presents a new model of asset price dynamics based on the CARMA(p,q) Hawkes model. It is constructed using a compound version of it with a random jump size that is independent of both the counting and the intensity processes and can be employed as the main block for pure jump and (stochastic volatility) jump-diffusion processes. The numerical results for pricing European options illustrate that the new model can replicate the volatility smile observed in financial markets. Through an empirical analysis, which is presented as a calibration exercise, we highlight the role of higher order autoregressive and moving average parameters in pricing options.
Comparative Statics of Trading Boundary in Finite Horizon Portfolio Selection with Proportional Transaction Costs
Authors: Jintao Li, Shuaijie Qian
Date: 2024-12-18 00:00:00

We consider the Merton's problem with proportional transaction costs. It is well-known that the optimal investment strategy is characterized by two trading boundaries, i.e., the buy boundary and the sell boundary, between which is the no-trading region. We study how the two trading boundaries vary with transaction costs. We reveal that the cost-adjusted trading boundaries are monotone in transaction costs. Our result indicates that (i) the Merton line must lie between two cost-adjusted trading boundaries; (ii) when the Merton line is positive, the buy boundary and the sell boundary are monotone in transaction costs and the Merton line lies in the no-trading region as a result.
Strictly monotone mean-variance preferences with applications to portfolio selection
Authors: Yike Wang, Yusha Chen
Date: 2024-12-18 00:00:00
Tasks: Management, Math

This paper extends the monotone mean-variance (MMV) preference to a broader class of strictly monotone mean-variance (SMMV) preferences, and demonstrates its applications to portfolio selection problems. For the single-period portfolio problem under the SMMV preference, we derive the gradient condition for the optimal strategy, and investigate its association with the optimal mean-variance (MV) static strategy. A novel contribution of this work is the reduction of the problem to solving a set of linear equations by analyzing the saddle point of some minimax problem. Building on this advancement, we conduct numerical experiments and compare our results with those of Maccheroni, et al. (Math. Finance 19(3): 487-521, 2009). The findings indicate that our SMMV preferences provide a more rational basis for assessing given prospects. For the continuous-time portfolio problem with the SMMV preference, we consider continuous price processes with random coefficients. We establish the condition under which the optimal dynamic strategies for SMMV and MV preferences coincide, and characterize the optimal solution using the dynamic programming principle and the martingale convex duality method, respectively. Consequently, the problem is reduced to solving a stochastic Hamilton-Jacobi-Bellman-Isaacs equation, or a multi-stage linear-quadratic optimization problem with the embedding technique.
Refining and Robust Backtesting of A Century of Profitable Industry Trends
Authors: Alessandro Massaad, Rene Moawad, Oumaima Nijad Fares, Sahaphon Vairungroj
Date: 2024-12-18 00:00:00

We revisit the long-only trend-following strategy presented in A Century of Profitable Industry Trends by Zarattini and Antonacci, which achieved exceptional historical performance with an 18.2% annualized return and a Sharpe Ratio of 1.39. While the results outperformed benchmarks, practical implementation raises concerns about robustness and evolving market conditions. This study explores modifications addressing reliance on T-bills, alternative fallback allocations, and industry exclusions. Despite attempts to enhance adaptability through momentum signals, parameter optimization, and Walk-Forward Analysis, results reveal persistent challenges. The results highlight challenges in adapting historical strategies to modern markets and offer insights for future trend-following frameworks.
Application of the Kelly Criterion to Prediction Markets
Authors: Bernhard K Meister
Date: 2024-12-18 00:00:00
Tasks: Prediction

Betting markets are gaining in popularity. Mean beliefs generally differ from prices in prediction markets. Logarithmic utility is employed to study the risk and return adjustments to prices. Some consequences are described. A modified payout structure is proposed. A simple asset price model based on flipping biased coins is investigated. It is shown using the Kullback-Leibler divergence how the misjudgment of the bias and the miscalculation of the investment fraction influence the portfolio growth rate.
Market-Neutral Strategies in Mid-Cap Portfolio Management: A Data-Driven Approach to Long-Short Equity
Authors: Saumya Kothari, Harsh Shah, Utkarsh Prajapati, Shrinjay Kaushik
Date: 2024-12-17 00:00:00
Tasks: Management, Portfolio Optimization

Mid-cap companies, generally valued between \$2 billion and \$10 billion, provide investors with a well-rounded opportunity between the fluctuation of small-cap stocks and the stability of large-cap stocks. This research builds upon the long-short equity approach (e.g., Michaud, 2018; Dimitriu, Alexander, 2002) customized for mid-cap equities, providing steady risk-adjusted returns yielding a significant Sharpe ratio of 2.132 in test data. Using data from 2013 to 2023, obtained from WRDS and following point-in-time (PIT) compliance, the approach guarantees clarity and reproducibility. Elements of essential financial indicators, such as profitability, valuation, and liquidity, were designed to improve portfolio optimization. Testing historical data across various markets conditions illustrates the stability and resilience of the tactic. This study highlights mid-cap stocks as an attractive investment route, overlooked by most analysts, which combine transparency with superior performance in managing portfolios.
Hunting Tomorrow's Leaders: Using Machine Learning to Forecast S&P 500 Additions & Removal
Authors: Vidhi Agrawal, Eesha Khalid, Tianyu Tan, Doris Xu
Date: 2024-12-17 00:00:00
Tasks: Decision Making, Feature Engineering

This study applies machine learning to predict S&P 500 membership changes: key events that profoundly impact investor behavior and market dynamics. Quarterly data from WRDS datasets (2013 onwards) was used, incorporating features such as industry classification, financial data, market data, and corporate governance indicators. Using a Random Forest model, we achieved a test F1 score of 0.85, outperforming logistic regression and SVC models. This research not only showcases the power of machine learning for financial forecasting but also emphasizes model transparency through SHAP analysis and feature engineering. The model's real world applicability is demonstrated with predicted changes for Q3 2023, such as the addition of Uber (UBER) and the removal of SolarEdge Technologies (SEDG). By incorporating these predictions into a trading strategy i.e. buying stocks announced for addition and shorting those marked for removal, we anticipate capturing alpha and enhancing investment decision making, offering valuable insights into index dynamics
Multivariate Rough Volatility
Authors: Ranieri Dugo, Giacomo Giorgio, Paolo Pigato
Date: 2024-12-18 00:00:00
Tasks: Time Series

Motivated by empirical evidence from the joint behavior of realized volatility time series, we propose to model the joint dynamics of log-volatilities using a multivariate fractional Ornstein-Uhlenbeck process. This model is a multivariate version of the Rough Fractional Stochastic Volatility model proposed in Gatheral, Jaisson, and Rosenbaum, Quant. Finance, 2018. It allows for different Hurst exponents in the different marginal components and non trivial interdependencies. We discuss the main features of the model and propose an estimator that jointly identifies its parameters. We derive the asymptotic theory of the estimator and perform a simulation study that confirms the asymptotic theory in finite sample. We carry out an extensive empirical investigation on all realized volatility time series covering the entire span of about two decades in the Oxford-Man realized library. Our analysis shows that these time series are strongly correlated and can exhibit asymmetries in their cross-covariance structure, accurately captured by our model. These asymmetries lead to spillover effects that we analyse theoretically within the model and then using our empirical estimates. Moreover, in accordance with the existing literature, we observe behaviors close to non-stationarity and rough trajectories.
AI-Enhanced Factor Analysis for Predicting S&P 500 Stock Dynamics
Authors: Jiajun Gu, Zichen Yang, Xintong Lin, Sixun Chen, Yuting Lu
Date: 2024-12-17 00:00:00

This project investigates the interplay of technical, market, and statistical factors in predicting stock market performance, with a primary focus on S&P 500 companies. Utilizing a comprehensive dataset spanning multiple years, the analysis constructs advanced financial metrics, such as momentum indicators, volatility measures, and liquidity adjustments. The machine learning framework is employed to identify patterns, relationships, and predictive capabilities of these factors. The integration of traditional financial analytics with machine learning enables enhanced predictive accuracy, offering valuable insights into market behavior and guiding investment strategies. This research highlights the potential of combining domain-specific financial expertise with modern computational tools to address complex market dynamics.
Productivity of Short Term Assets as a Signal of Future Stock Performance
Authors: Veer Vohra, Devyani Vij, Jehil Mehta, Arman Ozcan
Date: 2024-12-17 00:00:00

This paper investigates cash productivity as a signal for future stock performance, building on the cash-return framework of Faulkender and Wang (2006). Using financial and market data from WRDS, we calculate cash returns as a proxy for operational efficiency and evaluate a long-only strategy applied to Nasdaq-listed non-financial firms. Results show limited predictive power across the broader Nasdaq universe but strong performance in a handpicked portfolio, which achieves significant positive alpha after controlling for the Fama-French three factors. These findings underscore the importance of refined universe selection. While promising, the strategy requires further validation, including the incorporation of transaction costs and performance testing across economic cycles. Our results suggest that cash productivity, when combined with other complementary signals and careful universe selection, can be a valuable tool for generating excess returns.
An Application of the Ornstein-Uhlenbeck Process to Pairs Trading
Authors: Jirat Suchato, Sean Wiryadi, Danran Chen, Ava Zhao, Michael Yue
Date: 2024-12-17 00:00:00

We conduct a preliminary analysis of a pairs trading strategy using the Ornstein-Uhlenbeck (OU) process to model stock price spreads. We compare this approach to a naive pairs trading strategy that uses a rolling window to calculate mean and standard deviation parameters. Our findings suggest that the OU model captures signals and trends effectively but underperforms the naive model on a risk-return basis, likely due to non-stationary pairs and parameter tuning limitations.
A multi-factor market-neutral investment strategy for New York Stock Exchange equities
Authors: Georgios M. Gkolemis, Adwin Richie Lee, Amine Roudani
Date: 2024-12-16 00:00:00
Tasks: feature selection

This report presents a systematic market-neutral, multi-factor investment strategy for New York Stock Exchange equities with the objective of delivering steady returns while minimizing correlation with the market. A robust feature set is integrated combining momentum-based indicators, fundamental factors, and analyst recommendations. Using various statistical tests for feature selection, the strategy identifies key drivers of equity performance and ranks stocks to build a balanced portfolio of long and short positions. Portfolio construction methods, including equally weighted, risk parity, and minimum variance beta-neutral approaches, were evaluated through rigorous backtesting. Risk parity demonstrated superior performance with a higher Sharpe ratio, lower beta, and smaller maximum drawdown compared to the Standard and Poor's 500 index. Risk parity's market neutrality, combined with its ability to maintain steady returns and mitigate large drawdowns, makes it a suitable approach for managing significant capital in equity markets.
Stochastic Gradient Descent in the Optimal Control of Execution Costs
Authors: Simeon Kolev
Date: 2024-12-14 00:00:00

Bertsimas and Lo's seminal work laid the groundwork for addressing the implementation shortfall dilemma in institutional investing, emphasizing the significance of market microstructure and price dynamics in minimizing execution costs. However, the ability to derive a theoretical Optimum market order policy is an unrealistic assumption for many investors. This study aims to bridge this gap by proposing an approach that leverages stochastic gradient descent (SGD) to derive alternative solutions for optimizing execution cost policies in dynamic markets where explicit mathematical solutions may not yet exist. The proposed methodology assumes the existence of a mathematically derived optimal solution that is a function of the underlying market dynamics. By iteratively refining strategies using SGD, economists can adapt their approaches over time based on evolving execution strategies. While these SGD-based solutions may not achieve optimality, they offer valuable insights into optimizing policies under complex market frameworks. These results serve as a bridge for economists and mathematicians, facilitating the study of the Optimum policy volatile markets while offering SGD driven implementable policies that closely approximate optimal outcomes within shorter time frames.
RoadObstacle21
Authors: Robin Chan, Krzysztof Lis, Svenja Uhlemeyer, Hermann Blum, Sina Honari, Roland Siegwart, Pascal Fua, Mathieu Salzmann, Matthias Rottmann
Date: 2021-10-11 00:00:00
Proceeding: NeurIPS 2021 Track 2021 10
Tasks: Instance Segmentation, Object, Segmentation, Semantic Segmentation

State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes. As such, they are ill-equipped to handle previously-unseen objects. However, detecting and localizing such objects is crucial for safety-critical applications such as perception for automated driving, especially if they appear on the road ahead. While some methods have tackled the tasks of anomalous or out-of-distribution object segmentation, progress remains slow, in large part due to the lack of solid benchmarks; existing datasets either consist of synthetic data, or suffer from label inconsistencies. In this paper, we bridge this gap by introducing the "SegmentMeIfYouCan" benchmark. Our benchmark addresses two tasks: Anomalous object segmentation, which considers any previously-unseen object category; and road obstacle segmentation, which focuses on any object on the road, may it be known or unknown.We provide two corresponding datasets together with a test suite performing an in-depth method analysis, considering both established pixel-wise performance metrics and recent component-wise ones, which are insensitive to object sizes. We empirically evaluate multiple state-of-the-art baseline methods, including several models specifically designed for anomaly / obstacle segmentation, on our datasets and on public ones, using our test suite.The anomaly and obstacle segmentation results show that our datasets contribute to the diversity and difficulty of both data landscapes.
Spoofing-Robust Speaker Verification Based on Time-Domain Embedding
Authors: Avishai Weizman, Yehuda Ben-Shimol, Itshak Lapidot
Date: 2024-12-12 00:00:00
Proceeding: Cyber Security, Cryptology, and Machine Learning 2024 12
Tasks: Audio Deepfake Detection, DeepFake Detection, Speaker Verification, Synthetic Speech Detection, Voice Anti-spoofing

Spoofing-robust speaker verification technology serves to safeguard voice-based authentication systems from fraudulent attempts. Such a system should be capable of detecting spoofed voice segments and verifying voice segments identified as genuine as originating from the real speaker. This research employs an understandable and explainable embedding based on the probability mass function of waveform amplitudes in the time domain. The results demonstrate that the performance of the countermeasure (CM) system is enhanced when it is gender dependent. The ASVspoof2019 challenge, logical access (LA) database was employed for evaluation purposes. The CM system demonstrated an equal error rate (EER) of 9.2% on the evaluation set for the male gender, with an EER of 10.1% for the female gender. In contrast, a gender-independent CM system exhibited an EER of 10.2%. The system’s performance, as quantified by the detection cost function for tandem assessment (t-DCF), is 0.262 for the gender-dependent system and 0.328 for the gender-independent system.