Tootfinder

Rethinking Beta: A Causal Take on CAPM
Naftali Cohen
https://arxiv.org/abs/2509.05760 https://arxiv.org/pdf/2509.05760

Rethinking Beta: A Causal Take on CAPM
The CAPM regression is typically interpreted as if the market return contemporaneously \emph{causes} individual returns, motivating beta-neutral portfolios and factor attribution. For realized equity returns, however, this interpretation is inconsistent: a same-period arrow $R_{m,t} \to R_{i,t}$ conflicts with the fact that $R_m$ is itself a value-weighted aggregate of its constituents, unless $R_m$ is lagged or leave-one-out -- the ``aggregator contradiction.'' We formalize CAPM as a structura…

Quantum Leap in Finance: Economic Advantages, Security, and Post-Quantum Readiness
Gerhard Hellstern, Esra Yeniaras
https://arxiv.org/abs/2508.21548 https://

Quantum Leap in Finance: Economic Advantages, Security, and Post-Quantum Readiness
This paper provides an in-depth review of the evolving role of quantum computing in the financial sector, emphasizing both its computational potential and cybersecurity implications. Distinguishing itself from existing surveys, this work integrates classical quantum computing applications - such as portfolio optimization, risk analysis, derivative pricing, and Monte Carlo simulations with a thorough examination of blockchain technologies and post-quantum cryptography (PQC), which are crucial fo…

Rethinking Portfolio Risk: Forecasting Volatility Through Cointegrated Asset Dynamics
Gabriele Casto
https://arxiv.org/abs/2509.23533 https://arxiv.org/pdf…

Rethinking Portfolio Risk: Forecasting Volatility Through Cointegrated Asset Dynamics
We introduce the Historical and Dynamic Volatility Ratios (HVR/DVR) and show that equity and index volatilities are cointegrated at intraday and daily horizons. This allows us to construct a VECM to forecast portfolio volatility by exploiting volatility cointegration. On S&P 500 data, HVR is generally stationary and cointegration with the index is frequent; the VECM implementation yields substantially lower mean absolute percentage error (MAPE) than covariance-based forecasts at short- to mediu…

Neural-GASh: A CGA-based neural radiance prediction pipeline for real-time shading
Efstratios Geronikolakis, Manos Kamarianakis, Antonis Protopsaltis, George Papagiannakis
https://arxiv.org/abs/2507.13917

Neural-GASh: A CGA-based neural radiance prediction pipeline for real-time shading
This paper presents Neural-GASh, a novel real-time shading pipeline for 3D meshes, that leverages a neural radiance field architecture to perform image-based rendering (IBR) using Conformal Geometric Algebra (CGA)-encoded vertex information as input. Unlike traditional Precomputed Radiance Transfer (PRT) methods, that require expensive offline precomputations, our learned model directly consumes CGA-based representations of vertex positions and normals, enabling dynamic scene shading without pr…

Click A, Buy B: Rethinking Conversion Attribution in E- Commerce Recommendations
Xiangyu Zeng, Amit Jaspal, Bin Liu, Goutham Panneeru, Kevin Huang, Nicolas Bievre, Mohit Jaggi, Prathap Maniraju, Ankur Jain
https://arxiv.org/abs/2507.15113

Click A, Buy B: Rethinking Conversion Attribution in E- Commerce Recommendations
User journeys in e-commerce routinely violate the one-to-one assumption that a clicked item on an advertising platform is the same item later purchased on the merchant's website/app. For a significant number of converting sessions on our platform, users click product A but buy product B -- the Click A, Buy B (CABB) phenomenon. Training recommendation models on raw click-conversion pairs therefore rewards items that merely correlate with purchases, leading to biased learning and sub-optimal conv…

A Lightweight Incentive-Based Privacy-Preserving Smart Metering Protocol for Value-Added Services
Farid Zaredar, Morteza Amini
https://arxiv.org/abs/2508.14703 https://

A Lightweight Incentive-Based Privacy-Preserving Smart Metering Protocol for Value-Added Services
The emergence of smart grids and advanced metering infrastructure (AMI) has revolutionized energy management. Unlike traditional power grids, smart grids benefit from two-way communication through AMI, which surpasses earlier automated meter reading (AMR). AMI enables diverse demand- and supply-side utilities such as accurate billing, outage detection, real-time grid control, load forecasting, and value-added services. Smart meters play a key role by delivering consumption values at predefined …

On the efficiency roll-off in Perovskite Light Emitting Diodes
Pradeep R Nair, Advaith Kiran Marathi
https://arxiv.org/abs/2507.22741 https://arxiv.org/pdf…

On the efficiency roll-off in Perovskite Light Emitting Diodes
Here, we report a comprehensive modeling framework to unravel the efficiency roll-off in Perovskite light emitting diodes (PeLEDs). Our model self-consistently accounts for a positive feedback mechanism which involves diverse phenomena like temperature-dependent carrier recombination, space charge effects, Joule heating, and thermal transport. Model predictions compare well with experimental results such as dark current-voltage characteristics and efficiency as well as radiance roll-off under h…

Precision Spatio-Temporal Feature Fusion for Robust Remote Sensing Change Detection
Buddhi Wijenayake, Athulya Ratnayake, Praveen Sumanasekara, Nichula Wasalathilaka, Mathivathanan Piratheepan, Roshan Godaliyadda, Mervyn Ekanayake, Vijitha Herath
https://arxiv.org/abs/2507.11523

Precision Spatio-Temporal Feature Fusion for Robust Remote Sensing Change Detection
Remote sensing change detection is vital for monitoring environmental and urban transformations but faces challenges like manual feature extraction and sensitivity to noise. Traditional methods and early deep learning models, such as convolutional neural networks (CNNs), struggle to capture long-range dependencies and global context essential for accurate change detection in complex scenes. While Transformer-based models mitigate these issues, their computational complexity limits their applica…

Through-the-Cycle PD Estimation Under Incomplete Data -- A Single Risk Factor Approach
Barbara D\"om\"ot\"or, Ferenc Ill\'es
https://arxiv.org/abs/2508.15651 …

Through-the-Cycle PD Estimation Under Incomplete Data -- A Single Risk Factor Approach
Banks are required to use long-term default probabilities (PDs) of their portfolios when calculating credit risk capital under internal ratings-based (IRB) models. However, the calibration models and historical data typically reflect prevailing market conditions. According to Basel recommendations, averaging annual PDs over a full economic cycle should yield the long-term PD. In practice, the available data are often temporally incomplete - even for high-risk portfolios. In this paper, we prese…