pwc-archive/papers-with-abstracts huggingface dataset:
Smart leverage? Rethinking the role of Leveraged Exchange Traded Funds in constructing portfolios to beat a benchmark
Authors: Pieter van Staden, Peter Forsyth, Yuying Li
Date: 2024-12-06 00:00:00

Leveraged Exchange Traded Funds (LETFs), while extremely controversial in the literature, remain stubbornly popular with both institutional and retail investors in practice. While the criticisms of LETFs are certainly valid, we argue that their potential has been underestimated in the literature due to the use of very simple investment strategies involving LETFs. In this paper, we systematically investigate the potential of including a broad stock market index LETF in long-term, dynamically-optimal investment strategies designed to maximize the outperformance over standard investment benchmarks in the sense of the information ratio (IR). Our results exploit the observation that positions in a LETF deliver call-like payoffs, so that the addition of a LETF to a portfolio can be a convenient way to add inexpensive leverage while providing downside protection. Under stylized assumptions, we present and analyze closed-form IR-optimal investment strategies using either a LETF or standard/vanilla ETF (VETF) on the same equity index, which provides the necessary intuition for the potential and benefits of LETFs. In more realistic settings, we use a neural network-based approach to determine the IR-optimal strategies, trained on bootstrapped historical data. We find that IR-optimal strategies with a broad stock market LETF are not only more likely to outperform the benchmark than IR-optimal strategies derived using the corresponding VETF, but are able to achieve partial stochastic dominance over the benchmark and VETF-based strategies in terms of terminal wealth.
IntraLayer: A Platform of Digital Finance Platforms
Authors: Arman Abgaryan, Utkarsh Sharma
Date: 2024-12-10 00:00:00

IntraLayer presents an innovative framework that enables comprehensive interconnectivity in digital finance. The proposed framework comprises a core underlying infrastructure and an overarching strategy to create a pioneering "platform of platforms", serving as an algorithmic fiduciary. By design, this infrastructure optimises transactional efficiency for a broad spectrum of agents, thereby facilitating the sustainable creation of intrinsic economic value. Complementing the infrastructure, our forthcoming work will present an overarching adaptive fiscal policy to optimise IntraLayer's resources, striking a balance between sustaining the network and enhancing the proposal herein.
Log-Ergodic Dynamics in Stochastic Monetary Velocity: Theoretical Insights and Economic Implications
Authors: Kiarash Firouzi, Mohammad Jelodari Mamaghani
Date: 2024-11-27 00:00:00

We suggest employing log-ergodic processes to simulate the velocity of money in an ergodic manner. Our approach sheds light on economic behavior, policy implications, and financial dynamics by maintaining long-term stability. By bridging theory and practice, the partially ergodic model helps analysts and policymakers comprehend and forecast velocity of money. The empirical analysis, using historical U.S. GDP and money supply data, demonstrates the model's effectiveness in capturing the long-term stability of the velocity of money. Key findings indicate that the log-ergodic model offers superior predictive power compared to traditional models, making it a valuable tool for policymakers to control economic factors in vital situations.
Robust mean-variance stochastic differential reinsurance and investment games under volatility risk and model uncertainty
Authors: Guohui Guan, Zongxia Liang, Yi Xia
Date: 2024-12-12 00:00:00

This paper investigates robust stochastic differential games among insurers under model uncertainty and stochastic volatility. The surplus processes of ambiguity-averse insurers (AAIs) are characterized by drifted Brownian motion with both common and idiosyncratic insurance risks. To mitigate these risks, AAIs can purchase proportional reinsurance. Besides, AAIs allocate their wealth in a financial market consisting of cash, and a stock characterized by the 4/2 stochastic volatility model. AAIs compete with each other based on relative performance with the mean-variance criterion under the worst-case scenario. This paper formulates a robust time-consistent mean-field game in a non-linear system. The AAIs seek robust, time-consistent response strategies to achieve Nash equilibrium strategies in the game. We introduce $n$-dimensional extended Hamilton-Jacobi-Bellman-Isaacs (HJBI) equations and corresponding verification theorems under compatible conditions. Semi-closed forms of the robust $n$-insurer equilibrium and mean-field equilibrium are derived, relying on coupled Riccati equations. Suitable conditions are presented to ensure the existence and uniqueness of the coupled Riccati equation as well as the integrability in the verification theorem. As the number of AAIs increases, the results in the $n$-insurer game converge to those in the mean-field game. Numerical examples are provided to illustrate economic behaviors in the games, highlighting the herd effect of competition on the AAIs.
LLMs for Time Series: an Application for Single Stocks and Statistical Arbitrage
Authors: Sebastien Valeyre, Sofiane Aboura
Date: 2024-12-12 00:00:00
Tasks: Time Series, Time Series Prediction

Recently, LLMs (Large Language Models) have been adapted for time series prediction with significant success in pattern recognition. However, the common belief is that these models are not suitable for predicting financial market returns, which are known to be almost random. We aim to challenge this misconception through a counterexample. Specifically, we utilized the Chronos model from Ansari et al.(2024) and tested both pretrained configurations and fine-tuned supervised forecasts on the largest American single stocks using data from Guijarro-Ordonnez et al.(2022). We constructed a long/short portfolio, and the performance simulation indicates that LLMs can in reality handle time series that are nearly indistinguishable from noise, demonstrating an ability to identify inefficiencies amidst randomness and generate alpha. Finally, we compared these results with those of specialized models and smaller deep learning models, highlighting significant room for improvement in LLM performance to further enhance their predictive capabilities.
Tail Risk Alert Based on Conditional Autoregressive VaR by Regression Quantiles and Machine Learning Algorithms
Authors: Zong Ke, Yuchen Yin
Date: 2024-12-09 00:00:00

As the increasing application of AI in finance, this paper will leverage AI algorithms to examine tail risk and develop a model to alter tail risk to promote the stability of US financial markets, and enhance the resilience of the US economy. Specifically, the paper constructs a multivariate multilevel CAViaR model, optimized by gradient descent and genetic algorithm, to study the tail risk spillover between the US stock market, foreign exchange market and credit market. The model is used to provide early warning of related risks in US stocks, US credit bonds, etc. The results show that, by analyzing the direction, magnitude, and pseudo-impulse response of the risk spillover, it is found that the credit market's spillover effect on the stock market and its duration are both greater than the spillover effect of the stock market and the other two markets on credit market, placing credit market in a central position for warning of extreme risks. Its historical information on extreme risks can serve as a predictor of the VaR of other markets.
Multi-Factor Function-on-Function Regression of Bond Yields on WTI Commodity Futures Term Structure Dynamics
Authors: Peilun He, Gareth W. Peters, Nino Kordzakhia, Pavel V. Shevchenko
Date: 2024-12-08 00:00:00
Tasks: regression

In the analysis of commodity futures, it is commonly assumed that futures prices are driven by two latent factors: short-term fluctuations and long-term equilibrium price levels. In this study, we extend this framework by introducing a novel state-space functional regression model that incorporates yield curve dynamics. Our model offers a distinct advantage in capturing the interdependencies between commodity futures and the yield curve. Through a comprehensive empirical analysis of WTI crude oil futures, using US Treasury yields as a functional predictor, we demonstrate the superior accuracy of the functional regression model compared to the Schwartz-Smith two-factor model, particularly in estimating the short-end of the futures curve. Additionally, we conduct a stress testing analysis to examine the impact of both temporary and permanent shocks to US Treasury yields on futures price estimation.
A Decision Support System for Stock Selection and Asset Allocation Based on Fundamental Data Analysis
Authors: Ali Abrishami, Jafar Habibi, AmirAli Jarrahi, Dariush Amiri, Mohammadamin Fazli
Date: 2024-11-24 00:00:00

Financial markets are integral to a country's economic success, yet their complex nature raises challenging issues for predicting their behaviors. There is a growing demand for an integrated system that explores the vast and diverse data in financial reports with powerful machine-learning models to analyze financial markets and suggest appropriate investment strategies. This research provides an end-to-end decision support system (DSS) that pervasively covers the stages of gathering, cleaning, and modeling the stock's financial and fundamental data alongside the country's macroeconomic conditions. Analyzing and modeling the fundamental data of securities is a noteworthy method that, despite its greater power, has been used by fewer researchers due to its more complex and challenging issues. By precisely analyzing securities' fundamental data, the proposed system assists investors in predicting stock future prices and allocating assets in major financial markets: stock, bond, and commodity. The most notable contributions and innovations of this research are: (1) Developing a robust predictive model for mid- to long-term stock returns, tailored for investors rather than traders, (2) The proposed DSS considers a diverse set of features relating to the economic conditions of the company, including fundamental data, stock trading characteristics, and macro-economic attributes to enhance predictive accuracy, (3) Evaluating the DSS performance on the Tehran Stock Exchange that has specific characteristics of small to medium-sized economies with high inflation rates and showing the superiority to novel researches, and (4) Empowering the DSS to generate different asset allocation strategies in various economic situations by simulating expert investor decision-making.
Understanding the Impact of News Articles on the Movement of Market Index: A Case on Nifty 50
Authors: Subhasis Dasgupta, Pratik Satpati, Ishika Choudhary, Jaydip Sen
Date: 2024-11-22 00:00:00
Tasks: Articles, Sentiment Analysis

In the recent past, there were several works on the prediction of stock price using different methods. Sentiment analysis of news and tweets and relating them to the movement of stock prices have already been explored. But, when we talk about the news, there can be several topics such as politics, markets, sports etc. It was observed that most of the prior analyses dealt with news or comments associated with particular stock prices only or the researchers dealt with overall sentiment scores only. However, it is quite possible that different topics having different levels of impact on the movement of the stock price or an index. The current study focused on bridging this gap by analysing the movement of Nifty 50 index with respect to the sentiments associated with news items related to various different topic such as sports, politics, markets etc. The study established that sentiment scores of news items of different other topics also have a significant impact on the movement of the index.
Feature Group Tabular Transformer: A Novel Approach to Traffic Crash Modeling and Causality Analysis
Authors: Oscar Lares, Hao Zhen, Jidong J. Yang
Date: 2024-12-06 00:00:00

Reliable and interpretable traffic crash modeling is essential for understanding causality and improving road safety. This study introduces a novel approach to predicting collision types by utilizing a comprehensive dataset fused from multiple sources, including weather data, crash reports, high-resolution traffic information, pavement geometry, and facility characteristics. Central to our approach is the development of a Feature Group Tabular Transformer (FGTT) model, which organizes disparate data into meaningful feature groups, represented as tokens. These group-based tokens serve as rich semantic components, enabling effective identification of collision patterns and interpretation of causal mechanisms. The FGTT model is benchmarked against widely used tree ensemble models, including Random Forest, XGBoost, and CatBoost, demonstrating superior predictive performance. Furthermore, model interpretation reveals key influential factors, providing fresh insights into the underlying causality of distinct crash types.
I See, Therefore I Do: Estimating Causal Effects for Image Treatments
Authors: Abhinav Thorat, Ravi Kolla, Niranjan Pedanekar
Date: 2024-11-28 00:00:00

Causal effect estimation under observational studies is challenging due to the lack of ground truth data and treatment assignment bias. Though various methods exist in literature for addressing this problem, most of them ignore multi-dimensional treatment information by considering it as scalar, either continuous or discrete. Recently, certain works have demonstrated the utility of this rich yet complex treatment information into the estimation process, resulting in better causal effect estimation. However, these works have been demonstrated on either graphs or textual treatments. There is a notable gap in existing literature in addressing higher dimensional data such as images that has a wide variety of applications. In this work, we propose a model named NICE (Network for Image treatments Causal effect Estimation), for estimating individual causal effects when treatments are images. NICE demonstrates an effective way to use the rich multidimensional information present in image treatments that helps in obtaining improved causal effect estimates. To evaluate the performance of NICE, we propose a novel semi-synthetic data simulation framework that generates potential outcomes when images serve as treatments. Empirical results on these datasets, under various setups including the zero-shot case, demonstrate that NICE significantly outperforms existing models that incorporate treatment information for causal effect estimation.
RDGSL: Dynamic Graph Representation Learning with Structure Learning
Authors: Siwei Zhang, Yun Xiong, Yao Zhang, Yiheng Sun, Xi Chen, Yizhu Jiao, Yangyong Zhu
Date: 2023-09-05 00:00:00
Tasks: Graph Representation Learning, Graph structure learning, Representation Learning

Temporal Graph Networks (TGNs) have shown remarkable performance in learning representation for continuous-time dynamic graphs. However, real-world dynamic graphs typically contain diverse and intricate noise. Noise can significantly degrade the quality of representation generation, impeding the effectiveness of TGNs in downstream tasks. Though structure learning is widely applied to mitigate noise in static graphs, its adaptation to dynamic graph settings poses two significant challenges. i) Noise dynamics. Existing structure learning methods are ill-equipped to address the temporal aspect of noise, hampering their effectiveness in such dynamic and ever-changing noise patterns. ii) More severe noise. Noise may be introduced along with multiple interactions between two nodes, leading to the re-pollution of these nodes and consequently causing more severe noise compared to static graphs. In this paper, we present RDGSL, a representation learning method in continuous-time dynamic graphs. Meanwhile, we propose dynamic graph structure learning, a novel supervisory signal that empowers RDGSL with the ability to effectively combat noise in dynamic graphs. To address the noise dynamics issue, we introduce the Dynamic Graph Filter, where we innovatively propose a dynamic noise function that dynamically captures both current and historical noise, enabling us to assess the temporal aspect of noise and generate a denoised graph. We further propose the Temporal Embedding Learner to tackle the challenge of more severe noise, which utilizes an attention mechanism to selectively turn a blind eye to noisy edges and hence focus on normal edges, enhancing the expressiveness for representation generation that remains resilient to noise. Our method demonstrates robustness towards downstream tasks, resulting in up to 5.1% absolute AUC improvement in evolving classification versus the second-best baseline.
Safety Alignment Backfires: Preventing the Re-emergence of Suppressed Concepts in Fine-tuned Text-to-Image Diffusion Models
Authors: Sanghyun Kim, Moonseok Choi, Jinwoo Shin, Juho Lee
Date: 2024-11-30 00:00:00
Tasks: Safety Alignment

Fine-tuning text-to-image diffusion models is widely used for personalization and adaptation for new domains. In this paper, we identify a critical vulnerability of fine-tuning: safety alignment methods designed to filter harmful content (e.g., nudity) can break down during fine-tuning, allowing previously suppressed content to resurface, even when using benign datasets. While this "fine-tuning jailbreaking" issue is known in large language models, it remains largely unexplored in text-to-image diffusion models. Our investigation reveals that standard fine-tuning can inadvertently undo safety measures, causing models to relearn harmful concepts that were previously removed and even exacerbate harmful behaviors. To address this issue, we present a novel but immediate solution called Modular LoRA, which involves training Safety Low-Rank Adaptation (LoRA) modules separately from Fine-Tuning LoRA components and merging them during inference. This method effectively prevents the re-learning of harmful content without compromising the model's performance on new tasks. Our experiments demonstrate that Modular LoRA outperforms traditional fine-tuning methods in maintaining safety alignment, offering a practical approach for enhancing the security of text-to-image diffusion models against potential attacks.
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing
Authors: Xintian Sun, Benji Peng, Charles Zhang, Fei Jin, Qian Niu, Junyu Liu, Keyu Chen, Ming Li, Pohsun Feng, Ziqian Bi, Ming Liu, Yichao Zhang
Date: 2024-11-05 00:00:00
Tasks: Change Detection, Contrastive Learning, Disaster Response, Domain Adaptation, Image Retrieval, Image to text, object-detection, Object Detection, Question Answering, Text Generation, Visual Question Answering

Remote sensing has evolved from simple image acquisition to complex systems capable of integrating and processing visual and textual data. This review examines the development and application of multi-modal language models (MLLMs) in remote sensing, focusing on their ability to interpret and describe satellite imagery using natural language. We cover the technical underpinnings of MLLMs, including dual-encoder architectures, Transformer models, self-supervised and contrastive learning, and cross-modal integration. The unique challenges of remote sensing data--varying spatial resolutions, spectral richness, and temporal changes--are analyzed for their impact on MLLM performance. Key applications such as scene description, object detection, change detection, text-to-image retrieval, image-to-text generation, and visual question answering are discussed to demonstrate their relevance in environmental monitoring, urban planning, and disaster response. We review significant datasets and resources supporting the training and evaluation of these models. Challenges related to computational demands, scalability, data quality, and domain adaptation are highlighted. We conclude by proposing future research directions and technological advancements to further enhance MLLM utility in remote sensing.
Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning
Authors: Mingyu Cao, Gen Li, Jie Ji, JiaQi Zhang, Xiaolong Ma, Shiwei Liu, Lu Yin
Date: 2024-11-26 00:00:00
Tasks: Mixture-of-Experts

Mixture-of-Experts (MOE) has garnered significant attention for their ability to scale up neural networks while utilizing the same or even fewer active parameters. However, MoE does not relieve the massive memory requirements of networks, which limits their practicality in real-world applications, especially in the era of large language models (LLMs). While recent work explores the possibility of removing entire layers of MoE to reduce memory, the performance degradation is still notable. In this paper, we propose Condense-MoE (CD-MoE} that, instead of dropping the entire MoE layer, condenses the big, sparse MoE layer into a small but dense layer with only a few experts that are activated for all tokens. Our approach is specifically designed for fine-grained MoE with shared experts, where Feed-Forward Networks are split into many small experts, with certain experts isolated to serve as shared experts that are always activated. We demonstrate the effectiveness of our method across multiple MoE models such as DeepSeekMoE and QwenMoE on various benchmarks. Specifically, for the DeepSeekMoE-16B model, our approach maintains nearly 90% of the average accuracy while reducing memory usage by 30% and enhancing inference speed by 30%. Moreover, we show that with lightweight expert fine-tuning, the pruned model can achieve further improvements on specific tasks. Our code are available at https://github.com/duterscmy/CD-MoE/tree/main.
LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting
Authors: Lingzheng Zhang, Lifeng Shen, Yimin Zheng, Shiyuan Piao, Ziyue Li, Fugee Tsung
Date: 2024-11-24 00:00:00
Tasks: Computational Efficiency, Natural Language Understanding, Time Series, Time Series Forecasting, Time Series Generation

Recent research has shown that large language models (LLMs) can be effectively used for real-world time series forecasting due to their strong natural language understanding capabilities. However, aligning time series into semantic spaces of LLMs comes with high computational costs and inference complexity, particularly for long-range time series generation. Building on recent advancements in using linear models for time series, this paper introduces an LLM-enhanced mixture of linear experts for precise and efficient time series forecasting. This approach involves developing a mixture of linear experts with multiple lookback lengths and a new multimodal fusion mechanism. The use of a mixture of linear experts is efficient due to its simplicity, while the multimodal fusion mechanism adaptively combines multiple linear experts based on the learned features of the text modality from pre-trained large language models. In experiments, we rethink the need to align time series to LLMs by existing time-series large language models and further discuss their efficiency and effectiveness in time series forecasting. Our experimental results show that the proposed LeMoLE model presents lower prediction errors and higher computational efficiency than existing LLM models.
Sequence Covering Similarity for Symbolic Sequence Comparison
Authors: Pierre-François Marteau
Date: 2018-01-22 00:00:00

This paper introduces the sequence covering similarity, that we formally define for evaluating the similarity between a symbolic sequence (string) and a set of symbolic sequences (strings). From this covering similarity we derive a pair-wise distance to compare two symbolic sequences. We show that this covering distance is a semimetric. Few examples are given to show how this string metric in $O(n \cdot log n)$ compares with the Levenshtein's distance that is in $O(n^2)$. A final example presents its application to plagiarism detection.