[Workflow] Automatic Update Quant Scholar Paper List

Author: WinstonLiyt

README.md

@@ -1,7 +1,7 @@
 <p align="center"><h1 align="center">🌟 QUANT-SCHOLAR 🌟</h1><h2 align="center">Automatically Quantitative Finance Papers List</h2></p> 
 <p align="center"><img src="https://raw.githubusercontent.com/LLMQuant/quant-scholar/main/asset/icon.png" width="180"></p>
 
-## 🚩 Updated on 2025.04.20 
+## 🚩 Updated on 2025.04.21 
 <details>
   <summary><strong>📜 Contents</strong></summary>
   <ol>
@@ -57,7 +57,7 @@
 | 2025-02-20 | Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework | Arno Botha, Tanja Verster, Roland Breedt et.al. | [2502.14479](http://arxiv.org/abs/2502.14479) |  | 33 pages, 8192 words, 12 figures | <details><summary>Abstract (click to expand)</summary>The lifetime behaviour of loans is notoriously difficult to model, which can compromise a bank's financial reserves against future losses, if modelled poorly. Therefore, we present a data-driven comparative study amongst three techniques in modelling a series of default risk estimates over the lifetime of each loan, i.e., its term-structure. The behaviour of loans can be described using a nonstationary and time-dependent semi-Markov model, though we model its elements using a multistate regression-based approach. As such, the transition probabilities are explicitly modelled as a function of a rich set of input variables, including macroeconomic and loan-level inputs. Our modelling techniques are deliberately chosen in ascending order of complexity: 1) a Markov chain; 2) beta regression; and 3) multinomial logistic regression. Using residential mortgage data, our results show that each successive model outperforms the previous, likely as a result of greater sophistication. This finding required devising a novel suite of simple model diagnostics, which can itself be reused in assessing sampling representativeness and the performance of other modelling techniques. These contributions surely advance the current practice within banking when conducting multistate modelling. Consequently, we believe that the estimation of loss reserves will be more timeous and accurate under IFRS 9.</details> |
 | 2025-02-20 | Causality Analysis of COVID-19 Induced Crashes in Stock and Commodity Markets: A Topological Perspective | Buddha Nath Sharma, Anish Rai, SR Luwang et.al. | [2502.14431](http://arxiv.org/abs/2502.14431) |  |  | <details><summary>Abstract (click to expand)</summary>The paper presents a comprehensive causality analysis of the US stock and commodity markets during the COVID-19 crash. The dynamics of different sectors are also compared. We use Topological Data Analysis (TDA) on multidimensional time-series to identify crashes in stock and commodity markets. The Wasserstein Distance WD shows distinct spikes signaling the crash for both stock and commodity markets. We then compare the persistence diagrams of stock and commodity markets using the WD metric. A significant spike in the $WD$ between stock and commodity markets is observed during the crisis, suggesting significant topological differences between the markets. Similar spikes are observed between the sectors of the US market as well. Spikes obtained may be due to either a difference in the magnitude of crashes in the two markets (or sectors), or from the temporal lag between the two markets suggesting information flow. We study the Granger-causality between stock and commodity markets and also between different sectors. The results show a bidirectional Granger-causality between commodity and stock during the crash period, demonstrating the greater interdependence of financial markets during the crash. However, the overall analysis shows that the causal direction is from stock to commodity. A pairwise Granger-causal analysis between US sectors is also conducted. There is a significant increase in the interdependence between the sectors during the crash period. TDA combined with Granger-causality effectively analyzes the interdependence and sensitivity of different markets and sectors.</details> |
 
-<p align=right>(<a href=#-updated-on-20250420>back to top</a>)</p>
+<p align=right>(<a href=#-updated-on-20250421>back to top</a>)</p>
 
 ## 📌 Deep Learning in Finance
 
@@ -221,7 +221,7 @@
 | 2025-03-09 | Modular Photobioreactor Façade Systems for Sustainable Architecture: Design, Fabrication, and Real-Time Monitoring | Xiujin Liu et.al. | [2503.06769](http://arxiv.org/abs/2503.06769) |  | 21 pages, 22 figures, 3 tables | <details><summary>Abstract (click to expand)</summary>This paper proposes an innovative solution to the growing issue of greenhouse gas emissions: a closed photobioreactor (PBR) fa\c{c}ade system to mitigate greenhouse gas (GHG) concentrations. With digital fabrication technology, this study explores the transition from traditional, single function building facades to multifunctional, integrated building systems. It introduces a photobioreactor (PBR) fa\c{c}ade system to mitigate greenhouse gas (GHG) concentrations while addressing the challenge of large-scale prefabricated components transportation. This research introduces a novel approach by designing the fa\c{c}ade system as modular, user-friendly and transportation-friendly bricks, enabling the creation of a user-customized and self-assembled photobioreactor (PBR) system. The single module in the system is proposed to be "neutralization bricks", which embedded with algae and equipped with an air circulation system, facilitating the photobioreactor (PBR)'s functionality. A connection system between modules allows for easy assembly by users, while a limited variety of brick styles ensures modularity in manufacturing without sacrificing customization and diversity. The system is also equipped with an advanced microalgae status detection algorithm, which allows users to monitor the condition of the microalgae using monocular camera. This functionality ensures timely alerts and notifications for users to replace the algae, thereby optimizing the operational efficiency and sustainability of the algae cultivation process.</details> |
 | 2025-03-09 | Energy-Adaptive Checkpoint-Free Intermittent Inference for Low Power Energy Harvesting Systems | Sahidul Islam, Wei Wei, Jishnu Banarjee et.al. | [2503.06663](http://arxiv.org/abs/2503.06663) |  |  | <details><summary>Abstract (click to expand)</summary>Deep neural network (DNN) inference in energy harvesting (EH) devices poses significant challenges due to resource constraints and frequent power interruptions. These power losses not only increase end-to-end latency, but also compromise inference consistency and accuracy, as existing checkpointing and restore mechanisms are prone to errors. Consequently, the quality of service (QoS) for DNN inference on EH devices is severely impacted. In this paper, we propose an energy-adaptive DNN inference mechanism capable of dynamically transitioning the model into a low-power mode by reducing computational complexity when harvested energy is limited. This approach ensures that end-to-end latency requirements are met. Additionally, to address the limitations of error-prone checkpoint-and-restore mechanisms, we introduce a checkpoint-free intermittent inference framework that ensures consistent, progress-preserving DNN inference during power failures in energy-harvesting systems.</details> |
 
-<p align=right>(<a href=#-updated-on-20250420>back to top</a>)</p>
+<p align=right>(<a href=#-updated-on-20250421>back to top</a>)</p>
 
 ## 📌 Reinforcement Learning in Finance
 
@@ -231,7 +231,7 @@
 | 2022-06-28 | Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Network | Frensi Zejnullahu, Maurice Moser, Joerg Osterrieder et.al. | [2206.14267](http://arxiv.org/abs/2206.14267) |  |  | <details><summary>Abstract (click to expand)</summary>This paper presents a Double Deep Q-Network algorithm for trading single assets, namely the E-mini S&P 500 continuous futures contract. We use a proven setup as the foundation for our environment with multiple extensions. The features of our trading agent are constantly being expanded to include additional assets such as commodities, resulting in four models. We also respond to environmental conditions, including costs and crises. Our trading agent is first trained for a specific time period and tested on new data and compared with the long-and-hold strategy as a benchmark (market). We analyze the differences between the various models and the in-sample/out-of-sample performance with respect to the environment. The experimental results show that the trading agent follows an appropriate behavior. It can adjust its policy to different circumstances, such as more extensive use of the neutral position when trading costs are present. Furthermore, the net asset value exceeded that of the benchmark, and the agent outperformed the market in the test set. We provide initial insights into the behavior of an agent in a financial domain using a DDQN algorithm. The results of this study can be used for further development.</details> |
 | 2023-02-28 | Recent Advances in Reinforcement Learning in Finance | Ben Hambly, Renyuan Xu, Huining Yang et.al. | [2112.04553](http://arxiv.org/abs/2112.04553) |  | 60 pages, 1 figure | <details><summary>Abstract (click to expand)</summary>The rapid changes in the finance industry due to the increasing amount of data have revolutionized the techniques on data processing and data analysis and brought new theoretical and computational challenges. In contrast to classical stochastic control theory and other analytical approaches for solving financial decision-making problems that heavily reply on model assumptions, new developments from reinforcement learning (RL) are able to make full use of the large amount of financial data with fewer model assumptions and to improve decisions in complex financial environments. This survey paper aims to review the recent developments and use of RL approaches in finance. We give an introduction to Markov decision processes, which is the setting for many of the commonly used RL approaches. Various algorithms are then introduced with a focus on value and policy based methods that do not require any model assumptions. Connections are made with neural networks to extend the framework to encompass deep RL algorithms. Our survey concludes by discussing the application of these RL algorithms in a variety of decision-making problems in finance, including optimal execution, portfolio optimization, option pricing and hedging, market making, smart order routing, and robo-advising.</details> |
 
-<p align=right>(<a href=#-updated-on-20250420>back to top</a>)</p>
+<p align=right>(<a href=#-updated-on-20250421>back to top</a>)</p>
 
 ## 📌 Time Series Forecasting
 
@@ -308,5 +308,5 @@
 | 2025-03-04 | Lightweight Channel-wise Dynamic Fusion Model: Non-stationary Time Series Forecasting via Entropy Analysis | Tianyu Jia, Zongxia Xie, Yanru Sun et.al. | [2503.02609](http://arxiv.org/abs/2503.02609) |  |  | <details><summary>Abstract (click to expand)</summary>Non-stationarity is an intrinsic property of real-world time series and plays a crucial role in time series forecasting. Previous studies primarily adopt instance normalization to attenuate the non-stationarity of original series for better predictability. However, instance normalization that directly removes the inherent non-stationarity can lead to three issues: (1) disrupting global temporal dependencies, (2) ignoring channel-specific differences, and (3) producing over-smoothed predictions. To address these issues, we theoretically demonstrate that variance can be a valid and interpretable proxy for quantifying non-stationarity of time series. Based on the analysis, we propose a novel lightweight \textit{C}hannel-wise \textit{D}ynamic \textit{F}usion \textit{M}odel (\textit{CDFM}), which selectively and dynamically recovers intrinsic non-stationarity of the original series, while keeping the predictability of normalized series. First, we design a Dual-Predictor Module, which involves two branches: a Time Stationary Predictor for capturing stable patterns and a Time Non-stationary Predictor for modeling global dynamics patterns. Second, we propose a Fusion Weight Learner to dynamically characterize the intrinsic non-stationary information across different samples based on variance. Finally, we introduce a Channel Selector to selectively recover non-stationary information from specific channels by evaluating their non-stationarity, similarity, and distribution consistency, enabling the model to capture relevant dynamic features and avoid overfitting. Comprehensive experiments on seven time series datasets demonstrate the superiority and generalization capabilities of CDFM.</details> |
 | 2025-03-03 | Unify and Anchor: A Context-Aware Transformer for Cross-Domain Time Series Forecasting | Xiaobin Hong, Jiawen Zhang, Wenzhong Li et.al. | [2503.01157](http://arxiv.org/abs/2503.01157) |  | 20 pages, 12 figures, 8 tables, conference under review | <details><summary>Abstract (click to expand)</summary>The rise of foundation models has revolutionized natural language processing and computer vision, yet their best practices to time series forecasting remains underexplored. Existing time series foundation models often adopt methodologies from these fields without addressing the unique characteristics of time series data. In this paper, we identify two key challenges in cross-domain time series forecasting: the complexity of temporal patterns and semantic misalignment. To tackle these issues, we propose the ``Unify and Anchor" transfer paradigm, which disentangles frequency components for a unified perspective and incorporates external context as domain anchors for guided adaptation. Based on this framework, we introduce ContexTST, a Transformer-based model that employs a time series coordinator for structured representation and the Transformer blocks with a context-informed mixture-of-experts mechanism for effective cross-domain generalization. Extensive experiments demonstrate that ContexTST advances state-of-the-art forecasting performance while achieving strong zero-shot transferability across diverse domains.</details> |
 
-<p align=right>(<a href=#-updated-on-20250420>back to top</a>)</p>
+<p align=right>(<a href=#-updated-on-20250421>back to top</a>)</p>