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[1] Modern approaches to building effective interpretable models of the property market using machine learning
使用机器学习构建房地产市场有效可解释模型的现代方法
来源:ARXIV_20250623
[2] Transformers Beyond Order
超越秩序的变压器
来源:ARXIV_20250624
[3] Empirical Models of the Time Evolution of SPX Option Prices
SPX期权价格时间演化的实证模型
来源:ARXIV_20250624
[4] Causal Interventions in Bond Multi Dealer to Client Platforms
债券多交易商对客户平台的因果干预
来源:ARXIV_20250624
[5] American options valuation in time dependent jump diffusion models via integral equations and characteristic functions
基于积分方程和特征函数的时变跳跃扩散模型美式期权估值
来源:ARXIV_20250624
[6] Advanced Applications of Generative AI in Actuarial Science
生成性人工智能在精算科学中的高级应用
来源:ARXIV_20250625
[7] Predicting Major Stock Price Declines with Fundamentals: A Machine Learning Approach
用基本面预测主要股票价格下跌：一种机器学习方法
来源:SSRN_20250625
[8] Supervised Similarity for Firm Linkages
企业关联的监督相似性
来源:ARXIV_20250626
[9] FinAI-BERT: A Transformer-Based Model for Sentence-Level Detection of AI Disclosures in Financial Reports
FinAI BERT：一种基于变压器的财务报告人工智能披露句子级检测模型
来源:SSRN_20250626
[10] The Mathematics of Physics-Informed Neural Networks vs. Physics-Informed Neural Operators
基于物理的神经网络数学与基于物理的神经元算子
来源:SSRN_20250626
[11] Cross-Sectional Drivers of Stock-Treasury Correlations
股票与国债相关性的横截面驱动因素
来源:SSRN_20250626
[12] Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market
台湾股市板块轮动的量子强化学习交易代理
来源:ARXIV_20250627
[13] Deep Learning in Finance Time-series
金融深度学习时间序列
来源:SSRN_20250627
[1] Modern approaches to building effective interpretable models of the property market using machine learning
标题:使用机器学习构建房地产市场有效可解释模型的现代方法
作者:Irina G. Tanashkina, Alexey S. Tanashkin, Alexander S. Maksimchuik, Anna Yu. Poshivailo
来源:ARXIV_20250623
Abstract : In this article, we review modern approaches to building interpretable models of property markets using machine learning on the base of mass valuation of property in the Primorye region, Russia. The researcher, lacking expertise in this topic, encounters numerous difficulties in the effort to build a good model. The main source of this is the huge difference between noisy real market......(摘要翻译及全文见知识星球)
Keywords :
[2] Transformers Beyond Order
标题:超越秩序的变压器
作者:Arif Pathan
来源:ARXIV_20250624
Abstract : Short term sentiment forecasting in financial markets (e.g., stocks, indices) is challenging due to volatility, non linearity, and noise in OHLC (Open, High, Low, Close) data. This paper introduces a novel CMG (Chaos Markov Gaussian) framework that integrates chaos theory, Markov property, and Gaussian processes to improve prediction accuracy. Chaos theory captures nonlinear dynamics the Markov chain models regime shifts......(摘要翻译及全文见知识星球)
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[3] Empirical Models of the Time Evolution of SPX Option Prices
标题:SPX期权价格时间演化的实证模型
作者:Alessio Brini, David A. Hsieh, Patrick Kuiper, Sean Moushegian, David Ye
来源:ARXIV_20250624
Abstract : The key objective of this paper is to develop an empirical model for pricing SPX options that can be simulated over future paths of the SPX. To accomplish this, we formulate and rigorously evaluate several statistical models, including neural network, random forest, and linear regression. These models use the observed characteristics of the options as inputs their price,......(摘要翻译及全文见知识星球)
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[4] Causal Interventions in Bond Multi Dealer to Client Platforms
标题:债券多交易商对客户平台的因果干预
作者:Paloma Marín, Sergio Ardanza-Trevijano, Javier Sabio
来源:ARXIV_20250624
Abstract : The digitalization of financial markets has shifted trading from voice to electronic channels, with Multi Dealer to Client (MD2C) platforms now enabling clients to request quotes (RfQs) for financial instruments like bonds from multiple dealers simultaneously. In this competitive landscape, dealers cannot see each other s prices, making a rigorous analysis of the negotiation process crucial to ensure their profitability. This......(摘要翻译及全文见知识星球)
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[5] American options valuation in time dependent jump diffusion models via integral equations and characteristic functions
标题:基于积分方程和特征函数的时变跳跃扩散模型美式期权估值
作者:Andrey Itkin
来源:ARXIV_20250624
Abstract : Despite significant advancements in machine learning for derivative pricing, the efficient and accurate valuation of American options remains a persistent challenge due to complex exercise boundaries, near expiry behavior, and intricate contractual features. This paper extends a semi analytical approach for pricing American options in time inhomogeneous models, including pure diffusions, jump diffusions, and Levy processes. Building on prior work, we......(摘要翻译及全文见知识星球)
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[6] Advanced Applications of Generative AI in Actuarial Science
标题:生成性人工智能在精算科学中的高级应用
作者:Simon Hatzesberger, Iris Nonneman
来源:ARXIV_20250625
Abstract : This article demonstrates the transformative impact of Generative AI (GenAI) on actuarial science, illustrated by four implemented case studies. It begins with a historical overview of AI, tracing its evolution from early neural networks to modern GenAI technologies. The first case study shows how Large Language Models (LLMs) improve claims cost prediction by deriving significant features from unstructured textual data, significantly......(摘要翻译及全文见知识星球)
Keywords :
[7] Predicting Major Stock Price Declines with Fundamentals: A Machine Learning Approach
标题:用基本面预测主要股票价格下跌：一种机器学习方法
作者:Richard Wang
来源:SSRN_20250625
Abstract : This paper develops machine learning models to forecast major medium-and long-term stock price declines using firm-level accounting fundamentals and stock returns. Major stock declines are defined as a loss of 50% within one year or 75% within two years. Using US stocks from 1970 to 2024, the study evaluates eight machine learning algorithms-Logistic Regression, Random Forest, XGBoost, AdaBoost, Multi-layer Perceptron (MLP),......(摘要翻译及全文见知识星球)
Keywords : Forecasting, Stock Price Declines, Machine Learning, Fundamental Analysis, XGBoost, Random Forest, SVM
[8] Supervised Similarity for Firm Linkages
标题:企业关联的监督相似性
作者:Ryan Samson, Adrian Banner, Luca Candelori, Sebastien Cottrell, Tiziana Di Matteo, Paul Duchnowski, Vahagn Kirakosyan, Jose Marques, Kharen Musaelian, Stefano Pasquali, Ryan Stever, Dario Villani
来源:ARXIV_20250626
Abstract : We introduce a novel proxy for firm linkages, Characteristic Vector Linkages (CVLs). We use this concept to estimate firm linkages, first through Euclidean similarity, and then by applying Quantum Cognition Machine Learning (QCML) to similarity learning. We demonstrate that both methods can be used to construct profitable momentum spillover trading strategies, but QCML similarity outperforms the simpler Euclidean similarity.......(摘要翻译及全文见知识星球)
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[9] FinAI-BERT: A Transformer-Based Model for Sentence-Level Detection of AI Disclosures in Financial Reports
标题:FinAI BERT：一种基于变压器的财务报告人工智能披露句子级检测模型
作者:Muhammad Bilal Zafar
来源:SSRN_20250626
Abstract : The proliferation of artificial intelligence (AI) in financial services has prompted growing demand for tools that can systematically detect AI-related disclosures in corporate filings. While prior approaches often rely on keyword expansion or document-level classification, they fall short in granularity, interpretability, and robustness. This study introduces FinAI-BERT, a domain-adapted transformer-based language model designed to classify AI-related content at the sentence level......(摘要翻译及全文见知识星球)
Keywords : Artificial Intelligence Disclosure, Financial Natural Language Processing, Transformer Models, FinAI-BERT, Sentence-Level Classification, Explainable AI, Annual Reports, Financial Text Mining, SHAP Interpretation, Domain-Specific Language Models
[10] The Mathematics of Physics-Informed Neural Networks vs. Physics-Informed Neural Operators
标题:基于物理的神经网络数学与基于物理的神经元算子
作者:Miquel Noguer I Alonso
来源:SSRN_20250626
Abstract : Physics-informed machine learning has emerged as a transformative approach for solving partial differential equations (PDEs) by incorporating physical laws into neural network architectures. Two prominent paradigms have emerged: Physics-Informed Neural Networks (PINNs) and Physics-Informed Neural Operators (PINOs). While PINNs learn specific solutions to PDEs by embedding physical constraints into the loss function, PINOs learn operators that map between function spaces, enabling......(摘要翻译及全文见知识星球)
Keywords : Physics-Informed Neural Networks (PINNs), Physics-Informed Neural Operators (PINOs), Partial Differential Equations (PDEs), Operator Learning, Parametric Studies
[11] Cross-Sectional Drivers of Stock-Treasury Correlations
标题:股票与国债相关性的横截面驱动因素
作者:Sebastian Luber,Felix Akilles Lundén
来源:SSRN_20250626
Abstract : This study shows that incorporating stock-level characteristics enhances both the explanation and forecasting of stock–Treasury correlations. At the individual stock level, we find that (i) these correlations vary systematically with firm characteristics, and (ii) incorporating this cross-sectional information significantly improves out-of-sample forecast accuracy, resulting in meaningful reductions in realized portfolio risk. At the index level, (iii) adding aggregate stock market characteristics......(摘要翻译及全文见知识星球)
Keywords : correlation, stocks, treasury, machine learning
[12] Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market
标题:台湾股市板块轮动的量子强化学习交易代理
作者:Chi-Sheng Chen, Xinyu Zhang, Ya-Chuan Chen
来源:ARXIV_20250627
Abstract : We propose a hybrid quantum classical reinforcement learning framework for sector rotation in the Taiwan stock market. Our system employs Proximal Policy Optimization (PPO) as the backbone algorithm and integrates both classical architectures (LSTM, Transformer) and quantum enhanced models (QNN, QRWKV, QASA) as policy and value networks. An automated feature engineering pipeline extracts financial indicators from capital share data to ensure......(摘要翻译及全文见知识星球)
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[13] Deep Learning in Finance Time-series
标题:金融深度学习时间序列
作者:Xiaoyu Zong
来源:SSRN_20250627
Abstract : In this project, we develop a market timing strategy using a single ticker as required by the project and a market-neutral strategy as an extension. The methodology includes constructing alphas and detecting market cycles to estimate the look-back window for the Long-short-term Memory (LSTM) algorithm. The fixed alpha structures and pre-defined look-back window are critical to improving model robustness to market distribution shifts. This project......(摘要翻译及全文见知识星球)
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