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[1] Application of Deep Reinforcement Learning to At the Money S&P 500 Options Hedging
深度强化学习在标准普尔500指数期权套期保值中的应用
来源:ARXIV_20251013
[2] Interpretable Machine Learning for Predicting Startup Funding, Patenting, and Exits
用于预测创业资金、专利和退出的可解释机器学习
来源:ARXIV_20251013
[3] Learning the Exact SABR Model
学习精确的SABR模型
来源:ARXIV_20251014
[4] Integrating Large Language Models and Reinforcement Learning for Sentiment Driven Quantitative Trading
集成大型语言模型和强化学习用于情绪驱动的定量交易
来源:ARXIV_20251014
[5] Identifying and Quantifying Financial Bubbles with the Hyped Log Periodic Power Law Model
用对数周期幂律模型识别和量化金融泡沫
来源:ARXIV_20251014
[6] Orderbook Feature Learning and Asymmetric Generalization in Intraday Electricity Markets
日内电力市场中的订单特征学习和非对称泛化
来源:ARXIV_20251015
[7] (Non Parametric) Bootstrap Robust Optimization for Portfolios and Trading Strategies
（非参数）Bootstrap稳健优化投资组合和交易策略
来源:ARXIV_20251015
[8] Mitigating Overfitting in Hyperparameter Selection for Trading Strategies Using Randomized In-Sample Evaluation
使用随机样本评估减轻交易策略超参数选择中的过拟合
来源:SSRN_20251017
[9] Deep Learning in Asset Management: Architectures, Applications, and Challenges
资产管理中的深度学习：架构、应用和挑战
来源:SSRN_20251017
[10] Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging
深度强化学习在标准普尔500指数期权套期保值中的应用
来源:SSRN_20251017
[1] Application of Deep Reinforcement Learning to At the Money S&P 500 Options Hedging
标题:深度强化学习在标准普尔500指数期权套期保值中的应用
作者:Zofia Bracha, Paweł Sakowski, Jakub Michańków
来源:ARXIV_20251013
Abstract : This paper explores the application of deep Q learning to hedging at the money options on the S &P 500 index. We develop an agent based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, trained to simulate hedging decisions without making explicit model assumptions on price dynamics. The agent was trained on historical intraday prices of S &P 500......(摘要翻译及全文见知识星球)
Keywords :
[2] Interpretable Machine Learning for Predicting Startup Funding, Patenting, and Exits
标题:用于预测创业资金、专利和退出的可解释机器学习
作者:Saeid Mashhadi, Amirhossein Saghezchi, Vesal Ghassemzadeh Kashani
来源:ARXIV_20251013
Abstract : This study develops an interpretable machine learning framework to forecast startup outcomes, including funding, patenting, and exit. A firm quarter panel for 2010 2023 is constructed from Crunchbase and matched to U.S. Patent and Trademark Office (USPTO) data. Three horizons are evaluated next funding within 12 months, patent stock growth within 24 months, and exit through an initial public offering......(摘要翻译及全文见知识星球)
Keywords :
[3] Learning the Exact SABR Model
标题:学习精确的SABR模型
作者:Giorgia Rensi, Pietro Rossi, Marco Bianchetti
来源:ARXIV_20251014
Abstract : The SABR model is a cornerstone of interest rate volatility modeling, but its practical application relies heavily on the analytical approximation by Hagan et al., whose accuracy deteriorates for high volatility, long maturities, and out of the money options, admitting arbitrage. While machine learning approaches have been proposed to overcome these limitations, they have often been limited by simplified SABR dynamics......(摘要翻译及全文见知识星球)
Keywords :
[4] Integrating Large Language Models and Reinforcement Learning for Sentiment Driven Quantitative Trading
标题:集成大型语言模型和强化学习用于情绪驱动的定量交易
作者:Wo Long, Wenxin Zeng, Xiaoyu Zhang, Ziyao Zhou
来源:ARXIV_20251014
Abstract : This research develops a sentiment driven quantitative trading system that leverages a large language model, FinGPT, for sentiment analysis, and explores a novel method for signal integration using a reinforcement learning algorithm, Twin Delayed Deep Deterministic Policy Gradient (TD3). We compare the performance of strategies that integrate sentiment and technical signals using both a conventional rule based approach and a reinforcement......(摘要翻译及全文见知识星球)
Keywords :
[5] Identifying and Quantifying Financial Bubbles with the Hyped Log Periodic Power Law Model
标题:用对数周期幂律模型识别和量化金融泡沫
作者:Zheng Cao, Xingran Shao, Yuheng Yan, Helyette Geman
来源:ARXIV_20251014
Abstract : We propose a novel model, the Hyped Log Periodic Power Law Model (HLPPL), to the problem of quantifying and detecting financial bubbles, an ever fascinating one for academics and practitioners alike. Bubble labels are generated using a Log Periodic Power Law (LPPL) model, sentiment scores, and a hype index we introduced in previous research on NLP forecasting of stock return volatility.......(摘要翻译及全文见知识星球)
Keywords :
[6] Orderbook Feature Learning and Asymmetric Generalization in Intraday Electricity Markets
标题:日内电力市场中的订单特征学习和非对称泛化
作者:Runyao Yu, Ruochen Wu, Yongsheng Han, Jochen L. Cremer
来源:ARXIV_20251015
Abstract : Accurate probabilistic forecasting of intraday electricity prices is critical for market participants to inform trading decisions. Existing studies rely on specific domain features, such as Volume Weighted Average Price (VWAP) and the last price. However, the rich information in the orderbook remains underexplored. Furthermore, these approaches are often developed within a single country and product type, making it unclear whether the......(摘要翻译及全文见知识星球)
Keywords :
[7] (Non Parametric) Bootstrap Robust Optimization for Portfolios and Trading Strategies
标题:（非参数）Bootstrap稳健优化投资组合和交易策略
作者:Daniel Cunha Oliveira, Grover Guzman, Nick Firoozye
来源:ARXIV_20251015
Abstract : Robust optimization provides a principled framework for decision making under uncertainty, with broad applications in finance, engineering, and operations research. In portfolio optimization, uncertainty in expected returns and covariances demands methods that mitigate estimation error, parameter instability, and model misspecification. Traditional approaches, including parametric, bootstrap based, and Bayesian methods, enhance stability by relying on confidence intervals or probabilistic priors but often......(摘要翻译及全文见知识星球)
Keywords :
[8] Mitigating Overfitting in Hyperparameter Selection for Trading Strategies Using Randomized In-Sample Evaluation
标题:使用随机样本评估减轻交易策略超参数选择中的过拟合
作者:Karim Khalil
来源:SSRN_20251017
Abstract : In quantitative finance and algorithmic trading, hyperparameter tuning is often performed via a traditional train-test split, with the best-performing model on the in-sample data evaluated on a holdout set. However, this approach is prone to overfitting, particularly when evaluating a large grid of parameters, as it optimizes to a single realization of the in-sample period. In this study, we propose a......(摘要翻译及全文见知识星球)
Keywords : Overfitting, Randomized Subsampling, Financial Machine Learning, Trading Strategy Evaluation, Backtesting, Sharpe Ratio, Model Validation, Algorithmic Trading, Robustness Testing
[9] Deep Learning in Asset Management: Architectures, Applications, and Challenges
标题:资产管理中的深度学习：架构、应用和挑战
作者:Yoontae Hwang,Youngbin Lee,Junhyeong Lee,Stefan Zohren,Jang Ho Kim,Woo Chang Kim,Yongjae Lee,Frank J. Fabozzi
来源:SSRN_20251017
Abstract : This paper provides a critical survey of the essential considerations for applying deep learning models within the financial domain, particularly in asset management. While deep learning has shown immense promise, its direct application is hindered by formidable challenges unique to finance, including low signal-to-noise ratios, pervasive non-stationarity in time series data, and the adversarial and adaptive nature of markets where discovered......(摘要翻译及全文见知识星球)
Keywords : Asset Management, Financial Applications, Model Validation, Neural Network, Large Language Models
[10] Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging
标题:深度强化学习在标准普尔500指数期权套期保值中的应用
作者:Zofia Bracha,Paweł Sakowski,Jakub Michańków
来源:SSRN_20251017
Abstract : This paper explores the application of deep Q-learning to hedging at-the-money options on the S&P 500 index. We develop an agent based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, trained to simulate hedging decisions without making explicit model assumptions on price dynamics. The agent was trained on historical intraday prices of S&P 500 call options across years 2004......(摘要翻译及全文见知识星球)
Keywords : Deep Learning, Reinforcement Learning, Double Deep Q-Networks, Options Market, Options Hedging, Deep Hedging
