[1] Optimizing Portfolio with Two Sided Transactions and Lending利用双向交易和借贷优化投资组合来源:ARXIV_20240813[2] A GCN LSTM Approach for ES mini and VX Futures ForecastingES mini和VX期货预测的GCN-LSTM方法来源:ARXIV_20240813[3] Stochastic Calculus for Option Pricing with Convex Duality, Logistic Model, and Numerical Examination凸对偶期权定价的随机微积分、Logistic模型和数值检验来源:ARXIV_20240813[4] Why Groups Matter为什么群体很重要来源:ARXIV_20240813[5] A forward differential deep learning based algorithm for solving high dimensional nonlinear backward stochastic differential equations基于前向微分深度学习的求解高维非线性后向随机微分方程的算法来源:ARXIV_20240813[6] Harnessing Earnings Reports for Stock Predictions利用收益报告进行股票预测来源:ARXIV_20240814[7] Case based Explainability for Random Forest随机森林的基于案例的可解释性来源:ARXIV_20240814
[1] Optimizing Portfolio with Two Sided Transactions and Lending
标题:利用双向交易和借贷优化投资组合作者:Ali Habibnia, Mahdi Soltanzadeh来源:ARXIV_20240813Abstract : This study presents a Reinforcement Learning (RL) based portfolio management model tailored for high risk environments, addressing the limitations of traditional RL models and exploiting market opportunities through two sided transactions and lending. Our approach integrates a new environmental formulation with a Profit and Loss (PnL) based reward function, enhancing the RL agent s ability in downside risk management and capital......(摘要翻译及全文见知识星球)Keywords :
[2] A GCN LSTM Approach for ES mini and VX Futures Forecasting
标题:ES mini和VX期货预测的GCN-LSTM方法作者:Nikolas Michael, Mihai Cucuringu, Sam Howison来源:ARXIV_20240813Abstract : We propose a novel data driven network framework for forecasting problems related to E mini S &P 500 and CBOE Volatility Index futures, in which products with different expirations act as distinct nodes. We provide visual demonstrations of the correlation structures of these products in terms of their returns, realized volatility, and trading volume. The resulting networks offer insights into the......(摘要翻译及全文见知识星球)Keywords :
[3] Stochastic Calculus for Option Pricing with Convex Duality, Logistic Model, and Numerical Examination
标题:凸对偶期权定价的随机微积分、Logistic模型和数值检验作者:Zheng Cao来源:ARXIV_20240813
Abstract : This thesis explores the historical progression and theoretical constructs of financial mathematics, with an in depth exploration of Stochastic Calculus as showcased in the Binomial Asset Pricing Model and the Continuous Time Models. A comprehensive survey of stochastic calculus principles applied to option pricing is offered, highlighting insights from Peter Carr and Lorenzo Torricelli s Convex Duality in Continuous......(摘要翻译及全文见知识星球)Keywords :
[4] Why Groups Matter
标题:为什么群体很重要作者:Dangxing Chen, Jingfeng Chen, Weicheng Ye来源:ARXIV_20240813Abstract : Explainable machine learning methods have been accompanied by substantial development. Despite their success, the existing approaches focus more on the general framework with no prior domain expertise. High stakes financial sectors have extensive domain knowledge of the features. Hence, it is expected that explanations of models will be consistent with domain knowledge to ensure conceptual soundness.In this work, we study the......(摘要翻译及全文见知识星球)Keywords :
[5] A forward differential deep learning based algorithm for solving high dimensional nonlinear backward stochastic differential equations
标题:基于前向微分深度学习的求解高维非线性后向随机微分方程的算法作者:Lorenc Kapllani, Long Teng来源:ARXIV_20240813Abstract : In this work, we present a novel forward differential deep learning based algorithm for solving high dimensional nonlinear backward stochastic differential equations (BSDEs). Motivated by the fact that differential deep learning can efficiently approximate the labels and their derivatives with respect to inputs, we transform the BSDE problem into a differential deep learning problem. This is done by leveraging Malliavin calculus,......(摘要翻译及全文见知识星球)Keywords :
[6] Harnessing Earnings Reports for Stock Predictions
标题:利用收益报告进行股票预测作者:Haowei Ni, Shuchen Meng, Xupeng Chen, Ziqing Zhao, Andi Chen, Panfeng Li, Shiyao Zhang, Qifu Yin, Yuanqing Wang, Yuxi Chan来源:ARXIV_20240814Abstract : Accurate stock market predictions following earnings reports are crucial for investors. Traditional methods, particularly classical machine learning models, struggle with these predictions because they cannot effectively process and interpret extensive textual data contained in earnings reports and often overlook nuances that influence market movements. This paper introduces an advanced approach by employing Large Language Models (LLMs) instruction fine tuned with a......(摘要翻译及全文见知识星球)Keywords :
[7] Case based Explainability for Random Forest
标题:随机森林的基于案例的可解释性作者:Gregory Yampolsky, Dhruv Desai, Mingshu Li, Stefano Pasquali, Dhagash Mehta来源:ARXIV_20240814Abstract : The explainability of black box machine learning algorithms, commonly known as Explainable Artificial Intelligence (XAI), has become crucial for financial and other regulated industrial applications due to regulatory requirements and the need for transparency in business practices. Among the various paradigms of XAI, Explainable Case Based Reasoning (XCBR) stands out as a pragmatic approach that elucidates the output of a model......(摘要翻译及全文见知识星球)Keywords :
