[1] MLP, XGBoost, KAN, TDNN, and LSTM GRU Hybrid RNN with Attention for SPX and NDX European Call Option PricingMLP、XGBoost、KAN、TDNN和LSTM GRU混合RNN，关注SPX和NDX欧洲看涨期权定价来源:ARXIV_20240912[2] Leveraging RNNs and LSTMs for Synchronization Analysis in the Indian Stock Market利用RNN和LSTM进行印度股市同步分析来源:ARXIV_20240912[3] Machine Learning from the Best: Predicting the Holdings of Top Mutual Funds从最好的机器学习：预测顶级共同基金的持有量来源:SSRN_20240912[4] LSR IGRULSR-IGRU来源:ARXIV_20240916[5] Comparative Study of Long Short Term Memory (LSTM) and Quantum Long Short Term Memory (QLSTM)长短期记忆与量子长短期记忆的比较研究来源:ARXIV_20240916[6] COMEX Copper Futures Volatility ForecastingCOMEX铜期货波动预测来源:ARXIV_20240916[7] A Deep Reinforcement Learning Framework For Financial Portfolio Management金融投资组合管理的深度强化学习框架来源:ARXIV_20240916[8] Dynamic Link and Flow Prediction in Bank Transfer Networks银行转账网络中的动态链接和流量预测来源:ARXIV_20240916[9] Disentangling the sources of cyber risk premia理清网络风险溢价的来源来源:ARXIV_20240916[10] A Market for Lemons Strategic Directions for a Vigilant Application of Artificial Intelligence in Entrepreneurship Research柠檬市场——人工智能在创业研究中谨慎应用的战略方向来源:ARXIV_20240916[11] Anatomy of Machines for MarkowitzMarkowitz机器剖析来源:ARXIV_20240917[12] Research and Design of a Financial Intelligent Risk Control Platform Based on Big Data Analysis and Deep Machine Learning基于大数据分析和深度机器学习的金融智能风险控制平台的研究与设计来源:ARXIV_20240917[13] Robust Reinforcement Learning with Dynamic Distortion Risk Measures具有动态失真风险度量的鲁棒强化学习来源:ARXIV_20240917
[1] MLP, XGBoost, KAN, TDNN, and LSTM GRU Hybrid RNN with Attention for SPX and NDX European Call Option Pricing
标题:MLP、XGBoost、KAN、TDNN和LSTM GRU混合RNN，关注SPX和NDX欧洲看涨期权定价作者:Boris Ter-Avanesov, Homayoon Beigi来源:ARXIV_20240912Abstract : We explore the performance of various artificial neural network architectures, including a multilayer perceptron (MLP), Kolmogorov Arnold network (KAN), LSTM GRU hybrid recursive neural network (RNN) models, and a time delay neural network (TDNN) for pricing European call options. In this study, we attempt to leverage the ability of supervised learning methods, such as ANNs, KANs, and gradient boosted decision trees,......(摘要翻译及全文见知识星球)Keywords :
[2] Leveraging RNNs and LSTMs for Synchronization Analysis in the Indian Stock Market
标题:利用RNN和LSTM进行印度股市同步分析作者:Sanjay Sathish, Charu C Sharma来源:ARXIV_20240912Abstract : Our research presents a new approach for forecasting the synchronization of stock prices using machine learning and non linear time series analysis. To capture the complex non linear relationships between stock prices, we utilize recurrence plots (RP) and cross recurrence quantification analysis (CRQA). By transforming Cross Recurrence Plot (CRP) data into a time series format, we enable the use of Recurrent......(摘要翻译及全文见知识星球)Keywords :
[3] Machine Learning from the Best: Predicting the Holdings of Top Mutual Funds
标题:从最好的机器学习：预测顶级共同基金的持有量作者:Jean-Paul van Brakel来源:SSRN_20240912Abstract : I show that machine learning models, by exploiting the nonlinearities and interactions in stock characteristics, can better predict the stocks owned by top-performing mutual fund managers than suggested by their most recent holdings or a linear model. Previous ownership by mutual funds and the market cap and volume of the stock are identified as the most important predictors. The predictions also......(摘要翻译及全文见知识星球)Keywords : Machine learning, mutual funds, stock-picking, classification, active management
[4] LSR IGRU
标题:LSR-IGRU作者:Peng Zhu, Yuante Li, Yifan Hu, Qinyuan Liu, Dawei Cheng, Yuqi Liang来源:ARXIV_20240916Abstract : Stock price prediction is a challenging problem in the field of finance and receives widespread attention. In recent years, with the rapid development of technologies such as deep learning and graph neural networks, more research methods have begun to focus on exploring the interrelationships between stocks. However, existing methods mostly focus on the short term dynamic relationships of stocks and directly......(摘要翻译及全文见知识星球)Keywords :
[5] Comparative Study of Long Short Term Memory (LSTM) and Quantum Long Short Term Memory (QLSTM)
标题:长短期记忆与量子长短期记忆的比较研究作者:Tariq Mahmood, Ibtasam Ahmad, Malik Muhammad Zeeshan Ansar, Jumanah Ahmed Darwish, Rehan Ahmad Khan Sherwani来源:ARXIV_20240916Abstract : In recent years, financial analysts have been trying to develop models to predict the movement of a stock price index. The task becomes challenging in vague economic, social, and political situations like in Pakistan. In this study, we employed efficient models of machine learning such as long short term memory (LSTM) and quantum long short term memory (QLSTM) to predict the......(摘要翻译及全文见知识星球)Keywords :
[6] COMEX Copper Futures Volatility Forecasting
标题:COMEX铜期货波动预测作者:Zian Wang, Xinyi Lu来源:ARXIV_20240916Abstract : This paper investigates the forecasting performance of COMEX copper futures realized volatility across various high frequency intervals using both econometric volatility models and deep learning recurrent neural network models. The econometric models considered are GARCH and HAR, while the deep learning models include RNN (Recurrent Neural Network), LSTM (Long Short Term Memory), and GRU (Gated Recurrent Unit). In forecasting daily realized......(摘要翻译及全文见知识星球)Keywords :
[7] A Deep Reinforcement Learning Framework For Financial Portfolio Management
标题:金融投资组合管理的深度强化学习框架作者:Jinyang Li来源:ARXIV_20240916Abstract : In this research paper, we investigate into a paper named A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem arXiv 1706.10059 . It is a portfolio management problem which is solved by deep learning techniques. The original paper proposes a financial model free reinforcement learning framework, which consists of the Ensemble of Identical Independent Evaluators (EIIE)......(摘要翻译及全文见知识星球)Keywords :
[8] Dynamic Link and Flow Prediction in Bank Transfer Networks
标题:银行转账网络中的动态链接和流量预测作者:Shu Takahashi, Kento Yamamoto, Shumpei Kobayashi, Ryoma Kondo, Ryohei Hisano来源:ARXIV_20240916Abstract : The prediction of both the existence and weight of network links at future time points is essential as complex networks evolve over time. Traditional methods, such as vector autoregression and factor models, have been applied to small, dense networks, but become computationally impractical for large scale, sparse, and complex networks. Some machine learning models address dynamic link prediction, but few address......(摘要翻译及全文见知识星球)Keywords :
[9] Disentangling the sources of cyber risk premia
标题:理清网络风险溢价的来源作者:Loïc Maréchal, Nathan Monnet来源:ARXIV_20240916Abstract : We use a methodology based on a machine learning algorithm to quantify firms cyber risks based on their disclosures and a dedicated cyber corpus. The model can identify paragraphs related to determined cyber threat types and accordingly attribute several related cyber scores to the firm. The cyber scores are unrelated to other firms characteristics. Stocks with high cyber scores......(摘要翻译及全文见知识星球)Keywords :
[10] A Market for Lemons Strategic Directions for a Vigilant Application of Artificial Intelligence in Entrepreneurship Research
标题:柠檬市场——人工智能在创业研究中谨慎应用的战略方向作者:Martin Obschonka, Moren Levesque来源:ARXIV_20240916Abstract : The rapid expansion of AI adoption (e.g., using machine learning, deep learning, or large language models as research methods) and the increasing availability of big data have the potential to bring about the most significant transformation in entrepreneurship scholarship the field has ever witnessed. This article makes a pressing meta contribution by highlighting a significant risk of unproductive knowledge exchanges in......(摘要翻译及全文见知识星球)Keywords :
[11] Anatomy of Machines for Markowitz
标题:Markowitz机器剖析作者:Junhyeong Lee, Inwoo Tae, Yongjae Lee来源:ARXIV_20240917Abstract : Markowitz laid the foundation of portfolio theory through the mean variance optimization (MVO) framework. However, the effectiveness of MVO is contingent on the precise estimation of expected returns, variances, and covariances of asset returns, which are typically uncertain. Machine learning models are becoming useful in estimating uncertain parameters, and such models are trained to minimize prediction errors, such as mean squared......(摘要翻译及全文见知识星球)Keywords :
[12] Research and Design of a Financial Intelligent Risk Control Platform Based on Big Data Analysis and Deep Machine Learning
标题:基于大数据分析和深度机器学习的金融智能风险控制平台的研究与设计作者:Shuochen Bi, Yufan Lian, Ziyue Wang来源:ARXIV_20240917Abstract : In the financial field of the United States, the application of big data technology has become one of the important means for financial institutions to enhance competitiveness and reduce risks. The core objective of this article is to explore how to fully utilize big data technology to achieve complete integration of internal and external data of financial institutions, and create an......(摘要翻译及全文见知识星球)Keywords :
[13] Robust Reinforcement Learning with Dynamic Distortion Risk Measures
标题:具有动态失真风险度量的鲁棒强化学习作者:Anthony Coache, Sebastian Jaimungal来源:ARXIV_20240917Abstract : In a reinforcement learning (RL) setting, the agent s optimal strategy heavily depends on her risk preferences and the underlying model dynamics of the training environment. These two aspects influence the agent s ability to make well informed and time consistent decisions when facing testing environments. In this work, we devise a framework to solve robust risk aware RL problems where......(摘要翻译及全文见知识星球)Keywords :
