[1] EUR USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods基于大语言模型和深度学习方法的信息融合的欧元兑美元汇率预测来源:ARXIV_20240826[2] Loss based Bayesian Sequential Prediction of Value at Risk with a Long Memory and Non linear Realized Volatility Model基于损失的贝叶斯序列预测具有长期记忆和非线性实现波动率模型的风险价值来源:ARXIV_20240827[3] DeepVoting深度投票来源:ARXIV_20240827[4] Conditional Non-linear Asset Pricing with Generalized Random Forests; Evidence and Examples广义随机森林条件非线性资产定价；证据和例子来源:SSRN_20240827[5] Evaluating Credit VIX (CDS IV) Prediction Methods with Incremental Batch Learning用增量批处理学习评估信用VIX（CDS IV）预测方法来源:ARXIV_20240829[6] Directional Stock Price Forecasting Based on Quantitative Value Investing Principles for Loss Averted Bogle-Head Investing using Various Machine Learning Algorithms基于定量价值投资原理的定向股票价格预测，使用各种机器学习算法进行损失避免的转向架投资来源:SSRN_20240901
[1] EUR USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods
标题:基于大语言模型和深度学习方法的信息融合的欧元兑美元汇率预测作者:Hongcheng Ding, Xuanze Zhao, Zixiao Jiang, Shamsul Nahar Abdullah, Deshinta Arrova Dewi来源:ARXIV_20240826Abstract : Accurate forecasting of the EUR USD exchange rate is crucial for investors, businesses, and policymakers. This paper proposes a novel framework, IUS, that integrates unstructured textual data from news and analysis with structured data on exchange rates and financial indicators to enhance exchange rate prediction. The IUS framework employs large language models for sentiment polarity scoring and exchange rate movement classification......(摘要翻译及全文见知识星球)Keywords :
[2] Loss based Bayesian Sequential Prediction of Value at Risk with a Long Memory and Non linear Realized Volatility Model
标题:基于损失的贝叶斯序列预测具有长期记忆和非线性实现波动率模型的风险价值作者:Rangika Peiris, Minh-Ngoc Tran, Chao Wang, Richard Gerlach来源:ARXIV_20240827Abstract : A long memory and non linear realized volatility model class is proposed for direct Value at Risk (VaR) forecasting. This model, referred to as RNN HAR, extends the heterogeneous autoregressive (HAR) model, a framework known for efficiently capturing long memory in realized measures, by integrating a Recurrent Neural Network (RNN) to handle non linear dynamics. Loss based generalized Bayesian inference with......(摘要翻译及全文见知识星球)Keywords :
[3] DeepVoting
标题:深度投票作者:Leonardo Matone, Ben Abramowitz, Nicholas Mattei, Avinash Balakrishnan来源:ARXIV_20240827
Abstract : Aggregating the preferences of multiple agents into a collective decision is a common step in many important problems across areas of computer science including information retrieval, reinforcement learning, and recommender systems. As Social Choice Theory has shown, the problem of designing algorithms for aggregation rules with specific properties (axioms) can be difficult, or provably impossible in some cases. Instead of designing......(摘要翻译及全文见知识星球)Keywords :
[4] Conditional Non-linear Asset Pricing with Generalized Random Forests; Evidence and Examples
标题:广义随机森林条件非线性资产定价；证据和例子作者:Ahmad Aghapour,Hamid R. Arian,Marcos Escobar-Anel来源:SSRN_20240827Abstract : This short work proposes a methodology to improve Asset Pricing Models (APM), by conditioning on important market factors via Generalized Random Forests (GRFs). We assemble the evidence using two well-known examples of APM: the Capital Asset Pricing Model (CAPM) and the Three-factor Fama-French Model (3FF). We use the VIX, a popular volatility measure, as an example of a conditioning variable. The......(摘要翻译及全文见知识星球)Keywords : Asset Pricing, Machine Learning, Generalized Random Forest
标题:用增量批处理学习评估信用VIX（CDS IV）预测方法作者:Robert Taylor来源:ARXIV_20240829Abstract : This paper presents the experimental process and results of SVM, Gradient Boosting, and an Attention GRU Hybrid model in predicting the Implied Volatility of rolled over five year spread contracts of credit default swaps (CDS) on European corporate debt during the quarter following mid May 24, as represented by the iTraxx Cboe Europe Main 1 Month Volatility Index (BP Volatility).......(摘要翻译及全文见知识星球)Keywords :
[6] Directional Stock Price Forecasting Based on Quantitative Value Investing Principles for Loss Averted Bogle-Head Investing using Various Machine Learning Algorithms
标题:基于定量价值投资原理的定向股票价格预测，使用各种机器学习算法进行损失避免的转向架投资作者:Agnij Moitra来源:SSRN_20240901Abstract : Boglehead investing, founded on the principles of John C. Bogle is one of the classic time tested long term, low cost, and passive investment strategy. This paper uses various machine learning methods, and fundamental stock data in order to predict whether or not a stock would incur negative returns next year, and suggests a loss averted bogle-head strategy to invest in......(摘要翻译及全文见知识星球)Keywords : Stock market, Stock picking, Directional price forecasting, Machine learning
