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[1] Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books
探索加密货币限价订单簿中的微观结构动态
来源:ARXIV_20250609
[2] Explainable AI powered stock price prediction using time series transformers
使用时间序列变换器进行可解释的人工智能股票价格预测
来源:ARXIV_20250610
[3] Improving choice model specification using reinforcement learning
使用强化学习改进选择模型规范
来源:ARXIV_20250610
[4] Explaining Risks
解释风险
来源:ARXIV_20250610
[5] The impact of extracurricular education on socioeconomic mobility in Japan
课外教育对日本社会经济流动性的影响
来源:ARXIV_20250610
[6] Predicting Realized Variance Out of Sample
预测样本外的已实现方差
来源:ARXIV_20250610
[7] Advancing Exchange Rate Forecasting
推进汇率预测
来源:ARXIV_20250612
[8] Personal Finance Management System Using Java Swing and MySQL
基于Java Swing和MySQL的个人理财系统
来源:SSRN_20250612
[9] Integrated GARCH-GRU in Financial Volatility Forecasting
金融波动预测中的GARCH-GRU集成
来源:SSRN_20250612
[10] S shaped Utility Maximization with VaR Constraint and Partial Information
具有VaR约束和部分信息的S形效用最大化
来源:ARXIV_20250613
[11] An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data
使用支付系统数据预测通货膨胀的可解释机器学习方法
来源:ARXIV_20250613
[1] Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books
标题:探索加密货币限价订单簿中的微观结构动态
作者:Haochuan (Kevin)Wang
来源:ARXIV_20250609
Abstract : Cryptocurrency price dynamics are driven largely by microstructural supply demand imbalances in the limit order book (LOB), yet the highly noisy nature of LOB data complicates the signal extraction process. Prior research has demonstrated that deep learning architectures can yield promising predictive performance on pre processed equity and futures LOB data, but they often treat model complexity as an unqualified virtue.......(摘要翻译及全文见知识星球)
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[2] Explainable AI powered stock price prediction using time series transformers
标题:使用时间序列变换器进行可解释的人工智能股票价格预测
作者:Sukru Selim Calik, Andac Akyuz, Zeynep Hilal Kilimci, Kerem Colak
来源:ARXIV_20250610
Abstract : Financial literacy is increasingly dependent on the ability to interpret complex financial data and utilize advanced forecasting tools. In this context, this study proposes a novel approach that combines transformer based time series models with explainable artificial intelligence (XAI) to enhance the interpretability and accuracy of stock price predictions. The analysis focuses on the daily stock prices of the five highest......(摘要翻译及全文见知识星球)
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[3] Improving choice model specification using reinforcement learning
标题:使用强化学习改进选择模型规范
作者:Gabriel Nova, Sander van Cranenburgh, Stephane Hess
来源:ARXIV_20250610
Abstract : Discrete choice modelling is a theory driven modelling framework for understanding and forecasting choice behaviour. To obtain behavioural insights, modellers test several competing model specifications in their attempts to discover the true data generation process. This trial and error process requires expertise, is time consuming, and relies on subjective theoretical assumptions. Although metaheuristics have been proposed to assist choice......(摘要翻译及全文见知识星球)
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[4] Explaining Risks
标题:解释风险
作者:Dangxing Chen
来源:ARXIV_20250610
Abstract : In recent years, machine learning models have achieved great success at the expense of highly complex black box structures. By using axiomatic attribution methods, we can fairly allocate the contributions of each feature, thus allowing us to interpret the model predictions. In high risk sectors such as finance, risk is just as important as mean predictions. Throughout this work, we address......(摘要翻译及全文见知识星球)
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[5] The impact of extracurricular education on socioeconomic mobility in Japan
标题:课外教育对日本社会经济流动性的影响
作者:Yang Qiang
来源:ARXIV_20250610
Abstract : This paper explores the socioeconomic impacts of extracurricular education, specifically private tutoring, on social mobility in Japan. Using data from the 2015 National Survey on Social Stratification and Social Mobility (SSM), we employed a causal machine learning approach to evaluate this educational intervention on income, educational attainment, and occupational prestige. Our research suggests that while shadow education holds the potential for......(摘要翻译及全文见知识星球)
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[6] Predicting Realized Variance Out of Sample
标题:预测样本外的已实现方差
作者:Austin Pollok
来源:ARXIV_20250610
Abstract : The discrepancy between realized volatility and the market s view of volatility has been known to predict individual equity options at the monthly horizon. It is not clear how this predictability depends on a forecast s ability to predict firm level volatility. We consider this phenomenon at the daily frequency using high dimensional machine learning models, as well as low dimensional......(摘要翻译及全文见知识星球)
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[7] Advancing Exchange Rate Forecasting
标题:推进汇率预测
作者:Md. Yeasin Rahat, Rajan Das Gupta, Nur Raisa Rahman, Sudipto Roy Pritom, Samiur Rahman Shakir, Md Imrul Hasan Showmick, Md. Jakir Hossen
来源:ARXIV_20250612
Abstract : The prediction of foreign exchange rates, such as the US Dollar (USD) to Bangladeshi Taka (BDT), plays a pivotal role in global financial markets, influencing trade, investments, and economic stability. This study leverages historical USD BDT exchange rate data from 2018 to 2023, sourced from Yahoo Finance, to develop advanced machine learning models for accurate forecasting. A Long Short Term Memory......(摘要翻译及全文见知识星球)
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[8] Personal Finance Management System Using Java Swing and MySQL
标题:基于Java Swing和MySQL的个人理财系统
作者:Darshita Singh,Shreya Saini,Tanish Rashm,Anshu Rai,Punit Kumar
来源:SSRN_20250612
Abstract : This paper outlines the design and implementation of a Personal Finance Management System (PFMS) aimed at helping individuals manage their personal finances effectively. In this digital revolution era, sound financial planning and investment performance are crucial for individuals wanting to achieve long-term financial stability. Therefore, this study presents the design and implementation of an innovative Personal Finance Management and Investment System......(摘要翻译及全文见知识星球)
Keywords : Personal Finance Management, Java Swing, MySQL, Financial Tracking, Data Visualization
[9] Integrated GARCH-GRU in Financial Volatility Forecasting
标题:金融波动预测中的GARCH-GRU集成
作者:Jingyi Wei,Steve Y. Yang,Zhenyu Cui
来源:SSRN_20250612
Abstract : In this study, we propose a novel integrated Generalized Autoregressive Conditional Heteroskedasticity-Gated Recurrent Unit (GARCH-GRU) model for financial volatility modeling and forecasting. The model embeds the GARCH(1,1) formulation directly into the GRU cell architecture, yielding a unified recurrent unit that jointly captures both traditional econometric properties and complex temporal dynamics. This hybrid structure leverages the strengths of GARCH in modeling key......(摘要翻译及全文见知识星球)
Keywords : Volatility forecasting, Deep Learning, Neural Networks, GARCH models, Gated Recurrent Units, Long Short Term Memory, Value at Risk, Risk Management
[10] S shaped Utility Maximization with VaR Constraint and Partial Information
标题:具有VaR约束和部分信息的S形效用最大化
作者:Dongmei Zhu, Ashley Davey, Harry Zheng
来源:ARXIV_20250613
Abstract : We study S shaped utility maximisation with VaR constraint and unobservable drift coefficient. Using the Bayesian filter, the concavification principle, and the change of measure, we give a semi closed integral representation for the dual value function and find a critical wealth level that determines if the constrained problem admits a unique optimal solution and Lagrange multiplier or is infeasible. We......(摘要翻译及全文见知识星球)
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[11] An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data
标题:使用支付系统数据预测通货膨胀的可解释机器学习方法
作者:Wishnu Badrawani
来源:ARXIV_20250613
Abstract : This paper evaluates the performance of prominent machine learning (ML) algorithms in predicting Indonesia s inflation using the payment system, capital market, and macroeconomic data. We compare the forecasting performance of each ML model, namely shrinkage regression, ensemble learning, and super vector regression, to that of the univariate time series ARIMA and SARIMA models. We examine various out of bag sample......(摘要翻译及全文见知识星球)
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