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[1] GRU PFG
GRU PFG
来源:ARXIV_20241202
[2] Deep learning interpretability for rough volatility
粗糙波动性的深度学习可解释性
来源:ARXIV_20241202
[3] Dynamic ETF Portfolio Optimization Using enhanced Transformer Based Models for Covariance and Semi Covariance Prediction(Work in Progress)
使用基于增强变换器的模型进行协方差和半协方差预测的动态ETF投资组合优化（正在进行中）
来源:ARXIV_20241202
[4] BPQP
BPQP
来源:ARXIV_20241202
[5] Neural Network Approach to Demand Estimation and Dynamic Pricing in Retail
零售业需求估计和动态定价的神经网络方法
来源:ARXIV_20241203
[6] Deep Learning Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Market
基于深度学习的批发电力市场虚拟竞价电价预测
来源:ARXIV_20241203
[7] Research on Optimizing Real Time Data Processing in High Frequency Trading Algorithms using Machine Learning
基于机器学习的高频交易算法实时数据处理优化研究
来源:ARXIV_20241203
[8] Unsupervised Learning based Calibration Scheme for the Rough Bergomi Model
基于无监督学习的粗糙Bergomi模型校准方案
来源:ARXIV_20241204
[9] Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning
驯服维度的诅咒：深度学习的定量经济学
来源:SSRN_20241204
[10] Deep Learning Interpretability for Rough Volatility
粗糙波动的深度学习可解释性
来源:SSRN_20241205
[11] Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models
基于时间序列变换模型的银行高级风险预测和稳定性评估
来源:ARXIV_20241206
[12] Research on Financial Multi Asset Portfolio Risk Prediction Model Based on Convolutional Neural Networks and Image Processing
基于卷积神经网络和图像处理的金融多资产组合风险预测模型研究
来源:ARXIV_20241206
[13] Multi Scale Node Embeddings for Graph Modeling and Generation
用于图形建模和生成的多尺度节点嵌入
来源:ARXIV_20241206
[14] Projecting Financial Statements with Artificial Intelligence
用人工智能预测财务报表
来源:SSRN_20241207
[15] Does One Pattern Fit All? Image Analysis for Different Equity Styles
一种模式适合所有人吗？不同股权风格的图像分析
来源:SSRN_20241207
[1] GRU PFG
标题:GRU PFG
作者:Yonggai Zhuang, Haoran Chen, Kequan Wang, Teng Fei
来源:ARXIV_20241202
Abstract : The complexity of stocks and industries presents challenges for stock prediction. Currently, stock prediction models can be divided into two categories. One category, represented by GRU and ALSTM, relies solely on stock factors for prediction, with limited effectiveness. The other category, represented by HIST and TRA, incorporates not only stock factors but also industry information, industry financial reports, public sentiment, and......(摘要翻译及全文见知识星球)
Keywords :
[2] Deep learning interpretability for rough volatility
标题:粗糙波动性的深度学习可解释性
作者:Bo Yuan, Damiano Brigo, Antoine Jacquier, Nicola Pede
来源:ARXIV_20241202
Abstract : Deep learning methods have become a widespread toolbox for pricing and calibration of financial models. While they often provide new directions and research results, their black box nature also results in a lack of interpretability. We provide a detailed interpretability analysis of these methods in the context of rough volatility a new class of volatility models for......(摘要翻译及全文见知识星球)
Keywords :
[3] Dynamic ETF Portfolio Optimization Using enhanced Transformer Based Models for Covariance and Semi Covariance Prediction(Work in Progress)
标题:使用基于增强变换器的模型进行协方差和半协方差预测的动态ETF投资组合优化（正在进行中）
作者:Jiahao Zhu, Hengzhi Wu
来源:ARXIV_20241202
Abstract : This study explores the use of Transformer based models to predict both covariance and semi covariance matrices for ETF portfolio optimization. Traditional portfolio optimization techniques often rely on static covariance estimates or impose strict model assumptions, which may fail to capture the dynamic and non linear nature of market fluctuations. Our approach leverages the power of Transformer models to generate adaptive,......(摘要翻译及全文见知识星球)
Keywords :
[4] BPQP
标题:BPQP
作者:Jianming Pan, Zeqi Ye, Xiao Yang, Xu Yang, Weiqing Liu, Lewen Wang, Jiang Bian
来源:ARXIV_20241202
Abstract : Data driven decision making processes increasingly utilize end to end learnable deep neural networks to render final decisions. Sometimes, the output of the forward functions in certain layers is determined by the solutions to mathematical optimization problems, leading to the emergence of differentiable optimization layers that permit gradient back propagation. However, real world scenarios often involve large scale datasets and numerous......(摘要翻译及全文见知识星球)
Keywords :
[5] Neural Network Approach to Demand Estimation and Dynamic Pricing in Retail
标题:零售业需求估计和动态定价的神经网络方法
作者:Kirill Safonov
来源:ARXIV_20241203
Abstract : This paper contributes to the literature on parametric demand estimation by using deep learning to model consumer preferences. Traditional econometric methods often struggle with limited within product price variation, a challenge addressed by the proposed neural network approach. The proposed method estimates the functional form of the demand and demonstrates higher performance in both simulations and empirical applications. Notably, under low......(摘要翻译及全文见知识星球)
Keywords :
[6] Deep Learning Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Market
标题:基于深度学习的批发电力市场虚拟竞价电价预测
作者:Xuesong Wang, Sharaf K. Magableh, Oraib Dawaghreh, Caisheng Wang, Jiaxuan Gong, Zhongyang Zhao, Michael H. Liao
来源:ARXIV_20241203
Abstract : Virtual bidding plays an important role in two settlement electric power markets, as it can reduce discrepancies between day ahead and real time markets. Renewable energy penetration increases volatility in electricity prices, making accurate forecasting critical for virtual bidders, reducing uncertainty and maximizing profits. This study presents a Transformer based deep learning model to forecast the price spread between real time......(摘要翻译及全文见知识星球)
Keywords :
[7] Research on Optimizing Real Time Data Processing in High Frequency Trading Algorithms using Machine Learning
标题:基于机器学习的高频交易算法实时数据处理优化研究
作者:Yuxin Fan, Zhuohuan Hu, Lei Fu, Yu Cheng, Liyang Wang, Yuxiang Wang
来源:ARXIV_20241203
Abstract : High frequency trading (HFT) represents a pivotal and intensely competitive domain within the financial markets. The velocity and accuracy of data processing exert a direct influence on profitability, underscoring the significance of this field. The objective of this work is to optimise the real time processing of data in high frequency trading algorithms. The dynamic feature selection mechanism is responsible for......(摘要翻译及全文见知识星球)
Keywords :
[8] Unsupervised Learning based Calibration Scheme for the Rough Bergomi Model
标题:基于无监督学习的粗糙Bergomi模型校准方案
作者:Changqing Teng, Guanglian Li
来源:ARXIV_20241204
Abstract : Current deep learning based calibration schemes for rough volatility models are based on the supervised learning framework, which can be costly due to a large amount of training data being generated. In this work, we propose a novel unsupervised learning based scheme for the rough Bergomi (rBergomi) model which does not require accessing training data. The main idea is to use......(摘要翻译及全文见知识星球)
Keywords :
[9] Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning
标题:驯服维度的诅咒：深度学习的定量经济学
作者:Jesús Fernández-Villaverde,Galo Nuño,Jesse Perla
来源:SSRN_20241204
Abstract : We argue that deep learning provides a promising avenue for taming the curse of dimensionality in quantitative economics. We begin by exploring the unique challenges posed by solving dynamic equilibrium models, especially the feedback loop between individual agents’ decisions and the aggregate consistency conditions required by equilibrium. Following this, we introduce deep neural networks and demonstrate their application by solving the......(摘要翻译及全文见知识星球)
Keywords : deep learning, quantitative economics
[10] Deep Learning Interpretability for Rough Volatility
标题:粗糙波动的深度学习可解释性
作者:Bo Yuan,Damiano Brigo,Antoine (Jack) Jacquier,Nicola Pede
来源:SSRN_20241205
Abstract : Deep learning methods have become a widespread toolbox for pricing and calibration of financial models. While they often provide new directions and research results, their 'black box' nature also results in a lack of interpretability. We provide a detailed interpretability analysis of these methods in the context of rough volatility-a new class of volatility models for Equity and FX markets. Our......(摘要翻译及全文见知识星球)
Keywords : rough volatility, deep learning, interpretability, Shapley values, surrogate models, option pricing
[11] Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models
标题:基于时间序列变换模型的银行高级风险预测和稳定性评估
作者:Wenying Sun, Zhen Xu, Wenqing Zhang, Kunyuan Ma, You Wu, Mengfang Sun
来源:ARXIV_20241206
Abstract : This paper aims to study the prediction of the bank stability index based on the Time Series Transformer model. The bank stability index is an important indicator to measure the health status and risk resistance of financial institutions. Traditional prediction methods are difficult to adapt to complex market changes because they rely on single dimensional macroeconomic data. This paper proposes a......(摘要翻译及全文见知识星球)
Keywords :
[12] Research on Financial Multi Asset Portfolio Risk Prediction Model Based on Convolutional Neural Networks and Image Processing
标题:基于卷积神经网络和图像处理的金融多资产组合风险预测模型研究
作者:Zhuohuan Hu, Fu Lei, Yuxin Fan, Zong Ke, Ge Shi, Zichao Li
来源:ARXIV_20241206
Abstract : In today s complex and volatile financial market environment, risk management of multi asset portfolios faces significant challenges. Traditional risk assessment methods, due to their limited ability to capture complex correlations between assets, find it difficult to effectively cope with dynamic market changes. This paper proposes a multi asset portfolio risk prediction model based on Convolutional Neural Networks (CNN). By utilizing......(摘要翻译及全文见知识星球)
Keywords :
[13] Multi Scale Node Embeddings for Graph Modeling and Generation
标题:用于图形建模和生成的多尺度节点嵌入
作者:Riccardo Milocco, Fabian Jansen, Diego Garlaschelli
来源:ARXIV_20241206
Abstract : Lying at the interface between Network Science and Machine Learning, node embedding algorithms take a graph as input and encode its structure onto output vectors that represent nodes in an abstract geometric space, enabling various vector based downstream tasks such as network modelling, data compression, link prediction, and community detection. Two apparently unrelated limitations affect these algorithms. On one hand, it......(摘要翻译及全文见知识星球)
Keywords :
[14] Projecting Financial Statements with Artificial Intelligence
标题:用人工智能预测财务报表
作者:Paul Geertsema,Helen Lu,Guang Ma
来源:SSRN_20241207
Abstract : We introduce a novel artificial intelligence framework for projecting the entire set of financial statements. Our approach integrates multi-target learning and chained learning to predict interdependent financial statement items, capturing the intricate relationships across income statement and balance sheet components. Leveraging gradient boosting machines (GBMs) as the base learner, the framework employs a three-step process to optimise chaining sequences and expand......(摘要翻译及全文见知识星球)
Keywords : artificial intelligence, machine learning, multi-target forecasting, chained learning, financial statement projection
[15] Does One Pattern Fit All? Image Analysis for Different Equity Styles
标题:一种模式适合所有人吗？不同股权风格的图像分析
作者:YuChen Den,Kendro Vincent
来源:SSRN_20241207
Abstract : This paper investigates the predictability of stock returns using Convolutional Neural Networks (CNNs) applied to images generated from stock prices and volumes. We employ multi-class classification models to predict stock returns which outperform binary classification models. Additionally, our study examines the predictability of different stock styles, revealing that small-capital stocks generally exhibit higher annual Sharpe ratios and more pronounced monthly excess......(摘要翻译及全文见知识星球)
Keywords : stock chart pattern, cross-sectional return predictability, technical analysis No. 64
