投资者兴趣的深度预测:一种监督聚类方法

IF 0.3 Q4 BUSINESS, FINANCE

Algorithmic Finance Pub Date : 2019-09-11 DOI:10.3233/AF-200296

Baptiste Barreau, Laurent Carlier, D. Challet

{"title":"投资者兴趣的深度预测:一种监督聚类方法","authors":"Baptiste Barreau, Laurent Carlier, D. Challet","doi":"10.3233/AF-200296","DOIUrl":null,"url":null,"abstract":"We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given time frame. This architecture performs both investor clustering and modelling at the same time. We first verify its superior performance on a synthetic scenario inspired by real data and then apply it to two real-world databases, a publicly available dataset about the position of investors in Spanish stock market and proprietary data from BNP Paribas Corporate and Institutional Banking.1,2","PeriodicalId":42207,"journal":{"name":"Algorithmic Finance","volume":"8 1","pages":"77-89"},"PeriodicalIF":0.3000,"publicationDate":"2019-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/AF-200296","citationCount":"0","resultStr":"{\"title\":\"Deep Prediction of Investor Interest: a Supervised Clustering Approach\",\"authors\":\"Baptiste Barreau, Laurent Carlier, D. Challet\",\"doi\":\"10.3233/AF-200296\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given time frame. This architecture performs both investor clustering and modelling at the same time. We first verify its superior performance on a synthetic scenario inspired by real data and then apply it to two real-world databases, a publicly available dataset about the position of investors in Spanish stock market and proprietary data from BNP Paribas Corporate and Institutional Banking.1,2\",\"PeriodicalId\":42207,\"journal\":{\"name\":\"Algorithmic Finance\",\"volume\":\"8 1\",\"pages\":\"77-89\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2019-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.3233/AF-200296\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Algorithmic Finance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/AF-200296\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Algorithmic Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/AF-200296","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}

引用次数: 0

摘要

我们提出了一种新的深度学习架构，适用于在给定时间框架内预测投资者对给定资产的兴趣。该体系结构同时执行投资者聚类和建模。我们首先在一个由真实数据启发的合成场景上验证了它的卓越性能，然后将其应用于两个真实世界的数据库，一个是关于西班牙股市投资者头寸的公开数据集，另一个是来自法国巴黎银行公司和机构银行业务的专有数据

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Deep Prediction of Investor Interest: a Supervised Clustering Approach

We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given time frame. This architecture performs both investor clustering and modelling at the same time. We first verify its superior performance on a synthetic scenario inspired by real data and then apply it to two real-world databases, a publicly available dataset about the position of investors in Spanish stock market and proprietary data from BNP Paribas Corporate and Institutional Banking.1,2

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Algorithmic Finance BUSINESS, FINANCE-

CiteScore

0.40

自引率

0.00%

发文量

期刊介绍： Algorithmic Finance is both a nascent field of study and a new high-quality academic research journal that seeks to bridge computer science and finance. It covers such applications as: High frequency and algorithmic trading Statistical arbitrage strategies Momentum and other algorithmic portfolio management Machine learning and computational financial intelligence Agent-based finance Complexity and market efficiency Algorithmic analysis of derivatives valuation Behavioral finance and investor heuristics and algorithms Applications of quantum computation to finance News analytics and automated textual analysis.