Forecasting daily stock trend using multi-filter feature selection and deep learning

计算机科学 人工智能 特征选择 生成模型 库存(枪支) 股票市场 计量经济学 机器学习 数据挖掘 生成语法 经济 机械工程 生物 工程类 古生物学
作者
Anwar Ul Haq,Adnan Zeb,Zhenfeng Lei,Defu Zhang
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:168: 114444-114444 被引量:127
标识
DOI:10.1016/j.eswa.2020.114444
摘要

Abstract Stock market forecasting has attracted significant attention mainly due to the potential monetary benefits. Predicting these markets is a challenging task due to numerous interrelated factors, and needs a complete and efficient feature selection process to identify the most informative factors. As a time series problem, stock price movements are also dependent on movements on its previous trading days. Feature selection techniques have been widely applied in stock forecasting, but existing approaches usually use a single feature selection technique, which may overlook some important assumptions about the underlying regression function linking the input and output variables. In this study, we combine features selected by multiple feature selection techniques to generate an optimal feature subset and then use a deep generative model to predict future price movements. First, we compute an extended set of forty-four technical indicators from daily stock data of eighty-eight stocks and then compute their importance by independently training logistic regression model, support vector machine and random forests. Based on a prespecified threshold, the lowest ranked features are dropped and the rest are grouped into clusters. The variable importance measure is reused to select the most important feature from each cluster to generate the final subset. The input is then fed to a deep generative model comprising of a market signal extractor and an attention mechanism. The market signal extractor recurrently decodes market movement from the latent variables to deal with stochastic nature of the stock data and the attention mechanism discriminates between predictive dependencies of different temporal auxiliary outputs. The results demonstrate that combining features selected by multiple feature selection approaches and using them as input into a deep generative model outperforms state-of-the-art approaches.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
情怀应助嘉嘉琦采纳,获得10
刚刚
Ava应助WSGQT采纳,获得10
1秒前
Jiang发布了新的文献求助10
2秒前
超帅听枫完成签到,获得积分20
2秒前
SYLH应助poli采纳,获得10
3秒前
生菜完成签到,获得积分10
3秒前
bxxxxx应助FFF采纳,获得30
3秒前
senhoo完成签到,获得积分10
4秒前
4秒前
JamesPei应助Ana采纳,获得10
4秒前
superbada发布了新的文献求助30
4秒前
希望天下0贩的0应助雪碧采纳,获得10
7秒前
爆米花应助科研通管家采纳,获得10
8秒前
坦率的匪应助科研通管家采纳,获得10
8秒前
8秒前
隐形曼青应助科研通管家采纳,获得10
8秒前
pcr163应助科研通管家采纳,获得80
8秒前
大模型应助科研通管家采纳,获得30
8秒前
Jasper应助科研通管家采纳,获得10
8秒前
大个应助科研通管家采纳,获得10
8秒前
科研通AI5应助舒服的乐曲采纳,获得10
8秒前
传奇3应助科研通管家采纳,获得10
8秒前
坦率的匪应助科研通管家采纳,获得10
8秒前
汉堡包应助科研通管家采纳,获得10
8秒前
8秒前
坦率的匪应助科研通管家采纳,获得10
8秒前
8秒前
quhayley应助科研通管家采纳,获得10
8秒前
要减肥岩应助科研通管家采纳,获得10
9秒前
彭于彦祖应助培爷采纳,获得30
9秒前
Mininine完成签到,获得积分10
9秒前
9秒前
Akim应助科研通管家采纳,获得10
9秒前
quhayley应助科研通管家采纳,获得10
9秒前
LUJyyyy完成签到,获得积分10
9秒前
SciGPT应助科研通管家采纳,获得10
9秒前
爆米花应助科研通管家采纳,获得10
9秒前
9秒前
SYLH应助科研通管家采纳,获得50
10秒前
JamesPei应助科研通管家采纳,获得10
10秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988732
求助须知:如何正确求助?哪些是违规求助? 3531027
关于积分的说明 11252281
捐赠科研通 3269732
什么是DOI,文献DOI怎么找? 1804764
邀请新用户注册赠送积分活动 881869
科研通“疑难数据库(出版商)”最低求助积分说明 809021