Stock Price Prediction via Discovering Multi-Frequency Trading Patterns

计算机科学 计量经济学 高频交易 交易策略 股票市场 库存(枪支) 算法交易 时频分析 金融经济学 经济 电信 机械工程 古生物学 雷达 工程类 生物
作者
Liheng Zhang,Charų C. Aggarwal,Guo-Jun Qi
标识
DOI:10.1145/3097983.3098117
摘要

Stock prices are formed based on short and/or long-term commercial and trading activities that reflect different frequencies of trading patterns. However, these patterns are often elusive as they are affected by many uncertain political-economic factors in the real world, such as corporate performances, government policies, and even breaking news circulated across markets. Moreover, time series of stock prices are non-stationary and non-linear, making the prediction of future price trends much challenging. To address them, we propose a novel State Frequency Memory (SFM) recurrent network to capture the multi-frequency trading patterns from past market data to make long and short term predictions over time. Inspired by Discrete Fourier Transform (DFT), the SFM decomposes the hidden states of memory cells into multiple frequency components, each of which models a particular frequency of latent trading pattern underlying the fluctuation of stock price. Then the future stock prices are predicted as a nonlinear mapping of the combination of these components in an Inverse Fourier Transform (IFT) fashion. Modeling multi-frequency trading patterns can enable more accurate predictions for various time ranges: while a short-term prediction usually depends on high frequency trading patterns, a long-term prediction should focus more on the low frequency trading patterns targeting at long-term return. Unfortunately, no existing model explicitly distinguishes between various frequencies of trading patterns to make dynamic predictions in literature. The experiments on the real market data also demonstrate more competitive performance by the SFM as compared with the state-of-the-art methods.
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
徐进发布了新的文献求助10
1秒前
jiaolulu发布了新的文献求助10
1秒前
乐观银耳汤完成签到,获得积分10
2秒前
WJing完成签到,获得积分10
2秒前
lenetivy发布了新的文献求助20
2秒前
4秒前
linhanwenzhou发布了新的文献求助10
6秒前
yyy完成签到 ,获得积分10
6秒前
幽默的煎饼完成签到,获得积分10
6秒前
7秒前
搞怪不斜完成签到,获得积分10
7秒前
7秒前
xinxiangshicheng完成签到 ,获得积分10
8秒前
愤怒的小鸟完成签到,获得积分10
8秒前
MY完成签到,获得积分10
8秒前
顾矜应助lenetivy采纳,获得10
9秒前
自觉寒梦发布了新的文献求助10
9秒前
美好斓发布了新的文献求助10
9秒前
郑文涛完成签到,获得积分10
10秒前
JamesPei应助专注的白柏采纳,获得10
11秒前
YHY发布了新的文献求助10
13秒前
好吃发布了新的文献求助10
13秒前
拾光完成签到,获得积分10
14秒前
long完成签到 ,获得积分10
14秒前
天天向上发布了新的文献求助10
15秒前
6260完成签到,获得积分10
15秒前
pcr163应助linhanwenzhou采纳,获得50
16秒前
16秒前
酷酷元风完成签到,获得积分10
17秒前
18秒前
天才幸运鱼完成签到,获得积分10
18秒前
19秒前
19秒前
粥游天下完成签到,获得积分10
20秒前
jcc完成签到,获得积分10
20秒前
哈哈哈哈完成签到,获得积分10
20秒前
lighthouse完成签到,获得积分10
21秒前
平凡中的限量版完成签到,获得积分10
21秒前
大伟完成签到,获得积分10
21秒前
long关注了科研通微信公众号
22秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
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 4038368
求助须知:如何正确求助?哪些是违规求助? 3576068
关于积分的说明 11374313
捐赠科研通 3305780
什么是DOI,文献DOI怎么找? 1819322
邀请新用户注册赠送积分活动 892672
科研通“疑难数据库(出版商)”最低求助积分说明 815029