Stock Market Trend Prediction and Investment Strategy by Deep Neural Networks

被引：3

作者：

Shi, Mingze ^{[1
]}

Zhao, Qiangfu ^{[1
]}

机构：

[1] Univ Aizu, Syst Intelligence Lab, Aizu Wakamatsu, Fukushima, Japan

来源：

2020 11TH INTERNATIONAL CONFERENCE ON AWARENESS SCIENCE AND TECHNOLOGY (ICAST) | 2020年

关键词：

stock market; stock price change; trend; turning point; golden cross; deep neural networks; precision accuracy; threshold; investment; expectation; confidence; profit;

D O I：

10.1109/ICAST51195.2020.9319488

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This research is mainly about the prediction of the price change in the stock market. Instead of daily change, this paper analyzes the trend of price change for weeks by judging turning points. Deep neural networks will be used as the classifier of true and fake golden crosses to judge the growth trend of price change. Most stocks on the sample list have positive profits after simulated trading of 10 years. Based on the results we may conclude that deep neural networks are helpful to assist users positively for stock investment.

引用

页数：6

共 6 条

[1] Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data [J].

Kim, Taewook ;

Kim, Ha Young .

PLOS ONE, 2019, 14 (02)

[2]

Lokeswari R Y. V., 2018, STOCK PRICE PREDICTI, P3342

[3]

Nelson DMQ, 2017, IEEE IJCNN, P1419, DOI 10.1109/IJCNN.2017.7966019

[4]

Pang X., P 3 INT C COMPL FUT, P102, DOI [10.5220/0006749901020108, DOI 10.5220/0006749901020108]

[5]

Yang J., 2020, DEEP LEARNING STOCK

[6]

Zou Z, 2020, Using LSTM in Stock Prediction and Quantitative Trading

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