Forecast of Stock Price

DOI

10.2991/978-94-6463-052-7_169How to use a DOI?

Keywords

stock price; stock volume; regression model; prediction model; Long- Short Term Memory (LSTM)

Abstract

The stock market is an important part of the financial market. The stock price prediction based on the model has very important practical significance for individuals and enterprises. So this paper uses regression models to fit past stock prices and forecast their future volume. This paper uses the polynomial regression method to regression the stock price from 2012 to 2017, and then uses LSTM to predict the inventory. The data used in this paper is from 2012 to 2017. Training on the data of the past few years, predicting the output in 2017, and then comparing it with the actual output. After training, the result shows that the trend of the predicted volume is similar to the actual volume in 2017. Therefore, LSTM truly forecasts the stock volume.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zizhan Jiang
PY  - 2022
DA  - 2022/12/27
TI  - Forecast of Stock Price
BT  - Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022)
PB  - Atlantis Press
SP  - 1529
EP  - 1539
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-052-7_169
DO  - 10.2991/978-94-6463-052-7_169
ID  - Jiang2022
ER  -