Forecast of Stock Price

Zizhan Jiang ()
Additional contact information
Zizhan Jiang: The University of Bath

A chapter in Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022), 2022, pp 1529-1539 from Springer

Abstract: 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.

Keywords: stock price; stock volume; regression model; prediction model; Long- Short Term Memory (LSTM) (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-052-7_169

Ordering information: This item can be ordered from
http://www.springer.com/9789464630527

DOI: 10.2991/978-94-6463-052-7_169

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().