Prediction of Shanghai Composite Index Based on Macroeconomic Indicators and Artificial Intelligence Method

DOI

10.2991/978-94-6463-222-4_18How to use a DOI?

Keywords

Macroeconomics; Artificial intelligence; SSE Composite Index

Abstract

The stock market can be defined as a market that, on the one hand, facilitates companies that need financing and, on the other hand, provides opportunities for investors who need to invest. By predicting the rise and fall of stock indices, it can bring guidance to individuals and companies when to enter the financial market, and it can also provide theoretical implications for government economic policy making. However, the stock market is a complex system full of various information, it is not only affected by past information, but also by current political, economic and psychological factors, so it is difficult to accurately predict the rise and fall of the stock index. At present, the stock index rise and fall prediction methods are mainly applied technical analysis method and measurement time series analysis method, which applied technical method is used by more groups, because it almost does not need too much analysis but according to personal investment habits and experience, subjective color. The econometric time series method is a method that is effective only when used in an ideal situation, which requires the input of the independent variable indicators and the target variable is preferably linear, if it is a non-linear situation, the results will have no reference significance. In this paper, we combine the main capital flow model with support vector machine as a tool to construct a stock index up/down prediction scheme.

Copyright

© 2023 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  - Heng Lyu
AU  - Muqing Zhu
AU  - Hao Lin
AU  - Hanzhen Huang
AU  - Huiying Fang
AU  - Zili Chen
PY  - 2023
DA  - 2023/08/28
TI  - Prediction of Shanghai Composite Index Based on Macroeconomic Indicators and Artificial Intelligence Method
BT  - Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)
PB  - Atlantis Press
SP  - 185
EP  - 194
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-222-4_18
DO  - 10.2991/978-94-6463-222-4_18
ID  - Lyu2023
ER  -