The Study of Using Machine Learning Algorithms to Construct Portfolio Formation

DOI

10.2991/aebmr.k.220307.345How to use a DOI?

Keywords

Stock Prediction; EWMA Model; Clustering Algorithms; Machine Learning; Portfolio Formation

Abstract

The earning of the investors in the stock market is related to their investment choice and the behavior of the stocks. The key to investors making a more intelligent investment decision is to predict the price and volatility of the stocks accurately. In addition, building a portfolio formation is also an effective way to reduce investment risk. However, the stock price is beyond the predictive ability of any individual, and the portfolio formations are hard to build due to the stock market volatiles incessantly. In such a condition, this project attempts to propose a methodology for designing portfolio formations by using machine learning methods. The ultimate aim of this project is to predict the profit and volatility of stocks in the American stock market and use these predictions to construct portfolio formations. In this paper, the Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), and Long Short-Term Memory model (LSTM) are used to predict the tendency of stocks. The experiment result shows that the LGBM model has the best performance on stocks price prediction. Based on the prediction of price, investing profit is easy to predict. The financial model called exponential weighted moving average (EWMA) is used to calculate the volatility of the stocks. After getting the prediction of profit and volatility, using the spectral clustering algorithm can get the portfolio formation. The result shows that the portfolio formation given by the proposed model has high profit and low volatility.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Zefeng Chen
PY  - 2022
DA  - 2022/03/26
TI  - The Study of Using Machine Learning Algorithms to Construct Portfolio Formation
BT  - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
PB  - Atlantis Press
SP  - 2105
EP  - 2109
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220307.345
DO  - 10.2991/aebmr.k.220307.345
ID  - Chen2022
ER  -