# Research on Microsoft Stock Price Prediction Based on Various Models

### Yuanhao Fu

#### 2024

#### Abstract

With the development of social economy, stock investment is more and more popular. In the process of investing in stocks, people execute investment strategies in a quantitative trading manner, hoping to obtain the highest return with the least risk. To be successful in quantitative investing, the key is to build excellent mathematical models and grasp the accurate trading time node. The paper uses the dataset of Microsoft stock prices from April 2015 to April 2021 to build Machine learning models such as Linear regression, Time series models such as ARIMA, and LSTM model are used to fotcast the Microsoftâ€™s stock price. This thesis provides the theoretical knowledge of LSTM neural model and time series model, selects the actual stocks in the stock market, conducts modeling analysis and predicts the stock price, and then uses RMSE to compare the prediction results of several models. Since the time series model cannot get the utmost out of the non-linear part of the data and cannot carry out long-term memory, the LSTM neural network can make full use of it and long-term memory to obtain useful information in the stock data. In terms of root-mean-square error, LSTM neural network is smaller than the time series model, which indicates that LSTM neural network is a better method for prediction.

Download#### Paper Citation

#### in Harvard Style

Fu Y. (2024). **Research on Microsoft Stock Price Prediction Based on Various Models**. In *Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE*; ISBN 978-989-758-690-3, SciTePress, pages 11-16. DOI: 10.5220/0012807400004547

#### in Bibtex Style

@conference{icdse24,

author={Yuanhao Fu},

title={Research on Microsoft Stock Price Prediction Based on Various Models},

booktitle={Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE},

year={2024},

pages={11-16},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0012807400004547},

isbn={978-989-758-690-3},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Science and Engineering - Volume 1: ICDSE

TI - Research on Microsoft Stock Price Prediction Based on Various Models

SN - 978-989-758-690-3

AU - Fu Y.

PY - 2024

SP - 11

EP - 16

DO - 10.5220/0012807400004547

PB - SciTePress