Investment Market Trend Forecasting and Strategy Optimization based on Big Data Analytics

Yuyao Zhang

doi:10.54097/rwkp8y28

Authors

Yuyao Zhang

DOI:

https://doi.org/10.54097/rwkp8y28

Keywords:

Financial Time Series Forecasting; Investment Markets; Big Data Analytics; Trend Forecasting; Strategy Optimization; Machine Learning.

Abstract

The financial market is an important part of the global economy, and there exists a certain special internal regularity in its changes. With the rapid development of big data technology, the huge amount of data in the financial market provides new opportunities for the prediction of investment trends and strategy optimization. Based on big data analytics technology, combined with machine learning models, the study explores how to construct investment market trend prediction models through effective data preprocessing and feature engineering, and evaluates and compares the performance of different prediction models. How to accurately predict the direction of the financial market will be an important reference value for relevant practitioners as well as researchers. Traditional financial time series research mainly focuses on linear trend fitting based on statistical knowledge, however, the trend of the financial market is not limited to pure mathematical and rational function curves, the financial market is affected by a combination of various factors from all walks of life. Big data analytics has significant application potential in the investment market, providing investors with more scientific and accurate decision support.

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