A Study on Financial Market Trend Prediction Based on Machine Learning Algorithms

Abstract

Accurate prediction of short-term and long-term trends in financial markets remains a challenging task due to the complexity and noise characteristics of financial time series. To address the limitations of existing methods that often produce biased prediction results, this paper proposes a financial market trend prediction approach based on machine learning techniques. Financial data are collected at fixed time intervals to construct time series sequences. Wavelet analysis is employed to preprocess the financial time series, effectively removing noise while preserving the essential characteristics of the original data. Subsequently, a long short-term memory (LSTM) neural network is utilized to learn temporal dependencies within the processed data and establish a financial market prediction model. Experimental results demonstrate that the proposed method can effectively suppress noise, smooth the original data, and achieve high prediction accuracy in both short-term and long-term trend forecasting.