Financial Time Series Forecasting: Semantic Analysis Of Economic News

TL;DR

This paper introduces a method that uses semantic analysis of economic news, based on a dictionary of positive and negative words, combined with neural networks, to improve financial time series forecasting.

Contribution

It presents a novel approach integrating semantic analysis of news with neural network models for stock price prediction.

Findings

Neural network models showed high accuracy in forecasting stock prices.

Semantic analysis of news improves prediction performance.

The method automates news information extraction for financial forecasting.

Abstract

The paper proposes a method of financial time series forecasting taking into account the semantics of news. For the semantic analysis of financial news the sampling of negative and positive words in economic sense was formed based on Loughran McDonald Master Dictionary. The sampling included the words with high frequency of occurrence in the news of financial markets. For single-root words it has been left only common part that allows covering few words for one request. Neural networks were chosen for modeling and forecasting. To automate the process of extracting information from the economic news a script was developed in the MATLAB Simulink programming environment, which is based on the generated sampling of positive and negative words. Experimental studies with different architectures of neural networks showed a high adequacy of constructed models and confirmed the feasibility of using information from news feeds to predict the stock prices.