Forecasting Stock Price Trends by Analyzing Economic Reports With Analyst Profiles

Area of Science:

• Computational Finance
• Natural Language Processing
• Machine Learning

Background:

• Financial analysts play a crucial role in forecasting corporate performance and stock prices.
• Analyst reports contain valuable information but are often unstructured text.
• Predicting forecast accuracy is essential for investment decisions.

Purpose of the Study:

• To develop a methodology for forecasting analysts' estimated net income and stock prices.
• To investigate the impact of analyst profiles on forecast accuracy.
• To evaluate the contribution of opinion vs. non-opinion sentences in analyst reports.

Main Methods:

• Utilized natural language processing (NLP) to extract opinion sentences from analyst reports.
• Employed neural networks to forecast net income and stock prices.
• Incorporated analyst profiles (name, company, sector, ranking) as input features.

Main Results:

• Analyst profiles significantly enhance the accuracy of the forecasting models.
• The distinction between opinion and non-opinion sentences did not notably impact forecast performance.
• The methodology demonstrated the potential of NLP and neural networks in financial forecasting.

Conclusions:

• Analyst characteristics, captured in their profiles, are strong predictors of forecast accuracy.
• Future research should focus on leveraging richer analyst profile data.
• The NLP-based approach offers a novel way to extract predictive signals from financial text.