A Modelling Framework of an Intelligent News Recommendation Engine for Financial Analysts

Abstract

Referring to news articles has become a predominant task in the daily routine of each financial analyst, as it helps to make accurate financial insights. However, as there are thousands of news articles generated daily, finding out the most relevant articles becomes a time-consuming task. Hence, this study develops a modelling framework of an intelligent news recommendation engine for financial analysts in a particular financial company, which assists to recommend the most appropriate articles according to analysts' preferences in an efficient and effective manner without tedious browsing.
In this study, data collection phase of the recommendation engine is accomplished through retrieving news articles from online news websites using web scraping technique. The response variable, analysts’ preference level for each article is manually acquired from a group of financial analysts at a selected financial company. In the analysis phase of the recommendation engine, a classification-based method was utilized where three machine learning models, KNN, SVM, Random Forest and two deep learning models, LSTM and CNN with Natural Language Processing techniques are experimented to discover the best algorithm. Moreover, synonym replacement method is employed as a text data augmentation method to address the imbalanced problem in the dataset.
As CNN obtains the highest accuracy with comparison to other applied methods it is chosen as the most suitable approach in the analysis phase. Moreover, this study reveals that DL models perform considerably high performances in the context of news recommendation engines rather than ML approaches. Lastly, this framework can be adjustable to any financial company with minor modifications.