NLP in FinTech: Developing a Lightweight Text-to-Chart Application for Financial Analysis

Abstract

Natural language processing (NLP), a subcategory of artificial intelligence and linguistics, can involve a range of tasks such as identifying, categorizing, summarizing, contextualizing, sentiment analysis, and creating question and answer systems. These tasks are extensively utilized in industry for building question and answer systems and creating virtual assistants that interact with humans. For these purposes, large language models, customized language models or pre-trained models are widely used in the literature. In this study, we aim to develop a lightweight application tailored for the financial analysis sector using NLP. This study contributes to the literature by introducing the development of a pre-trained language model for effective use in financial analysis tasks. In financial analysis, tasks such as time series analysis, investment decision-making, and automated trading (using robots called algos) require expert analysts and investors to use specialized languages like Metastock, MQL, Pine, and other low-code environments We developed a practical application natively in Python that utilizes text processing, NLP tasks, and RegEx libraries. The application processes input data consisting of texts in the natural languages of analysts and/or investors.