Constructing Robust Financial Models Utilizing Deep Learning
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This project proposes the use of Attention-based LSTMs for Aspect-Level Sentiment Classification in financial documents. Extracting sentiment in this method can enable more accurate sentiment metrics which can be used to both automate and improve the financial modelling process for public firms. ...learn more
Project status: Concept
Groups
Student Developers for AI
Intel Technologies
AI DevCloud / Xeon
Overview / Usage
Proposed Application:
This project seeks to improve the robustness of financial modeling by leveraging cutting-edge deep learning technology to develop sentiment metrics for financial performance measures from several sources. Sentiment metrics will be extracted from a few sources including but not limited to the “Management Discussion and Analysis” & Factors” portion of 10-K/10-Q (Annual/Quarterly Financial Reports) documents, quarterly and annual earnings call discussions, as well as any other public sources of data such as interviews or social media in which management discussed financial performance of the respective firm. Often the 10-K/10-Q documents as well as other commentary that is provided by management is referred to as guidance for predicting future financial performance by firms.
The sentiment metrics generated can be utilized by Financial Modelers as an alternative data point consideration to build more accurate financial models. Improving the accuracy of Financial Modeling can lead to better strategic business decision-making within a firm, better investment insights when considering equity and credit-based investments in companies, enable more accurate pricing of securities, and grant more insight for M&A decision-making.
Technologies Used
This project will leverage Intel AI DevCloud, Intel’s Distribution Of Python, Intel’s Optimization of Tensorflow, and pWord2Vec (C++ implementation of Word2Vec which is optimized for Intel Xeon and Xeon Phi Processers.
Most notably, this project will leverage NLP Architect, an open source library of AI models, coding notebooks, and frameworks purpose-built for a range of Natural Language Processing (NLP) tasks. NLP architect also recently launched, 05/02/2019 in version 0.4, models for sentiment analysis for Aspect-Based Sentiment Analysis (ABSA). The ABSA feature is a perfect fit for this project as it’s a lightly supervised model which enables it to ingest unlabeled text and output opinion and aspect lexicons after domain-specific lexicons are defined.
This project leverages other frameworks, libraries, API’s and technologies not mentioned within this paper.