Iowa State University Capstones, Theses and Creative Components Dissertations Spring 2021 Leveraging Text Mining and Analytical Technology to Enhance Financial Planning and Analysis Yih-Shan Sheu Follow this and additional works at: https://lib.dr.iastate.edu/creativecomponents Part of the Business Analytics Commons Recommended Citation Sheu, Yih-Shan, "Leveraging Text Mining and Analytical Technology to Enhance Financial Planning and Analysis" (2021). Creative Components. 810. https://lib.dr.iastate.edu/creativecomponents/810 This Creative Component is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Creative Components by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Leveraging Text Mining and Analytical Technology to Enhance Financial Planning and Analysis by Yih-Shan Sheu A creative component submitted to the graduate faculty in partial fulfillment of the requirements for the degree of MASTER OF BUSINESS ADMINISTRATION (MBA) and MASTER OF SCIENCE (MS) Major: Business Analytics (MBA) and Information Systems (MS) Program of Study Committee: Dr. Anthony M. Townsend, Major Professor (MSIS) Dr. Valentina Salotti (MBA) The student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this creative component. The Graduate College will ensure this creative component is globally accessible and will not permit alterations after a degree is conferred. Iowa State University Ames, Iowa 2021 Copyright © Yih-Shan Sheu, 2021. All rights reserved. 1 TABLE OF CONTENTS LIST OF FIGURES ............................................................................................................ 3 ABSTRACT ....................................................................................................................... 4 1. INTRODUCTION .......................................................................................................... 4 2. RESEARCH QUESTIONS (RQs) ................................................................................. 6 3. LITERATURE REVIEW ............................................................................................... 9 3.1. Text Mining / Textual Analytics in Financial Reporting (Qualitative analysis) ... 10 3.2. Algorithmic Financial Forecasting in Corporate Finance (Quantitative analysis) 13 3.2.1 Big-Data Demand-Driven Forecasting ............................................................. 14 3.2.2 Uber's Financial Intelligence- Financial Cloud Platforms ............................... 18 3.2.3 Foreseen Modernized Forecasting .................................................................... 20 4. METHODOLOGY ....................................................................................................... 21 4.1. Textual Analytics (Qualitative analysis) ............................................................... 21 4.2. AI-based Forecasting Modeling (Quantitative analysis) ....................................... 40 5. CHALLENGES AND FUTURE SCOPE .................................................................... 50 6. CONCLUSION ............................................................................................................ 50 ACKNOWLEDGMENTS ................................................................................................ 52 REFERENCES ................................................................................................................. 53 RESOURCES ................................................................................................................... 56 2 LIST OF FIGURES Figure 1- Porter's Value Chain ...................................................................................................... 7 Figure 2- Forecasting using Machine Learning .......................................................................... 17 Figure 3- Kumar et al. (2020) 's big data-driven framework for demand-driven forecasting. .... 17 Figure 4- Performance matrices result on demand data based on Kumar et al. (2020) 's model 18 Figure 5- Uber's Financial Cloud Platform System Architecture ............................................... 19 Figure 6- Fog Index ..................................................................................................................... 23 Figure 7- RapidMiner: Process Documents from Files Operators .............................................. 28 Figure 8- RapidMiner: Edit Parameters: text directories ............................................................ 28 Figure 9- RapidMiner: Edit Parameters: vector creation ............................................................ 29 Figure 10- RapidMiner: Passive Verbs Analysis ........................................................................ 30 Figure 11- RapidMiner: Filter Tokens (by Content) Operator .................................................... 31 Figure 12- RapidMiner: Edit Regular Expressions ..................................................................... 31 Figure 13- RapidMiner: Uncertainty Words analysis (Sears) ..................................................... 32 Figure 14- RapidMiner: Uncertainty Words analysis (Target) ................................................... 32 Figure 15- RapidMiner: Uncertainty Words analysis for Continuous Four Quarters ................. 33 Figure 16- RapidMiner: Complexity Words analysis for Continuous Four Quarters ................. 34 Figure 17- RapidMiner: Extract Sentiment Operator .................................................................. 37 Figure 18- RapidMiner: VADER Sentiment Analysis for Continuous Four Quarters ............... 37 Figure 19- RapidMiner: Scoring String (Positivity) .................................................................... 38 Figure 20- RapidMiner: Scoring String (Negativity) .................................................................. 38 Figure 21- RapidMiner: Scoring Heatmap (Total Score/ Positivity/ Negativity) ....................... 39 Figure 22- RapidMiner: Score range ........................................................................................... 39 Figure 23- JMP: Retail Sales Dataset .......................................................................................... 42 Figure 24- JMP: Merging Retail Sales Dataset ........................................................................... 42 Figure 25- JMP: Time-Series Analysis ....................................................................................... 43 Figure 26- JMP: Seasonal ARIMA / Seasonal Exponential Smoothing models ........................ 44 Figure 27- JMP: “Time-Series Forecast” Analysis with Fitted Model (Store 1) ........................ 45 Figure 28- JMP: “Time-Series Forecast” Analysis with Fitted Model (Store 20) ...................... 46 Figure 29- JMP: Multivariate Method- Scatterplot Matrix ......................................................... 47 Figure 30- JMP: Bootstrap Forest ............................................................................................... 48 Figure 31- JMP: Boosted Tree .................................................................................................... 48 3 ABSTRACT Big data technologies have substantially affected various industries. Though data science has been the most valuable evolution in the age of technological innovation, the financial sector is lagging behind other sectors through leveraging data science to evolve quickly and emphasize competency in data analytics. Although big data technology used in financial services, such as FinTech and stock trending models, has grown immensely in the past few years, there is still little research in Corporate Finance. This paper focuses on the big-data technology application in corporate finance via text mining and algorithmic forecasting model. This study aims to answer the following two research questions: (i) How to handle unstructured information to gain an in- depth understanding of qualitative data that will impact the financial performance; (ii) How could machine learning help Corporate Finance acquire better market trend insights and achieve precise sales prediction as well as financial forecasting? In order to answer these questions, a qualitative analysis of literature is carried out comprehensively. Recent research and study indicate that such applications in corporate finance can significantly benefit the corporate decision-making process due to more timely, more relevant, and customer-oriented factors involving qualitative data sources. Finally, the paper briefly discusses the current challenges and limitations and points out the potential future scope of data technology in corporate finance. 1. INTRODUCTION Today, finance talent with a robust professional background is no longer sufficiently competitive to become a value-added business partner over the long term. Data science1 has been emerging within business sectors such as financial markets, internet banking, and financial service management. "Data science" in this paper would refer to specific 1Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big