Rochester Institute of Technology RIT Scholar Works Theses 12-2020 Application of Machine Learning Models: Stock Price Forecasting of the Philippines' Top Six Conglomerates Anthony Rey Llanos [email protected] Follow this and additional works at: https://scholarworks.rit.edu/theses Recommended Citation Llanos, Anthony Rey, "Application of Machine Learning Models: Stock Price Forecasting of the Philippines' Top Six Conglomerates" (2020). Thesis. Rochester Institute of Technology. Accessed from This Master's Project is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For more information, please contact [email protected]. APPLICATION OF MACHINE LEARNING MODELS: STOCK PRICE FORECASTING OF THE PHILIPPINES’ TOP SIX CONGLOMERATES by Anthony Rey Llanos A Capstone Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science in Professional Studies: Data Analytics Department of Graduate Programs & Research Rochester Institute of Technology December 2020 RIT Master of Science in Professional Studies: Data Analytics Graduate Capstone Approval Student Name: Anthony Rey Llanos Graduate Capstone Title: Application of Machine Learning Models: Stock Price Forecasting of the Philippines’ Top Six Conglomerates Graduate Capstone Committee: Name: Dr. Sanjay Modak Date: Chair of committee Name: Dr. Ehsan Warriach Date: Member of committee Acknowledgements To my beautiful and loving wife, Ana Mae, my love and inspiration, my prayer partner and warrior. Special thanks to my mentor, Dr. Ehsan Warriach, for his guidance and support all throughout the process. My gratitude also goes to Dr. Sanjay Modak, Chair of Graduate Studies, for his valuable insights. Finally and most importantly, I thank the Lord Most High for it is He who accomplishes everything for me (Ps. 57) and who is the source of every good and perfect gift (James 1). AMDG. ii Abstract The advent of digital age dramatically changed the way all aspects of commerce is conducted. From the largest multi-national conglomerates to the least small-and-medium enterprises and to the unassuming business-savvy individuals have adapted to take advantage of the benefits afforded by the resulting digital technology. Investing in, and profiting from, shares of stocks of companies listed in an organized stock exchange is one such instance. Gone are the days wherein stock investors and brokers are inseparable from their telephones to handle trades. Online platforms, powered by machine learning algorithms, have made investing in stocks not only accessible and convenient but also more lucrative. These online stock brokerage platforms account for the bulk of daily trades across the globe. Stocks investing, by its nature, is risky and, investors’ gains or losses are guided primarily by their risk appetite and ability to analyse movements in stock prices and the fundamentals of the company’s underlying financial information. The purpose of this study is to create a model that predicts or forecasts stock prices using machine learning algorithms. By designing a model that has predictive ability, investors are able to optimize gains or minimize losses. Stated differently, the availability of data on stock price forecasts allows investors to either buy, hold or sell stocks thereby enabling them to realize the highest possible gain or cut losses to the lowest possible level. This study aims to forecast stock prices for the next end-of-day trading of the top six publicly-listed Philippine conglomerates for the year 2019 as reported by Forbes. These conglomerates include: Ayala Corporation BDO Unibank Inc. SM Investments Corporation Top Frontier Investment Holdings (the controlling shareholder of San Miguel Corporation) Metropolitan Bank & Trust Corp JG Summit Holdings Inc. Machine learning algorithms such as facebook prophet, random forest, auto regressive integrated moving average (ARIMA) will be used in this study to design a predictive model that generates the next end-of-day closing stock price with the highest accuracy. iii List of Figures Figure 3.4.1: Comparative Average Stock Price – Ayala Figure 3.4.2: Comparative Average Stock Price and Daily Percentage Change – Ayala Figure 3.4.3: Comparative Volume of Traded Shares – Ayala Figure 3.4.4: Comparative Average Stock Price - BDO Figure 3.4.5: Comparative Average Stock Price and Daily Percentage Change - BDO Figure 3.4.6: Comparative Volume of Traded Shares – BDO Figure 3.4.7: Comparative Average Stock Price - JG Summit Figure 3.4.8: Comparative Average Stock Price and Daily Percentage Change - JG Summit Figure 3.4.9: Comparative Volume of Traded Shares - JG Summit Figure 3.4.10: Comparative Average Stock Price – Metrobank Figure 3.4.11: Comparative Average Stock Price and Daily Percentage Change- Metrobank Figure 3.4.12: Comparative Volume of Traded Shares - Metrobank Figure 3.4.13: Comparative Average Stock Price – SM Figure 3.4.14: Comparative Average Stock Price and Daily Percentage Change – SM Figure 3.4.15: Comparative Volume of Traded Shares– SM Figure 3.4.16: Comparative Average Stock Price – Top Frontier Figure 3.4.17: Comparative Average Stock Price and Daily Percentage Change – Top Frontier Figure 3.4.18: Comparative Volume of Trade Shares – Top Frontier Figure 4.1.1: Neural Network Forecast – Ayala Figure 4.1.2: Neural Network Forecast – BDO . Figure 4.1.3: Neural Network Forecast – JG Summit Figure 4.1.4: Neural Network Forecast – Metrobank Figure 4.1.5: Neural Network Forecast – SM Figure 4.1.6: Neural Network Forecast – Top Frontier Figure 4.2.1: Facebook Prophet Forecast – Ayala Figure 4.2.2: Facebook Prophet Forecast – BDO Figure 4.2.3: Facebook Prophet Forecast – JG Summit Figure 4.2.4: Facebook Prophet Forecast – Metrobank Figure 4.2.5: Facebook Prophet Forecast – SM Figure 4.2.6: Facebook Prophet Forecast – Top Frontier Figure 4.3.1: ARIMA Forecast – Ayala Figure 4.3.2: ARIMA Forecast – BDO Figure 4.3.3: ARIMA Forecast – JG Summit Figure 4.3.4: ARIMA Forecast – Metrobank Figure 4.3.5: ARIMA Forecast – SM Figure 4.3.6: ARIMA Forecast – Top Frontier iv TABLE OF CONTENTS CHAPTER 1 INTRODUCTION ............................................................................................................ 1 1.1 Background ............................................................................................................................. 1 1.2 Statement of Problem .............................................................................................................. 4 1.3 Project Goals ........................................................................................................................... 4 1.4 Methodology ........................................................................................................................... 5 1.5 Limitations of the Study .......................................................................................................... 6 CHAPTER 2 LITERATURE REVIEW ................................................................................................. 7 2.1 Complex Machine Learning Model: Neural Networks ........................................................... 7 2.2 Emerging Machine Learning Model: Facebook Prophet ........................................................ 8 2.3 Established Machine Learning Model: Auto Regressive Integrated Moving Average........... 9 CHAPTER 3 PROJECT DESCRIPTION............................................................................................. 11 3.1 Data Dictionary ..................................................................................................................... 11 3.2 Understanding the Dataset .................................................................................................... 12 3.3 Cleaning the Dataset ............................................................................................................. 18 3.4 Transforming the Dataset ...................................................................................................... 18 3.4 Preliminary Exploratory Analysis of the Dataset.................................................................. 20 CHAPTER 4 PROJECT ANALYSIS ................................................................................................... 33 4.1 Neural Network ..................................................................................................................... 33 4.2 Facebook Prophet .................................................................................................................. 37 4.3 ARIMA ................................................................................................................................. 41 CHAPTER 5 CONCLUSION ............................................................................................................... 45 5.1 Conclusion ............................................................................................................................ 45 5.2 Recommendations ................................................................................................................. 46 5.3 Future Work .......................................................................................................................... 47 BIBLIOGRAPHY ................................................................................................................................. 48 CHAPTER 1 INTRODUCTION 1.1 BACKGROUND As individuals