– third layer state Xt Data input ht Data in epochs Ct Cell state
MINISTRY OF EDUCATION AND TRAINING HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION GRADUATION THESIS MAJOR: ELECTRONICS AND COMMUNICATION ENGINEERING PREDICTION OF RISK AND RETURN BY USING MACHINE LEARNING INSTRUCTOR: PHẠM NGỌC SƠN, PhD STUDENT: LÂM MINH NHẬT SKL011197 Ho Chi Minh City, July 2023 HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION FACULTY FOR HIGH QUALITY TRAINING DEPARTMENT OF COMPUTER AND COMMUNICATIONS GRADUATION PROJECT PREDICTION OF RISK AND RETURN BY USING MACHINE LEARNING Student’s name: LÂM MINH NHẬT Student ID: 19161037 Major: ELECTRONICS AND COMMUNICATION ENGINEERING Advisor: PHẠM NGỌC SƠN, PhD Ho Chi Minh City, July 2023 THE SOCIALIST REPUBLIC OF VIETNAM Independence – Freedom– Happiness -Ho Chi Minh City, July 5th, 2023 GRADUATION PROJECT ASSIGNMENT Student name: LÂM MINH NHẬT Student ID: 19161037 Major: ELECTRONICS AND COMMUNICATION ENGINEERING Advisor: PHẠM NGỌC SƠN, PhD Class: 19161CLA Date of assignment: June 22th 2023 Date of submission: June 23th 2023 Phone number: Project title: RISK AND RETURN BY USING MACHINE LEARNING Initial materials provided by the advisor: _ Content of the project: _ Final product: CHAIR OF THE PROGRAM (Sign with full name) ADVISOR (Sign with full name) THE SOCIALIST REPUBLIC OF VIETNAM Independence – Freedom– Happiness -Ho Chi Minh City, July , 2023 ADVISOR’S EVALUATION SHEET Student name: Student ID: Student name: Student ID: Student name: Student ID: Major: Project title: Advisor: EVALUATION Content of the project: Strengths: Weaknesses: Approval for oral defense? (Approved or denied) Overall evaluation: (Excellent, Good, Fair, Poor) Ho Chi Minh City, (month day, year) ADVISOR (Sign with full name) THE SOCIALIST REPUBLIC OF VIETNAM Independence – Freedom– Happiness -Ho Chi Minh City, July, 2023 PRE-DEFENSE EVALUATION SHEET Student name: Student ID: Student name: Student ID: Student name: Student ID: Major: Project title: Name of Reviewer: EVALUATION Content and workload of the project Strengths: Weaknesses: Approval for oral defense? (Approved or denied) Reviewer questions for project valuation Mark:……………….(in words: ) Ho Chi Minh City, (month day, year) REVIEWER (Sign with full name) Faculty for High Quality Training – HCMC University of Technology and Education PREAMBLE During the implementation of the graduation project, our team has received a lot of help, suggestions and indicators from teachers and friends To complete the project “RISK AND RETURN BY USING MACHINE LEARNING.” We sincerely thank PhD Phạm Ngọc Sơn - Lecturer of the Department of Computer Engineering - Telecommunications, Faculty of Electrical - Electronics, University of Technology and Education of Ho Chi Minh City With dedicated guidance, guidance, facilitating and supporting the team to successfully complete this project Individual would also like to thank the authors of the reference sources who helped the group to have more knowledge and choices in the process of implementing the topic Although the group has tried to complete this topic in the most complete way, certain errors in research work, practical approach, as well as limitations in knowledge and time cannot be avoided implementation time Looking forward to receiving your comments so that the group can supplement and correct the topic to be more complete Faculty for High Quality Training – HCMC University of Technology and Education ABBREVIATION Short writing Meaning NaN Not a Number 2D Two-direction I/O Input / Output LSTMs Long-Short Term Memory networks RNN Recurrent Neural Network Stdv Standard deviation Rmse Root-mean-square deviation S&P500 Standard & Poor's 500 Index CAGR Compound Annual Growth Rate rfr Risk-free-rate CHP Central Hydropower JSC SAB Saigon Beer Alcohol Beverage Corp FPT FPT Corp VND Vietnam dong Faculty for High Quality Training – HCMC University of Technology and Education MATHEMATICAL SYMBOL Symbol Tanh Meaning The function Tanh is the ratio of Sinh and Cosh – third layer state Xt Data input ht Data in epochs Ct Cell state 𝜎 Sigmoid ft First layer state it Second layer state ot Fourth layer state rfr Risk-free-rate stdv Standard deviation PercentGrowth Daily growth percentage 𝑆ℎ𝑎𝑟𝑝𝑒𝑅𝑎𝑡𝑖𝑜 The amount of return received per unit of risk Faculty for High Quality Training – HCMC University of Technology and Education Table of Contents CHAPTER 1: OVERVIEW OF PROJECT 1.1 1.2 1.3 1.4 INTRODUCTION OBJECTIVES OF THE PROJECT LIMITATION OF THE PROJECT PROJECT’S LAYOUT CHAPTER 2: THEORETICAL BASIS 2.1 PYTHON PROGRAMING LANGUAGE 2.1.1 NUMPY 2.1.2 PANDAS 2.1.3 MATPLOTLIB 2.1.4 KERAS 2.2 MACHINE LEARNING 2.3 LSTM MODEL 2.4 THE CORE IDEA OF LSTM 2.5 LSTM’S OPERATION 2.6 INVESTMENT PORTFOLIO 10 2.7 SHARPE’S RATIO CONCEPT .11 2.8 REVIEW ANOTHER PROJECT .12 CHAPTER 3: IMPLEMENTATION PROCESS 18 3.1 MODEL SYSTEM 18 3.2 MAIN FLOWCHART AND SIMULATION PARAMETERS .23 CHAPTER 4: RESULT OF SIMULATION ANALYSIS AND ASSESSMENT 24 4.1 4.2 4.3 4.4 CHP ( CENTRAL HYDROPOWER CORP ) 24 SAB ( SAIGON BEER ALCOHOL BEVERAGE CORP ) 26 FPT ( FPT CORP ) 28 EVALUATION 30 CHAPTER 5: CONCLUSION AND DEVELOPMENT 33 5.1 CONCLUSION 33 5.2 DEVELOPMENT DIRECTION OF THE PROJECT 33 REFERENCES 34 Faculty for High Quality Training – HCMC University of Technology and Education List of figure Figure 1) The repeating module in a standard RNN contains a single layer Figure 2) The repeating module in an LSTM contains four interacting layers Figure 3) The cell state runs straight down the entire chain with only some minor linear interactions Figure 4) They are combined by a sigmoid lattice layer and a multiplication Figure 5) Decision of what information is going to throw away from the cell state Figure 6) Decision of what new information is going to store in the cell state Figure 7) Updating the old cell state 10 Figure 8) Decision of what is going to output 10 Figure 9) William Forsyth Sharpe 11 Figure 10) Summarize daily prices for Amazon and Facebook 12 Figure 11) Plot daily prices for Amazon and Facebook 12 Figure 12) Summarize daily values for the S&P 500 13 Figure 13) Plot daily values for the S&P 500 13 Figure 14) Visualize the daily return of Amazon and Facebook 14 Figure 15) Visualize the daily return of S&P 500 14 Figure 16) Calculating Excess Returns for Amazon and Facebook vs S&P 500 15 Figure 17) The Average Difference in Daily Returns Stocks vs S&P 500 15 Figure 18) Standard Deviation of the Return Difference 16 Figure 19) Comparation the Sharpe ratio between Amzazon and Facebook 16 Figure 20) Model system 18 Figure 21) Flowchart 23 Figure 22) Development chart of CHP 24 Figure 23) The predicted price compares with valid price 24 Figure 24) Comparasion of Sharpe Ratio 26 Figure 25) Development chart of SAB 26 Figure 26) The predicted price compares with valid price 27 Figure 27) Comparasion of Sharpe Ratio 28 Figure 28) Development chart of FPT 28 Figure 29) The predicted price compares with valid price 29 Figure 30) Comparasion of Sharpe Ratio 30 Figure 31) CHP’s Open Price in 23-06-09 31 Figure 32) SAB’s Open Price in 23-06-09 31 Figure 33) FPT’s Open Price in 23-06-09 32