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研究生：	林家輝 Lin, Chia-Hui
論文名稱：	以強化學習演算法建構外資衍伸性金融商品之理財機器人 Constructing Robo-Advisor for Foreign-funded Financial Derivatives with Reinforcement Learning Algorithms
指導教授：	李昇暾 Li, Sheng-Tun
共同指導教授:	林清河 Lin, Chin-Ho
學位類別：	碩士 Master
系所名稱：	管理學院 - 資訊管理研究所 Institute of Information Management
論文出版年：	2019
畢業學年度：	107
語文別：	英文
論文頁數：	46
中文關鍵詞：	理財機器人、Q-learning 、未平倉量、布林通道
外文關鍵詞：	Robo-Advisor, Q-learning, Open Interest, Bollinger Bands
相關次數：	點閱：75 下載：0
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世界經濟論壇在2015年指出金融科技(Fintech)將會是一個破壞性的創新，會全面改造金融產業的未來。其中，在智慧理財領域，Business Insider提出全世界被理財機器人所管理的資金將從2015年的一千億美元上漲到2020的八兆美元。相較於電腦，透過人力做投資會存在三大劣勢，包含記憶能力、決策速度，及情緒管理能力。因此本篇論文將建立一個理財機器人藉此提升交易的競爭能力。
本篇論文的理財機器人不同之處在於未平倉量的處理方式。過去文獻都將未平倉的數量直接作為輸入變數，本篇論文則是將未平倉量轉換成金額單位建立能夠同時評估現貨、期貨、與選擇權未平倉量的綜合指標作為輸入變數。在做投資決策時，若沒有同時評估外資衍生性金融商品的未平倉量，很容易被外資現貨的進出狀況誤導，做出錯誤決策。在訓練模型上，我們將使用Q-learning演算法作為訓練模型，並透過所建立綜合指標以及技術分析資料建立State，以台灣期貨指數的賺賠金額建立Reward。並利用台灣加權指數作為訓練市場，實作兩個SSCI期刊中的金融方法，分別為MACD以及EMA方法。最後再以Sharpe ratio來當作三個方法的評估依據，得到利用Q-learning演算法去做交易策略能獲得較高的Sharpe ratio.

The World Economic Forum pointed out in 2015 that Fintech would be a disruptive innovation that will revolutionize the future of the financial industry. In the field of investment management, Business Insider proposed that the world's funds managed by Robo-Advisors will rise from USD 100 billion in 2015 to USD 8 trillion in 2020. Investing through humans have three disadvantages compared to computers, including memory, the speed of making decision, and the ability of emotion management. Therefore, this study established a Robo-Advisor to enhance the competitiveness of investing.
The novelty of the proposed Robo-Advisor is that we convert the volume of open interest into a unit of value as an input variable. When making investment decisions, if not assessing the open interest of foreign investment institution in different financial derivatives at the same time, it is easy for investor to confuse by foreign investment institution to make the wrong decision. Therefore, we will establish a comprehensive evaluation indicator of open interest as the input variable of Q-learning algorithm. We define the states by the comprehensive indicator and technical analysis data, and define the rewards by the profit of trading on(The future of Capitalization Weighted Stock Index) TX. We use the Taiwan Capitalization Weighted Stock Index as a training market, and implement two financial methods including moving average convergence divergence (MACD), and exponential moving average (EMA). Finally, we used the Sharpe ratio as the basis for the evaluation of the three methods, then the Q-learning algorithm achieve a higher Sharpe ratio.

CONTENTS
摘要	I
Abstract	II
誌謝	III
CONTENTS	V
List of Tables	VII
List of Figures	VIII
Chapter 1 Introduction	1
1 Research Background	1
2 Research Motivation	3
3 Research Objectives	4
4 Research Process	5
Chapter 2 Literature Review	7
1 Candlestick Chart	7
2 Open Interest	8
3 The Pricing Model of Financial Derivatives	10
3.1 Cost of Carry Model	10
3.2 The Options Pricing Model of Black and Scholes	11
4 The Strategy of Trading	12
4.1 Bollinger Bands	12
4.2 Way of the Turtle	13
5 Model of Forecasting Stock Market	14
5.1 Related Studies and Comparison	14
5.2 Reinforcement Learning	15
5.3 Q-learning Algorithm	16
6 Summary	18
Chapter 3 Research Methodology	19
1 Problem Definition	19
2 Research Method Flow	19
3 Data Preprocessing	21
4 Construct the Comprehensive Indicator of Open Interest	21
4.1 HyperParameter	22
4.2 Evaluate the Profit of TAIEX	23
4.3 Evaluate the Profit of Future	24
4.4 Evaluate the Profit of Option	24
4.5 Comprehensive Indicator of Open Interest	25
5 Robo-Advisor Design	26
5.1 State Space	26
5.2 State Definition	26
5.3 Action Definition	29
5.1 Reward Definition	29
5.2 The Algorithm of Robo-Advisor	30
Chapter 4 Experiment and Analysis	31
1 Experimental Architecture	31
2 Experiment Implementation	32
2.1 Data Set Description	32
2.2 Hyperparameter Setting	34
3 Baseline methods	34
3.1 Exponential Moving Average	34
3.2 Moving Average Convergence Divergence	35
4 Experimental Results and Analysis	35
4.1 Q-table Analysis	36
4.2 Trading Performance Analysis	38
Chapter 5 Conclusion and Future work	41
1 Conclusion	41
2 Managerial Implication	42
3 Future work and Limitation	43
3.1 Limitation	43
3.2 Future work	43
References	44


                                    

Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction. Journal of Applied Mathematics, 2014, 1-7.

Almahdi, S., & Yang, S. Y. (2017). An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown. Expert Systems with Applications, 87, 267-279.

Bates, R. G., Dempster, M. A., & Romahi, Y. S. (2003). Evolutionary reinforcement learning in FX order book and order flow analysis. Proceedings of the Computational Intelligence for Financial Engineering, 355-362.

Bhuyan, R., & Chaudhury, M. (2005). Trading on the information content of open interest: Evidence from the US equity options market. Derivatives Use, Trading & Regulation, 11(1), 16-36.

Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. The Journal of Political Economy, 81(3), 637-654.

Chen, W. H., Shih, J. Y., & Wu, S. (2006). Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets. Electronic Finance, 1(1), 49-67.

Cho, S., Cho, S., & Yow, K. C. (2017). A robust time series prediction model using POMDP and data analysis. Advances in Information Technology, 8, 154-158.

Cornell, B., & French, K. R. (1983). The pricing of stock index futures. Futures markets, 3(1), 1-14.

Day, M. Y., Cheng, T. K., & Li, J. G. (2018). AI Robo-Advisor with Big Data Analytics for Financial Services. Proceedings of the Advances in Social Networks Analysis and Mining (ASONAM), 1027-1031.

Gao, X., & Chan, L. (2000). An algorithm for trading and portfolio management using Q-learning and sharpe ratio maximization. Proceedings of the Neural information processing, 832-837.

Houlihan, P., & Creamer, G. G. (2017). Can Sentiment Analysis and Options Volume Anticipate Future Returns? Computational Economics, 50(4), 669-685.

Hsu, H., & Wang, J. (2004). Price expectation and the pricing of stock index futures. Review of Quantitative Finance and Accounting, 23(2), 167-184.

Huang, S. C., & Wu, T. K. (2008). Integrating GA-based time-scale feature extractions with SVMs for stock index forecasting. Expert Systems with Applications, 35(4), 2080-2088.

Kamble, R. A. (2017). Short and long term stock trend prediction using decision tree. Proceedings of the Intelligent Computing and Control Systems (ICICCS), 1371-1375.

Kannan, K. S., Sekar, P. S., Sathik, M. M., & Arumugam, P. (2010). Financial stock market forecast using data mining techniques. Proceedings of the International Multiconference of Engineers and computer scientists.

Kocianski, S. (2016). THE ROBO-ADVISING REPORT: Market forecasts, key growth drivers, and how automated asset management will change the advisory industry. Retrieved November 7, 2018 from https://nordic.businessinsider.com/the-robo-advising-report-market-forecasts-key-growth-drivers-and-how-automated-asset-management-will-change-the-advisory-industry-2016-6/.

Lai, H.-C., Tseng, T.-C., & Huang, S.-C. (2016). Combining value averaging and Bollinger Band for an ETF trading strategy. Applied Economics, 48(37), 3550-3557.

Lee, J. W., Park, J., Jangmin, O., Lee, J., & Hong, E. (2007). A Multiagent Approach to Q-Learning for Daily Stock Trading. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 37(6), 864-877.

Lee, K., & Jo, G. (1999). Expert system for predicting stock market timing using a candlestick chart. Expert Systems with Applications, 16(4), 357-364.

Lehoczky, J., & Schervish, M. (2018). Overview and History of Statistics for Equity Markets. Annual Review of Statistics and Its Application, 5, 265-288.

Li, B., Zhang, D., & Zhou, Y. (2017). Do trend following strategies work in Chinese futures markets? The journal of futures markets, 37(12), 1226-1254.

Moody, J., Wu, L., Liao, Y., & Saffell, M. (1998). Performance functions and reinforcement learning for trading systems and portfolios. Forecasting, 17(5‐6), 441-470.

Neuneier, R. (1998). Enhancing Q-learning for optimal asset allocation. Proceedings of the Advances in neural information processing systems, 936-942.

Pendharkar, P. C., & Cusatis, P. (2018). Trading financial indices with reinforcement learning agents. Expert Systems with Applications, 103, 1-13.

Seyhun, H. N. (1986). Insiders' profits, costs of trading, and market efficiency. Financial Economics, 16(2), 189-212.

Yong, L., Bo, Z., & Yong, T. (2015). Dynamic optimal capital growth of diversified investment. Journal of Applied Statistics, 42(3), 577-588.

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