運用個案實證法模擬理財機器人投資策略的績效與風險研究;Using case-based empirical method to simulate financial robots Research on the Performance and Risk of Investment Strategy

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Title:	運用個案實證法模擬理財機器人投資策略的績效與風險研究;Using case-based empirical method to simulate financial robots Research on the Performance and Risk of Investment Strategy
Authors:	王瑤玲;Yao-Ling, Wang
Contributors:	資訊管理學系在職專班
Keywords:	理財機器人;指數型基金;凱特通道;強基金;R語言;Robo-advisor;ETF;Keltner Channels;FundHot;R programming
Date:	2018-06-25
Issue Date:	2018-08-31 14:48:28 (UTC+8)
Publisher:	國立中央大學
Abstract:	近年隨著金融科技（FinTech）與人工智慧（AI）的研究興起，智能理財在台灣金融市場掀起卡位戰。一般投資大眾對各家金融業者所推出的多種機器人理財（Robo-advisor）服務，所標榜的大數據演算與客製投組說法，莫衷一是。原因是其所施行的投資策略是一個你我看不見的黑箱，對不理解又無法對外說得很清楚的投資工具要如何決擇? 本文提出一個簡易程式回測的方法來銓釋理財機器人的投資策略與步驟，使其隱含的投資策略更加清晰明確。本研究採用個案實證法，以科學實據來實證最佳「理財機器人」。取自網路上免費可得的市場指標訊號，強基金FBI（Fundhot Bias Index）乖離率的強弱訊號與合併相對應的ETF歷史收盤價格，篩檢出適合進場的時點。過程中以強基金素人網站的投資心法與一般基本理財常識設計成投資個案，利用R語言將FBI訊號加共同基金與ETF淨值做成回測（back test）模型，取得模擬投資的總獲利與承受風險高低來比較投資策略績效，藉由凱特通道交易模擬來推估FBI指數訊號的準確性，並將其投資步驟作成一實用SOP。個案實證法中使用FBI指數訊號投資法、凱特通道最適化與季線交易策略法的ETF個案回測驗證結果，FBI短期回測收益表現佳，而凱特通道的策略獲利性一般會接近甚至可優於FBI與季線策略。 ;Abstract In recent years, with the rise of Financial Technology (FinTech) and Artificial Intelligence (AI), the smart money set off a battle for position in Taiwan′s financial markets. Investors confused on financial operators has launched a variety of Robo-advisor, advertised big data algorithm and custom-made portfolio statement, today. The reason is that the investment strategy which implementing is a black box, how do you make decisions about investment tools that cannot be clearly to state to the public? The proposes of this article try to use a simple program backtesting method to explain the investment strategy and steps of the financial robot, making its implicit investment strategy more clear and clear. In this study, the best empirical scientific evidence to "Robo-Advisor". According to freely available market indicators signal on the fundhot web, FBI (Fundhot Bias Index) deviation from the rate corresponding to the closing price of the ETF historical, suitable for buy point in time. During the process, we designed the investment cases based on the fundhot′s investment attitude and general basic financial management knowledge, and used R language to make the FBI signal plus the mutual fund and ETF′s net value as the back test model to obtain the total profit of the simulation investment. The investment strategy performance is compared with the level of risk exposure, and the accuracy of the FBI index signal is estimated through the Keltner Channel(KC) trading simulation, and a practical SOP is created for its investment steps. The FBI investment method, KC optimization, and MA60 strategy are used in the cases empirical method to verify the results of ETF case backtesting. FBI short-term backtesting returns perform well, and the KC’s strategy profitability is generally close to or even possible. Better than FBI and quarterly strategy.
Appears in Collections:	[Executive Master of Information Management] Electronic Thesis & Dissertation

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