摘要(英) |
Investment securities are always main topics in academic circle, industry or public. The issue of investment management is more and more important since FinTech has been discussed over the past few years. Most study in stock selection and market timing only surveyed in specific part, there was not only a few systematic and overall discussion in portfolio investment or profit and risk evaluation, but also a few study discussed the performance of combining program trading, portfolio investment and risk evaluation together. In this study, there are three research purposes. First, combining each process in investment securities to build an investment strategy process model. Second, discussing the performance of combining trading strategy, position sizing and style investment. Finally, providing an automatic return and risk evaluation platform for investors to use.
This study uses MultiCharts, Python, Microsoft SQL Server, Django and Plotly to implement this system. It includes three sub system, program trading strategy optimization sub system (optimizes the parameters of the technical analysis strategies), portfolio investment management sub system (creates the classification of portfolio investment) and profit and risk evaluation sub system (provides the performance of portfolio investment).
Mid-Cap 100 ETF in Taiwan Stock is representative and has enough number of stocks to verify this system. Channel strategy and moving average strategy are trend indicators in technical analysis, this study chooses them as experiment. Portfolio investment uses value, size, momentum and volatility effect in style investment. Portfolio in each effect is equally classified into five categories and invests different ratio of position sizing. Based on the fundamentals above, this study compares portfolio performance between the combination of stock selection and market timing (fundamental and technical analysis) and stock selection (fundamental analysis).
In this experiment, no matter which style investment and trading strategy, the performance of combination of stock selection and market timing is better than stock selection, the rate of return is higher and the risk is lower. This result proves that the combination of stock selection and market timing is feasible to use in investment trading and management. |
參考文獻 |
1. Ahmed, P., & Nanda, S. (2001). Style investing: Incorporating growth characteristics in value stocks. The Journal of Portfolio Management, 27(3), 47-59.
2. Bender, Jennifer, et al. (2013). Foundations of factor investing: MSCI.
3. Business Insider. (2016). THE FINTECH REPORT 2016: Financial industry trends and investment. from http://www.businessinsider.com/the-fintech-report-2016-financial-industry-trends-and-investment-2016-12
4. Clyne, Miles A, & Townrow, Michael Lawson. (2010). Apparatus for automatic financial portfolio monitoring and associated methods: Google Patents.
5. Davey, Kevin. (2014). Building winning algorithmic trading systems: Wiley.
6. Evans, Jeffrey L. (2004). Wealthy investor attitudes, Expectations, and Behaviors toward risk and return. The Journal of Wealth Management, 7(1), 12-18.
7. Fama, Eugene F, & French, Kenneth R. (1992). The cross‐section of expected stock returns. the Journal of Finance, 47(2), 427-465.
8. Fama, Eugene F, & French, Kenneth R. (1997). Industry costs of equity. Journal of financial economics, 43(2), 153-193.
9. Graham, Benjamin, & Dodd, David L. (1934). Security analysis: Principles and technique: McGraw-Hill.
10. Investment Statistics Guide. (2017). Retrieved June 10, 2017, from https://www.evestment.com/resources/investment-statistics-guide/
11. Jones, Charles P. (1999). Investment Analysis and Management: John Willey and Sons.
12. Keltner, Chester W. (1960). How to make money in commodities: Keltner Statistical Service.
13. Ledoit, Olivier, & Wolf, Michael. (2003). Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of empirical finance, 10(5), 603-621.
14. Lintner, John. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. The review of economics and statistics, 13-37.
15. MacroAxis. (2017). Portfolio optimization and digital investment analytics. Retrieved June 10, 2017, from https://www.macroaxis.com/
16. Markowitz, Harry. (1952). Portfolio selection. The journal of finance, 7(1), 77-91.
17. Motif. (2017). Motif | Smart Investing Made Simple. Retrieved June 10, 2017, from https://www.motifinvesting.com/
18. Nicholas Barberis, Andrei Shleiferb. (2003). Style investing. Journal of financial Economics, 68(2), 161-199.
19. Portfolio Visualizer. (2017). Backtest Portfolio Asset Class Allocation. Retrieved June 10, 2017, from https://www.portfoliovisualizer.com/
20. Pruitt, Ronald Earl. (2010). Portfolio management system: Google Patents.
21. PwC. (2016). Blurred lines: How FinTech is shaping Financial Services - Global FinTech Report
22. Ross, Stephen A. (1976). The arbitrage theory of capital asset pricing. Journal of economic theory, 13(3), 341-360.
23. Sharpe, William F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance, 19, 425-442.
24. Sharpe, William F. (1978). Major investment styles. The Journal of Portfolio Management, 4(2), 68-74.
25. Sharpe, William F. (1992). Asset allocation: Management style and performance measurement. The Journal of Portfolio Management, 18(2), 7-19.
26. Statman, Meir. (1987). How many stocks make a diversified portfolio? Journal of Financial and Quantitative Analysis, 22(03), 353-363.
27. Tharp, Van K. (2007). Trade your way to financial freedom: McGraw-Hill.
28. Tharp, Van K. (2008). Van Tharp′ s Definite Guide to Position Sizing SM: How to Evaluate Your System and Use Position Sizing SM to Meet Your Objectives, 25ff.
29. World Economic Forum. (2015). The Future of Financial Services: How disruptive innovations are reshaping the way financial services are structured, provisioned and consumed.
30. Zvi Bodie, Alex Kane and Alan J. Marcus. (2014). Investments (10th ed.): McGraw-Hill Education.
31. 王雍智、張澤、戴宏廩 (2011),風格投資-台灣股市的實證,東海管理評論 (Vol. 第十三卷, pp. 1-46)。
32. 江沛勳 (2014),布林通道策略成效分析-以台灣 50 成分股為例 (pp. 1-45),成功大學企業管理學系學位論文。
33. 江孟育 (2015),通道突破系統之探討-以台灣五十成分股為例,國立臺灣科技大學財務金融研究所碩士論文。
34. 吳秀月 (2013),以均線為基礎技術分析投資績效之探討,國立中山大學財務管理學系研究所碩士論文。
35. 吳忠輝 (2015),股價策略研究-以 Bollinger Bands 之應用在台灣 50 ETF 成分股,國立高雄應用科技大學金融系金融資訊碩士在職專班。
36. 周忠樑 (2004),規模與價值多重風格投資策略實證分析—以台灣股票市場為例,國立政治大學企業管理學系碩士班碩士論文。
37. 邱科毓 (2014),均線分析投資績效探討:以臺灣 50 指數成份股為例 (pp. 1-46), 中山大學財務管理學系研究所碩士論文。
38. 姜林杰祐 (2012),程式交易:方法與實務應用,新陸書局。
39. 范秉航 (2004),風格投資策略:動能風格與基本風格之比較,世新大學管理學院財務金融學系碩士學位論文。
40. 張林忠 (2014),分析師關鍵報告2:張林忠教你程式交易,寰宇出版社。
41. 張金龍 (2006),台股風格投資法之研究與探討,國立中央大學財務金融研究所碩士論文。
42. 陳重信 (2009),利用風險指標幫助投資人衡量其所委託之資產,國立中正大學企業管理研究所碩士論文。
43. 楊宗慶 (2015),多市場多商品程式交易績效之決策支援平台的整合設計與實作,國立中央大學資訊管理學系研究所碩士論文。
44. 樓禎祺、何培基 (2003),股價移動平均線之理論與實證-以台灣股市模擬投資操作為例,育達研究叢刊,5_6 (pp. 27-51)。
45. 賴宣名 (2013),包寧傑帶狀搭配均線與 KD 指標之多層次股票篩選模式-以台灣股市為例 (pp. 1-54),嶺東科技大學經營管理研究所碩士論文。
46. 謝政遠 (2004),以移動平均線、相對強弱指標與成交量檢驗台灣股票市場的效率性,逢甲大學財務金融學系研究所碩士論文。
47. 顏茂城 (2017),應用資料探勘於自動化交易策略之獲利能力評估的模型與平台設計及建置,國立中央大學資訊管理學系研究所碩士論文。 |