達成基金目標獲利的羅吉斯迴歸模型探討

在投資領域中，為了能取得更好的報酬，投資人總是不斷的在進行抉擇。例如選擇投資方式、投資地區、投資類型、投資時點。然而該如何選擇才能有效提升獲利，一直都是廣大投資人尋找答案的問題。故本研究之動機即在眾多影響投資報酬率的變數中進行相關分析，希望能從中提供投資人了解各變數對於報酬率的影響，並進而建置有效提升獲利之模型，使投資人皆能有效提升其投資報酬率。研究中針對過往被認為重要、有效且具影響力等變數進行研究，探討各變數其對於投資報酬的影響力大小。這些變數分別為基金類型、投資區域、基金規模、計價幣別、投資方式、信託方式、持有期間、淨值是否在10、50、200日均線之上、短中長期均線是否呈現多頭排序、10、50、200日乖離率(Bias)、14日相對強弱指標(RSI)、隨機指標中的快線(K9)、台股加權指數、台股加權指數10日、50日、200日均線。本研究最後依投資人所預期的獲利比率，分別透過Logistic Regression篩選出真正有效且具影響力之變數，並利用這些變數建置模型，以供投資人在投資前能加以試算其獲利達成之可能機率，而非任意進行投資，從而提升投資人在投資市場上之勝算與存活率。關鍵字：羅吉斯廻歸、技術分析指標、投資報酬

關鍵字

羅吉斯廻歸、技術分析指標、投資報酬

並列摘要

With regard to investment, investors are always making decision to achieve better returns. For example, how to select investment selection, investment region, investment types, investment timing is always significant to the investors. And how to make decision to effectively improve the profitability is always the major problem which investors are eager to find the answers. The motivation for this study that affect the return on investment for a number of variables in the research, the study hopes to provide investors understand the impact of the variable for the return, and building an effective model, the investor could improve their return on investment. Some valid and influential variables considered in the past study will be analyzed in this study to find their influence to the expected profit of investing. These variables are the type of fund, the investment area, fund size, currency type, investment period, EMA 10, EMA 50, EMA 200, BIAS 10, BIAS 50, BIAS 200, RSI, %K9, and Taiwan stocks weighted index. Finally, this study according to the expected return of an investor established Logistic Regression Models to find out valid and influence variables for each respectively. These models can offer a strategy for investors to enhance the probability to achieve his/her goal in the investment market. Keywords: Logistic Regression, Technical Analysis Indicator, Invest return

並列關鍵字

Logistic Regression、Technical Analysis Indicator、Invest return

參考文獻

1.David A. Latzko, “ECONOMIES OF SCALE IN MUTUAL FUND ADMINISTRATION”, 1998, Pennsylvania State University, York Campus

4.Michael S. Rozeff(1994),” Lump-Sum Investing versus Dollar-Averaging”, Journal of Portfolio Management, pp. 45-50

5.Moshe A. Milevsky, Steven E. Posner(1999),” A Continuous-Time Re-examination of the Inefficiency of Dollar-Cost Averaging”, York University - Schulich School of Business

6.P.J. Detry(2001),” Other Evidences of the Predictive Power of Technical Analysis: The Moving Averages Rules on European Indexes”, University of Namur

7.The predictability of security returns with simple technical trading rules

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達成基金目標獲利的羅吉斯迴歸模型探討

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