Applying run technical analysis in investment:experiment of NASDAQ index

• Main Authors: Yang,Yu-Hsiang, 楊喻翔
• Other Authors: Chiang,Yao-Ming
• Format: Others
• Language: en_US
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/12013219888435250319

碩士 === 國立政治大學 === 財務管理學系 === 88 === “TECHNICAL ANALYSIS” has been enjoying a renaissance on Wall Street to predict the market. The term “TECHNICAL ANALYSIS” is a general heading for a myriad for trading techniques. Technical analysts attempt to forecast prices by the study of past prices and of a few other related summary statistics about security trading. They believe that shifts in supply and demand can be detected in charts of market action (Brock et al., 1992). For example, Gencay and Stengos (1998) used the daily Dow Jones Industrial Average Index from 1963 to 1988 to examine the linear and non-linear predictability of stock market returns with some simple technical trading rules. They found some evidence of nonlinear predictability in stock market returns by using the past buy and sell signals of the moving average rules. Moreover, they suggest that it is worthwhile to investigate more elaborate rules and the profitability of these rules after accounting for transaction costs and brokerage fees. Many others researchers like Alexander (1961) also provide different models for technical analysis. Here, we try to build a more elaborate rule by introducing the concept of runs after considering the transaction costs. The idea of runs comes from Elliott Wave Principle, another pattern trading rules. Our objective is to model a trading formula by applying the concept of runs. We want to compare the performance of moving average rule with the concept of runs will be with the performance without the concept of runs under linear condition. (I.e. the simple moving average rules). The second objective is to develop the wave moving average model under wave return basis. This is different from previous literatures that construct moving average based on daily, weekly, monthly, or even yearly return. Just like Neftci (1991) said, “Technical analysis is a broad class of prediction rules with unknown statistical properties, developed by practitioners without reference to any formalism. … Most patterns used by technical analysts need to be characterized by appropriate sequences of local minima and/or maxima and will lead to nonlinear prediction problems.” We therefore want to construct a non-linear wave moving average model with artificial neural network (ANN) to investigate the non-linear characteristics of stock prediction.