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題名 應用連串技術分析於投資決策：以NASDAQ指數為例
Applying run technical analysis in investment: experimen of NASDAQ index
作者 楊喻翔
Yang, Yu-Hsiang
貢獻者 姜堯民<br>蔡瑞煌
Chiang, Yao-Ming<br>Ray Tsaih
楊喻翔
Yang, Yu-Hsiang
關鍵詞 技術分析
投資決策
連串
techinical analysis
investment
runs
日期 2000
上傳時間 31-Mar-2016 15:32:59 (UTC+8)
摘要 本文主旨是利用連串理論（RUNS）的觀念引入現行的簡單移動平均法則的技術分析中，實證發現在以逐日作投資決策而進行的交易中，引入連串的簡單移動平圴預測來作交易決策的績效可以跟買入後持有的績效相同，而根據不引用連串觀念的簡單移動平均所作旳預測來進行交易的績效則明顯不如買入後持有的績效， 這樣的結果說明了有連串觀念的簡單平均含有某些獲利訊息。另以逐波作投資決策進行的交易中，研究結果顯示以類神經網路預測而進行交易決策的績效比以多元迴歸預測的為佳，但二者皆可獲得正的超額報酬。
     就理論而言，本文延續Gencay and Stengos（1998）所作的簡單移動平均研究，關於簡單移動平均等此類的技術分析探討自Alexander（1964）用濾嘴法則開始研究後，就陸陸續續在財務領域中被探討，例如Brock et al.（1992）發現這些技術分析法則在高報酬及低波動度（returns are high and volatility is low）時可以進行作多獲利（to be in the market or long the index）。本文首先嘗試引用連串移動平均法則來進行預測，文中的３個連串移動平均（the moving average of 3 runs）實是在計量驗證下求得的。以連串移動平均預測而進行交易操作是一種順勢而為的交易方法，其研究實證所獲得超額報酬是正的。
參考文獻 References
     [1] Alexander, S.S., 1964 Price movements in speculative markets: trends or random walks. In: Cootner, P. (Ed.), The Random Character of Stock Market Prices, vol. 2, MIT Press, Cambridge, 338-372.
     [2] Allen, Franklin., Karjalainen, Risto. (1999)"Using genetic algorithms to find technical trading rules", Journal of Financial Economics 51, 245-271.
     [3] Azoff, Eitan Michael, Neural network time series forecasting of financial markets. Chichester, England ; New York : John Wiley & Sons, 1994.
     [4] Battiti, R. (1992), “First- and second-order methods for learning between steepest descent and Newton’s method,” Neural Networks, Vol. 5, pp. 507-529.
     [5] Blume, L., Easley, D. and O’hara, M., 1994, “Market statistics and technical analysis: The role of volume’, Journal of finance, 49(1994), 153-181.
     [6] Brock, W. A., Lakonishok, J. and LeBaron, B.,1992, ‘Simple technical trading rules and the stochastic properties of stock returns’, Journal of Finance, 47(1992), 1731-17643.
     [7] Feller, William, An introduction to probability theory and its applications. 2nd ed. New York : Wiley, (1957).
     [8] Frost, A. J, and Prechter Jr., R.R., Elliott Wave Principle, 5th ed. New classics Library, 1992.
     [9] Gencay, Ramazan; and Stengos, Thanasis, 1998, “Moving Average Rules, Volume and the Predictability of security Returns with Feedforward Networks,”Journal of Forecasting, Forecast 17, 401-414(1998).
     [10] Godbole, Anant P. and Papastavridis, Stavros G., Runs and patterns in probability: selected papers. Dordrecht ; Boston : Kluwer Academic, 1994.
     [11] Hanke, M. (1997), “Neural network approximation of option-pricing formulas for analytical intractable option-pricing models,” Journal of Computational Intelligence in Finance, pp. 20-27.
     [12] Hansen, J.V. and Nelson, R.D. (1997), “Neural networks and traditional time series methods: a synergistic combination in state economic forecasts,” IEEE Transactions on Neural Networks, v.8, pp. 863-873.
     [13] Hutchinson, J., Lo, A.W. and Poggio, T. (1994), “A nonparametric approach to pricing and hedging derivative securities via learning networks,” The Journal of Finance, Vol. XLXI, No. 3, pp. 851-859.
     [14] Jacobs, R.A. (1988), “Increased rate of convergence through learning rate adaptation,” Neural Networks, Vol. 1, pp. 295-307.
     [15] Jain, B.A. and Nag, B.N. (1996), “Artificial neural network models for pricing initial public offerings,” Decision Sciences, Vol. 26, No. 3, pp. 283-302.
     [16] Kuo, Chin; and Reitsch, Arthur, 1995/1996, “Neural networks vs. conventional methods of forecasting ,” The Journal of Business forecasting Methods & Systems; Flushing; Winter, 17-24.
     [17] Lajbcygier, P., Boek, C., Flitman, A., and Palaniswami, M. (1996), “Comparing conventional and artificial neural network models for the pricing of options on futures,” NeuralVest Journal, pp. 16-24.
     [18] Neely, Christopher.,Weller, Paul., Dittmar,Rob,1997, "Is technical analysis in the foreign exchange market profitable? A genetic programming approach," Journal of Financial & Quantitative Analysis, v32n4, Dec p.405-426.
     [19] Neftci, Salih N,1991,"Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of Technical Analysis", Journal of business, vol. 64, no. 4.
     [20] Pring, M. J. Technical Analysis Explained, Second Ed. New York, NY: McGraw-Hill (1991).
     [21] Rosenberg, Barr, Kenneth Reid, and Ronald Lanstein, 1985, “Persuasive evidence of market inefficiency,” Journal of Portfolio Management 11, 9-17.
     [22] Rosenblatt, F. (1958), “The perceptron: a probabilistic model for information storage and organization in the brain,” Psychological Review, Vol. 65, pp. 386-408.
     [23] Rumelhart, D.E., Hinton, G.E., and Williams, R. (1986), “Learning internal representation by error propagation,” Parallel Distributed Processing, Cambridge, MA: MIT Press, Vol. 1, pp. 318-362.
     [24] Sarkar, D. 1995, “Methods to speed up error back-propagation learning algorithm,” ACM Computer Surveys, Vol. 27, No. 4, pp. 519-542.
     [25] Sharma, T.C., 1996, “Simulation of the Kenyan longest dry and wet spells and the largest rain-sums using a Markov model, “ Journal of Hydrology 178(1996) 55-67.
     [26] Sharpe, William F., 1975, “Likely gains from market timing,” Financial Analysts Journal 31.
     [27] Ross, Sheldon, A first course in Probability, 3rd Ed. New York, NY: Macmillan (1989).
     [28] Sweeney, R. J. , 19988, “Some new filter rule tests: methods and results. “, Journal of Fianacial and Quantitaative Analysis 23, 285-300.
     [29] Takechi, H., Murakami, K. and Izumida, M. 1995, “Back propagation learning algorithm with different learning coefficients for each layer,” Systems and Computers in Japan, Vol. 26-(7), pp. 47-56.
     [30] Tsaih R., Y. Hsu, C. Lai, 1998, “Forecasting S&P 500 stock index futures with a hybrid AI system,” Decision Support Systems 23, 161-174.
     [31] Tsaih R., W. K. Chen, and Y. P. Lin, 1998, “Application of Reasoning Neural Networks to Financial Swaps,” Journal of Computational Intelligence in Finance, 27-37.
     [32] Trippi, Robert R. and Turban, Efraim., Neural networks in finance and investing : using artificial intelligence to improve real-world performance., Chicago : Irwin Professional Pub., c1996.
     [33] Venugopal, V. and Baets, W. 1994, “Neural networks and statistical techniques in marketing research,” Marketing Intelligence & Planning, Vol. 12, No. 7, pp. 30-38.
     [34] Wang, S.,1995, “The unpredictability of standard back propagation neural networks in classification applications,” Management Science, Vol.41, No. 3, 555-559.
     [35] Van Wezel, M.C. and Baets, W.R.J. ,1995, “Predicting market responses with a neural networks: the case of fast moving consumer goods,” Marketing Intelligence & Planning, Vol. 13, No. 7, pp. 23-30.
     [36] Wasserman, G.S. and Sudjianto, A. ,1996, “A comparison of three strategies for forecasting warranty claims,” IIE Transactions, 28, pp. 967-977.
     [37] Yoon, Y., Swales, G., and Margavio, T.M. ,1993, “A comparison of discriminant analysis versus artificial neural networks,” Journal of Operational Research Society, 44, pp. 51-60.
     [38] 梁馨尹, 民國87年,"台灣股價行程的連串類型與其分析技術", 中興大學統計學研究所未出版碩士論文.
     [39] 蔡瑞煌著，類神經網路概論，初版 ， 臺北市 : 三民，民國86年.
     [40] 顏月珠著, 商用統計學, 修訂七版, 臺北市 : 三民, 民國80年.
描述 碩士
國立政治大學
財務管理研究所
86357010
資料來源 http://thesis.lib.nccu.edu.tw/record/#A2002002084
資料類型 thesis
dc.contributor.advisor 姜堯民<br>蔡瑞煌zh_TW
dc.contributor.advisor Chiang, Yao-Ming<br>Ray Tsaihen_US
dc.contributor.author (Authors) 楊喻翔zh_TW
dc.contributor.author (Authors) Yang, Yu-Hsiangen_US
dc.creator (作者) 楊喻翔zh_TW
dc.creator (作者) Yang, Yu-Hsiangen_US
dc.date (日期) 2000en_US
dc.date.accessioned 31-Mar-2016 15:32:59 (UTC+8)-
dc.date.available 31-Mar-2016 15:32:59 (UTC+8)-
dc.date.issued (上傳時間) 31-Mar-2016 15:32:59 (UTC+8)-
dc.identifier (Other Identifiers) A2002002084en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/83281-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 財務管理研究所zh_TW
dc.description (描述) 86357010zh_TW
dc.description.abstract (摘要) 本文主旨是利用連串理論（RUNS）的觀念引入現行的簡單移動平均法則的技術分析中，實證發現在以逐日作投資決策而進行的交易中，引入連串的簡單移動平圴預測來作交易決策的績效可以跟買入後持有的績效相同，而根據不引用連串觀念的簡單移動平均所作旳預測來進行交易的績效則明顯不如買入後持有的績效， 這樣的結果說明了有連串觀念的簡單平均含有某些獲利訊息。另以逐波作投資決策進行的交易中，研究結果顯示以類神經網路預測而進行交易決策的績效比以多元迴歸預測的為佳，但二者皆可獲得正的超額報酬。
     就理論而言，本文延續Gencay and Stengos（1998）所作的簡單移動平均研究，關於簡單移動平均等此類的技術分析探討自Alexander（1964）用濾嘴法則開始研究後，就陸陸續續在財務領域中被探討，例如Brock et al.（1992）發現這些技術分析法則在高報酬及低波動度（returns are high and volatility is low）時可以進行作多獲利（to be in the market or long the index）。本文首先嘗試引用連串移動平均法則來進行預測，文中的３個連串移動平均（the moving average of 3 runs）實是在計量驗證下求得的。以連串移動平均預測而進行交易操作是一種順勢而為的交易方法，其研究實證所獲得超額報酬是正的。		zh_TW
dc.description.tableofcontents 封面頁
     證明書
     致謝詞
     論文摘要
     目錄
     圖表目錄
     Chapter 1 Introduction
     1.1 Motivation and Goal
     1.2 Structure of the thesis
     Chapter 2 Literature Review
     2.1 The theory of runs and the concept of waves
     2.2 The technical analysis/Technical trading rules
     2.3 Artificial Neural Network and it’s applications in finance
     Chapter 3 Experiment Design and Methodology
     3.1 Estimator of simple moving average without waves
     3.2 Waves of stock process
     3.3 Estimator of simple moving average with waves
     3.4 Wave’s moving average rules with multiple regression
     3.5 Wave’s moving average rules with multiple regression
     3.6 Data and the calculation of return
     Chapter 4 Performance and Analysis
     4.1 Results of simple moving average rules with waves and simple moving average rules without waves
     4.2 Waves moving average rules with OLS and BP
     Chapter 5 Conclusion and Suggestion
     5.1 Conclusion
     5.2 Suggestion
     Reference		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#A2002002084en_US
dc.subject (關鍵詞) 技術分析zh_TW
dc.subject (關鍵詞) 投資決策zh_TW
dc.subject (關鍵詞) 連串zh_TW
dc.subject (關鍵詞) techinical analysisen_US
dc.subject (關鍵詞) investmenten_US
dc.subject (關鍵詞) runsen_US
dc.title (題名) 應用連串技術分析於投資決策：以NASDAQ指數為例zh_TW
dc.title (題名) Applying run technical analysis in investment: experimen of NASDAQ indexen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) References
     [1] Alexander, S.S., 1964 Price movements in speculative markets: trends or random walks. In: Cootner, P. (Ed.), The Random Character of Stock Market Prices, vol. 2, MIT Press, Cambridge, 338-372.
     [2] Allen, Franklin., Karjalainen, Risto. (1999)"Using genetic algorithms to find technical trading rules", Journal of Financial Economics 51, 245-271.
     [3] Azoff, Eitan Michael, Neural network time series forecasting of financial markets. Chichester, England ; New York : John Wiley & Sons, 1994.
     [4] Battiti, R. (1992), “First- and second-order methods for learning between steepest descent and Newton’s method,” Neural Networks, Vol. 5, pp. 507-529.
     [5] Blume, L., Easley, D. and O’hara, M., 1994, “Market statistics and technical analysis: The role of volume’, Journal of finance, 49(1994), 153-181.
     [6] Brock, W. A., Lakonishok, J. and LeBaron, B.,1992, ‘Simple technical trading rules and the stochastic properties of stock returns’, Journal of Finance, 47(1992), 1731-17643.
     [7] Feller, William, An introduction to probability theory and its applications. 2nd ed. New York : Wiley, (1957).
     [8] Frost, A. J, and Prechter Jr., R.R., Elliott Wave Principle, 5th ed. New classics Library, 1992.
     [9] Gencay, Ramazan; and Stengos, Thanasis, 1998, “Moving Average Rules, Volume and the Predictability of security Returns with Feedforward Networks,”Journal of Forecasting, Forecast 17, 401-414(1998).
     [10] Godbole, Anant P. and Papastavridis, Stavros G., Runs and patterns in probability: selected papers. Dordrecht ; Boston : Kluwer Academic, 1994.
     [11] Hanke, M. (1997), “Neural network approximation of option-pricing formulas for analytical intractable option-pricing models,” Journal of Computational Intelligence in Finance, pp. 20-27.
     [12] Hansen, J.V. and Nelson, R.D. (1997), “Neural networks and traditional time series methods: a synergistic combination in state economic forecasts,” IEEE Transactions on Neural Networks, v.8, pp. 863-873.
     [13] Hutchinson, J., Lo, A.W. and Poggio, T. (1994), “A nonparametric approach to pricing and hedging derivative securities via learning networks,” The Journal of Finance, Vol. XLXI, No. 3, pp. 851-859.
     [14] Jacobs, R.A. (1988), “Increased rate of convergence through learning rate adaptation,” Neural Networks, Vol. 1, pp. 295-307.
     [15] Jain, B.A. and Nag, B.N. (1996), “Artificial neural network models for pricing initial public offerings,” Decision Sciences, Vol. 26, No. 3, pp. 283-302.
     [16] Kuo, Chin; and Reitsch, Arthur, 1995/1996, “Neural networks vs. conventional methods of forecasting ,” The Journal of Business forecasting Methods & Systems; Flushing; Winter, 17-24.
     [17] Lajbcygier, P., Boek, C., Flitman, A., and Palaniswami, M. (1996), “Comparing conventional and artificial neural network models for the pricing of options on futures,” NeuralVest Journal, pp. 16-24.
     [18] Neely, Christopher.,Weller, Paul., Dittmar,Rob,1997, "Is technical analysis in the foreign exchange market profitable? A genetic programming approach," Journal of Financial & Quantitative Analysis, v32n4, Dec p.405-426.
     [19] Neftci, Salih N,1991,"Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of Technical Analysis", Journal of business, vol. 64, no. 4.
     [20] Pring, M. J. Technical Analysis Explained, Second Ed. New York, NY: McGraw-Hill (1991).
     [21] Rosenberg, Barr, Kenneth Reid, and Ronald Lanstein, 1985, “Persuasive evidence of market inefficiency,” Journal of Portfolio Management 11, 9-17.
     [22] Rosenblatt, F. (1958), “The perceptron: a probabilistic model for information storage and organization in the brain,” Psychological Review, Vol. 65, pp. 386-408.
     [23] Rumelhart, D.E., Hinton, G.E., and Williams, R. (1986), “Learning internal representation by error propagation,” Parallel Distributed Processing, Cambridge, MA: MIT Press, Vol. 1, pp. 318-362.
     [24] Sarkar, D. 1995, “Methods to speed up error back-propagation learning algorithm,” ACM Computer Surveys, Vol. 27, No. 4, pp. 519-542.
     [25] Sharma, T.C., 1996, “Simulation of the Kenyan longest dry and wet spells and the largest rain-sums using a Markov model, “ Journal of Hydrology 178(1996) 55-67.
     [26] Sharpe, William F., 1975, “Likely gains from market timing,” Financial Analysts Journal 31.
     [27] Ross, Sheldon, A first course in Probability, 3rd Ed. New York, NY: Macmillan (1989).
     [28] Sweeney, R. J. , 19988, “Some new filter rule tests: methods and results. “, Journal of Fianacial and Quantitaative Analysis 23, 285-300.
     [29] Takechi, H., Murakami, K. and Izumida, M. 1995, “Back propagation learning algorithm with different learning coefficients for each layer,” Systems and Computers in Japan, Vol. 26-(7), pp. 47-56.
     [30] Tsaih R., Y. Hsu, C. Lai, 1998, “Forecasting S&P 500 stock index futures with a hybrid AI system,” Decision Support Systems 23, 161-174.
     [31] Tsaih R., W. K. Chen, and Y. P. Lin, 1998, “Application of Reasoning Neural Networks to Financial Swaps,” Journal of Computational Intelligence in Finance, 27-37.
     [32] Trippi, Robert R. and Turban, Efraim., Neural networks in finance and investing : using artificial intelligence to improve real-world performance., Chicago : Irwin Professional Pub., c1996.
     [33] Venugopal, V. and Baets, W. 1994, “Neural networks and statistical techniques in marketing research,” Marketing Intelligence & Planning, Vol. 12, No. 7, pp. 30-38.
     [34] Wang, S.,1995, “The unpredictability of standard back propagation neural networks in classification applications,” Management Science, Vol.41, No. 3, 555-559.
     [35] Van Wezel, M.C. and Baets, W.R.J. ,1995, “Predicting market responses with a neural networks: the case of fast moving consumer goods,” Marketing Intelligence & Planning, Vol. 13, No. 7, pp. 23-30.
     [36] Wasserman, G.S. and Sudjianto, A. ,1996, “A comparison of three strategies for forecasting warranty claims,” IIE Transactions, 28, pp. 967-977.
     [37] Yoon, Y., Swales, G., and Margavio, T.M. ,1993, “A comparison of discriminant analysis versus artificial neural networks,” Journal of Operational Research Society, 44, pp. 51-60.
     [38] 梁馨尹, 民國87年,"台灣股價行程的連串類型與其分析技術", 中興大學統計學研究所未出版碩士論文.
     [39] 蔡瑞煌著，類神經網路概論，初版 ， 臺北市 : 三民，民國86年.
     [40] 顏月珠著, 商用統計學, 修訂七版, 臺北市 : 三民, 民國80年.		zh_TW