| Reference: | 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. |
