自2001年以來，黃金價格受到新興市場成長等多重影響下不斷攀升，雖於2008年3月至2008年12月間進行了大幅修正，但近期仍不斷的創新高，在經濟或金融市場不穩定的環境下，突顯出它兼具保值與避險的功能，因此本研究以黃金作為研究對象，以發展出穩定的波段預測模型作為研究目的。
本研究樣本為2001年1月2日至2011年6月30日之黃金現貨日資料，採用技術指標及總體經濟資料等作為輸入變數；並以隔日波段分別作為輸出變數共兩種模型，以平滑支撐向量機進行模型訓練後比較，同時也探討加入停損策略前後對於報酬率的影響；另也加入第三種投資決策，買入持有到期賣出策略之懶人投資法來進行比較。

本研究發現：

一、使用隔日波段作為輸出變數並使用總體經濟資料作為輸入變數的準確率及平均報酬率高於使用技術指標作為 輸入變數之模型，此能提供給波段操作投資人參考。
二、輸入變數使用總體經濟資料，並使用停損策略，有較高之報酬，統計上顯著優於使用技術指標的模型。
三、無論使用總體經濟資料作為輸入變數或使用技術指標作為輸入變數之投資報酬率都高於買入持有到期賣出之懶人投資法報酬率。得知運用人工智慧預測方法能幫助投資人有效提升投資報酬率。

關鍵字: 平滑支撐向量機,黃金現貨,波段預測 

From year 2001, the gold price has been raised quickly and consistently by multiple reasons including the growth of emerging market. Although there was a drawdown between March 2008 and December 2008, the gold price continues to rise, In the conditions of economic or financial market instability, but also highlights concurrently hedge against inflation and hedging functions; therefore we use gold price as our subject in order to develop a steady prediction model.
In this study, we employ a recent data between January 28th, 2008 and December 31th, 2010. Using technical indicators and macroeconomics data as our feature variables. We proposed two models based on different predict variables and feature variables, compared each other after model training with Smooth SVM and investigate the difference between with and without the stop-loss trading strategy. We has also joined the third investment decisions, lazy investment to compare.

Our findings are as follows:

1.Using next-day band and macroeconomics data rather than using technical indicators as predict variable has higher accuracy and the average rate of return, which could assist trend investors when making decisions.
2.Using macroeconomic data as feature variables and stop-loss trading strategy could have better accumulated payoff with steadily growth. Significantly better than technical indicators models.
3.No matter using next-day band with macroeconomics data or technical indicators as predict variable, their rate of return is higher then lazy investment; Learned that the use of artificial intelligence forecasting methods can effectively enhance the return on investment to help investors.

Keywords: Smooth Support Vector Machine (SSVM),Gold Price,Trend Forecasting.

中文部份
[1]李映潔 (2007)，影響黃金價格因素其穩定性之研究，國立成功大學國際企業研究所碩士論文。
[2]吳宗諭 (2010)，影響台銀黃金存摺價格因素之研究，國立中央大學產業經濟研究所碩士在職專班碩士論文。
[3]余尚武、劉憶瑩、黃泓瑋 (2009)，動態避險模型之建立–以遠期外匯與一籃子貨幣避險策略為例，第15屆海峽兩岸資訊管理發展與策略研討會。
[4]曹鈞斐 (2006)，運用平滑支撐向量機於期貨市場擇時策略之研究，國立台灣科技大學資訊管理系碩士論文。
[5]熊金志,胡金蓮,袁華強等 (2008)，光滑支持向量機的原理和進展，計算機工程第34卷第13期，pp172-173。
[6]趙智穎 (2010)，我國黃金期貨市場投資價值之分析，私立元智大學管理研究所碩士論文。
[7]蕭翔薰 (2006)，遺傳演化類神經網路於黃金期貨價格預測之研究，私立真理大學財經研究所碩士論文。
[8]闕彥菱 (2008)，利率、美元、黃金價格及原油價格之動態傳遞效果，國立高雄第一科技大學金融營運所碩士論文。
[9]林蔚宗 (2011)，應用平滑支撐向量機於台幣黃金期貨的投資策略，國立台灣科技大學資訊管理系碩士論文。
[10]余尚武、黃雅蘭 (2003)，台灣股價指數期貨套利之研究：類神經網路與灰色理論之應用，國立台灣科技大學資訊管理系碩士論文。
[11]洪琳美 (2006)，運用支撐向量機與類神經網路於銀行授信之研究，國立台灣科技大學資訊管理系碩士論文。
[12]楊天立 (2009)，「第一次買黃金就賺錢」，高寶書版。
[13]楊天立 (2006)，「黃金再起」，高寶書版。

英文部分
[1]Cohen, G. and Qadan , M. (2010), “Is gold still a shelter to fear?”, American Journal of Social and Management Sciences, pp. 39-43
[2]Hsu, C. W, Change, C. C., and Lin, C. J. (2003), “A Practical Guide to Support Vector Classification”, Available at http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf
[3]Cao, L. J. and Tay, F. E. H. (2003), “Support vector machine with adaptive parameters in financial time series forecasting”, IEEE Transactions on Neural Networks 14, No.6.
[4]Lee, Y. J. and Mangasarian, O. L. (2001), “SSVM: a smooth support vector machine for classification”. Computational Optimization and Applications, 2001,22(1) : 5-21.
[5]Pai, P. F. and Wei, W. R. (2007), “Predicting movement directions of stock index futures by support vector models with data preprocessing”, Industrial Engineering and Engineering Management, IEEE International Conference , pp.169-173
[6]Lu , S. X. and Wang, X. Z. (2004), “A comparison among four SVM classification methods: LSVM, NLSVM, SSVM and NSVM”, in: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol. 7, pp. 4277–4282.
[7]Stephen, A. B., and Roger, C.V. T. (1984), “Forecasting the Price of Gold: A Fundamentalist Approach,” Atlantic Economic Association Conference in Montreal, October 11-14, 1984.
[8]Simpson, M. J., Svendsen, A., and Chan, P. L. (2007), “Gold, Platinum, Silver. Demand and Supply in the International Finance Market: An Empirical Analysis”, City University of Hong Kong, Department of Economics and Finance.
[9]Sharpe, W.F. (1994), “The Sharpe ratio,” Journal of Portfolio Management 21 (1), pp. 49-58.
[10]Thomas, D. K. and Richard, A.W. (1989), “ The price of gold: A simple model,” Resources Policy, vol. 15, Issue 4 December 1989, pp.309-313.
[11]Vapnik, V. N. (1995), “The Nature of Statistical Learning Theory”, New York: Springer.
[12]Huang , W., Nakamoria, Y. and Wang, S.Y., “Forecasting stock market movement direction with support vector machine,” Computers & Operations Research, pp. 2513–2522
[13]Zhang , Y. J. and Wei, Y. M. (2010), “The crude oil market and the gold market: Evidence for cointegration, causality and price discovery”, Resources Policy, Volume 35, Issue 3, September 2010, pp.168-177
[14]Oxford Economics (2011), “ The impact of inflation and deflation on the case of gold,” Oxford Economics, July, 2011.