運用自組織映射圖神經網路於股票型基金市場行為發現

Abstract

投資理財是一門重要的學問，也是門不太容易的學問，必須敏銳地趨利避險，才能提高獲利的機會。共同基金也被一般人稱為「懶人投資法」。基金投資門檻極低，像是國內的共同基金單筆申購僅需新台幣一萬元，定期定額投資最低更只要三千元，不論您是大富翁或是小散戶，都可以藉由投資基金敲開累積財富之門，是一種「共聚資金、共擔風險、共同分享投資利潤」的投資方法。

然而投資是存在風險的，投資者在進行基金投資時，總希望趨吉避凶，在多頭時，能夠買入報酬率最佳的基金，在空頭時，能夠不持有基金而避免損失，甚至是用期貨做反向獲利；本研究從國內眾多的基金市場中挑選189檔股票型基金，以及13個總體經濟指標，經過適當的資料處理後，做為自組織映射圖神經網路的輸入值，從中建構基金市場行為趨勢發現模型，並依此模型進行投資交易模擬。

實證結果發現於2002年1月至2008年12月期間，採用本模型模擬交易策略3，進行基金市場模擬交易，總報酬率可達122%，而同時期的加權指數為 -22%，隨機交易策略平均報酬率為 -25%，明顯說明運用本研究模型進行基金投資，將可有效擊敗大盤及隨機交易；實驗過程中亦發現依據模型的買賣訊號可對大盤的多空趨勢進行掌握，並且也提供投資人進行基金標的物選擇時的參考依據。

Investment strategy is something that is important to the general public though at the same time is difficult to be formulated. Mutual fund, as one implementation of the investment strategy, may be a good fit to people without much time or willingness to track their investment portfolio on a frequent basis. The threshold for the mutual fund investment is relatively low. For example, to make single mutual fund investment locally may only cost ten thousands (10,000) NTD. And when people choose to investment their money on a monthly basis only three thousands (3,000) NTD may be enough. Regardless of the investor’s financial status, the mutual fund creates an opportunity for people to properly manage their fortunes by pooling their investment money, sharing the risks associated with the investment, and enjoying the profit together. However, any investment comes with the risks. Any investor including the mutual fund investor desires to maximize the profit of his/her investment portfolio while minimizing the loss. Any investor desires to purchase the mutual fund with a superior investment return ratio in the bull market. The investor may not want to be in possession of any mutual fund during the downturn of economy in order to avoid the loss, and even likes to reap certain profits by investment in futures. The present research picks and processes one hundred and eighty nine (189) domestic equity mutual funds and thirteen (13) macroeconomics indices to serve as inputs for self-organizing map neural networks so as to formulate a model for mutual fund market behavior and trend discovery. With such model in place, the present research further simulates investment transactions to verify its efficiency. Our verification shows the investment strategy formulated on the basis of the established model the overall investment return could be as high as one hundred and twenty two (122) percents of the investment while the Taiwan weighted stock index was down by twenty two (22) percents during the period from Jan., 2002 to Dec., 2008. That the average ratio of investment return stands at minus twenty-five (25) percents during the same period further suggests utilizing the model proposed by the present research for the mutual fund investment could outperform the random investment and stock transactions. The present research might also predict the trend of the stock market through buy/sell signals according to the established model, which further provides the investors with a valuable reference when it comes to selecting a target for the mutual fund investment.