整合關聯規則探勘與類神經網路於ETF投資之行為知識發現

Abstract

世界金融市場自次級房貸風暴以後經歷了嚴重的大衰退，造成許多投資人蒙受重大損失甚至血本無歸。儘管一般投資人無力去避免類似的金融風暴之再度發生，但透過資訊科技理論上可以讓他們能提早發現市場的趨勢，而進一步有避險的動作，而非等到重大金融危機發生之後才能有所反應。 近二十年來，衍生性金融商品逢勃發展，投資人也試圖運用不同之金融商品，期望獲取正向報酬。指數股票型基金(ETF)由於具有低交易成本、低風險、流動性高之特性，亦逐漸地得到一般投資人的歡迎。除此之外，ETF的發展已經超越單一國界甚至有專門針對某種商品價格如黃金以及天然資源的ETF之發行，給了投資人很多投資獲利的機會。如前所述，ETF相對於一般的基金，ETF可以是針對不同國家或是特定地區國家的股票市場，在風險管理上勝過於一般的共同基金。考量研究之適用性，本研究選擇不限於在台灣交易之iShare 發行之EWT 做為研究對象，實驗之交易天數為2762天，研究期間為 2000/6/23~2011/6/16 。 在本論文中，關聯規則探勘以及類神經網路係被整合來發現ETF相關之投資行為，並藉以提出ETF相關之投資策略，經實驗證明所提出之模型可以增加獲取正向報酬的機會並可以有76%以上的準確率在掌握次日之走勢。期藉由此研究之發現，能輔助投資者在面對瞬息萬變的金融環境下做出正確的投資決策。

Global financial markets have been through an extreme downturn in the wake of crisis associated with sub-prime mortgages, causing many investors to suffer from an enormous loss of their investment. Despite the fact that investors generally will not be able to avoid any financial crisis of the similar scale from occurring again as is the case in high risk stock market which is based on the framework of speculations and business growth variance, one can reduce the risks probability with the aid of information technology. In the past two decades, financial derivatives have been gaining a significant popularity with the investors, employing a variety of financial derivatives as new tools and medium of investments for profiting.. Since an ETF (exchange-traded fund) is associated with characteristics of relatively lower transactional costs and risks along with relatively higher fluidity, more and more investors have been exploring and pursuing the ETF-related opportunities. Additionally, ETF-related financial products have been reaching beyond the country borders into different markets and to certain specific goods such as gold and minerals. The country-specific ETFs offer the profits of an international portfolio diversification at a lower cost with a lower tracking error in a more tax-efficient way, than passive open or closed-end country funds. The present research utilizes EWT at iShares and the corresponding researching period starts from 06/23/2000 to 06/16/2011. In the present research, data mining (DM) and neural network are both utilized for identifying the investment patterns associated with an exchange-traded fund (ETF). The present research further capitalizes on characteristics including low cost, tax and trading flexibility associated with an ETF to develop the investment strategy with high probability and a positive investment returns. Specifically, an approach proposed in the present study utilizes DM and BPNN. Experiment result shows that, “following the investment strategy prepared and utilized according to the present research will ensure better investment return than Random Walk methods”. As such, the present research work and model could provide the investors with appropriate guidance at the time of the decision-making in this constantly-changing financial market Investments.