| 摘要: | 在低利率時代怎麼學習投資理財?如何保有資金不被通貨膨脹折價?有效的投資就變成大家關心的課題，『財富的累積，從投資理財開始』在理財意識高漲的今日，投資商品相當多元化，而股票市場更是深受歡迎的標的之一，然而如何規避風險，進而找出投資標的進場的最佳時機，以獲取高報酬、低風險的理財成果，將是投資者必須研究的課題. 投資者除了選擇自己所感興趣的股票標的之外，所面臨的共同難題為買賣時間點的決定，因此在股票市場上各種技術分析方法則被廣泛的研究及使用。
本研究利用多種技術指標搭配使用，並探討在多種技術指標的關係中，找出技術指標與大盤走勢的關聯性及預測適合投資的時機點，研究中建立貝氏分類法、SVM分類法、KNN分類法三種分類演算法模組以預測股票市場之買進切入時機的正確性。最後並利用綜合性的多重分類模組預測買賣時間點，以幫助投資者能夠增加投資報酬率，並降低不必要的風險。
本研究經實作實驗証明研究中使用的整合性分類模型整體預測大盤漲跌的平均報酬比利用各單一分類器預測的平均報酬更佳，其中利用票選法篩選過的投資策略在大類別相同就進場的積極型的投資策略建議交易天數279天，每次的交易平均可獲得12.88點大盤指數，而在小類別相同才進場的保守型的投資策略建議交易天數87天，每次的交易平均可獲得27.6點大盤指數，均可以有效幫助投資決策者作出正確的決策及避免不必要的投資風險。
In the era of low interest rate, how does one learn to invest and manage the wealth? How to retain the capital on hands and not let it depreciate through inflation? Effective investment suddenly becomes the subject that everyone cares about. Adage like “The accumulation of wealth starts from investment and wealth management” is well said under backdrop of high conscientiousness for wealth management nowadays. Since investment merchandises have considerably been diversified and stock market naturally becomes one of the most popular targets for investment monies. Nonetheless, how to hedge the risk so as to locate the best timing for targeting your investment, to acquire high investment returns and low risk exposures as the outcomes for wealth management, would be the absolute necessities for all investor studies. Therefore, investors not only select the stocks of their own interest as the stock investment targets, but also will jointly face the dilemma of when for the actual transactions to take place. Hence, all kinds of technical analysis methods for stock market are widely researched and applied as well.
This research employs a variety of technical indicators in the sense of complementing each other. In addition, this research also intends to locate the relevancies between technical indicators and the market trends, and predict the timing for adequate investment between the relationships from a variety of these indicators. During research, Bayesian, SVM, KNN classifications were established accordingly. And these three classification algorithm modules were used to predict the validity of timing for the buy-transaction in the stock market. Lastly, the composite multiple classification module will be used to predict the time points for buy and sell transactions so as to facilitate the investors in increasing the investment returns in addition to lower the unnecessary risks.
This research has been proven through lab experiments that, in the performance for integrated classification model used in the research, the average return ratio for integrated prediction for the market index is better than that from using the single classifier approach. Within the investment strategies selected by voting method, for those aggressive investment strategies that invest during identical large categories, the respective trading lasts 279 days with 12.88 points gain of the market index during each transaction. And those conservative types of investment strategy lasting 87 days which would trade during identical small categories, they managed with 27.6 points gain of the market index. We can see that all of these can effectively facilitate the investors in making the right decisions so as to prevent from exposures to unnecessary investment risks. |
