本研究使用卷積神經網路(Convolutional Neural Network，CNN)針對股票型基金於未來20天後的漲跌趨勢做出預測，輸入資料(Input Data)先使用技術分析建立資料矩陣後，再用Sliding Window的方式在上方滑動擷取出二維的image做為訓練資料，資料標籤(Label)則是觀察上漲強度與收益率之間的關係並加上時間權重來設計出漲跌門檻，模型設計是以LeNet-5為基礎進行延伸。當CNN預測出一群未來會上漲的基金後，我們再以高關聯篩選或機率門檻排序來挑出5支欲購買的基金，買入後固定持有20天後賣出。
實驗結果顯示在資料標籤設計時若考慮時間權重所設計出的上漲強度加上高關聯篩選能讓我們有較好的實驗結果，其中在2016年以距離平方型加上高關聯篩選能獲得23.89%的年化報酬率，在2017年以距離型加上高關聯篩選能獲得32.69%的年化報酬率。

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