電機資訊學院: 資訊工程學研究所指導教授: 蕭旭君李啟為Lee, Chi-WeiChi-WeiLee2017-03-032018-07-052017-03-032018-07-052016http://ntur.lib.ntu.edu.tw//handle/246246/275611隨著電腦的計算能力以及類神經網路之應用越發成熟，使用人工智慧的方式進行金融商品價格預測變得越來越可行。這篇論文中，我們結合了移動平均逆轉以及循環類神經網路進行「線上資產組合選擇」；而「線上資產組合選擇」在金融市場中是非常熱門的題材。移動平均逆轉的策略已經被證實十分有用在線上資產選擇的領域，所謂的線上資產選擇，簡而言之，就是預測隔一天的價格做為根據以調整目前的投資組合，而移動平均逆轉的策略則利用了股票價格會維持在一個平均值的特性下，以最大化最後交易日的總資產。循環類神經網路在時間序列預測的領域得到關注，如手寫辨識以及語音辨識，其使用內部神經元記憶歷史序列的方使，正好依循著其他時間序列預測的方法。因此，我們結合了移動平均逆轉以及循環類神經網路，並且在一些知名的資料中得到了一些進步。As the development of computing power and the neural network methods become more and more mature, the prediction on financial instruments using artificial related methods becomes more and more viable. In this thesis, we combined the moving average reversion and recurrent neural network on online portfolio selection, which is one of the most popular topics in the area of financial industry. Moving average reversion technique has been proved powerful on online portfolio selection. On-line portfolio selection, in short, is to predict the stocks’prices of the next trading day and adjust the previous portfolio accordingly, while moving average reversion exploits the mean reversion characteristic of stock to better maximize the total wealth in the final trading day. Recurrent neural network (RNN) has been attracting increasing interest in time series prediction like handwriting recognition and speech recognition. RNNs use their internal memory to better utilize the historical sequences, which follows other time series prediction methods. Combining RNN and moving average reversion method, we’ve shown some improvement on popular datasets.714904 bytesapplication/pdf論文公開時間: 2026/12/31論文使用權限: 同意無償授權循環類神經網路移動平均逆轉線上學習資產組合選擇Recurrent neural networkMoving average reversionOn-line learningPortfolio selectionthesis10.6342/NTU201601689http://ntur.lib.ntu.edu.tw/bitstream/246246/275611/1/ntu-105-R03922054-1.pdf