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| Title: | 利用深度學習模型建構基金最適資產配置
Using Deep Learning Model to Construct The Optimal Asset Allocation In Mutual Fund |
| Authors: | 黃勝彥
Huang, Sheng-Yan |
| Contributors: | 黃泓智
Huang, Hong-Chih
黃勝彥
Huang, Sheng-Yan |
| Keywords: | 基金分群
長短期記憶模型
波動度控制
最適化權重
Mutual fund
LSTM
Volatility control
Optimal asset allocation |
| Date: | 2020 |
| Issue Date: | 2020-08-03 17:41:33 (UTC+8) |
| Abstract: | 本研究主要是以長短期記憶模型（LSTM）進行基金報酬率預測，搭配波動度控制的方法，建構穩健的投資組合。為了達成風險分散的目的，本論文將股票型基金與債券型基金分別處理，其中，股票型基金劃分為六大市場，包含日本、美國、新興市場、歐洲、亞太不含日本與台灣，而債券型基金劃分為三大類別，包含投資級別債券、高收益債券以及新興市場債券，於資產配置時，從各大地區與類別挑選出預期表現較佳之基金，達成風險分散之目的。在波動度控制的部分，本文以下方標準差做為波動度的衡量，並嘗試以固定波動度與變動波動度的方法進行資產配置，最終比較其結果之差異。實證結果發現，透過每月檢視投資組合的風險，變動波動度控制能夠迅速反應市場狀況，且較為保守，整體績效優於固定波動度控制。
The purpose of this study is to use LSTM model to predict the return of mutual fund, and build a stable portfolio. In order to achieve the purpose of risk diversification, this study treats bond fund and stock fund separately. Moreover, stock funds are divided into six major markets, and bond funds are divided into three major categories. The final portfolio will include funds from each category in order to diversify the risk. This study uses two volatility control methods to determine the asset allocation, including fixed volatility control and variable volatility control. The empirical results find that through monthly review of portfolio risk, variable volatility control method can quickly reflect market conditions, and therefore the overall performance is better than fixed volatility control. |
| Reference: | 1.Abe Masaya and Nakayama Hideki（2018）.“Deep learning for forecasting stock returns in the cross-section,” Pacific-Asia Conference on Knowledge Discovery and Data Mining. 273-284.
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| Description: | 碩士
國立政治大學
風險管理與保險學系
107358012 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107358012 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202000778 |
| Appears in Collections: | [風險管理與保險學系] 學位論文
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