臺灣博碩士論文加值系統

在這篇論文裡，我們結合李承翰經由基因規劃法(gen expression programming)產生的交易訊號與江學者經由卷積神經網路(convolutional neural network)產生的投資組合，形成一個股票組合管理的投資方法。
江學者的方法主要是對比特幣進行投資，我們轉換江學者的投資物從比特幣到股票。當李承翰的方法產生一個買賣的交易訊號(買或賣)，我們會重新計算投資組合的權重。為了測試我們的方法，我們選擇存在於1995/1/5 到2017/12/29之間的213支股票，我們的訓練區間開始於1995/1/5，我們從2002/1/2開始交易到2017/12/29為止。
在我們的實驗裡有三種狀況的測試:利用100支股票的特徵去交易100支股票，利用213支股票的特徵去交易100支股票和利用213支股票的特徵去交易213支股票，這三個測試的年報酬率分別為25.00%、26.52%、27.32%。我們的方法比buy-and-hold 的方式好，100支股票使用buy-and-hold方式的年報酬率為12.36%，213支股票使用buy-and-hold方式的年報酬率為12.21%。我們的方法也比李承翰無投資組合管理的方法好，李承翰的方法100支股票的年報酬率為12.94%，213支股票的年報酬率為12.67%。

In this thesis, we combine the trading signals generated by the gen expression programming (GEP) method of Lee et al. and the portfolio generated by convolutional neural network (CNN) structure of Jiang et al. to form a stock investment method with portfolio management.
The method of Jiang et al. focuses on the investment of the cryptocurrency. We change the invested target of Jiang et al. from cryptocurrency to stocks. We recompute the weights of the portfolio when the method of Lee et al. generates a trading signal (buy or sell). To test our method, we choose 213 stocks which always exist during 1995/1/5 to 2017/12/29 on stock market in Taiwan. Our training period starts from 1995/1/5. We perform the trading from 2002/1/2 until 2017/12/29.
There are three cases in our experiments: Trading 100 stocks with the 100-stock features, trading 100 stocks with the 213-stock features, and trading 213 stocks with the 213-stock features. The annualized returns for the three cases are 25.00%, 26.52% and 27.32%, respectively. Our method is better than the buy-and-hold 12.36% for 100 stocks, and 12.21% for 213 stocks. Our method is also better than the method of Lee et al. without portfolio management 12.94% for 100 stocks, and 12.67% for 213 stocks.

VERIFICATION FORM . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
THESIS AUTHORIZATION FORM . . . . . . . . . . . . . . . . . . . . iii
ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
CHINESE ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
ENGLISH ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
LIST OF SYMBOLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
Chapter 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 2. Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1 The Trading Strategy of Lee et al. . . . . . . . . . . . . . . . . . . . . 4
2.2 Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . 8
Chapter 3. Our Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.1 Generation of Rebalancing Signals . . . . . . . . . . . . . . . . . . . . 15
3.2 The Proposed Convolutional Neural Network Models and Training
Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Chapter 4. Experimental Results . . . . . . . . . . . . . . . . . . . . . . 22
Chapter 5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

[1] C. Y. Chou, C. B. Yang, and H. H. Chen, “Portfolio investment based on
gene expression programming,” Proc. of the 32nd International Conference on
Computers and Their Applications, Honolulu, Hawaii, USA, pp. 225–230, Mar.
2017.
[2] Y. Deng, F. Bao, Y. Kong, Z. Ren, and Q. Dai, “Deep direct reinforcement
learning for financial signal representation and trading,” IEEE Transatction on
Neural Networks and Learning Systems, Vol. 28, No. 3, pp. 653–664, 2017.
[3] C. Ferreira, “Gene expression programming: A new adaptive algorithm for
solving problems,” Complex Systems, Vol. 13, pp. 87–129, 2001.
[4] C. M. Hsu, “A hybrid precedure for stock price prediction by integrating self-
organizing map and genetic programming,” Expert Systems with Applications,
Vol. 38, pp. 14026–14036, 2011.
[5] C. F. Huang, B. R. Chang, D. W. Cheng, and C. H. Chang, “Feature selection
and parameter optimization of a fuzzy-based stock selection model using genetic
algorithms,” International Journal of Fuzzy Systems, Vol. 14, No. 1, pp. 65–75,
2012.
[6] D. H. Hubel and T. N. Wiesel, “Receptive fields and functional architecture
of monkey striate cortex,” The Journal of Physiology, Vol. 195, pp. 215–243,
1968.
[7] S. M. Jhou, C. B. Yang, and H. H. Chen, “Taiwan stock forecasting with the
genetic programming,” Proc. of the 16th Conference on Artificial Intelligence
and Application (Domestic Track), Chungli, Taiwan, pp. 151–157, Nov. 2011.
[8] Z. Jiang, D. Xu, and J. Liang, “A deep reinforcement learning framework for the
financial portfolio management problem.” https://arxiv.org/abs/1706.10059,
2017.
[9] J. Kiefer and J. Wolfowitz, “Stochastic estimation of the maximum of a regres-
sion function,” The Annals of Mathematical Statistics, Vol. 23, 1952.
[10] J. R. Koza, Genetic Programming: On the programming of computers by means
of natural selection, Vol. 1. Cambridge, USA, MA: MIT press, 1992.
[11] C.-H. Lee, C.-B. Yang, and H.-H. Chen, “Taiwan stock investment with gene
expression programming,” Procedia Computer Science, Vol. 35, pp. 137–146,
2014.
[12] B. Li and S. C. H. Hoi, “Online portfolio selection: A survey,” ACM Computing
Surveys, Vol. 46, pp. 1–32, 2014.
[13] H. Robbins and S. Monro, “A stochastic approximation method,” The Annals
of Mathematical Statistics, Vol. 22, pp. 400–407, 1951.
[14] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations
by back-propagating errors,” Nature, Vol. 323, pp. 533–536, 1986.
[15] D. Silver, J. Schrittwieser, K. Simonyan, ioannis Antonoglou, A. Huang,
A. Guez, T. Hubert, L. baker, M. Lai, A. bolton, Y. chen, T. Lillicrap, F. Hui,
L. Sifre, G. van den Driessche, T. Graepel, and D. Hassabis, “Mastering the
game of Go without human knowledge,” Nature, Vol. 550, pp. 254–359, 2017.
[16] I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with
neural networks,” 27th International Conference on Neural Information Pro-
cessing Systems, Vol. 2, Montreal, Canada, pp. 3104–3112, dec 2014.
[17] Taiwan Economic Journal Co., Ltd, “TEJ.” http://www.tej.com.tw/twsite/,
1991.
[18] Z. Tan, C. Quek, and P. Y. Cheng, “Stock trading with cycles: A financial
application of anfis and reinforcement learning,” Expert Systems with Applica-
tions, Vol. 38, pp. 4711–4755, 2011.
[19] M. Thakur and D. Kumar, “A hybrid financial trading support system using
multi-category classifiers and random forest,” Applied Soft Computing, Vol. 67,
pp. 337–349, 2018.
[20] J. L. Ticknor, “A Bayesian regularized artificial neural network for stock market
forecasting,” Expert Systems with Applications, Vol. 40, pp. 5501–5506, 2013.
[21] T. J. Tsai, C. B. Yang, and Y. H. Peng, “Genetic algorithms for the invest-
ment of the mutual fund with global trend indicator,” Expert Systems with
Applications, Vol. 38, pp. 1697–1701, 2011.
[22] L.-X. Wang, “Dynamical models of stock prices based on technical trading rules-
part i: The models,” IEEE Transatction on Fuzzy Systems, Vol. 23, pp. 787–
800, 2015.
[23] L.-X. Wang, “Dynamical models of stock prices based on technical trading
rules-part ii: Analysis of the model,” IEEE Transatction on Fuzzy Systems,
Vol. 23, pp. 1127–1141, 2015.
[24] L.-X. Wang, “Dynamical models of stock prices based on technical trading
rules-part iii: Application to hong kong stocks,” IEEE Transatction on Fuzzy
Systems, Vol. 23, pp. 1680–1697, 2015.
[25] Y. Zhan, D. Hu, Y. Wang, and X. Yu, “Semisupervised hyperspectral image
classification based on generative adversarial networks,” IEEE Geoscience and
Remote Sensng Letters, Vol. 15, No. 2, pp. 212–216, 2018.