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1 AI
2 2017 CSL GPIF (1) AI (2) AI GPIF GPIF AI GPIF etc Style Detector Array Style Detector Array GPIF GPIF CSL
3 1 1 GPIF GPIF 28 [1] % -0.29% +0.64% -0.78% [2, 3] GPIF 3K+1D AI Two sigma[4, 5] Renaissance Technologies[6, 7] Investifai[8, 9, 10] AI AI AI GPIF GPIF
4 2 2 GPIF AI Style Detector Array B P L O
5 2.1: 3
![4 2.2 Style Detector Array AI Style Detector Array 100 Python tensorflow[11] Keras[12] matplotlib[13] plotly[14] SciPy [15] numpy[16]](/docs-images/93/113465075/images/6-0.jpg "pandas[17] Scikit-learn[18] ipython[19] R [20] 2.2.1 Style Detector Array 2.")
6 4 2.2 Style Detector Array AI Style Detector Array 100 Python tensorflow[11] Keras[12] matplotlib[13] plotly[14] SciPy [15] numpy[16] pandas[17] Scikit-learn[18] ipython[19] R [20] Style Detector Array 2.2: Style Detector Array Style Detector Array Detector Detector 2.2
7 5 Style Detector Array Detector Style Detector Array 8 Style Detector Array High Dividend Minimum Volatility 20 Momentum 20 Value PBR Growth PER Quality / Fixed Weight Technical Style Detector Array Detector Detector PBR PER 100
8 6 (a) High Dividend (b) Minimum Volatility (c) Momentum (d) Value (e) Growth (f) Quality (g) Fixed Weight (h) Technical 2.3: Detector Detector , Detector Style Detector Array Detector [21, 22, 23] 20 Style Detector Array Style Detector Array 2.3 8
9 Style Detector Array (a) J (b) B (c) O (d) Q (e) L (f) E (g) V (h) D (i) X (j) N 2.4: Style Detector Array Style Detector Array 10 Style Detector Array 2.4 Detector
10 8 V D 1 V (a) (b) 2.5: V Growth 2.4g Momentum Growth Growth Technical Growth Growth Style Detector Array V GPIF Style Detector Array Style Detector Array Detector Style Detector Array 8 Detector Momentum Growth Technical 3 3 V Style Detector Array Momentum Growth Technical Detector
11 9 Momentum Growth 2.5a 2.4g Growth Momentum D 2.4h Momentum Fixed Weight High Dividend (a) (b) 2.6: D O 2.4c L 2.4e 2.7a 2.7b
12 10 (a) O (b) L 2.7: 2.3 t-sne[24] 2.8: 2015/ /04
13 11 2.9: 2006/ / High Dividend Growth Minimum Volatility Momentum Fixed Weight Technical Momentum Technical High Dividend Minimum Volatility Growth
14 : (a) (b)
15 13 (a) 絶対リターンおよびリスク (b) 対ベンチマークの相対リターンおよびリスク 図 2.11: Risk-Return 相関図 2015/01/ /04/06 (a) 絶対リターンおよびリスク (b) 対ベンチマークの相対リターンおよびリスク 図 2.12: Risk-Return 相関図 2015/05/ /10/01 これらの図から共通して言えるのは 絶対リターンとリスクとの相関を見たとき 各図の左側 には 個々のファンドの違いがはっきりと現れないことである 特に市場が大きな変動があった時のみを取り出 した図 2.12a を見ると 全てのファンドがほぼ同調して動いていることが分かりやすく示されている 一 方で 対ベンチマークの相対リターンで見ると 各図の右側 それぞれのファンドの違いが多少現れて くる L 社や N 社がそれぞれのベンチマークに対してリスクを抑えた運用スタイルを採っているのに対 して これらに比べて E 社や B 社はより高いリスクまで許容する運用スタイルを採っている このよう に対ベンチマークで見た時には各ファンドの運用スタイルには多様性があるとみなすことは出来るもの
16 Style Detector Array Style Detector Array Barra Aladdin GPIF Style Detector Array 8 Style Detector Array Style Detector Array PC AI AI Style Detector Array Style Detector Array
17 AI AI AI AI AI AI AI AI AI AI N-Player Game AI INDEX Two Sigma AI AI INDEX AI INDEX
18 GPIF AI AI GPIF GPIF GPIF GPIF GPIF GPIF AI INDEX GPIF AI AI GPIF GPIF AI GPIF
19 17 4 GPIF AI 4.1 3, Style Detector Array 8
20 (a) (b) 4.1: 4.1a 4.1b
21 : GPIF GPIF: GPIF CSL: CSL GPIF GPIF GPIF : GPIF (EDA: Exploratory Data Analysis) Python, R SQL : off-memory OS Git *1 Git (Github/Bitbucket/Gitlab) PDCA *1
22 20 5 AI GPIF AI AI Style Detector Array Style Detector Array Style Detector Array GPIF AI AI AI N-Player Game AI INDEX INDEX AI INDEX GPIF GPIF
23 21 [1] GPIF. 28, [2] GPIF 3. operation/management/pdf/keieiiinkai_306.pdf. [3] GPIF 4. operation/management/pdf/keieiiinkai_405.pdf. [4] Paul Rosa David Weisberger. Automated equity trading: The evolution of market structure and its effect on volatility and liquidity. Technical report, Two Sigma Securities, [5] Top page of Two Sigma. [6] Michael Markov. The law of large numbers: An analysis of the renaissance fund. Technical report, Markov Processes International, September [7] Top page of Renaissance Technologies. [8] Mathieu Lemay Mary Kate MacPherson Miodrag Bolic Samer Obeidat, Daniel Shapiro. Adaptive portfolio asset allocation optimization with deep learning. International Journal on Advances in Intelligent Systems, Vol. 11, No. 1&2, pp , [9] Samer Obeidat. Five ways artificial intelligence is disrupting asset management. entrepreneur.com/article/312672, April [10] Top page of Investifai. [11] Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, Software available from tensorflow.org. [12] François Chollet, et al. Keras [13] John D. Hunter. Matplotlib: A 2d graphics environment. Computing in Science and Engineering, Vol. 9, No. 3, pp , [14] Plotly Technologies Inc. Collaborative data science, [15] Eric Jones, Travis Oliphant, Pearu Peterson, et al. SciPy: Open source scientific tools for Python, [Online; accessed today ].
24 22 [16] Travis E. Oliphant. Guide to NumPy. Trelgol, [17] Wes Mckinney. pandas: a foundational python library for data analysis and statistics [18] Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikitlearn: Machine learning in python. Journal of Machine Learning Research, Vol. 12, No. Oct, pp , [19] Fernando Pérez and Brian E. Granger. IPython: a system for interactive scientific computing. Computing in Science and Engineering, Vol. 9, No. 3, pp , May [20] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, [21] Yi Shang and Benjamin W. Wah. Global optimization for neural network training. IEEE Computer, Vol. 29, No. 3, pp , [22] M. S. Iyer and R. R. Rhinehart. A method to determine the required number of neural-network training repetitions. IEEE Trans. Neural Networks, Vol. 10, No. 2, pp , [23] Akarachai Atakulreka and Daricha Sutivong. Avoiding local minima in feedforward neural networks by simultaneous learning. In Mehmet A. Orgun and John Thornton, editors, Australian Conference on Artificial Intelligence, Vol of Lecture Notes in Computer Science, pp Springer, [24] Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research, Vol. 9, pp , 2008.
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九州大学学術情報リポジトリ Kyushu University Institutional Repository 回帰分析による CGM 動画再生回数推定 柴田, 知親九州大学工学部電気情報工学科 伊東, 栄典九州大学情報基盤研究開発センター : 准教授 http://hdl.handle.net/2324/1912137 出版情報 :2018-03-05. 電子情報通信学会データ工学研究専門委員会バージョン
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