-
Notifications
You must be signed in to change notification settings - Fork 13
/
Copy patholpsR.bib
187 lines (162 loc) · 12.4 KB
/
olpsR.bib
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
% This file was created with JabRef 2.10b2.
% Encoding: UTF8
@Article{BEG04,
Title = {Can we learn to beat the best stock},
Author = {Borodin, Allan and El-Yaniv, Ran and Gogan, Vincent},
Journal = {Journal of Artificial Intelligence Research},
Year = {2004},
Month = {May},
Number = {1},
Pages = {579-594},
Volume = {21},
Abstract = {A novel algorithm for actively trading stocks is presented. While traditional expert advice and ``universal'' algorithms (as well as standard technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirical results on historical markets provide strong evidence that this type of technical trading can ``beat the market'' and moreover, can beat the best stock in the market. In doing so we utilize a new idea for smoothing critical parameters in the context of expert learning.},
Acmid = {1622484},
Address = {USA},
Issue_date = {January 2004},
Numpages = {16},
Publisher = {AI Access Foundation},
Timestamp = {2014.11.12},
Url = {http://www.jair.org/papers/paper1336.html}
}
@Article{Cov91,
Title = {Universal Portfolios},
Author = {Cover, Thomas M.},
Journal = {Mathematical Finance},
Year = {1991},
Number = {1},
Pages = {1-29},
Volume = {1},
Abstract = {We exhibit an algorithm for portfolio selection that asymptotically outperforms the best stock in the market. Let xi= (xi, xi2,…, xim)t denote the performance of the stock market on day i, where xii is the factor by which the jth stock increases on day i. Let bi= (bi1 bi2, bim)t, b;ij 0, bij= 1, denote the proportion bij of wealth invested in the j th stock on day i. Then Sn= IIin= bitxi is the factor by which wealth is increased in n trading days. Consider as a goal the wealth Sn*= maxb IIin=1 btxi that can be achieved by the best constant rebalanced portfolio chosen after the stock outcomes are revealed. It can be shown that Sn * exceeds the best stock, the Dow Jones average, and the value line index at time n. In fact, Sn* usually exceeds these quantities by an exponential factor. Let x1, x2, be an arbitrary sequence of market vectors. It will be shown that the nonanticipating sequence of portfolios db yields wealth such that , for every bounded sequence x1, x2…, and, under mild conditions, achievewhere J, is an (m - 1) x (m - I) sensitivity matrix. Thus this portfolio strategy has the same exponential rate of growth as the apparently unachievable S*n.},
Doi = {10.1111/j.1467-9965.1991.tb00002.x},
ISSN = {1467-9965},
Keywords = {portfolio selection, robust trading strategies, performance weighting, rebalancing},
Publisher = {Blackwell Publishing Ltd},
Review = {Ursprung der Universal Portfolio Theory},
Timestamp = {2012.05.11},
Url = {http://dx.doi.org/10.1111/j.1467-9965.1991.tb00002.x}
}
@Inproceedings{DSSC08,
Title = {Efficient projections onto the l 1-ball for learning in high dimensions},
Author = {Duchi, John and Shalev-Shwartz, Shai and Singer, Yoram and Chandra, Tushar},
Booktitle = {Proceedings of the 25th international conference on Machine learning},
Year = {2008},
Organization = {ACM},
Pages = {272--279},
Url = {https://web.stanford.edu/~jduchi/projects/DuchiShSiCh08.pdf}
}
@Article{GS00,
Title = {Stochastic Nonstationary Optimization for Finding Universal Portfolios},
Author = {Gaivoronski, Alexei A. and Stella, Fabio},
Journal = {Annals of Operations Research},
Year = {2000},
Number = {1-4},
Pages = {165-188},
Volume = {100},
Doi = {10.1023/A:1019271201970},
ISSN = {0254-5330},
Keywords = {constant rebalanced portfolios; optimal growth; stochastic programming; nonstationary optimization},
Language = {English},
Publisher = {Kluwer Academic Publishers},
Url = {http://dx.doi.org/10.1023/A%3A1019271201970}
}
@Article{HSS98,
Title = {On-Line Portfolio Selection Using Multiplicative Updates},
Author = {Helmbold, David P. and Schapire, Robert E. and Singer, Yoram and Warmuth, Manfred K.},
Journal = {Mathematical Finance},
Year = {1998},
Number = {4},
Pages = {325--347},
Volume = {8},
Abstract = {We present an on-line investment algorithm that achieves almost the same wealth as the best constant-rebalanced portfolio determined in hindsight from the actual market outcomes. The algorithm employs a multiplicative update rule derived using a framework introduced by Kivinen and Warmuth. Our algorithm is very simple to implement and requires only constant storage and computing time per stock in each trading period. We tested the performance of our algorithm on real stock data from the New York Stock Exchange accumulated during a 22-year period. On these data, our algorithm clearly outperforms the best single stock as well as Cover's universal portfolio selection algorithm. We also present results for the situation in which the investor has access to additional “side information.”},
Doi = {10.1111/1467-9965.00058},
ISSN = {1467-9965},
Keywords = {portfolio selection, rebalancing, machine learning algorithms},
Publisher = {Blackwell Publishers Inc},
Timestamp = {2014.05.07},
Url = {http://dx.doi.org/10.1111/1467-9965.00058}
}
@Article{KO12,
Title = {It’s All in the Timing: Simple Active Portfolio Strategies that Outperform Naïve Diversification},
Author = {Kirby,Chris and Ostdiek,Barbara},
Journal = {Journal of Financial and Quantitative Analysis},
Year = {2012},
Month = {3},
Pages = {437--467},
Volume = {47},
Abstract = {ABSTRACT DeMiguel, Garlappi, and Uppal (2009) report that naïve diversification dominates mean-variance optimization in out-of-sample asset allocation tests. Our analysis suggests that this is largely due to their research design, which focuses on portfolios that are subject to high estimation risk and extreme turnover. We find that mean-variance optimization often outperforms naïve diversification, but turnover can erode its advantage in the presence of transaction costs. To address this issue, we develop 2 new methods of mean-variance portfolio selection (volatility timing and reward-to-risk timing) that deliver portfolios characterized by low turnover. These timing strategies outperform naïve diversification even in the presence of high transaction costs.},
Doi = {10.1017/S0022109012000117},
ISSN = {1756-6916},
Issue = {02},
Numpages = {31},
Timestamp = {2014.11.24},
Url = {http://journals.cambridge.org/article_S0022109012000117}
}
@Article{LH14,
Title = {Online Portfolio Selection: A Survey},
Author = {Li, Bin and Hoi, Steven C. H.},
Journal = {ACM Comput. Surv.},
Year = {2014},
Month = jan,
Number = {3},
Pages = {35:1--35:36},
Volume = {46},
__markedentry = {[ng:1]},
Abstract = {Online portfolio selection is a fundamental problem in computational finance, which has been extensively studied across several research communities, including finance, statistics, artificial intelligence, machine learning, and data mining. This article aims to provide a comprehensive survey and a structural understanding of online portfolio selection techniques published in the literature. From an online machine learning perspective, we first formulate online portfolio selection as a sequential decision problem, and then we survey a variety of state-of-the-art approaches, which are grouped into several major categories, including benchmarks, Follow-the-Winner approaches, Follow-the-Loser approaches, Pattern-Matching--based approaches, and Meta-Learning Algorithms. In addition to the problem formulation and related algorithms, we also discuss the relationship of these algorithms with the capital growth theory so as to better understand the similarities and differences of their underlying trading ideas. This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in the financial industry to help them understand the state of the art and facilitate their research and practical applications. We also discuss some open issues and evaluate some emerging new trends for future research.},
Acmid = {2512962},
Address = {New York, NY, USA},
Articleno = {35},
Doi = {10.1145/2512962},
ISSN = {0360-0300},
Issue_date = {January 2014},
Keywords = {algorithms, economics, financial, machine learning, optimization, portfolio selection},
Numpages = {36},
Publisher = {ACM},
Url = {http://doi.acm.org/10.1145/2512962}
}
@Article{LHZ13,
Title = {Confidence Weighted Mean Reversion Strategy for Online Portfolio Selection},
Author = {Li, Bin and Hoi, Steven C. H. and Zhao, Peilin and Gopalkrishnan, Vivekanand},
Journal = {ACM Trans. Knowl. Discov. Data},
Year = {2013},
Month = mar,
Number = {1},
Pages = {4:1--4:38},
Volume = {7},
Acmid = {2435213},
Address = {New York, NY, USA},
Articleno = {4},
Doi = {10.1145/2435209.2435213},
ISSN = {1556-4681},
Issue_date = {March 2013},
Keywords = {Portfolio selection, confidence weighted learning, mean reversion, online learning},
Numpages = {38},
Publisher = {ACM},
Timestamp = {2014.11.24},
Url = {http://doi.acm.org/10.1145/2435209.2435213}
}
@Article{LZH12,
Title = {PAMR: Passive aggressive mean reversion strategy for portfolio selection},
Author = {Li, Bin and Zhao, Peilin and Hoi, Steven C. H. and Gopalkrishnan, Vivekanand},
Journal = {Machine Learning},
Year = {2012},
Number = {2},
Pages = {221-258},
Volume = {87},
Doi = {10.1007/s10994-012-5281-z},
ISSN = {0885-6125},
Keywords = {Portfolio selection; Mean reversion; Passive aggressive learning; Online learning},
Language = {English},
Publisher = {Springer US},
Timestamp = {2014.11.24},
Url = {http://dx.doi.org/10.1007/s10994-012-5281-z}
}
@comment{jabref-meta: groupsversion:3;}
@comment{jabref-meta: groupstree:
0 AllEntriesGroup:;
1 ExplicitGroup:On-line Portfolio Selection\;0\;BEG04\;BK99\;BYG00\;Be
W05\;CB03\;CO96\;Cov91\;GS00\;GS03\;GUW07\;GZ12\;HK09\;HK12\;HSS98\;HZ
95\;Hak71\;KG12\;KO12\;KS11\;KV02\;LH14\;LHZ13\;LS08\;LZH12\;OC98\;SL0
5\;Sin97\;TaS12\;VW98\;YTL06\;ZZYX12\;;
1 ExplicitGroup:Competitive Analysis\;0\;BY98\;EFK92\;FW98\;KG12\;KP00
\;Kar92\;ST85\;;
}