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@article{doi:10.1002/for.3980130102,
author = {Lin, Jin-Lung and Granger, C. W. J.},
title = {Forecasting from non-linear models in practice},
journal = {Journal of Forecasting},
volume = {13},
number = {1},
pages = {1-9},
keywords = {Non-linear models, multi-step forecasts, bootstrap estimates},
doi = {10.1002/for.3980130102},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/for.3980130102},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/for.3980130102},
abstract = {Abstract If a simple non-linear autoregressive time-series model is suggested for a series, it is not straightforward to produce multi-step forecasts from it. Several alternative theoretical approaches are discussed and then compared with a simulation study only for the two-step case. It is suggested that fitting a new model for each forecast horizon may be a satisfactory strategy.},
year = {1994}
}
@Misc{Greenwich_2019,
author = "Johnson, Richard",
title = {Demystifying Alternative Data},
year = {2019},
Howpublished = {https://www.greenwich.com/asset-management/demystifying-alternative-data},
publisher={Greenwich Associates},
Note = {Last accessed on July 6, 2019}
}
@book{Wickham:2017:RDS:3086927,
author = {Wickham, Hadley and Grolemund, Garrett},
title = {R for Data Science: Import, Tidy, Transform, Visualize, and Model Data},
year = {2017},
isbn = {1491910399, 9781491910399},
edition = {1st},
publisher = {O'Reilly Media, Inc.},
}
@Book{Choudhry:2007,
title={An Introduction to Value-at-Risk},
author={Choudhry, M. and Tanna, K.},
isbn={9780470033777},
series={Securities Institute},
year={2007},
publisher={Wiley}
}
@book{McCulloh:2013:SNA:2829081,
author = {McCulloh, I. and Armstrong, H. and Johnson, A.},
title = {Social Network Analysis with Applications},
year = {2013},
isbn = {1118169476, 9781118169476},
edition = {1st},
publisher = {Wiley Publishing},
}
@book{CaseBerg:01,
abstract = {{This book builds theoretical statistics from the first
principles of probability theory. Starting from the basics
of probability, the authors develop the theory of
statistical inference using techniques, definitions, and
concepts that are statistical and are natural extensions
and consequences of previous concepts. Intended for
first-year graduate students, this book can be used for
students majoring in statistics who have a solid
mathematics background. It can also be used in a way that
stresses the more practical uses of statistical theory,
being more concerned with understanding basic statistical
concepts and deriving reasonable statistical procedures for
a variety of situations, and less concerned with formal
optimality investigations.}},
added-at = {2009-10-28T04:42:52.000+0100},
author = {Casella, G. and Berger, R.},
biburl = {https://www.bibsonomy.org/bibtex/21597678f36e23439610affbf46adec1c/jwbowers},
citeulike-article-id = {105644},
date-added = {2007-09-03 22:45:16 -0500},
date-modified = {2007-09-03 22:45:16 -0500},
howpublished = {{Textbook Binding}},
interhash = {2dd8caad6c0b6fb80e6334986a231a05},
intrahash = {1597678f36e23439610affbf46adec1c},
isbn = {0534243126},
keywords = {methodology probability statistics},
month = {June},
opturl = {http://www.amazon.fr/exec/obidos/ASIN/0534243126/citeulike04-21},
publisher = {{Duxbury Resource Center}},
timestamp = {2009-10-28T04:42:57.000+0100},
title = {Statistical Inference},
year = 2001
}
@article{Sun2016,
abstract = {•Transitions between patterns are emergent properties in spatial epidemics.•Two types of pattern transitions in infectious diseases are shown.•We provide possible mechanisms of pattern transition in spatial epidemics.•Pattern transition promotes complexity in spatial epidemics.•The results are applicable in medical science, ecology, quantitative finance and so on.},
affiliation = {Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi 030006, PR China; School of Mathematical Sciences, Fudan University, Shanghai 200433, PR China; Department of Vector Ecology and Environment, Nagasaki University Institute of Tropical Medicine (NEKKEN), Nagasaki 852-8523, Japan; Center of Mathematics for Social Creativity, Hokkaido University, Sapporo 060-0812, Japan; Department of Mathematics, Southeast University, Nanjing 210096, PR China; Department of Mathematics and Statistics, University of Victoria, Victoria BC V8W 3R4, Canada; Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka, 816-8580, Japan},
author = {Sun, G. and Jusup, M. and Jin, Z. and Wang, Y. and Wang, Z.},
doi = {10.1016/j.plrev.2016.08.002},
journal = {Physics of Life Reviews},
keywords = {Reaction–diffusion equation; Cellular automata; Spatial heterogeneity; Seasonality and noise; Coherence resonance; Cyclic evolution},
language = {English},
number = {Complete},
pages = {43-73},
title = {Review},
volume = {19},
year = {2016},
}
@article{DBLP:journals/amc/Li15a,
author = {Li L.},
title = {Patch invasion in a spatial epidemic model},
journal = {Applied Mathematics and Computation},
volume = {258},
pages = {342--349},
year = {2015},
url = {http://dx.doi.org/10.1016/j.amc.2015.02.006},
doi = {10.1016/j.amc.2015.02.006},
timestamp = {Sat, 21 Mar 2015 13:18:42 +0100},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/amc/Li15a},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@article{Sun20141507,
title = "Influence of time delay and nonlinear diffusion on herbivore outbreak ",
journal = "Communications in Nonlinear Science and Numerical Simulation ",
volume = "19",
number = "5",
pages = "1507 - 1518",
year = "2014",
note = "",
issn = "1007-5704",
doi = "http://doi.org/10.1016/j.cnsns.2013.09.016",
url = "http://www.sciencedirect.com/science/article/pii/S1007570413004164",
author = "G. Sun and A. Chakraborty and Q. Liu and Z. Jin and K. E. Anderson and B. Li",
keywords = "Herbivore-plant",
keywords = "Time delay",
keywords = "Spatial diffusion",
keywords = "Outbreak",
keywords = "Synchrony "
}
@article{PhysRevE.90.042807,
title = {Nonlinear growth and condensation in multiplex networks},
author = {Nicosia, V. and Bianconi, G. and Latora, V. and Barthelemy, M.},
journal = {Phys. Rev. E},
volume = {90},
issue = {4},
pages = {042807},
numpages = {13},
year = {2014},
month = {Oct},
publisher = {American Physical Society},
doi = {10.1103/PhysRevE.90.042807},
url = {https://link.aps.org/doi/10.1103/PhysRevE.90.042807}
}
@Article{Lacasa2015,
author={Lacasa, L.
and Nicosia, V.
and Latora, V.},
title={Network structure of multivariate time series},
journal={Scientific Reports},
year={2015},
month={Oct},
day={21},
publisher={The Author(s) SN -},
volume={5},
pages={15508 EP -},
note={Article},
url={http://dx.doi.org/10.1038/srep15508}
}
@article{Lü20111150,
title = "Link prediction in complex networks: A survey ",
journal = "Physica A: Statistical Mechanics and its Applications ",
volume = "390",
number = "6",
pages = "1150 - 1170",
year = "2011",
note = "",
issn = "0378-4371",
doi = "http://doi.org/10.1016/j.physa.2010.11.027",
url = "http://www.sciencedirect.com/science/article/pii/S037843711000991X",
author = "L. Lü and T. Zhou",
keywords = "Link prediction",
keywords = "Complex networks",
keywords = "Node similarity",
keywords = "Maximum likelihood methods",
keywords = "Probabilistic models "
}
@article{1367-2630-17-7-073029,
author={E. Cozzo and M. Kivelä and M. De Domenico and A. Solé-Ribalta and A. Arenas and S. Gómez and M. A.
Porter and Y. Moreno},
title={Structure of triadic relations in multiplex networks},
journal={New Journal of Physics},
volume={17},
number={7},
pages={073029},
url={http://stacks.iop.org/1367-2630/17/i=7/a=073029},
year={2015},
abstract={Recent advances in the study of networked systems have highlighted that our interconnected world is composed of networks that are coupled to each other through different ‘layers’ that each represent one of many possible subsystems or types of interactions. Nevertheless, it is traditional to aggregate multilayer networks into a single weighted network in order to take advantage of existing tools. This is admittedly convenient, but it is also extremely problematic, as important information can be lost as a result. It is therefore important to develop multilayer generalizations of network concepts. In this paper, we analyze triadic relations and generalize the idea of transitivity to multiplex networks. By focusing on triadic relations, which yield the simplest type of transitivity, we generalize the concept and computation of clustering coefficients to multiplex networks. We show how the layered structure of such networks introduces a new degree of freedom that has a fundamental effect on transitivity. We compute multiplex clustering coefficients for several real multiplex networks and illustrate why one must take great care when generalizing standard network concepts to multiplex networks. We also derive analytical expressions for our clustering coefficients for ensemble averages of networks in a family of random multiplex networks. Our analysis illustrates that social networks have a strong tendency to promote redundancy by closing triads at every layer and that they thereby have a different type of multiplex transitivity from transportation networks, which do not exhibit such a tendency. These insights are invisible if one only studies aggregated networks.}
}
@article{PhysRevLett.111.058702,
title = {Coevolution and Correlated Multiplexity in Multiplex Networks},
author = {Kim, J. Y. and Goh, K. I.},
journal = {Phys. Rev. Lett.},
volume = {111},
issue = {5},
pages = {058702},
numpages = {5},
year = {2013},
month = {Jul},
publisher = {American Physical Society},
doi = {10.1103/PhysRevLett.111.058702},
url = {https://link.aps.org/doi/10.1103/PhysRevLett.111.058702}
}
@Article{Battiston2017,
author="Battiston, F.
and Nicosia, V.
and Latora, V.",
title="The new challenges of multiplex networks: Measures and models",
journal="The European Physical Journal Special Topics",
year="2017",
volume="226",
number="3",
pages="401--416",
abstract="What do societies, the Internet, and the human brain have in common? They are all examples of complex relational systems, whose emerging behaviours are largely determined by the non-trivial networks of interactions among their constituents, namely individuals, computers, or neurons, rather than only by the properties of the units themselves. In the last two decades, network scientists have proposed models of increasing complexity to better understand real-world systems. Only recently we have realised that multiplexity, i.e. the coexistence of several types of interactions among the constituents of a complex system, is responsible for substantial qualitative and quantitative differences in the type and variety of behaviours that a complex system can exhibit. As a consequence, multilayer and multiplex networks have become a hot topic in complexity science. Here we provide an overview of some of the measures proposed so far to characterise the structure of multiplex networks, and a selection of models aiming at reproducing those structural properties and quantifying their statistical significance. Focusing on a subset of relevant topics, this brief review is a quite comprehensive introduction to the most basic tools for the analysis of multiplex networks observed in the real-world. The wide applicability of multiplex networks as a framework to model complex systems in different fields, from biology to social sciences, and the colloquial tone of the paper will make it an interesting read for researchers working on both theoretical and experimental analysis of networked systems.",
issn="1951-6401",
doi="10.1140/epjst/e2016-60274-8",
url="http://dx.doi.org/10.1140/epjst/e2016-60274-8"
}
@article{SandovalJunior2012187,
title = "Correlation of financial markets in times of crisis ",
journal = "Physica A: Statistical Mechanics and its Applications ",
volume = "391",
number = "1–2",
pages = "187 - 208",
year = "2012",
note = "",
issn = "0378-4371",
doi = "http://dx.doi.org/10.1016/j.physa.2011.07.023",
url = "http://www.sciencedirect.com/science/article/pii/S037843711100570X",
author = "L. S. Junior and I. De P. Franca",
keywords = "Financial markets",
keywords = "Crisis",
keywords = "Correlation matrix",
keywords = "Random matrix theory "
}
@article{10.1371/journal.pone.0107056,
author = {Tan, F. AND Xia, Y. AND Zhu, B.},
journal = {PLOS ONE},
publisher = {Public Library of Science},
title = {Link Prediction in Complex Networks: A Mutual Information Perspective},
year = {2014},
month = {09},
volume = {9},
url = {http://dx.doi.org/10.1371%2Fjournal.pone.0107056},
pages = {1-8},
abstract = {Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity.},
number = {9},
doi = {10.1371/journal.pone.0107056}
}
@article{PhysRevLett.86.3200,
title = {Epidemic Spreading in Scale-Free Networks},
author = {Pastor-Satorras, R. and Vespignani, A.},
journal = {Phys. Rev. Lett.},
volume = {86},
issue = {14},
pages = {3200--3203},
numpages = {0},
year = {2001},
month = {Apr},
publisher = {American Physical Society},
doi = {10.1103/PhysRevLett.86.3200},
url = {http://link.aps.org/doi/10.1103/PhysRevLett.86.3200}
}
@Inbook{Barabasi2003,
author="Barab{\'a}si, Albert-L{\'a}szl{\'o}
and Ravasz, Erzs{\'e}bet
and Oltvai, Zolt{\'a}n",
editor="Pastor-Satorras, R.
and Rubi, M.
and Diaz-Guilera, A.",
title="Hierarchical Organization of Modularity in Complex Networks",
bookTitle="Statistical Mechanics of Complex Networks",
year="2003",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="46--65",
isbn="978-3-540-44943-0",
doi="10.1007/978-3-540-44943-0_4",
url="http://dx.doi.org/10.1007/978-3-540-44943-0_4"
}
@article{0295-5075-97-6-68006,
author={G. D'Agostino and A. Scala and V. Zlatić and G. Caldarelli},
title={Robustness and assortativity for diffusion-like processes in scale-free networks},
journal={EPL (Europhysics Letters)},
volume={97},
number={6},
pages={68006},
url={http://stacks.iop.org/0295-5075/97/i=6/a=68006},
year={2012}
}
@article{10.1371/journal.pone.0031929,
author = {Song, W.-M. AND Di Matteo, T. AND Aste, T.},
journal = {PLoS ONE},
publisher = {Public Library of Science},
title = {Hierarchical Information Clustering by Means of Topologically Embedded Graphs},
year = {2012},
month = {03},
volume = {7},
url = {http://dx.doi.org/10.1371%2Fjournal.pone.0031929},
pages = {1-14},
abstract = {<p>We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network structure. For a planar embedding, this method provides both the intra-cluster hierarchy, which describes the way clusters are composed, and the inter-cluster hierarchy which describes how clusters gather together. We discuss performance, robustness and reliability of this method by first investigating several artificial data-sets, finding that it can outperform significantly other established approaches. Then we show that our method can successfully differentiate meaningful clusters and hierarchies in a variety of real data-sets. In particular, we find that the application to gene expression patterns of lymphoma samples uncovers biologically significant groups of genes which play key-roles in diagnosis, prognosis and treatment of some of the most relevant human lymphoid malignancies.</p>},
number = {3},
doi = {10.1371/journal.pone.0031929}
}
@article{doi:10.1137/070710111,
author = {A. Clauset and C. R. Shalizi and M. E. J. Newman},
title = {Power-Law Distributions in Empirical Data},
journal = {SIAM Review},
volume = {51},
number = {4},
pages = {661-703},
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http://dx.doi.org/10.1137/070710111
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http://dx.doi.org/10.1137/070710111
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{Aste}, T.},
title = "{Parsimonious modeling with Information Filtering Networks}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprint = {1602.07349},
primaryClass = "cs.IT",
keywords = {Computer Science - Information Theory, Statistics - Machine Learning},
year = 2016,
month = feb
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keywords = "Nonlinearity",
keywords = "Fast double bootstrap test "
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keywords = "Underreaction",
keywords = "Overreaction "
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year = {2007},
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publisher = {IEEE Computer Society},
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}
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year = {2010}
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year = {2013}
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author = {FANG, LILY and PERESS, JOEL},
title = {Media Coverage and the Cross-section of Stock Returns},
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volume = {64},
number = {5},
publisher = {Blackwell Publishing Inc},
issn = {1540-6261},
doi = {10.1111/j.1540-6261.2009.01493.x},
pages = {2023--2052},
year = {2009},
}
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year = {2015},
doi = {10.1080/14697688.2015.1039865},
URL = {
http://dx.doi.org/10.1080/14697688.2015.1039865
},
eprint = {
http://dx.doi.org/10.1080/14697688.2015.1039865
}
}
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year = {2009},
doi = {10.1108/19405979200900002}
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year = 2004
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doi={10.1007/978-1-4612-1768-8_18},
title={Predictability: An Information-Theoretic Perspective},
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author={Darbellay, GeorgesA.},
pages={249-262},
language={English}
}
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author = {Matthias W. Uhl},
title = {Emotions Matter: Sentiment and Momentum in Foreign Exchange},
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volume = {18},
number = {3},
pages = {249-257},
year = {2017},
publisher = {Routledge},
doi = {10.1080/15427560.2017.1332061},
URL = {
https://doi.org/10.1080/15427560.2017.1332061
},
eprint = {
https://doi.org/10.1080/15427560.2017.1332061
}
}
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author = {Daniel Tsvetanov and Jerry Coakley and Neil Kellard},
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pages = {1887-1899},
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https://doi.org/10.1080/14697688.2016.1211797
},
eprint = {
https://doi.org/10.1080/14697688.2016.1211797
}
}
@ARTICLE{RePEc:eee:empfin:v:18:y:2011:i:2:p:321-340,
title = {When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions},
author = {Groß-Klußmann, Axel and Hautsch, Nikolaus},
year = {2011},
journal = {Journal of Empirical Finance},
volume = {18},
number = {2},
pages = {321-340},
abstract = {We examine high-frequency market reactions to an intraday stock-specific news flow. Using unique pre-processed data from an automated news analytics tool based on linguistic pattern recognition we exploit information on the indicated relevance, novelty and direction of company-specific news. Employing a high-frequency VAR model based on 20 s data of a cross-section of stocks traded at the London Stock Exchange we find distinct responses in returns, volatility, trading volumes and bid-ask spreads due to news arrivals. We show that a classification of news according to indicated relevance is crucial to filter out noise and to identify significant effects. Moreover, sentiment indicators have predictability for future price trends though the profitability of news-implied trading is deteriorated by increased bid-ask spreads.},
keywords = {Firm-specific news News sentiment High-frequency data Volatility Liquidity Abnormal returns},
url = {https://EconPapers.repec.org/RePEc:eee:empfin:v:18:y:2011:i:2:p:321-340}
}
@article{doi:10.1111/j.1540-6261.2009.01493.x,
author = {FANG, LILY and PERESS, JOEL},
title = {Media Coverage and the Cross-section of Stock Returns},
journal = {The Journal of Finance},
volume = {64},
number = {5},
pages = {2023-2052},
doi = {10.1111/j.1540-6261.2009.01493.x},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-6261.2009.01493.x},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1540-6261.2009.01493.x},
abstract = {ABSTRACT By reaching a broad population of investors, mass media can alleviate informational frictions and affect security pricing even if it does not supply genuine news. We investigate this hypothesis by studying the cross-sectional relation between media coverage and expected stock returns. We find that stocks with no media coverage earn higher returns than stocks with high media coverage even after controlling for well-known risk factors. These results are more pronounced among small stocks and stocks with high individual ownership, low analyst following, and high idiosyncratic volatility. Our findings suggest that the breadth of information dissemination affects stock returns.}
}
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year={2011},
issn={0929-5313},
journal={Journal of Computational Neuroscience},
volume={30},
number={1},
doi={10.1007/s10827-010-0262-3},
title={Transfer entropy—a model-free measure of effective connectivity for the neurosciences},
url={http://dx.doi.org/10.1007/s10827-010-0262-3},
publisher={Springer US},
author={Vicente, Raul and Wibral, Michael and Lindner, Michael and Pipa, Gordon},
pages={45-67},
language={English}
}
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}
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year={2014},
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booktitle={Discovery Science},
volume={8777},
series={Lecture Notes in Computer Science},
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doi={10.1007/978-3-319-11812-3_2},
title={Big Data Analysis of StockTwits to Predict Sentiments in the Stock Market},
url={http://dx.doi.org/10.1007/978-3-319-11812-3_2},
publisher={Springer International Publishing},
keywords={Wrapper feature selection; Bayesian Networks; Stock microblogging sentiment},
author={Al Nasseri, Alya and Tucker, Allan and de Cesare, Sergio},
pages={13-24}
}
@Article {RePEc:dur:durham:2011_06,
title = {Media Sentiment and UK Stock Returns},
journal = {Working Papers},
year = {2011},
author = {Nicky J. Ferguson and Jie Michael Guo and Nicky Herbert Y.T. Lam and Dennis Philip},
publisher = {Durham University Business School}
}
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author = {Ranco, G. AND Aleksovski, D. AND Caldarelli, G. AND Gr\u{c}ar, M. AND Mozeti\u{c}, I.},
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publisher = {Public Library of Science},
title = {The Effects of \uppercase{t}witter Sentiment on Stock Price Returns},
year = {2015},
month = {09},
volume = {10},
url = {http://dx.doi.org/10.1371\%2Fjournal.pone.0138441},
pages = {e0138441},
number = {9},
doi = {10.1371/journal.pone.0138441}
}
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author = {Eric Gilbert and Karrie Karahalios},
title = {Widespread Worry and the Stock Market},
conference = {International AAAI Conference on Web and Social Media},
year = {2010},
pages = {58--65},
url = {https://www.aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/view/1513/1833}
}
@incollection{Olaniyan:2015,
year={2015},
isbn={978-3-319-17090-9},
booktitle={Statistical Learning and Data Sciences},
volume={9047},
series={Lecture Notes in Computer Science},
editor={Gammerman, Alexander and Vovk, Vladimir and Papadopoulos, Harris},
doi={10.1007/978-3-319-17091-6_15},
title={Social Web-Based Anxiety Index's Predictive Information on S\&P 500 Revisited},
url={http://dx.doi.org/10.1007/978-3-319-17091-6\_15},
publisher={Springer International Publishing},
author={Olaniyan, Rapheal and Stamate, Daniel and Logofatu, Doina},
pages={203-213},
language={English}
}
@article{DBLP:journals/eswa/NasseriTC15,
author = {Alya Al Nasseri and
Allan Tucker and
Sergio de Cesare},
title = {Quantifying StockTwits semantic terms' trading behavior in financial
markets: An effective application of decision tree algorithms},
journal = {Expert Syst. Appl.},
volume = {42},
number = {23},
pages = {9192--9210},
year = {2015},
url = {http://dx.doi.org/10.1016/j.eswa.2015.08.008},
doi = {10.1016/j.eswa.2015.08.008}
}
@Article {HestonRanjan:2014,
author = {Heston, Steven L. and Sinha, Nitish Ranjan},
title = {News versus Sentiment: Comparing Textual Processing Approaches for Predicting Stock Returns},
journal = {Robert H. Smith School Research Paper},
URL = {http://ssrn.com/abstract=2311310},
year = {2014}
}
@article{ENGELBERG2012260,
title = "How are shorts informed?: Short sellers, news, and information processing",
journal = "Journal of Financial Economics",
volume = "105",
number = "2",
pages = "260 - 278",
year = "2012",
issn = "0304-405X",
doi = "https://doi.org/10.1016/j.jfineco.2012.03.001",
url = "http://www.sciencedirect.com/science/article/pii/S0304405X12000384",
author = "Joseph E. Engelberg and Adam V. Reed and Matthew C. Ringgenberg",
keywords = "Asymmetric information, Manipulation, News media, Short sales"