thesisbib.bib

######
Introduction
######

@book{ISL,
  added-at = {2019-10-12T20:03:56.000+0200},
  author = {James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert},
  biburl = {https://www.bibsonomy.org/bibtex/2444186c86d18bddb4433c12fa126f6be/lopusz_kdd},
  interhash = {b3febabdc45a8629023cee7323dfbd86},
  intrahash = {444186c86d18bddb4433c12fa126f6be},
  keywords = {general_machine_learning},
  publisher = {Springer},
  timestamp = {2019-10-12T23:45:37.000+0200},
  title = {An Introduction to Statistical Learning: with Applications in R },
  url = {https://faculty.marshall.usc.edu/gareth-james/ISL/},
  year = 2013
}

@book{Networks,
  abstract = {"The scientific study of networks, including computer networks, social networks, and biological networks, has received an enormous amount of interest in the last few years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks.  The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.    Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks"--},
  added-at = {2012-11-14T13:41:26.000+0100},
  address = {Oxford; New York},
  author = {Newman, M. E. J.},
  biburl = {https://www.bibsonomy.org/bibtex/294f82a12bd7c95a9c781b6636188ee3f/lopusz_kdd},
  description = {Networks: An Introduction: Mark Newman: 9780199206650: Amazon.com: Books},
  interhash = {f76c00ea14de2458944b349efe9fc7ea},
  intrahash = {94f82a12bd7c95a9c781b6636188ee3f},
  isbn = {9780199206650 0199206651},
  keywords = {networks},
  publisher = {Oxford University Press},
  refid = {456837194},
  timestamp = {2012-11-20T16:24:19.000+0100},
  title = {Networks: an introduction},
  url = {http://www.amazon.com/Networks-An-Introduction-Mark-Newman/dp/0199206651/ref=sr_1_5?ie=UTF8&qid=1352896678&sr=8-5&keywords=complex+networks},
  year = 2010
}

@TechReport{DieboldYilmaz2014,
  author={Francis X. Diebold and Kamil Yilmaz},
  title={{On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms}},
  year=2011,
  month=Oct,
  institution={Koc University-TUSIAD Economic Research Forum},
  type={Koç University-TUSIAD Economic Research Forum Working Papers},
  url={https://ideas.repec.org/p/koc/wpaper/1124.html},
  number={1124},
  abstract={We propose several connectedness measures built from pieces of variance decompositions, and we argue that they provide natural and insightful measures of connectedness among financial asset returns and volatilities. We also show that variance decompositions define weighted, directed networks, so that our connectedness measures are intimately-related to key measures of connectedness used in the network literature. Building on these insights, we track both average and daily time-varying connectedness of major U.S. financial institutions’ stock return volatilities in recent years, including during the financial crisis of 2007-2008.},
  keywords={Risk measurement; risk management; portfolio allocation; market risk; credit risk; systemic risk; as},
  doi={},
}

@ARTICLE{Billio2012,
title = {Econometric measures of connectedness and systemic risk in the finance and insurance sectors},
author = {Billio, Monica and Getmansky, Mila and Lo, Andrew and Pelizzon, Loriana},
year = {2012},
journal = {Journal of Financial Economics},
volume = {104},
number = {3},
pages = {535-559},
abstract = {We propose several econometric measures of connectedness based on principal-components analysis and Granger-causality networks, and apply them to the monthly returns of hedge funds, banks, broker/dealers, and insurance companies. We find that all four sectors have become highly interrelated over the past decade, likely increasing the level of systemic risk in the finance and insurance industries through a complex and time-varying network of relationships. These measures can also identify and quantify financial crisis periods, and seem to contain predictive power in out-of-sample tests. Our results show an asymmetry in the degree of connectedness among the four sectors, with banks playing a much more important role in transmitting shocks than other financial institutions.},
keywords = {Systemic risk; Financial institutions; Liquidity; Financial crises;},
url = {https://EconPapers.repec.org/RePEc:eee:jfinec:v:104:y:2012:i:3:p:535-559}
}


@ARTICLE{Baginiski1987,
title = {INTRAINDUSTRY INFORMATION TRANSFERS ASSOCIATED WITH MANAGEMENT FORECASTS OF EARNINGS},
author = {Baginski, Sp},
year = {1987},
journal = {Journal of Accounting Research},
volume = {25},
number = {2},
pages = {196-216},
keywords = {Voluntary Disclosure; Management forecasts; Intraindustry effect; Information dissemination},
url = {https://EconPapers.repec.org/RePEc:bla:joares:v:25:y:1987:i:2:p:196-216}
}

@article{PownallWaymire1989,
 ISSN = {00218456, 1475679X},
 URL = {http://www.jstor.org/stable/2491066},
 author = {Grace Pownall and Gregory Waymire},
 journal = {Journal of Accounting Research},
 pages = {85--105},
 publisher = {[Accounting Research Center, Booth School of Business, University of Chicago, Wiley]},
 title = {Voluntary Disclosure Choice and Earnings Information Transfer},
 volume = {27},
 year = {1989}
}

@article{ClinchSinclair1987,
title = {Intra-industry information releases: A recursive systems approach},
journal = {Journal of Accounting and Economics},
volume = {9},
number = {1},
pages = {89-106},
year = {1987},
issn = {0165-4101},
doi = {https://doi.org/10.1016/0165-4101(87)90018-8},
url = {https://www.sciencedirect.com/science/article/pii/0165410187900188},
author = {Greg J. Clinch and Norman A. Sinclair},
abstract = {This paper investigates the extent of intra-industry information transfers associated with the half-yearly earnings announcements of a sample of Australian firms. The ‘omitted factor’ interpretation of Foster's (1981) results is examined using a recursive systems specification of the return generating process to model extra-market return covariation in cross-section. Although some aspects of the results do appear sensitive to the alternative methodologies, the overall conclusion is consistent with Foster (1981) and supports the existence of intra-industry information transfers associated with firms' earnings releases.}
}

@article{HanWild1990,
  title={Unexpected Earnings And Intraindustry Information Transfers - Further Evidence},
  author={Jerry C. Y. Han and J. Wild},
  journal={Journal of Accounting Research},
  year={1990},
  volume={28},
  pages={211-219}
}

@article{Wang2014,
author = {WANG, CLARE},
title = {Accounting Standards Harmonization and Financial Statement Comparability: Evidence from Transnational Information Transfer},
journal = {Journal of Accounting Research},
volume = {52},
number = {4},
pages = {955-992},
doi = {https://doi.org/10.1111/1475-679X.12055},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1475-679X.12055},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/1475-679X.12055},
abstract = {ABSTRACT This paper investigates whether accounting standards harmonization enhances the comparability of financial information across countries. I hypothesize that a firm yet to announce earnings reacts more strongly to the earnings announcement of a foreign firm when both report under the same rather than different accounting standards. My analysis of abnormal price reactions for a global sample of firms supports the prediction. Next, in an attempt to control for the underlying economic comparability and the effects of changes in reporting quality, I use a difference-in-differences design around the mandatory introduction of International Financial Reporting Standards. I find that mandatory adopters experience a significant increase in market reactions to the release of earnings by voluntary adopters compared to the period preceding mandatory adoption. This increase is not observed for nonadopters. Taken together, the results show that accounting standards harmonization facilitates transnational information transfer and suggest financial statement comparability as a direct mechanism.},
year = {2014}
}

@Article{HannKimZheng2019,
  author={Rebecca N. Hann and Heedong Kim and Yue Zheng},
  title={{Intra-industry information transfers: evidence from changes in implied volatility around earnings announcements}},
  journal={Review of Accounting Studies},
  year=2019,
  volume={24},
  number={3},
  pages={927-971},
  month={September},
  keywords={Second-moment information transfer; Implied volatility; Volatility risk; Uncertainty; Earnings annou},
  doi={10.1007/s11142-019-9487-1},
  abstract={ We examine whether there is intra-industry information transfer with respect to the second moment of returns around earnings announcements. Using implied volatility from option prices to proxy for uncertainty about firm fundamentals, we find a significantly positive association between changes in the implied volatility of each industry’s first announcer and its peers around the first announcer’s earnings announcement, suggesting that earnings announcements help resolve uncertainty about the value of not only the announcing firm but also its peers. This result holds after controlling for information transfer with respect to the first moment of returns. We further find that the extent of second-moment information transfer is stronger for long-duration options, when the announcer has higher earnings quality, reports positive earnings news, or is a bellwether firm and during periods of greater macroeconomic uncertainty. Our findings suggest that peers’ earnings announcements represent an important disclosure that conveys timely information about industry uncertainty.},
  url={https://ideas.repec.org/a/spr/reaccs/v24y2019i3d10.1007_s11142-019-9487-1.html}
}

@book{InfoTheoryApplication,
author = {Stone, James},
year = {2015},
month = {02},
pages = {},
title = {Information Theory: A Tutorial Introduction},
isbn = {978-0956372857},
doi = {10.13140/2.1.1633.8240}
}

@article{IntroToTransferEntropy,
  added-at = {2009-10-17T03:44:59.000+0200},
  author = {Schreiber, T.},
  biburl = {https://www.bibsonomy.org/bibtex/29811d2f7e1210a19c2d53a27774580cc/enfascination},
  citeulike-article-id = {5908555},
  description = {citeulike export},
  interhash = {afba69e10fdbf8fd737112be0a59ceb1},
  intrahash = {9811d2f7e1210a19c2d53a27774580cc},
  journal = {Physical review letters},
  keywords = {file-import-09-10-08},
  number = 2,
  pages = {461--464},
  posted-at = {2009-10-08 05:42:00},
  priority = {2},
  publisher = {APS},
  timestamp = {2009-10-17T03:44:59.000+0200},
  title = {{Measuring information transfer}},
  volume = 85,
  year = 2000
}

@article{IntroToTransferEntropy2,
  title = {Synchronization as adjustment of information rates: Detection from bivariate time series},
  author = {Palu\ifmmode \check{s}\else \v{s}\fi{}, Milan and Kom\'arek, Vladim\'{\i}r and Hrn\ifmmode \check{c}\else \v{c}\fi{}\'{\i}\ifmmode \check{r}\else \v{r}\fi{}, Zbyn\ifmmode \check{e}\else \v{e}\fi{}k and \ifmmode \check{S}\else \v{S}\fi{}t\ifmmode \check{e}\else \v{e}\fi{}rbov\'a, Katalin},
  journal = {Phys. Rev. E},
  volume = {63},
  issue = {4},
  pages = {046211},
  numpages = {6},
  year = {2001},
  month = {Mar},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.63.046211},
  url = {https://link.aps.org/doi/10.1103/PhysRevE.63.046211}
}

@article{MIdiffTE,
author = {Kaiser, A. and Schreiber, T.},
year = {2002},
month = {06},
pages = {},
title = {Information transfer in continuous processes},
volume = {166},
journal = {Physica D, v.166, 43-62 (2002)},
doi = {10.1016/S0167-2789(02)00432-3}
}

@book{TEBook,
author = {Bossomaier, Terry and Barnett, Lionel and Harré, Michael and Lizier, Joseph},
year = {2016},
month = {01},
pages = {},
title = {An Introduction to Transfer Entropy},
doi = {10.1007/978-3-319-43222-9}
}


@article{b359,
  title={Generalized Measures of Information Transfer},
  author={Paul L. Williams and Randall D. Beer},
  journal={ArXiv},
  year={2011},
  volume={abs/1102.1507}
}

@article{Kaiser2002,
author = {Kaiser, A. and Schreiber, Thomas},
year = {2002},
month = {06},
pages = {},
title = {Information transfer in continuous processes},
volume = {166},
journal = {Physica D, v.166, 43-62 (2002)},
doi = {10.1016/S0167-2789(02)00432-3}
}


###### 
SML PAPER
######

@article{SML,
author = {Kelechi  Ikegwu and Micheal Hao and Nerraj Asthana and Robert Brunner},
title = {Standard Machine Learning Language: A Language Agnostic Framework for Streamlining the Development of Machine Learning Pipelines},
url = {https://github.com/lcdm-uiuc/Publications/blob/master/2017_Kelechi_Mike_Brunner/main.pdf}, 
year = {2017}
}

@article{winsor,
  title={Trimming and winsorization: A review},
  author={Dixon, Wilfrid J and Yuen, Kareb K},
  journal={Statistische Hefte},
  volume={15},
  number={2-3},
  pages={157--170},
  year={1974},
  publisher={Springer}
}



@INPROCEEDINGS{ML-UseCase1,
  author={K. {Bakshi} and K. {Bakshi}},
  booktitle={2018 IEEE Aerospace Conference}, 
  title={Considerations for artificial intelligence and machine learning: Approaches and use cases}, 
  year={2018},
  volume={},
  number={},
  pages={1-9},
  doi={10.1109/AERO.2018.8396488}}



@inproceedings{pedros:fewUsefulThings,
 author = {Domingos, Pedro},
 title = {A Few Useful Things to Know About Machine Learning},
 journal = {Commun. ACM},
 issue_date = {October 2012},
 volume = {55},
 number = {10},
 month = oct,
 year = {2012},
 issn = {0001-0782},
 pages = {78--87},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/2347736.2347755},
 doi = {10.1145/2347736.2347755},
 acmid = {2347755},
 publisher = {ACM},
 address = {New York, NY, USA}
} 

@article{TPOT,
  author    = {Randal S. Olson and
               Nathan Bartley and
               Ryan J. Urbanowicz and
               Jason H. Moore},
  title     = {Evaluation of a Tree-based Pipeline Optimization Tool for Automating
               Data Science},
  journal   = {CoRR},
  volume    = {abs/1603.06212},
  year      = {2016},
  url       = {http://arxiv.org/abs/1603.06212},
  timestamp = {Sat, 02 Apr 2016 11:49:48 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/OlsonBUM16},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@inproceedings{RizzoloRo10,
    author = {N. Rizzolo and D. Roth},
    title = {Learning Based Java for Rapid Development of NLP Systems},
    booktitle = {LREC},
    month = {5},
    year = {2010},
    address = {Valletta, Malta},
    url = " http://cogcomp.cs.illinois.edu/papers/RizzoloRo10.pdf",
    funding = {NSF-SoD},
    projects = {LBP, CCM},
    comment = {Learning Based Java: a Modeling language that facilitates development of systems with learning and inference componenets.},
}

@article{Roth05,
    author = {D. Roth},
    title = {Learning based Programming},
    year = {2005},
    publisher = {Springer-Verlag},
    editor = {L.C. Jain and D. Holmes},
    journal = {Innovations in Machine Learning: Theory and Applications},
    url = " http://cogcomp.cs.illinois.edu/papers/Roth05.pdf",
    funding = {ITR-BI,MURI,TRECC},
    projects = {LBP},
    comment = {Suggests a paradigm and a programming language for the programming of learning intensive computer systems. Springer: http://dx.doi.org/10.1007/3-540-33486-6_3},
}

@misc{Lichman:2013 ,
author = "M. Lichman",
year = "2013",
title = "{UCI} Machine Learning Repository",
url = "http://archive.ics.uci.edu/ml",
institution = "University of California, Irvine, School of Information and Computer Sciences" }

@inbook{frank2005weka,
  added-at = {2009-11-11T16:04:07.000+0100},
  address = {Berlin},
  author = {Frank, E. and Hall, M. A. and Holmes, G. and Kirkby, R. and Pfahringer, B. and Witten, I. H.},
  biburl = {https://www.bibsonomy.org/bibtex/213435afd510be944e56862b0523643b9/unhammer},
  booktitle = {Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers},
  editor = {Maimon, O. and Rokach, L.},
  interhash = {efd9b5dadfd832fc98d173bf4a1bc6a3},
  intrahash = {13435afd510be944e56862b0523643b9},
  keywords = {DASP303 ML Master WEKA},
  pages = {1305--1314},
  publisher = {Springer},
  timestamp = {2009-11-11T16:04:07.000+0100},
  title = {{Weka: A machine learning workbench for data mining.}},
  url = {http://researchcommons.waikato.ac.nz/handle/10289/1497},
  year = 2005
}


@inproceedings{kotthoff_auto_2019,
    author = {Kotthoff, Lars and Thornton, Chris and Hoos, Holger H. and Hutter, Frank and Leyton-Brown, Kevin},
    title = {Auto-WEKA: Automatic model selection and hyperparameter optimization in WEKA},
    pages = {89-103},
    chapter = {4},
    crossref = {automl}
}

@inproceedings{komer_hyperopt_2019,
    author = {Komer, Brent and Bergstra, James and Eliasmith, Chris},
    title = {Hyperopt-Sklearn},
    pages = {105-121},
    chapter = {5},
    crossref = {automl}
}

@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}

@inproceedings{feurer_auto_2018,
    author = {Feurer, Matthias and Klein, Aaron and Eggensperger, Katharina and Springenberg, Jost Tobias and Blum, Manuel and Hutter, Frank},
    title = {Auto-sklearn: Efficient and Robust Automated Machine Learning},
    pages = {123-143},
    chapter = {6},
    crossref = {automl}
}

@inproceedings{Backus59,
  added-at = {2019-07-26T00:00:00.000+0200},
  author = {Backus, John W.},
  biburl = {https://www.bibsonomy.org/bibtex/24b7c6cb9169134e6f628a8c6d0c8e757/dblp},
  booktitle = {IFIP Congress},
  crossref = {conf/ifip/1959},
  interhash = {1a6b9bfef4927677fb04602f26d3ed82},
  intrahash = {4b7c6cb9169134e6f628a8c6d0c8e757},
  keywords = {dblp},
  pages = {125-131},
  publisher = {Butterworths, London},
  timestamp = {2019-07-27T11:38:29.000+0200},
  title = {The syntax and semantics of the proposed international algebraic language of the Zurich ACM-GAMM Conference.},
  year = 1959
}



###### 
ABIS Chapter
######


@Article{LiTuna2014,
  author={Li, Ningzhong and Richardson, Scott and Tuna, İrem},
  title={{Macro to micro: Country exposures, firm fundamentals and stock returns}},
  journal={Journal of Accounting and Economics},
  year=2014,
  volume={58},
  number={1},
  pages={1-20},
  month={},
  keywords={Macroeconomic exposures; Earnings; Stock returns; Geographic segments},
  doi={10.1016/j.jacceco.2014.04},
  abstract={We outline a systematic approach to incorporate macroeconomic information into firm level forecasting from the perspective of an equity investor. Using a global sample of 198,315 firm-years over the 1998–2010 time period, we find that combining firm level exposures to countries (via geographic segment data) with forecasts of country level performance, is able to generate superior forecasts for firm fundamentals. This result is particularly evident for purely domestic firms. We further find that this forecasting benefit is associated with future excess stock returns. These relations are stronger after periods of higher dispersion in expected country level performance.},
  url={https://ideas.repec.org/a/eee/jaecon/v58y2014i1p1-20.html}
}

@article{ABIS, title={Predicting Profitability Using Machine Learning}, url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3466478}, journal={SSRN}, author={Anand, Vic and Brunner, Robert and Ikegwu, Kelechi and Sougiannis, Theodore}, year={2019}, month={Oct}, url = "http://dx.doi.org/10.2139/ssrn.3466478"}

@incollection{IntAccBook,
    author = {Spiceland, J. David and James F. Sepe, and Mark Nelson.},
    title = {ntermediate Accounting},
    year = {2018},
    publisher = {McGraw-Hill Education},
    chapter = {1}
}

@inproceedings{Monahan,
  title={Financial Statement Analysis and Earnings Forecasting},
  author={S. Monahan},
  year={2018}
}

@article{Bradshaw,
  title={A re-examination of analysts’ superiority over time-series forecasts of annual earnings},
  author={Bradshaw, Mark T. and Drake, Michael S. and Myers, James N. and Myers, Linda A.},
  journal={Review of Accounting Studies},
  url="https://doi.org/10.1007/s11142-012-9185-8",
  year={2012}
}

@book{ISL,
author = {James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert},
title = {An Introduction to Statistical Learning: With Applications in R},
year = {2014},
isbn = {1461471370},
publisher = {Springer Publishing Company, Incorporated},
abstract = {An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.}
}

@article{ABIS:ML:EX1,
author = {Samer Muthana Sarsam and Hosam Al-Samarraie and Ahmed Ibrahim Alzahrani and Bianca Wright},
title ={Sarcasm detection using machine learning algorithms in Twitter: A systematic review},
journal = {International Journal of Market Research},
volume = {62},
number = {5},
pages = {578-598},
year = {2020},
doi = {10.1177/1470785320921779},
}

@article{ABIS:ML:EX2,
author = {Weiqiang Hang and Timothy Banks},
title ={Machine learning applied to pack classification},
journal = {International Journal of Market Research},
volume = {61},
number = {6},
pages = {601-620},
year = {2019},
doi = {10.1177/1470785319841217},
}

@article{ABIS:ML:EX3,
author = {Choudhury, Prithwiraj and Allen, Ryan T. and Endres, Michael G.},
title = {Machine learning for pattern discovery in management research},
journal = {Strategic Management Journal},
volume = {42},
number = {1},
pages = {30-57},
keywords = {abduction, decision trees, exploratory data analysis, induction, machine learning, neural networks, partial dependence plots, pattern discovery, random forests, ROC curve, supervised machine learning},
doi = {https://doi.org/10.1002/smj.3215},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/smj.3215},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/smj.3215},
year = {2021}
}

@article{ABIS:ML:EX4,
Abstract = {At present, with the continuous development of the intelligent system, it is used in many industries. In e-commerce industry, the intelligent system has also been used, especially in supplier management. Based on the machine learning theory, this paper studies the optimization of the supplier management intelligent system of cross-border e-commerce platforms. Based on the wisdom algorithm and machine learning perspective, the optimization of cross-border e-commerce platform supplier credit system is studied in this paper. Firstly, the calculation of the traditional supplier credit evaluation is optimized by introducing the decision matrix algorithm of the difference matrix and the cloud model evaluation method. Then a multi-objective joint decision model of supplier selection and order allocation is established, and the multi-objective evolutionary algorithm combined with actual examples is applied to verify the effectiveness and feasibility of the algorithm and model. Finally, the de},
Author = {Yang, Ying},
ISSN = {16179846},
Journal = {Information Systems \& e-Business Management},
Keywords = {Cross-border e-commerce, Machine theory, Suppliers, Algorithms, Machine learning, System optimization},
Number = {4},
Pages = {851 - 870},
Title = {Research on the optimization of the supplier intelligent management system for cross-border e-commerce platforms based on machine learning.},
Volume = {18},
Year = {2020},
}

@ARTICLE{BB68,
title = {EMPIRICAL EVALUATION OF ACCOUNTING INCOME NUMBERS},
author = {Ball, Ray and Brown, P},
year = {1968},
journal = {Journal of Accounting Research},
volume = {6},
number = {2},
pages = {159-178},
keywords = {Value relevance; Accounting; Earnings announcements; Usefulness of Accounting},
}

@article{OU1989295,
title = "Financial statement analysis and the prediction of stock returns",
journal = "Journal of Accounting and Economics",
volume = "11",
number = "4",
pages = "295 - 329",
year = "1989",
issn = "0165-4101",
doi = "https://doi.org/10.1016/0165-4101(89)90017-7",
url = "http://www.sciencedirect.com/science/article/pii/0165410189900177",
author = "Jane A. Ou and Stephen H. Penman",
abstract = "This paper performs a financial statement analysis that combines a large set of financial statement items into one summary measure which indicates the direction of one-year-ahead earnings changes. Positions are taken in stocks on the basis of this measure during the period 1973–1983, which involve canceling long and short positions with zero net investment. The two-year holding-period return to the long and short positions is in the order of 12.5%. After adjustment for ‘size effects’ the return is about 7.0%. These returns cannot be explained by nominated firm risk characteristics."
}

@article{HOU2012504,
title = "The implied cost of capital: A new approach",
journal = "Journal of Accounting and Economics",
volume = "53",
number = "3",
pages = "504 - 526",
year = "2012",
issn = "0165-4101",
doi = "https://doi.org/10.1016/j.jacceco.2011.12.001",
url = "http://www.sciencedirect.com/science/article/pii/S0165410111000966",
author = "Kewei Hou and Mathijs A. {van Dijk} and Yinglei Zhang",
keywords = "Cross-sectional earnings model, Earnings forecasts, Expected returns, Implied cost of capital, Asset pricing tests",
abstract = "We use earnings forecasts from a cross-sectional model to proxy for cash flow expectations and estimate the implied cost of capital (ICC) for a large sample of firms over 1968–2008. The earnings forecasts generated by the cross-sectional model are superior to analysts' forecasts in terms of coverage, forecast bias, and earnings response coefficient. Moreover, the model-based ICC is a more reliable proxy for expected returns than the ICC based on analysts' forecasts. We present evidence on the cross-sectional relation between firm-level characteristics and ex ante expected returns using the model-based ICC."
}


@misc{cerqueira2019evaluating,
      title={Evaluating time series forecasting models: An empirical study on performance estimation methods}, 
      author={Vitor Cerqueira and Luis Torgo and Igor Mozetic},
      year={2019},
      eprint={1905.11744},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@book{Campbell1997,
 ISBN = {9780691043012},
 URL = {http://www.jstor.org/stable/j.ctt7skm5},
 abstract = { The past twenty years have seen an extraordinary growth in the use of quantitative methods in financial markets. Finance professionals now routinely use sophisticated statistical techniques in portfolio management, proprietary trading, risk management, financial consulting, and securities regulation. This graduate-level textbook is intended for PhD students, advanced MBA students, and industry professionals interested in the econometrics of financial modeling. The book covers the entire spectrum of empirical finance, including: the predictability of asset returns, tests of the Random Walk Hypothesis, the microstructure of securities markets, event analysis, the Capital Asset Pricing Model and the Arbitrage Pricing Theory, the term structure of interest rates, dynamic models of economic equilibrium, and nonlinear financial models such as ARCH, neural networks, statistical fractals, and chaos theory.  Each chapter develops statistical techniques within the context of a particular financial application. This exciting new text contains a unique and accessible combination of theory and practice, bringing state-of-the-art statistical techniques to the forefront of financial applications. Each chapter also includes a discussion of recent empirical evidence, for example, the rejection of the Random Walk Hypothesis, as well as problems designed to help readers incorporate what they have read into their own applications.},
 author = {John Y. Campbell and Andrew W. Lo and A.Craig MacKinlay},
 publisher = {Princeton University Press},
 title = {The Econometrics of Financial Markets},
 year = {1997}
}

@book{CART,
  added-at = {2020-05-07T22:53:11.000+0200},
  address = {Monterey, CA},
  author = {Breiman, L. and Friedman, J. H. and Olshen, R. A. and Stone, C. J.},
  biburl = {https://www.bibsonomy.org/bibtex/27f293aa2bdfd10960ef36928f2795f1d/flashspys},
  interhash = {61f3e6d61ba17bb493014bd1c6dfa670},
  intrahash = {7f293aa2bdfd10960ef36928f2795f1d},
  keywords = {ma treelearning},
  publisher = {Wadsworth and Brooks},
  serial = {bre84a},
  timestamp = {2020-05-07T22:53:11.000+0200},
  title = {Classification and Regression Trees},
  year = 1984
}

@book{koning2017decision,
  title={Decision Trees and Random Forests: A Visual Introduction for Beginners},
  author={Koning, M. and Smith, C.},
  isbn={9781549893759},
  url={https://books.google.com/books?id=Hi\_CtAEACAAJ},
  year={2017},
  publisher={Amazon Digital Services LLC - Kdp Print Us}
}


####
PyIF Chapter
####

@INPROCEEDINGS{PyIF,
  author={K. M. {Ikegwu} and J. {Trauger} and J. {McMullin} and R. J. {Brunner}},
  booktitle={2020 SoutheastCon}, 
  title={PyIF: A Fast and Light Weight Implementation to Estimate Bivariate Transfer Entropy for Big Data}, 
  year={2020},
  volume={},
  number={},
  pages={1-6},
  doi={10.1109/SoutheastCon44009.2020.9249650}}

@article{Kantz,
  title={Analysing the information flow between financial time series
},
  author={R. Marschinski and H. Kantz},
  journal={The European Physical Journal B - Condensed Matter and Complex Systems},
  year={2002},
  volume={30},
  pages={275-281}
}

@article{Sandoval,
  title={Structure of a Global Network of Financial Companies Based on Transfer Entropy},
  author={L. Sandoval},
  journal={Entropy},
  year={2014},
  volume={16},
  pages={4443-4482}
}

@article{EstimatingTE,
author = {Khan, Shiraj and Bandyopadhyay, Sharba and Ganguly, Auroop and Saigal, Sunil and Erickson, David and Protopopescu, Vladimir and Ostrouchov, George},
year = {2007},
month = {09},
pages = {026209},
title = {Relative performance of mutual information estimation methods for quantifying the dependence among short and noisy data},
volume = {76},
journal = {Physical review. E, Statistical, nonlinear, and soft matter physics},
doi = {10.1103/PhysRevE.76.026209}
}

@article{kraskovEstimator,
  title = {Estimating mutual information},
  author = {Kraskov, Alexander and St\"ogbauer, Harald and Grassberger, Peter},
  journal = {Phys. Rev. E},
  volume = {69},
  issue = {6},
  pages = {066138},
  numpages = {16},
  year = {2004},
  month = {Jun},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.69.066138},
}

@Up{JeffTE,
  title = {Detecting Information Flows in Markets},
  author = {Anderson, Matthew and McMullin, Jeff},
  numpages = {132},
  year = {2018},
  month = {Nov},
}

@misc{CUDA, 
  author={NVIDIA and Vingelmann, Péter and Fitzek, Frank H.P.}, 
  title={CUDA, release: 10.2.89}, 
  year={2020}, 
  url={https://developer.nvidia.com/cuda-toolkit}, 
}

@ARTICLE{scipy,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}

@ARTICLE{numpy,
  author  = {Harris, Charles R. and Millman, K. Jarrod and
            van der Walt, Stéfan J and Gommers, Ralf and
            Virtanen, Pauli and Cournapeau, David and
            Wieser, Eric and Taylor, Julian and Berg, Sebastian and
            Smith, Nathaniel J. and Kern, Robert and Picus, Matti and
            Hoyer, Stephan and van Kerkwijk, Marten H. and
            Brett, Matthew and Haldane, Allan and
            Fernández del Río, Jaime and Wiebe, Mark and
            Peterson, Pearu and Gérard-Marchant, Pierre and
            Sheppard, Kevin and Reddy, Tyler and Weckesser, Warren and
            Abbasi, Hameer and Gohlke, Christoph and
            Oliphant, Travis E.},
  title   = {Array programming with {NumPy}},
  journal = {Nature},
  year    = {2020},
  volume  = {585},
  pages   = {357–362},
  doi     = {10.1038/s41586-020-2649-2}
}

@ARTICLE{nose,
  author  = {Pellerin, Jason},
  title   = {Nose},
  year    = {2021},
  url={https://nose.readthedocs.io/en/latest/}
}


@inproceedings{numba, 
  title={Numba: A llvm-based python jit compiler}, 
  author={Lam, Siu Kwan and Pitrou, Antoine and Seibert, Stanley}, 
  booktitle={Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC}, 
  pages={1--6}, 
  year={2015} 
}

@article{IDTxl,
  doi = {10.21105/joss.01081},
  url = {https://doi.org/10.21105/joss.01081},
  year = {2019},
  publisher = {The Open Journal},
  volume = {4},
  number = {34},
  pages = {1081},
  author = {Patricia Wollstadt and Joseph T. Lizier and Raul Vicente and Conor Finn and Mario Martinez-Zarzuela and Pedro Mediano and Leonardo Novelli and Michael Wibral},
  title = {IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks},
  journal = {Journal of Open Source Software}
}


@ARTICLE{cffi,
  author  = {Rigo,Armin  and Fijalkowski, Maciej},
  title   = {"CFFI documentation"},
  year    = {2018},
  url={https://cffi.readthedocs.io/en/latest/}
}

@Book{hdf5, 
        keywords = {python, hdf5}, 
        year = {2013}, 
        publisher = {O'Reilly}, 
        title = {Python and HDF5}, 
        author = {Andrew Collette} 
}

@ARTICLE{jpype,
  author  = {Menard, Steve and Nell, Luis },
  title   = {"JPype documentation"},
  year    = {2018},
  url={https://jpype.readthedocs.io/en/latest/}
}

@Manual{TransEnt,
title = {TransferEntropy: The Transfer Entropy Package},
author = {ANN Library: David Mount and Sunil Arya. Transfer Entropy Packge: Ghazaleh Haratinezhad Torbati and Glenn Lawyer.},
year = {2015},
note = {R package version 1.5},
url = {https://CRAN.R-project.org/package=TransferEntropy},
}
@inproceedings{ANN,
  title={ANN: library for approximate nearest neighbor searching},
  author={S. Arya and D. Mount},
  year={1998}
}

@Article{RTransferEntropy,
title = {RTransferEntropy — Quantifying information flow between
  different time series using effective transfer entropy},
author = {Behrendt Simon and Dimpfl Thomas and Peter {Franziska J.}
  and Zimmermann {David J.}},
journal = {SoftwareX},
year = {2019},
volume = {10},
number = {100265},
pages = {1-9},
url = {https://doi.org/10.1016/j.softx.2019.100265},
}


@Article{TransferEntropyToolbox,
  author = {Lindner, Michael and Vicente, Raul and Priesemann, Viola and Wibral, Michael},
  title = {TRENTOOL: A Matlab Open Source Toolbox to Analyse Information Flow in Time Series Data With Transfer Entropy},
  year = {2011},
  month = {01},
  URL = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3287134\&tool=pmcentrez\&rendertype=abstract},
  abstract = {BACKGROUND: Transfer entropy (TE) is a measure for the detection of directed interactions. Transfer entropy is an information theoretic implementation of Wiener's principle of observational causality. It offers an approach to the detection of neuronal interactions that is free of an explicit model of the interactions. Hence, it offers the power to analyze linear and nonlinear interactions alike. This allows for example the comprehensive analysis of directed interactions in neural networks at various levels of description. Here we present the open-source MATLAB toolbox TRENTOOL that allows the user to handle the considerable complexity of this measure and to validate the obtained results using non-parametrical statistical testing. We demonstrate the use of the toolbox and the performance of the algorithm on simulated data with nonlinear (quadratic) coupling and on local field potentials (LFP) recorded from the retina and the optic tectum of the turtle (Pseudemys scripta elegans) where a neuronal one-way connection is likely present. RESULTS: In simulated data TE detected information flow in the simulated direction reliably with false positives not exceeding the rates expected under the null hypothesis. In the LFP data we found directed interactions from the retina to the tectum, despite the complicated signal transformations between these stages. No false positive interactions in the reverse directions were detected. CONCLUSIONS: TRENTOOL is an implementation of transfer entropy and mutual information analysis that aims to support the user in the application of this information theoretic measure. TRENTOOL is implemented as a MATLAB toolbox and available under an open source license (GPL v3). For the use with neural data TRENTOOL seamlessly integrates with the popular FieldTrip toolbox.},
  journal = {BMC Neuroscience},
  volume = {12},
  number = {1},
  note = {Citations 56},
  doi = {10.1186/1471-2202-12-119}
}

@article{Foster1981,
title = {Intra-industry information transfers associated with earnings releases},
journal = {Journal of Accounting and Economics},
volume = {3},
number = {3},
pages = {201-232},
year = {1981},
issn = {0165-4101},
doi = {https://doi.org/10.1016/0165-4101(81)90003-3},
url = {https://www.sciencedirect.com/science/article/pii/0165410181900033},
author = {George Foster},
abstract = {The impact that a firm's earnings releases have on the stock prices of other firms in its industry is examined. For an identifiable sub-set of firms, the results are consistent with a significant information transfer occurring between the earnings release firm and the other firms in its industry. This subset is identified by examining the impact of the release on the stock price of the announcing firm. The magnitude of this impact is more significant for a sample of firms which have a larger percentage of their revenues in the same line of business as the earnings release firm vis-á-vis a sample with a lower percentage of their revenues from the same line of business. Alternative interpretations of the empirical results are also discussed. The research findings have implications for information content and market efficiency research and for research on policy issues associated with disclosure regulation.}
}

@article{OlsenDietrich1985,
 ISSN = {00218456, 1475679X},
 URL = {http://www.jstor.org/stable/2490695},
 author = {Chris Olsen and J. Richard Dietrich},
 journal = {Journal of Accounting Research},
 pages = {144--166},
 publisher = {[Accounting Research Center, Booth School of Business, University of Chicago, Wiley]},
 title = {Vertical Information Transfers: The Association between Retailers' Sales Announcements and Suppliers' Security Returns},
 volume = {23},
 year = {1985}
}


@article{AhernHarford2014,
 ISSN = {00221082, 15406261},
 URL = {http://www.jstor.org/stable/43611161},
 abstract = {We represent the economy as a network of industries connected through customer and supplier trade flows. Using this network topology, we find that stronger product market connections lead to a greater incidence of cross-industry mergers. Furthermore, mergers propagate in waves across the network through customer-supplier links. Merger activity transmits to close industries quickly and to distant industries with a delay. Finally, economy-wide merger waves are driven by merger activity in industries that are centrally located in the product market network. Overall, we show that the network of real economic transactions helps to explain the formation and propagation of merger waves.},
 author = {Kenneth R.Ahren and Jarrad Harford}, 
 journal = {The Journal of Finance},
 number = {2},
 pages = {527--576},
 publisher = {[American Finance Association, Wiley]},
 title = {The Importance of Industry Links in Merger Waves},
 volume = {69},
 year = {2014}
}

@article{Billio2012,
title = {Econometric measures of connectedness and systemic risk in the finance and insurance sectors},
journal = {Journal of Financial Economics},
volume = {104},
number = {3},
pages = {535-559},
year = {2012},
note = {Market Institutions, Financial Market Risks and Financial Crisis},
issn = {0304-405X},
doi = {https://doi.org/10.1016/j.jfineco.2011.12.010},
url = {https://www.sciencedirect.com/science/article/pii/S0304405X11002868},
author = {Monica Billio and Mila Getmansky and Andrew W. Lo and Loriana Pelizzon},
keywords = {Systemic risk, Financial institutions, Liquidity, Financial crises},
abstract = {We propose several econometric measures of connectedness based on principal-components analysis and Granger-causality networks, and apply them to the monthly returns of hedge funds, banks, broker/dealers, and insurance companies. We find that all four sectors have become highly interrelated over the past decade, likely increasing the level of systemic risk in the finance and insurance industries through a complex and time-varying network of relationships. These measures can also identify and quantify financial crisis periods, and seem to contain predictive power in out-of-sample tests. Our results show an asymmetry in the degree of connectedness among the four sectors, with banks playing a much more important role in transmitting shocks than other financial institutions.}
}


 @article {Serrano,
	author = {Serrano, M. {\'A}ngeles and Bogu{\~n}{\'a}, Mari{\'a}n and Vespignani, Alessandro},
	title = {Extracting the multiscale backbone of complex weighted networks},
	volume = {106},
	number = {16},
	pages = {6483--6488},
	year = {2009},
	doi = {10.1073/pnas.0808904106},
	publisher = {National Academy of Sciences},
	abstract = {A large number of complex systems find a natural abstraction in the form of weighted networks whose nodes represent the elements of the system and the weighted edges identify the presence of an interaction and its relative strength. In recent years, the study of an increasing number of large-scale networks has highlighted the statistical heterogeneity of their interaction pattern, with degree and weight distributions that vary over many orders of magnitude. These features, along with the large number of elements and links, make the extraction of the truly relevant connections forming the network{\textquoteright}s backbone a very challenging problem. More specifically, coarse-graining approaches and filtering techniques come into conflict with the multiscale nature of large-scale systems. Here, we define a filtering method that offers a practical procedure to extract the relevant connection backbone in complex multiscale networks, preserving the edges that represent statistically significant deviations with respect to a null model for the local assignment of weights to edges. An important aspect of the method is that it does not belittle small-scale interactions and operates at all scales defined by the weight distribution. We apply our method to real-world network instances and compare the obtained results with alternative backbone extraction techniques.},
	issn = {0027-8424},
	URL = {https://www.pnas.org/content/106/16/6483},
	eprint = {https://www.pnas.org/content/106/16/6483.full.pdf},
	journal = {Proceedings of the National Academy of Sciences}
}

@ARTICLE{Brochet2018,
title = {Information transfer and conference calls},
author = {Brochet, Francois and Kolev, Kalin and Lerman, Alina},
year = {2018},
journal = {Review of Accounting Studies},
volume = {23},
number = {3},
pages = {907-957},
abstract = {Abstract A long-standing literature documents intra-industry capital market co-movements around earnings releases, yet the dynamics of these information transfers remain largely unexplored. We provide evidence on both the sources and channels of information transfers by separating two distinct events within the reporting window using intra-day data and by exploring potential mechanisms of information flows. We document that the co-movement of absolute and signed stock returns over the conference call windows of announcing firms and their industry peers are statistically and economically larger than the co-movement over the corresponding earnings announcement windows. Turning to mechanisms, we find that shared analyst coverage, coverage by analysts providing industry recommendations, shared institutional ownership, and joint financial media mentions are each individually and incrementally associated with higher rate of information transfer over both the earnings announcement and conference call windows. Textual analyses reveal that peer mentions and macroeconomic discussions both significantly contribute to conference call information transfers.},
keywords = {Conference calls; Information transfer; Intra-day; TAQ; Information intermediaries},
url = {https://EconPapers.repec.org/RePEc:spr:reaccs:v:23:y:2018:i:3:d:10.1007_s11142-018-9444-4}
}


@article{Clauset2005,
  title = {Finding community structure in very large networks},
  author = {Clauset, Aaron and Newman, M. E. J. and Moore, Cristopher},
  journal = {Phys. Rev. E},
  volume = {70},
  issue = {6},
  pages = {066111},
  numpages = {6},
  year = {2004},
  month = {Dec},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.70.066111},
  url = {https://link.aps.org/doi/10.1103/PhysRevE.70.066111}
}

@Up{EA-TE,
  title = {Cross-Firm Information Transfers During Earnings Season: A Network Approach},
  author = {Brunner,Robert and Ikegwu,Kelechi and Schonberger,Bryce and McMullin, Jeff},
  year = {2021},
  month = {April},
}

@article{ThomasZhang2008,
author = {Thomas, Jacob and Zhang, Frank},
year = {2008},
month = {09},
pages = {909-940},
title = {Overreaction to Intra-Industry Information Transfers?},
volume = {46},
journal = {Journal of Accounting Research},
doi = {10.2139/ssrn.1028285}
}

@misc{ WRDS,
  author = {{\firstsecond{Wharton School}{The Wharton School}}},
  title = {Wharton research data services},
  year = {1993},
  note = {data retrieved from Wharton research data services, 
          \url{https://wrds-web.wharton.upenn.edu/wrds/}},
}


@ARTICLE{HoldenJacobsen2014,
title = {Liquidity Measurement Problems in Fast, Competitive Markets: Expensive and Cheap Solutions},
author = {Holden, Craig W. and Jacobsen, Stacey},
year = {2014},
journal = {Journal of Finance},
volume = {69},
number = {4},
pages = {1747-1785},
abstract = {type="main"> Do fast, competitive markets yield liquidity measurement problems when using the popular Monthly Trade and Quote (MTAQ) database? Yes. MTAQ yields distorted measures of spreads, trade location, and price impact compared with the expensive Daily Trade and Quote (DTAQ) database. These problems are driven by (1) withdrawn quotes, (2) second (versus millisecond) time stamps, and (3) other causes, including canceled quotes. The expensive solution, using DTAQ, is first-best. For financially constrained researchers, the cheap solution—using MTAQ with our new Interpolated Time technique, adjusting for withdrawn quotes, and deleting economically nonsensical states—is second-best. These solutions change research inferences.},
url = {https://EconPapers.repec.org/RePEc:bla:jfinan:v:69:y:2014:i:4:p:1747-1785}
}