Can a corporate network and news sentiment improve portfolio optimization using the Black–Litterman model?

References

• Adamic, L., Brunetti, C., Harris, J. and Kirilenko, A., Information flow in trading networks. Paper presented at 1st Workshop on Information in Networks, NYU, New York, NY, 2009.
   Google Scholar
• Aral, S., Ipeirotis, P.G. and Taylor, S., Content and context: Identifying the impact of qualitative information on consumer choice. In Proceedings of the 32nd International Conference on Information Systems, 2011 (AIS: Shanghai).
   Google Scholar
• Baker, C.F., Fillmore, C.J. and Lowe, J.B., The Berkeley framenet project. In Proceedings of the 17th International Conference on Computational Linguistics, pp. 86–90, 1998 (Montreal, QC).
   Google Scholar
• Bao, Y. and Datta, A., Simultaneously discovering and quantifying risk types from textual risk disclosures. Manage. Sci., 2014, 60, 1371–1391.
   Web of Science ®Google Scholar
• Bar-Haim, R., Dinur, E., Feldman, R., Fresko, M. and Goldstein, G., Identifying and following expert investors in stock microblogs. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 1310–1319, 2011 (Association for Computational Linguistics: Edinburgh, Scotland, UK).
   Google Scholar
• Beach, S. and Orlov, A., An application of the Black–Litterman model with EGARCH-M derived views for international portfolio management. Financ. Markets Portfolio Manage., 2007, 21, 147–166.
   Google Scholar
• Becker, F. and Gurtler, M., Quantitative forecast model for the application of the Black–Litterman approach. 2009 Finance International Meeting AFFI – EUROFIDAI, Paris, 2010.
   Google Scholar
• Black, F. and Litterman, R., Asset Allocation: Combining Investor Views With Market Equilibrium. Fixed Income Research, 1990 (Goldman Sachs: New York).
   Google Scholar
• Black, F. and Litterman, R., Global portfolio optimization. Financ. Anal. J., 1992, 48, 28–43.
   Google Scholar
• Black, F. and Litterman, R., The Intuition Behind Black–Litterman Model Portfolios. Investment Management Research, 1999 (Goldman Sachs: New York).
   Google Scholar
• Blei, D.M., Ng, A.Y. and Jordan, M.I., Latent Dirichlet allocation. J. Machine Learn. Res., 2003, 3, 993–1022.
   Web of Science ®Google Scholar
• Borgatti, S.P. and Everett, M., A graph-theoretic perspective on centrality. Soc. Netw., 2006, 28, 466–484.
   Web of Science ®Google Scholar
• Breiman, L., Friedman, J., Stone, C.J. and Olshen, R., Classification and Regression Trees, 1984 (Wadsworth and Brooks: Belmont).
   Google Scholar
• Cho, V., Wuthrich, B. and Zhang, J., Text processing for classification. J. Comput. Intell. Finance, 1999, 7, 6–22.
   Google Scholar
• Chua, C., Milosavljevic, M. and Curran, J.R., A sentiment detection engine for internet stock message boards. In Proceedings of the Australasian Language Technology Association Workshop 2009, pp. 89–93, 2009 (Sydney, Australia).
   Google Scholar
• Collins, M., Schapire, R.E. and Singer, Y., Logistic regression. AdaBoost and Bregman distances. Machine Learn., 2004, 48, 253–285.
   Web of Science ®Google Scholar
• Creamer, G. and Freund, Y., Predicting performance and quantifying corporate governance risk for Latin American ADRs and banks. In Proceedings of the Second IASTED International Conference Financial Engineering and Applications, pp. 91–101, 2004 (Acta Press: Cambridge, MA).
   Google Scholar
• Creamer, G. and Freund, Y., Using AdaBoost for equity investment scorecards. In Proceedings of the Machine Learning in Finance Workshop in NIPS 2005, 2005 (Whistler, BC).
   Google Scholar
• Creamer, G. and Freund, Y., A boosting approach for automated trading. J. Trading, 2007, 2, 84–95.
   Google Scholar
• Creamer, G. and Stolfo, S., A link mining algorithm for earnings forecast and trading. Data Min. Knowl. Disc., 2009, 18, 419–445.
   Web of Science ®Google Scholar
• Creamer, G.G., Ren, Y. and Nickerson, J.V., Impact of dynamic corporate news networks on asset return and volatility. In 2010 IEEE International Conference on Social Computing/IEEE International Conference on Privacy, Security, Risk and Trust, Alexandria, VA, pp. 809–814, 2013.
   Google Scholar
• Das, D., Schneider, N., Chen, D. and Smith, N.A., Probabilistic frame-semantic parsing. In Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Los Angeles, CA, 2010.
   Google Scholar
• de Nooy, W., Mrvar, A. and Batagelj, V., Exploratory Social Network Analysis with Pajek, 2005 (Cambridge University Press: New York).
   Google Scholar
• Decker, K., Sycara, K. and Zeng, D., Designing a multi-agent portfolio management system. In Proceedings of the AAAI Workshop on Internet Information Systems, 1996 (AAI Press: Menlo Park, CA).
   Google Scholar
• Devitt, A. and Ahmad, K., Sentiment polarity identification in financial news: A cohesion based approach. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 984–991, 2007 (Association for Computational Linguistics: Prague, Czech Republic).
   Google Scholar
• Dhar, V. and Chou, D., A comparison of nonlinear methods for predicting earnings surprises and returns. IEEE Trans. Neural Netw., 2001, 12, 907–921.
   PubMed Web of Science ®Google Scholar
• Domingos, P. and Richardson, M., Mining the network value of customers. In Proceedings of the KDD ’01: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, pp. 57–66, 2001 (ACM: New York, NY, USA).
   Google Scholar
• Fawcett, T. and Provost, F., Activity Monitoring: Noticing interesting changes in behavior. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), San Diego, CA, USA, 15–18 August, 1999, pp. 53–62, 1999 (ACM: New York).
   Google Scholar
• Fillmore, C.J., Frame semantics. In Linguistics in the Morning Calm, edited by T.L.S. of America, 1982 (Hanshin Publishing Co.: Seoul, South Korea).
   Google Scholar
• Forman, G., An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res., 2003, 3, 1289–1305.
   Google Scholar
• Freeman, L., Centrality in social networks conceptual clarification. Soc. Netw., 1979, 1, 215–39.
   Web of Science ®Google Scholar
• Freund, Y. and Mason, L., The alternating decision tree learning algorithm. In Proceedings of the Machine Learning: Proceedings Sixteenth International Conference, pp. 124–133, 1999 (Morgan Kaufmann Publishers Inc.: San Francisco, CA).
   Google Scholar
• Freund, Y. and Schapire, R.E., A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci., 1997, 55, 119–139.
   Web of Science ®Google Scholar
• Friedman, J., Hastie, T. and Tibshirani, R., Additive logistic regression: A statistical view of boosting. Ann. Stat., 2000, 38, 337–374.
   Web of Science ®Google Scholar
• Goldberg, H.G., Kirkland, J.D., Lee, D., Shyr, P. and Thakker, D., The NASD securities observation, news analysis and regulation system (SONAR). In Proceedings of the IAAI 2003, 2003 (Acapulco, Mexico).
   Google Scholar
• Hagenau, M., Liebmann, M., Hedwig, M. and Neumann, D., Automated news reading: Stock price prediction based on financial news using context-specific features. In Proceedings of the Hawaii International Conference on System Sciences (HICSS), 2012 (Big Island).
   Google Scholar
• Haider, S.A. and Mehrotra, R., Corporate news classification and valence prediction: A supervised approach. In Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2011), pp. 175–181, 2011 (Association for Computational Linguistics: Portland, Oregon).
   Google Scholar
• Hill, S., Provost, F. and Volinsky, C., Network-based marketing: Identifying likely adopters via consumer networks. Stat. Sci., 2006, 21, 256–276.
   Web of Science ®Google Scholar
• Idzorek, T., A step-by-step guide to the Black–Litterman model. Working Paper, 2009.
   Google Scholar
• Jegadeesh, N., Kim, J., Krische, S.D. and Lee, C.M.C., Analyzing the analysts: When do recommendations add value? J. Finance, 2004, 59, 1083–1124.
   Web of Science ®Google Scholar
• Khrishnan, H. and Mains, N., The two-factor Black–Litterman model, Risk, 2005, 69–73.
   Google Scholar
• Kirkland, J.D., Senator, T.E., Hayden, J.J., Dybala, T., Goldberg, H.G. and Shyr, P., The NASD regulation advanced detection system (ADS). AI Mag., 1999, 20, 55–67.
   Web of Science ®Google Scholar
• Lavrenko, V., Schmill, M., Lawrie, D., Oglivie, P., Jensen, D. and Allan, J., Language models for financial news recommendation. In Proceedings of the Ninth International Conference on Information and Knowledge Management, McLean, VA, 6–11 November, 2000, pp. 389–396, 2000 (ACM: New York).
   Google Scholar
• Leskovec, J., Adamic, L.A. and Huberman, B.A., The dynamics of viral marketing. In Proceedings of the EC ’06: Proceedings of the 7th ACM Conference on Electronic Commerce, Ann Arbor, MI, USA, pp. 228–237, 2006 (ACM: New York, NY, USA).
   Google Scholar
• Markowitz, H., Portfolio selection. J. Finance, 1952, 7, 77–91.
   Web of Science ®Google Scholar
• Markowitz, H., Portfolio Selection: Efficient Diversification of Investments, 1959 (John Wiley & Sons: New York).
   Google Scholar
• Richardson, M. and Domingos, P., Markov logic networks. Mach. Learn., 2006, 62, 107–136.
   Web of Science ®Google Scholar
• Ruiz, E.J., Hristidis, V., Castillo, C., Gionis, A. and Jaimes, A., Correlating financial time series with micro-blogging activity. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining, pp. 513–522, 2012 (ACM: Portland, OR).
   Google Scholar
• Schumaker, R.P. and Chen, H., Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Trans. Inform. Syst. 2009, 27, 12:1–12:19.
   Web of Science ®Google Scholar
• Senator, T.E., Link mining applications: Progress and challenges. SIGKDD Explor., 2005, 7, 76–83.
   Google Scholar
• Seo, Y.W., Giampapa, J. and Sycara, K., Financial news analysis for intelligent portfolio management. Technical report CMU-RI-TR-04-04, Robotics Institute, Carnegie Mellon University, 2004.
   Google Scholar
• Sparrow, M.K., The application of network analysis to criminal intelligence: An assessment of the prospects. Soc. Netw., 1991, 13, 251–274.
   Web of Science ®Google Scholar
• Steyvers, M. and Griffiths, T., Probabilistic topic models. In Latent Semantic Analysis: A Road to Meaning, edited by T. Landauer, D. McNamara, S. Dennis and W. Kintsch, 2007 (Erlbaum: Mahwah, NJ).
   Google Scholar
• Thomas, J.D., News and trading rules. PhD Thesis, Carnegie Mellon University, 2003.
   Google Scholar
• Walters, J., The Black–Litterman model in detail. Working Paper, 2009.
   Google Scholar
• Watts, D. and Strogatz, S., Collective dynamics of small world networks. Nature, 1998, 393, 440–442.
   PubMed Web of Science ®Google Scholar
• Wuthrich, B., Permunetilleke, D., Leung, S., Cho, V., Zhang, J. and Lam, W., Daily prediction of major stock indices from textual www data. In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, New York, 27–31 August, 1998, pp. 364–368, 1998 (AAAI Press: New York).
   Google Scholar
• Xie, B., Passonneau, R.J., Wu, L. and Creamer, G., Semantic frames to predict stock price movement. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, 2013 (Association for Computational Linguistics: Sofia, Bulgaria).
   Google Scholar