Korean J. Math. Vol. 22 No. 3 (2014) pp.529-552
DOI: https://doi.org/10.11568/kjm.2014.22.3.529

Financial models induced from auxiliary indices and twitter data

Main Article Content

Jae-pill Oh


As we know, some indices and data are strong influence to the price movement of some assets now, but not to another assets and in future. Thus we define some asset models for several time intervals; intraday, weekly, monthly, and yearly asset models. We define these asset models by using Brownian motion with volatility and Poisson process, and several deterministic functions(index function, twitter data function and big-jump simple function etc). In our asset models, these deterministic functions are the positive or negative levels of auxiliary indices, of analyzed data, and for imminent and extreme state(for example, financial shock or the highest popularity in the market). These functions determined by indices, twitter data and shocking news are a kind of one of speciality of our asset models. For reasonableness of our asset models, we introduce several real data, figurers and tables, and simulations. Perhaps from our asset models, for short-term or long-term investment, we can classify and reference many kinds of usual auxiliary indices, information and data.

Article Details

Supporting Agencies

This study was supported by 2013 Research Grant of Kangwon National University(C1010184-01-01).


[1] J. Bollen, H. Mao and X. Zeng, Twitter mood predicts the stock market, http://arxiv.org/abs /1010.3003 Google Scholar

[2] R. Chen and M. Lazer, Sentiment analysis of twitter feeds for the prediction of stock market movement, CS 229:Machine Learning, 2011, cs229.stanford.deu Google Scholar

[3] C. Castillo, M. Mendoza and B. Poblete, Information credibility on twitter, Proceedings of World Wide Web Conference (2011), 675–684. Google Scholar

[4] E. F. Fama, Efficient capital market: A review of theory and empirical work, J. Finance 25 (2) (1970), 383–417. Google Scholar

[5] M. Jeanblanc, V. Lacoste and S. Roland, Portfolio optimization under a partially observed jump-diffusion model, Prepublications de l,Equipe d’Analyse et probabilitie’s 2010. Google Scholar

[6] S. S. Kim, A study on the relationship between volume of corporate web news and stock prices, Master’s Thesis, KAIST, 2011. Google Scholar

[7] S. S. Kwon and J. H. Lee, The function of intraday implied volatility in the KOISP200 options, Asia-Pacific J. of Financial Studies (2008), 913–948. Google Scholar

[8] S. KumarF J. Morstatter and H. Liu, Twitter Data Analysis, Springer, August 19, 2013 Google Scholar

[9] X. Liang, Mining associations between web stock news volumes and stock prices, International Journal of Systems Science 37 (13) (2006), 919–930. Google Scholar

[10] C. Lindberg, Portfolio optimization and statistics in stochastic volatility markets, Thesis for Doctor of Philosophy, Chalmers Univ. Goteborg, Sweden, 2005 Google Scholar

[11] D. H. Lee, H. G. Kang and C. M. Lee, Autocorrelation analysis of the sentiment with stock information appearing on gig-data, 한국금용공학회 학술발표논문집 2013, 282–304. Google Scholar

[12] J. Oh, Multi-type financial asset models for portfolio construction, J. KSIAM 14 (4) (2010), 211–224. Google Scholar

[13] C. Park, L. Le, J. S. Marron, J. Park, V. Pipiras, F. D. Smith, R. L. Smith, M. Trovero and Z. Zhu, Long range dependence analysis of internet traffic, Journal of Applied Statistics 38 (7) (2011), 1407–1433. Google Scholar

[14] B. L. S. Prakasa Rao, Self-similar processes, fractional Brownian motion and statistical inference, A Festschrift for Herman Rubin Institute of Math. Statistics Lecture Notes - Monograph Series V. 45, 98–124, 2004 Google Scholar

[15] P. Protter, Stochastic Integration and Differential Equations, Berlin Heidelberg N.Y., Springer, 2nd Printing, 1992 Google Scholar

[16] S. A. Ross, Information and volatility: The no-arbitrage martingale approach to timing and resolution irrelevancy, J. of Finance 44 (1) (1989), 1–18. Google Scholar

[17] W. Sun, S. Rachev, F. J. Fabozzi and P. S. Kalev, Fractals in trade dulation: Capturing long-range dependence and heavy tailednes in modeling trade duration, Annals of Finance 4 (2008), 217–241. Google Scholar