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FINANCIAL MODELS INDUCED FROM AUXILIARY INDICES AND TWITTER DATA
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  • FINANCIAL MODELS INDUCED FROM AUXILIARY INDICES AND TWITTER DATA
  • FINANCIAL MODELS INDUCED FROM AUXILIARY INDICES AND TWITTER DATA
저자명
Oh. Jae-Pill
간행물명
Korean Journal of mathematics
권/호정보
2014년|22권 3호|pp.529-552 (24 pages)
발행정보
강원경기수학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
서지반출

기타언어초록

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.