By defining a novel finance-computer time series W, this paper
describes a web information-based triangle framework for conducting
studies on quantitative relations among P, V, and W, and
further explores the relationship between P and W quantitatively
using a model based on nonlinear SVR. Comparing with the
experimental results purely based on P, the mean predicting
errors forecasted by P and W were obviously reduced. The
addition of W thereof enhanced the forecasting performance.
In theory, the investigation provides an insightful understanding
of the functions of financial information time series from a
new angle, and it can be theoretically treated as a new and
promising way to probe into complex problems in security
markets.
As more and more ordinary people around the world share
information on the web, the ‘‘long-tail’’ formed by these ‘‘grass
roots’’ has a noticeable effect on the market. The rapid responses
of computer processing of financial information can help financial
managers quickly find the aggregated effect of opinions from
these ‘‘grass roots,’’ as well as other enormous web financial
information. The bridge we built in this paper between P and W
containing the views of ‘‘grass roots’’ offers a novel clue to P from
the W side for financial analysts, and enables them to rapidly
By defining a novel finance-computer time series W, this paperdescribes a web information-based triangle framework for conductingstudies on quantitative relations among P, V, and W, andfurther explores the relationship between P and W quantitativelyusing a model based on nonlinear SVR. Comparing with theexperimental results purely based on P, the mean predictingerrors forecasted by P and W were obviously reduced. Theaddition of W thereof enhanced the forecasting performance.In theory, the investigation provides an insightful understandingof the functions of financial information time series from anew angle, and it can be theoretically treated as a new andpromising way to probe into complex problems in securitymarkets.As more and more ordinary people around the world shareinformation on the web, the ‘‘long-tail’’ formed by these ‘‘grassroots’’ has a noticeable effect on the market. The rapid responsesof computer processing of financial information can help financialmanagers quickly find the aggregated effect of opinions fromthese ‘‘grass roots,’’ as well as other enormous web financialinformation. The bridge we built in this paper between P and Wcontaining the views of ‘‘grass roots’’ offers a novel clue to P fromthe W side for financial analysts, and enables them to rapidly
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