<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">ME</journal-id><journal-title-group><journal-title>Modern Economy</journal-title></journal-title-group><issn pub-type="epub">2152-7245</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/me.2014.58079</article-id><article-id pub-id-type="publisher-id">ME-47808</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>BUSINESS &amp; ECONOMICS</subject></subj-group></article-categories><title-group><article-title>Application of Intervention Analysis Model in Yu Ebao Yield Prediction</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Jian</surname><given-names>Su</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Guangming</surname><given-names>Deng</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib></contrib-group><aff id="aff1"><addr-line>College of Science, Guilin University of Technology, Guilin, China</addr-line></aff><author-notes><corresp id="cor1">* E-mail:<email>sujian2233@163.com(JS)</email>;</corresp></author-notes><pub-date pub-type="epub"><day>07</day><month>07</month><year>2014</year></pub-date><volume>05</volume><issue>08</issue><fpage>864</fpage><lpage>868</lpage><history><date date-type="received"><day>20</day>	<month>May</month>	<year>2014</year></date><date date-type="rev-recd"><day>9</day>	<month>June</month>	<year>2014</year>	</date><date date-type="accepted"><day>5</day>	<month>July</month>	<year>2014</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
	This paper used the
intervention analysis model to fit the data of seven-day annualized yield of Yu
Ebao, by regarding the Niu’s comment as intervention. We constructed the linear
model and the intervention model. The result showed that though Niu’s comment
was not the most important cause of the decline of the yield, its effects
cannot be ignored. And it caused the yield of Yu Ebao fallen 0.148% faster than
before.
</p></abstract><kwd-group><kwd>Yu Ebao</kwd><kwd> Yield</kwd><kwd> Intervention Analysis Model</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. Introduction</title><p>Recently, a series of internet financial products were launched by many internet companies and fund management companies, which attracted a lot of investors’ attention. Yu Ebao is one of these products. Yu Ebao was a balance value-added service built by the third party payment platform Alipay, which was launched in June 13, 2013. Essentially, it is a monetary fund launched by the Alipay cooperated with the Tianhong Asset Management Company. The users will access certain benefits by transferring the money to Yu Ebao. Due to that fact that its yield was higher than the bank current deposit interest rate, and the account funds could be used for online shopping or transferring at any time, it attracted a large number of young people fascinated in online shopping. As of February 27, 2014, Yu Ebao user amount has exceeded 81 million, and its asset size was larger than 5 hundred billion.</p><p>The yield of Yu Ebao was increased steadily since its launch (<xref ref-type="fig" rid="fig1">Figure 1</xref>), even broke through 6.7%, and lasted for 10 days long. However, nothing gold can stay, at the beginning of 2014, major banks had teamed up with other fund companies to launch their own internet financial products. Besides, under the pressure of other like products which came from Baidu and Jingdong companies, the yield of Yu Ebao was all the way down after the</p><fig id="fig1"><label>Figure 1</label><caption><p> The seven-day annualized yield of Yu Ebao</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\1ad08a96-0f4d-4560-88aa-6b95a2c25442.png"/></fig><p>peak.</p><p>On the day of February 21, 2014, Wenxin Niu whom is the chief executive editor and chief commentator of CCTV security information channel, accused that Yu Ebao was a “vampire” lying on the bank, a typical “financial parasite”, and advocated to ban this product. Niu’s comment was reported by many authoritative media, and sent shock waves throughout the internet. Because of this, the yield of Yu Ebao fallen sharply. Even though on March 4, Xiaochuan Zhou, president of the people’s bank of China, indicated that they won’t ban the Yu Ebao, that’s unable to block the yield decline. Based on the background above, we regarded Niu’s comment as the intervention, applying the intervention analysis model to analyze and predict the trend of seven-day annualized yield, quantitatively studying its running track.</p></sec><sec id="s2"><title>2. Intervention Analysis Model</title><p>The intervention analysis model is proposed by Box and Tiao, professors of University of Wisconsin, department of statistics [<xref ref-type="bibr" rid="scirp.47808-ref1">1</xref>] . Intervention analysis is to evaluate the impacts of policy events or emergency on the economic environment and process from the angle of quantitative analysis [<xref ref-type="bibr" rid="scirp.47808-ref2">2</xref>] . The intervention parameters fall into two categories, continuous and transitory. The continuous parameter refers that the intervention event still affects the changes after the moment T. The basic variable of intervention analysis model is the intervention variable, i.e.</p><disp-formula id="scirp.47808-formula4454"><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\52693c09-7095-476f-8cd6-fdfcb3d8adbe.png"/></disp-formula><p>The intervention model is<inline-formula><inline-graphic xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\b38a01c0-0972-491d-a3f0-4d19e9a7da2a.png" xlink:type="simple"/></inline-formula>, where <inline-formula><inline-graphic xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\061366db-fe36-48ff-a542-3b05868e8046.png" xlink:type="simple"/></inline-formula> represents the unknown parameter of the intervention</p><p>effect strength, <inline-formula><inline-graphic xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\7e83a2f2-9119-415a-8cad-2caf9d6d540d.png" xlink:type="simple"/></inline-formula>represents the intervention duration, B is delay operator.</p><p>Four kinds of intervention type concluded as below:</p><p>1) The influence of intervention appears all of a sudden, and continues for a long time;</p><p>2) The influence of intervention appears gradually, and continues for a long time;</p><p>3) The intervention occurs suddenly, and the influence is temporary;</p><p>4) The intervention occurs gradually, and the influence is temporary.</p><p>There are four steps to modeling the intervention analysis model. First of all, we construct a univariate time series model by using the data before intervention, then applying the model to make the prediction, the prediction data we get is without intervention. Secondly, with the real value minus the predicted value, the difference is the intervention value. Then we estimate the parameter of the intervention model. Thirdly, we combine the real data before the intervention and the prediction data which came from the intervention model as a complete data. Then we construct another univariate time series model. At last, with the last univariate time series model and the intervention model, the combination is the final intervention analysis model.</p></sec><sec id="s3"><title>3. Data Analysis</title><sec id="s3_1"><title>3.1. Data Specification</title><p>This paper uses the seven-day annualized yield of Yu Ebao from January 1 to April 30, 2014 as data. The data is collected from the official website of the Tianhong Asset Management Company (http://www.thfund.com.cn). The data is shown in the Appendix.</p></sec><sec id="s3_2"><title>3.2. Model Construction before the Intervention</title><p>According to the observation of the data before the intervention (before February 21), and the comparison of several models fitted effect, we chose the linear model. The model we constructed is:</p><disp-formula id="scirp.47808-formula4455"><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\6ed245d7-c6c7-4e19-ac3b-eff2417ea963.png"/></disp-formula><p>where the model goodness of fit<inline-formula><inline-graphic xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\dfc91f78-4677-4362-ad06-2c7c2254f3b0.png" xlink:type="simple"/></inline-formula>, model F Test<inline-formula><inline-graphic xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\bee30ebb-64f4-47a8-9394-c96ea88a4d1f.png" xlink:type="simple"/></inline-formula>, and all the parameters in the model is significant under the significant level 0.05, all of these show that the model fitting effect is great.</p></sec><sec id="s3_3"><title>3.3. Intervention Model Construction</title><p>With the linear model we got above, we predict the yield <inline-formula><inline-graphic xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\4c6c6318-a382-4af6-b817-a0d87d328ee8.png" xlink:type="simple"/></inline-formula> from February 21 to April 30. Then we compute the intervention effect value<inline-formula><inline-graphic xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\12b40817-3a19-4887-a89f-04b4dfd86a3e.png" xlink:type="simple"/></inline-formula>, <inline-formula><inline-graphic xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\00c5b5bb-2c1d-49f8-a7a6-82e294e86dea.png" xlink:type="simple"/></inline-formula>is the quantitative impact value of Niu’s comment on Yu Ebao’s yield. We estimate the parameters of the intervention model and get this:</p><disp-formula id="scirp.47808-formula4456"><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\4b0b239d-e1c9-4519-8531-e0aad14868e5.png"/></disp-formula><p>where the model goodness of fit<inline-formula><inline-graphic xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\4c7024df-16a7-4c00-9449-9a6ff9562134.png" xlink:type="simple"/></inline-formula>, model F Test<inline-formula><inline-graphic xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\e756948b-8eca-44be-8662-8cb954face63.png" xlink:type="simple"/></inline-formula>, and all the parameters in the model is significant under the significant level 0.05, all of these show that the model fitting is great.</p></sec><sec id="s3_4"><title>3.4. Purified Series Model Construction</title><p>The purified series refer to the series without the intervention impact, i.e. <inline-formula><inline-graphic xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\8ec91a2a-804e-45fb-ad47-cb2f46737477.png" xlink:type="simple"/></inline-formula></p><p><inline-formula><inline-graphic xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\af4ae97a-3fc2-4340-bea9-72e2b064ec62.png" xlink:type="simple"/></inline-formula>. We still chose the linear model to fit the purified series, the result is:</p><disp-formula id="scirp.47808-formula4457"><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\00745eaa-a0f2-41e8-9bca-87ce35d2a830.png"/></disp-formula><p>where the model goodness of fit<inline-formula><inline-graphic xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\b15f25f5-90bb-4f7e-b037-59969a20ae84.png" xlink:type="simple"/></inline-formula>, model F Test<inline-formula><inline-graphic xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\97789eb4-a0d3-46ae-9194-0b1cb909de39.png" xlink:type="simple"/></inline-formula>, and all the parameters in the model is significant5, all of these show that the model fitting is great.</p></sec><sec id="s3_5"><title>3.5. The Final Intervention Analysis Model</title><p>Combining the intervention model and the purified series model, we get the final intervention analysis model:</p><disp-formula id="scirp.47808-formula4458"><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\34427671-b545-4f4f-be86-48547bcededc.png"/></disp-formula><disp-formula id="scirp.47808-formula4459"><inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\34427671-b545-4f4f-be86-48547bcededc.png"/></disp-formula><p>By comparing the prediction value of intervention analysis model and the raw data (<xref ref-type="fig" rid="fig2">Figure 2</xref>), we know that the two series are highly consistent, which shows that the model we constructed is reasonable.</p><p>According to the analysis above, we conclude that Niu’s comment made the yield fallen faster and faster, the average impact is −0.148%, i.e. after the comment, the yield of Yu Ebao fallen 0.148% faster than before.</p></sec></sec><sec id="s4"><title>4. Conclusion and Improvement</title><p>There are a lot of factors affecting the financial product yield [<xref ref-type="bibr" rid="scirp.47808-ref3">3</xref>] . The direct factors include national polity, rate changing, the company’s own development, etc. The indirect factors include economic environment, comment, especially the famous scholars’ comment, which will first affect the investors’ psychology. Then the investors</p><fig id="fig2"><label>Figure 2</label><caption><p> Intervention analysis model prediction effect</p></caption><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://file.scirp.org/Html/htmlimages\4-7200847x\6d70fab0-6f33-45b0-b217-25f44283ba92.png"/></fig><p>will make their own choices to enter or exit the market according to the good or bad news; what’s more, that will affect yield of the product. Through the intervention analysis model, we fitted the impact of “ban Yu Ebao” comment on its yield accurately.</p><p>Yu Ebao as a monetary fund product, is restricted by the deposit rate of the bank, so that its yield won’t never keep on the high level [<xref ref-type="bibr" rid="scirp.47808-ref4">4</xref>] . Dengfeng Wang, fund manager of the Tianhong asset management company, explained that the reason why the yield was so high is that the money is in short supply at the end of the year and before the Spring Festival, so the investors who invest the monetary fund enjoyed a higher income. But after the Spring Festival, the money supply tension reduced, the yield of Yu Ebao is returned to the normal level. Therefore, though Niu’s comment was not the most important cause of the decline of the yield, with the analysis above, its effects cannot be ignored [<xref ref-type="bibr" rid="scirp.47808-ref5">5</xref>] .</p><p>This paper only considered the linear and curve regression model when we constructed the model. We haven’t considered applying some general time series model, such as ARIMA model, ARCH model, etc. So in the future research work, we will apply more types of model and compare their fitting effect.</p></sec><sec id="s5"><title>Acknowledgements</title><p>This work was jointly supported by the National Social Science Fund (No. 13BTJ009).</p></sec></body><back><ref-list><title>References</title><ref id="scirp.47808-ref1"><label>1</label><mixed-citation publication-type="journal" xlink:type="simple"><name name-style="western"><surname>BOX</surname><given-names> G.E.P. </given-names></name>,<name name-style="western"><surname> TIAO</surname><given-names> G.C. </given-names></name>,<etal>et al</etal>. 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