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Predicting he InterbankCapital
AdequacyLevelBased nFinancialData
Analysis
YaojunDUa,FangjunWANGa andLipingSHENa
aSchoolofElectronic InformationandElectricalEngineering
Shanghai JiaoTongUniversity, Shanghai,China
Abstract.Theadequacyof interbankcapital is affectedbymanyfactors, including
the tightnessof theentirefinancialmarket and theups-and-downsof interest rates.
It has great significance for commercial banks and other non-bank institutions to
diagnose thedisturbance factorsof interbankcapital, andpredict futurecapital ad-
equacy level in advance to deploy appropriate countermeasures accordingly. This
paper attempts to analyze the relevant factors affecting the interbank capital and
topredict the adequacy level of interbankcapital basedon structured andunstruc-
turedfinancial data. For unstructureddata,we crawl the texts fromSinaFinancial
Newsandthenmakepre-processing, includingwordsegmentation,emotionalword
extraction andword-to-vector transformation. For structured data the preprocess-
ing includes paddingmissing value, data normalization, feature selection and da-
ta dimensionality reduction. The predictionmodelswe tried includeGBDT,XG-
Boost, LSTM,SVM, andPerceptron. Experiments show that two-category (loose
and tight) average accuracy of the overall adequacy level of interbank capital can
achievemore than94.5%.
Keywords. Interbank Capital Adequacy Level, Prediction, Natural Language
Processing (NLP),FinancialDataAnalysis
1. Introduction
Commercial banks have a special and important role in the entire financial systemand
even in thenational economy.Thecapital adequacy level is ameasureof abank’s avail-
able capital to protect depositors and promote the stability and efficiency of financial
systemsaround theworld. It usually indicatesmoney supply and the ability of themar-
ket regulationpolicy to support financial products. The indicators includebroadmoney
supply (M2), stamp duty, central bank interest rates, etc. The prediction of the capital
adequacy of commercial banks can provide decision-making support for the assets al-
location, risk control and interbank lending, and enhance the liquidity of the inter-bank
moneymarket, andvery important,playsapositive role in theearlywarningoffinancial
risks.
With the rapiddevelopment of information technology, bigdata analysis andartifi-
cial intelligence, thefinancial industry is also actively trying tousenew technologies to
solve traditional problems.At present, academic researches onfinancialmarketmostly
focuson theanalysis of structureddata, and fewresearch studies thepredictionof capi-
o
t Intelligent Environments 2019
A. Muñoz et al. (Eds.)
© 2019 The authors and IOS Press.
This article is published online with Open Access by IOS Press and distributed under the terms
of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
doi:10.3233/AISE190020
36
Intelligent Environments 2019
Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Title
- Intelligent Environments 2019
- Subtitle
- Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Authors
- Andrés Muñoz
- Sofia Ouhbi
- Wolfgang Minker
- Loubna Echabbi
- Miguel Navarro-Cía
- Publisher
- IOS Press BV
- Date
- 2019
- Language
- German
- License
- CC BY-NC 4.0
- ISBN
- 978-1-61499-983-6
- Size
- 16.0 x 24.0 cm
- Pages
- 416
- Category
- Tagungsbände