Stock market prediction utilizing central bank’s policy statements

Moiseiev, Roman

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2020
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dc.contributor.author	Moiseiev, Roman
dc.date.accessioned	2020-02-21T14:21:16Z
dc.date.available	2020-02-21T14:21:16Z
dc.date.issued	2020
dc.identifier.citation	Moiseiev, Roman. Stock market prediction utilizing central bank’s policy statements : Master Thesis : manuscript rights / Roman Moiseiev ; Supervisor Andriy Zhovtanetskyy ; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2020. – 35 p. : ill.	uk
dc.identifier.uri	http://er.ucu.edu.ua/handle/1/2036
dc.language.iso	en	uk
dc.subject	Stock market prediction	uk
dc.subject	Policy statement classiﬁcation	uk
dc.subject	Multinomial Logistic Regression	uk
dc.title	Stock market prediction utilizing central bank’s policy statements	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	The stock market is quite unpredictable and affected by a vast number of factors. Moreover, many central banks, banks, hedge funds, and other ﬁnancial institutions target their R&D departments to try to predict probabilities of market movements, possible black swans, and other risks. In this work, I target inefﬁciencies in the prediction of the market reaction on central bank policy statements. Such statements have two parts: action and information. Therefore in complicated cases, automatic trading systems react to actions and may not recognize vital insights from the informational component. To improve this, I collected historical data for monetary actions and press releases by Federal Reserve, stock price data, Fed Fund futures contract prices. Based on that, I build several classiﬁcation models to predict the classofpolicystatements. Afterward,preparedpipelineandtheeconometricmodel that can incorporate a class of a policy statement for stock market reaction evaluation	uk