TECHNIQUES OF DATA ANALYSIS. AN APPROACH TO BUSINESS STATISTICS AND ECONOMETRICS

Andreea Dumitrache and Alexandra Nastu
Additional contact information
Andreea Dumitrache: Bucharest University of Economic Studies
Alexandra Nastu: Bucharest University of Economic Studies

Network Intelligence Studies, 2019, issue 13, 61-70

Abstract: Data can be analysed by using a wide range of techniques. The present article focuses on the use of the basic approaches to statistics and econometrics (descriptive analysis, distribution analysis, logistic regression and tests for model validation) in order to establish a correlation between the individual returns of the securities and a macroeconomic factor. The novelty of the paper consists in designing well-defined steps according to objective criteria of the financial market when evaluating financial assets. Thus, a unifactorial model consisting of several data science techniques is used, which assumes that the profitability of any financial title is in a linear relationship with a macroeconomic variable.The study is based on Apple and market portfolio data series and the results show that there is a strong positive dependence between them.

Keywords: Statistics; Time series; Stationarity; Normality; Linearity test; Data Science (search for similar items in EconPapers)
JEL-codes: H20 H50 (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://seaopenresearch.eu/Journals/articles/NIS_13_7.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cmj:networ:y:2019:i:13:p:61-70

Access Statistics for this article

Network Intelligence Studies is currently edited by Romanian Foundation for Business Intelligence

More articles in Network Intelligence Studies from Romanian Foundation for Business Intelligence, Editorial Department
Bibliographic data for series maintained by Serghie Dan ().