Browsing by Author "Tvaronaviciene, Manuela"

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Citation - Scopus: 2
Linkage between company scores and stock returns
(Centre of Sociological Research, 2017) Şaban Çelik; Bora Aktan; Manuela Tvaronavičienė; Pelin Bengitöz; Celik, Saban; Aktan, Bora; Bengitoz, Pelin; Tvaronaviciene, Manuela
Previous studies on company scores conducted at firm-level generally concluded that there exists a positive relation between company scores and stock returns. Motivated by these studies this study examines the relationship between company scores (Corporate Governance Score Economic Score Environmental Score and Social Score) and stock returns both at portfolio-level analysis and firm-level cross-sectional regressions. In portfolio-level analysis stocks are sorted based on each company scores and quintile portfolio are formed with different levels of company scores. Then existence and significance of raw returns and risk-adjusted returns difference between portfolios with the extreme company scores (portfolio 10 and portfolio 1) is tested. In addition firm-level cross-sectional regression is performed to examine the significance of company scores effects with control variables. While portfolio-level analysis results indicate that there is no significant relation between company scores and stock returns, firm-level analysis indicates that economic environmental and social scores have effect on stock returns however significance and direction of these effects change depending on the included control variables in the cross-sectional regression. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 9
SUSTAINABLE RISK MANAGEMENT: FUZZY APPROACH TO VOLATILITY AND APPLICATION ON FTSE 100 INDEX
(ENTERPRENEURSHIP & SUSTAINABILITY CENTER, 2014) Sinem Peker; Manuela Tvaronaviciene; Bora Aktan; Peker, Sinem; Aktan, Bora; Tvaronaviciene, Manuela
In this paper a fuzzy volatility labeling algorithm is offered to detect the periods with abnormal activities on daily share returns. Considering the vagueness in the switches of the time periods the membership functions of high and normal volatility classes are introduced. In the assignments both the density structure and membership degree are used. It is believed that this algorithm may be helpful to construct different estimation models for the time periods with normal and abnormal activities. Authors offer algorithm which can be used as a tool for sustainable risk management.