A time series data contains a large amount of information in itself. Chaos data and volatility data which calculated by any time series are also derivative information included in the same time series. According to these assumptions, it is very important to question the ability of chaos and volatility information to affect each other, and which information affects and which information is affected. It is very important to determine the causes of volatility, which is an important result indicator for the finance literature, and especially with this study, it was tried to determine whether the chaos data is in a causal relationship with volatility.If some of the chaos data can be identified as the cause of volatility, the detected chaos data can be used in other research as a leading indicator of volatility. The data set used in the study is the daily €/$ exchange rate index between 01.01.2005 and 10.11.2022. In the study, time series of chaos data were created with Windowed RQA method and Hatemi-J asymmetric causality analysis research was carried out between these time series and €/$ exchange rate index volatility. The findings of the study conclude that the chaos data LnRR, LnEntr and LnLAM could be used as leading indicators of the €/$ exchange rate index volatility.
The aim of this study is to try to identify the presence of a relationship between index results of the Altman Z'' Score and MFA Score Models and the market values of the firms and to determine which model is more effective among these models. In a comparison of the two models, which is the study subject, the service sector was specially chosen. The main reason for preferring the service sector is that Altman Z'' Score Model was formed by firstly modifying the original Altman Z'' Score Model for the firms in the USA (United States of America) Service Sector. However, later, it was identified and recommended that this model was also valid for the firms of developing countries, MFA Score Model is a model developed specifically for Turkey. It was desired to identify that it can be measured not only the financial failures of the firms of interest but also their possible achievements in the future and to compare both models. Thus, for middle and long-term investors, investment support information based on more scientific fundamentals will be introduced. In addition, a dataset which will support the decision processes of in-firm stakeholders other than investors will be reached. In order to be able to reach the aims of interest, BIST in the service sector was used in the study. In the study, panel time series co-integration data were used and, as a result, it was understood that Altman Z'' –Score Model made an effect of 36.3% to the firm value for a lagging of one period, while MFA Model made an effect of 51.9% to the firm value for a lagging of one period. According to this, it was identified that MFA model data were more effective in the prediction of firm value.
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