Anomalies, which are incompatible with the efficient market hypothesis and mean a deviation from normality, have attracted the attention of both financial investors and researchers. A salient research topic is the existence of anomalies in cryptocurrencies, which have a different financial structure from that of traditional financial markets. This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market, which is hard to predict. It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods. An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies. On October 6, 2021, Bitcoin (BTC), Ethereum (ETH), and Cardano (ADA), which are the top three cryptocurrencies in terms of market value, were selected for this study. The data for the analysis, consisting of the daily closing prices for BTC, ETH, and ADA, were obtained from the Coinmarket.com website from January 1, 2018 to May 31, 2022. The effectiveness of the established models was tested with mean squared error, root mean squared error, mean absolute error, and Theil’s U1, and $${R}_{OOS}^{2}$$ R OOS 2 was used for out-of-sample. The Diebold–Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models. When the models created with feedforward artificial neural networks are examined, the existence of the day-of-the-week anomaly is established for BTC, but no day-of-the-week anomaly for ETH and ADA was found.

Turkey has benefitted from financial assistance of the EU in order to enhance the institutional capacity and the quality of legislation in different areas since 2001. It is aimed that Turkey could integrate easily to common policies when she became a full member of the EU via projects funded by the EU. In this context, financial assistance is distributed to both public and private sectors and also non-governmental organizations via Central Finance and Contracts Unit (CFCU), National Agency, Agriculture and Rural Development Support Institution (ARDSI), and Ministries. At least 50% or all of the funds in some projects have been taken from the EU. After recognition of Turkey as a candidate country by the EU in December 1999, accession negotiations started between Turkey and the EU in October 2005. Therefore, the study covers the period of 2000-2015. The method of STEEPLED Analysis was used in the study. EU grant projects implemented in the last 15 years were investigated various point of view (Social, Technological, Economics, Environmental, Politics, Legal, European and Demographic) and in the light of the findings, contribution of the EU grant schemes to the local/regional development, employment, environmental conservation and reduction of poverty were determined.

Major scale projects have been implemented in Turkey in the context of FYDPs to reduce interregional disparities. Funds for these projects were provided from either WB and EU or Turkey’s internal resources. A new application was started in 2006 as parallel to the establishment of Development Agencies in the context of harmonisation process to the EU. There are 26 Development Agencies in Turkey. In this study, KUZKA was selected as research area. Secondary data (call for projects, reports, data of the Ministry of Development, statistics, etc) were used and grants distributed by the KUZKA were examined via PESTLE Analysis. In substance, effects of grants were handled point schemes of politics, economics, social, technological, legal and environmental point of view and also contribution of the KUZKA to regional development in Turkey have been determined.