25:T53f,Cryptocurrencies, such as Bitcoin, are one of the most controversial and complex technological innovations in today's financial system. This study aims to forecast the movements of Bitcoin prices at a high degree of accuracy. To this aim, four different Machine Learning (ML) algorithms are applied, namely, the Support Vector Machines (SVM), the Artificial Neural Network (ANN), the Naï ve Bayes (NB) and the Random Forest (RF) besides the logistic regression (LR) as a benchmark model. In order to test these algorithms, besides existing continuous dataset, discrete dataset was also created and used. For the evaluations of algorithm performances, the F statistic, accuracy statistic, the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE) and the Root Absolute Error (RAE) metrics were used. The t test was used to compare the performances of the SVM, ANN, NB and RF with the performance of the LR. Empirical findings reveal that, while the RF has the highest forecasting performance in the continuous dataset, the NB has the lowest. On the other hand, while the ANN has the highest and the NB the lowest performance in the discrete dataset. Furthermore, the discrete dataset improves the overall forecasting performance in all algorithms (models) estimated.26:T518,With this study, we try to explore new insights about the existence of an asymmetric relationship between financial innovation, banking sector development, and economic growth of Asian countries for the period of 1974Q1 to 2016Q4. In this study, we apply Autoregressive Distributed Lag (ARDL) bound testing to capture long-run association and nonlinear Autoregressive Distributed Lagged (NARDL) to capture the existence of an asymmetric relationship between studied variables. ARDL-bound testing confirms long-run cointegration and nonlinear ARDL confirms the existence of an asymmetric relationship between financial innovation, banking sector development and economic growth of Asian countries. For a directional relationship, we perform Granger-causality under error correction model. Study support feedback hypothesis between financial innovation and economic growth and banking sector development and economic growth both in short and long run. The study suggests, the government should encourage financial innovation in the financial system through technological advancement and institutional integration and formulated economic policy favor of banking sector development by allowing institutional development, enterprise risk management and encouraging healthy competition in the financial system.27:T40a, Based on the Environmental Kuznets's Curve theory, this study seeks to investigate the asymmetric relationship between the financial globalization uncertainty and the environmental quality alongside the test of Kuznets's hypothesis. The research covers the data set of nine Sub Saharan African countries from 1980-2019. The Kuznets's hypothesis of the relationship between economic growth and the environment quality has been validated, identifying the pivotal point of the relationship's transition. The results further reveal that the positive shock of FGU is inversely related to CO₂ emissions, implying that as the foreign capital flow increases, the accompanying CO₂ emissions decreases. Whereas, the negative decomposed component of the financial globalization uncertainty indicates a negative and significance coefficient. While, renewable energy reduces the deterioration of the environmental quality. In the last part of the study, policy implications are recommended accordingly. 28:T477,The rapid development of VFAs allows investors to diversify their choices of investment products. In this paper, we measure the return risk of VFAs based on GARCH-type model. By establishing a Markov regime-switching Regression (MSR) Model, we explore the asymmetric effects of speculation, investor attention, and market interoperability on return risks in different risk regimes of

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