Ιn this dissertation I forecast financial time series with machine learning methodologies.During my research I propose various novel forecasting schemes and attack four problems in a machine learning approach: short and long-term exchange rate, housing prices and bank insolvencies forecasting. More specifically, I propose a novel forecasting methodology in short-term exchange rate forecasting that couples a machine learning with a signal processing technique. In the same field I consider machine learning in long-term forecasting, that has rarely been used before in the relevant literature. The machine learning models outperform all the econometric models examined in this dissertation in terms of forecasting error and directional forecasting accuracy Overall, the empirical findings reveal the superiority of machine learning to econometric models in forecasting the selected financial time series examined in this dissertation.

This volume presents new methods and applications in longitudinal data estimation methodology in applied economic. Featuring selected papers from the 2020 the International Conference on Applied Economics (ICOAE 2020) held virtually due to the corona virus pandemic, this book examines interdisciplinary topics such as financial economics, international economics, agricultural economics, marketing and management. Country specific case studies are also featured.

This booklet discusses some major methodological issues relating to the construction of house price models on a macro level. There is no single method that always produces the optimal results; the choice of a particular approach, method, theory, model and technique is context-dependent. This is especially true in housing markets, where a multitude of different submarkets exist. The methodology chosen should be based on sound theory, from which the basic concepts of analysis can be derived. This booklet discusses the use of potential models, which can be constructed using a general field theory, and which act as a theoretical foundation for further analysis. If we use potential models for house price analysis we can discover additional features from the data set that other approaches would simply miss. This e-book presents a pragmatic overview of key methodological concerns with the emphasis on the use of potential models. Theoretical methodological questions are left unanswered, and are not even presented in this text, since they have little relevancy to real-world modelling questions.

The tremendous rise in house prices over the last decade has been both a national and a global phenomenon. The growth of secondary mortgage holdings and the increased impact of house prices on consumption and other components of economic activity imply ever-greater importance for accurate forecasts of home price changes. Given the boom-bust nature of housing markets, nonlinear techniques seem intuitively very well suited to forecasting prices, and better, for volatile markets, than linear models which impose symmetry of adjustment in both rising and falling price periods. Accordingly, Crawford and Fratantoni (2003) apply a Markov-Switching model to U.S. home prices, and compare the performance with ARMA and GARCH models. While the switching model shows great promise with excellent in-sample fit, its out-of-sample forecasts are generally inferior to more standard forecasting techniques. Since these results were published, some researchers have discovered that the Markov-Switching model is particularly ill-suited for forecasting. We thus consider other non-linear models besides the Markov Switching, and after evaluating alternatives, employ the Generalized Autoregressive (GAR) model. We find the GAR does a better job at out-of-sample forecasting than ARMA and GARCH models in many cases, especially in those markets traditionally associated with high home-price volatility.

Existing literature on housing prices is predominantly in a linear framework, and an important question that has not been addressed is whether housing prices exhibit non-linearity. We examine Smooth Transition Autoregressive (STAR) model based nonlinear properties of housing prices over the 1969-2004 period for the entire US and the four regions. Our main findings are (i) housing price for the entire US and all regions except for the Midwest show non-linearity; (ii) the dynamic properties implied by the nonlinear estimation explain the typical patterns that have characterized each housing market; and (iii) results of Granger causality tests look more plausible in the nonlinear framework where we find stronger evidence of Granger causality from housing price to employment and also from mortgage rates to housing price.

This proceedings volume analyzes the impact of globalization on international financial flow as well as harmonized financial reporting. Featuring contributions presented at the 18th Annual Conference on Finance and Accounting held at the University of Economics in Prague, this book examines the economic consequences of the globalized world in the sphere of corporate and public finance, monetary systems, banking, financial reporting and management accounting. The global perspective is accompanied by local specific cases studies, including those from emerging markets. In addition, the combination of micro- and macroeconomic approaches provide insights on the behavior of all relevant stakeholders in the process and the results of dynamic pressures surrounding global capital markets and international investments. This book will serve as a useful resource for scholars and researchers, practitioners and policy makers in the fields of finance, economics and accounting.