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This text explains the methods and aspects of exchange rate forecasting, including purchasing power, parity, interest rate differentials and technical analysis. Guidelines for reducing risk with forecasting strategies are included, as are techniques for co
This book focuses on forecasting foreign exchange rates via artificial neural networks (ANNs), creating and applying the highly useful computational techniques of Artificial Neural Networks (ANNs) to foreign-exchange rate forecasting. The result is an up-to-date review of the most recent research developments in forecasting foreign exchange rates coupled with a highly useful methodological approach to predicting rate changes in foreign currency exchanges.
Models and Strategies for Exchange Rate ForecastingMichael R. RosenbergGetting an accurate exchange rate is critical for any company doing business in today's global economy. Exchange Rate Determination--written by the number one-ranked foreign exchange team in the world--examines the methods used to accurately and profitably forecast foreign exchange rates. This hands-on guidebook uses extensive charts and tables to examine currency option markets, productivity trends and exchange rates; technical analysis methods to improve currency forecasting accuracy; and more.
Currency Strategy, Second Edition develops new techniques and explains classic tools available for predicting, managing, and optimizing fluctuations in the currency markets. Author Callum Henderson shows readers ho to use mathematical models to assist in the prediction of crises and gives practical advice on how to use these and other tools successfully. Given there such huge focus on China at the moment, the timing of this new edition is particularly important. The new edition will feature a thorough update on the key developments in the past 3 years, new chapters on emerging markets, an in-depth review of the markets of China and India and their currencies and much more.
Forecasting exchange rates is a variable that preoccupies economists, businesses and governments, being more critical to more people than any other variable. In Exchange Rate Forecasting the author sets out to provide a concise survey of the techniques of forecasting - bringing together the various forecasting methods and applying them to the exchange rate in a highly accessible and readable manner. Highly practical in approach, the book provides an understanding of the techniques of forecasting with an emphasis on its applications and use in business decision-making, such as hedging, speculation, investment, financing and capital budgeting. In addition, the author also considers recent developments in the field, notably neural networks and chaos, again, with easy-to-understand explanations of these "rocket science" areas. The practical approach to forecasting is also reflected in the number of examples that pepper the text, whilst descriptions of some of the software packages that are used in practice to generate forecasts are also provided.
This paper examines the dynamics of the foreign exchange market. The first half addresses a number of key questions regarding the forecasts of future exchange rates made by market participants, by means of updated estimates using survey data. Here we follow most of the theoretical and empirical literature in acting as if all market participants share the same expectation. The second half then addresses the possibility of heterogeneous expectations, particularly the distinction between “chartists” and “fundamentalists,” and the implications for trading in the foreign exchange market and for the formation of speculative bubbles.
Historical and recent developments at international ?nancial markets show that it is easy to loose money, while it is dif?cult to predict future developments and op- mize decision-making towards maximizing returns and minimizing risk. One of the reasons of our inability to make reliable predictions and to make optimal decisions is the growing complexity of the global economy. This is especially true for the f- eign exchange market (FX market) which is considered as one of the largest and most liquid ?nancial markets. Its grade of ef?ciencyand its complexityis one of the starting points of this volume. From the high complexity of the FX market, Christian Ullrich deduces the - cessity to use tools from machine learning and arti?cial intelligence, e.g., support vector machines, and to combine such methods with sophisticated ?nancial mod- ing techniques. The suitability of this combination of ideas is demonstrated by an empirical study and by simulation. I am pleased to introduce this book to its - dience, hoping that it will provide the reader with interesting ideas to support the understanding of FX markets and to help to improve risk management in dif?cult times. Moreover, I hope that its publication will stimulate further research to contribute to the solution of the many open questions in this area.
There are several books on exchange rate modeling, none with an empirical and practical focus. In Exchange Rate Modeling and Forecasting the author presents the main empirical developments in the exchange rate modelling literature from a practitioner's perspective.
The recent financial crisis has troubled the US, Europe, and beyond, and is indicative of the integrated world in which we live. Today, transactions take place with the use of foreign currencies, and their values affect the nations' economies and their citizens' welfare. Exchange Rates and International Financial Economics provides readers with the historic, theoretical, and practical knowledge of these relative prices among currencies. While much of the previous work on the topic has been simply descriptive or theoretical, Kallianiotis gives a unique and intimate understanding of international exchange rates and their place in an increasingly globalized world.
R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. - Presents an introduction into using R for data mining applications, covering most popular data mining techniques - Provides code examples and data so that readers can easily learn the techniques - Features case studies in real-world applications to help readers apply the techniques in their work