Forecasting directional changes in the FX markets

Most of existing studies sample markets' prices as time series when developing models to predict market's trend. Directional Changes (DC) is an approach to summarize market prices other than time series. DC marks the market as downtrend or uptrend based on the magnitude of prices changes. In this paper we address the problem of forecasting trend's direction in the foreign exchange (FX) market under the DC framework. In particularly we aim to answer the question of whether the current trend will continue for a specific percentage before the trend ends. We propose one single independent variable to make the forecast. We assess the accuracy of our approach using three currency pairs in the FX market; namely EUR/CHF, GBP/CHF, and USD/JPY. The experimental results show that the accuracy of the proposed forecasting model is very good; in some cases, forecasting accuracy was over 80%. However, under particular settings the accuracy may not outperform dummy prediction. The results confirm that directional changes are predictable, and the identified independent variable is useful for forecasting under the DC framework

Forecasting Foreign Exchange Rates with the use of Artificial Neural Networks/Learning Machines and comparison with Traditional Concepts and Linear Models

2014 dissertation for MSc in Finance & Risk. Selected by academic staff as a good example of a masters level dissertation. The prediction of Foreign Exchange has been an ever-going learning process. The development of methods of prediction has come a long way, from the beginning where the though there was no ability to predict the future, and behavior is an unpredictable entity to the development of simple statistical linear models that has come a long way to todays technology world where computers and their computational powers have made it possible for Artificial Intelligence to be born. This paper will be going through previous studies on these Neural Networks to forecast the EUR/USD, GBP/USD and USD/JPY to test and review their ability to forecast one day ahead

The Forecast of Exchange Rates using Artificial Neural Networks, and their comparison to classic models

2014 dissertation for MSc in Financial Management. Selected by academic staff as a good example of a masters level dissertation. Predicting Foreign Exchange rates has forever been a task of great importance to any individual, business or organization having to deal with a foreign currency. In the wake of a world where global transactions are an everyday activity, readiness and skill when dealing with the forecasting of international monetary movements is a key factor in the success of any operation; be it that of an individual investor, or that of multi-national index listed company. The motivation behind the desire of conquering the skill of forecasting may range from the simple desire to hedge one‟s investments and dealings in a foreign currency, to that of a speculative investor, looking for arbitrage opportunities in trading foreign exchange markets. This paper had for motivation to test and compare various models in their ability to forecast the return generated by price movements of three globally available and traded currencies; notable the Euro – US Dollar, the Euro-Swiss Franc and the Pound Sterling – US Dollar. Recent studies have been showing great promise in the use of Artificial Neural Networks in the field of forecasting exchange traded assets and currencies; which is why this paper has discussed the performance of 4 Learning Machine models in comparison to 3 base models and 2 linear models. The learning machine models being studied are the Multi-Layer Perceptron, the Higher Order Neural Network, Gene Expression and Rolling Genetic-Support Vector Regression. These models were compared using various methods of statistical evaluation, in order to measure the discrepancy of the forecasted values from the actual values, as well as the annualized return and the risk to return ratio. It was concluded that modern forecasting technique do outweigh the classic base and linear models in terms of forecasting accuracy as well as potential gain and risk to return

Forecasting foreign exchange rates using Support Vector Regression

Support Vector Regression (SVR) algorithms have received increasing interest in forecasting, promising nonlinear, non-parametric and data driven regression capabilities for time series prediction. But despite evidence on the nonlinear properties of foreign exchange markets, applications of SVR in price or return forecasting have demonstrated only mixed results. However, prior studies were limited to using only autoregressive time series inputs to SVR. This paper evaluates the efficacy of SVR to predict the Euro-US Dollar exchange rate using input vectors enhanced with explanatory variables on mean-reversion movements derived from Bollinger Bands technical indicators. Using a rigorous empirical out-of-sample evaluation of multiple rolling forecast origins, we assess the accuracy of different SVR input vectors, including upper and lower BB, binary trading signals of BB, and combinations of the above. As a result, a local SVR model using autoregressive lags in conjunction with BB bands and BB indicators, and recalibrated yearly, outperforms the random walk on directional and all other error metrics, showing some promise for an SVR application

Developing trading strategies under the Directional Changes framework, with application in the FX Market

Directional Changes (DC) is a framework for studying price movements. Many studies have reported that the DC framework is useful in analysing financial markets. Other studies have suggested that, theoretically, a trading strategy that exploits the full promise of the DC framework could be astonishingly profitable. However, such a strategy is yet to be discovered. In this thesis, we explore, and consequently provide proof of, the usefulness of the DC framework as the basis of a profitable trading strategy. Existing trading strategies can be categorised into two groups: the first comprising those that rely on forecasting models; the second comprising all other strategies. In line with existing research, this thesis develops two trading strategies: the first relies on forecasting Directional Changes in order to decide when to trade; whereas the second strategy, whilst based on the DC framework, uses no forecasting models at all. This thesis comprises three original research elements: 1. We formalize the problem of forecasting the change of a trend’s direction under the DC framework. We propose a solution for the defined forecasting problem. Our solution includes discovering a novel indicator, which is based on the DC framework. 2. We develop the first trading strategy that relies on the forecasting approach established above (Point 1) to decide when to trade. 3. We develop a second trading strategy which does not rely on any forecasting model. This is trading strategy employs a DC-based procedure to examine historical prices in order to discover profitable trading rules. We examine the performance of these two trading strategies in the foreign exchange market. The results indicate that both can be profitable and that both outperform other DC-based trading strategies. The results additionally suggest that none of these two trading strategies outperforms the other in terms of profitability and risk simultaneously