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.

Abstract. Directional Change (DC) is a new way to summarize price movements in a financial market. Unlike time series, it samples data at irregular time intervals. According to the DC concept, the data is sampled only when the magnitude of price changes is significant according to the investor. In this paper, we propose a contrarian trading strategy which is based on the DC concept. We test our trading strategy using two currency pairs; namely EUR/CHF and EUR/USD. The results show that our proposed trading strategy is consistently profitable; it produce a profit of up to 145% within seven months; whereas the buy-andhold approach incurred a loss of -14% during the same trading period.

Directional Change (DC) is a technique to summarize price movements in a financial market. According to the DC concept, data is sampled only when the magnitude of price change is significant according to the investor. In this paper, we develop a contrarian trading strategy named TSFDC. TSFDC is based on a forecasting model which aims to predict the change of the direction of market's trend under the DC context. We examine the profitability, risk and risk-adjusted return of TSFDC in the FX market using eight currency pairs. The results suggest that TSFDC outperforms the buy and hold approach and another DC-based trading strategy.

This paper addresses the problem of forecasting daily stock trends. The key consideration is to predict whether a given stock will close on uptrend tomorrow with reference to today’s closing price. We propose a forecasting model that comprises a features selection model, based on the Genetic Algorithm (GA), and Random Forest (RF) classifier. In our study, we consider four international stock indices that follow the concept of distributed lag analysis. We adopted a genetic algorithm approach to select a set of helpful features among these lags’ indices. Subsequently, we employed the Random Forest classifier, to unveil hidden relationships between stock indices and a particular stock’s trend. We tested our model by using it to predict the trends of 15 stocks. Experiments showed that our forecasting model had 80% accuracy, significantly outperforming the dummy forecast. The S&P 500 was the most useful stock index, whereas the CAC40 was the least useful in the prediction of daily stock trends. This study provides evidence of the usefulness of employing international stock indices to predict stock trends.

The Directional Changes (DC) framework is an approach to summarize price movement in financial time series. Some studies have tried to develop trading strategies based on the DC framework. Dynamic Backlash Agent (DBA) is a trading strategy that has been developed based on the DC framework. Despite the promising results of DBA, DBA employed neither an order size management nor risk management components. In this paper, we present an improved version of DBA named Intelligent DBA (IDBA). IDBA overcomes the weaknesses of DBA as it embraces an original order size management and risk management modules. We examine the performance of IDBA in the forex market. The results suggest that IDBA can provide significantly greater returns than DBA. The results also show that the IDBA outperforms another DC-based trading strategy and that it can generate annualized returns of about 30% after deducting the bid and ask spread (but not the transaction costs).

Resource discovery is a real challenge in grid systems due to the dynamicity of nodes (i.e. any node can join or leave the system at any moment). This paper proposes a new protocol for resource discovery in dynamic grid systems. The hypothesis is that a grid is composed from a set of Virtual Organization (VO). The idea is to define a Distributed Hash Tables (DHTs) for each VO. The discovery inside a VO is a traditional discovery based on DHTs. The resource discovery between Virtual Organizations, i.e. between DHTs, is achieved through a new protocol enabling a persistent communication between all the VOs. The main advantage of the proposed protocol is to enable a robust global discovery between unstable VOs of a grid (any node or even VO can leave the system at any moment). We evaluate the proposed protocol by experiments showing its feasibility and benefits.

Abstract-Representing causal relation between set of variables is a challenged objective. Causal Bayesian Networks has been classified as good modeling technique for this purpose. However structure learning for causal Bayesian networks still suffering from several problems including the causal interpretation of the model and the complexity of the learning algorithm. In this research the author presents an approach for learning causal graph based on Wiener-Granger causal-theory, with minor modifications, and use Genetic Programming to determine the parameters of Granger formula. This approach enjoys necessary advantages: reasonable complexity and cover nonlinear equation. A case study of 5 global stock markets is presented to experimentally explain and support this approach. The finding show that SP500 has Granger-causal influence on NIKKE: the accuracy of forecasting NIKKE stock market can be incremented by 24% when integrating past data from SP500. Whereas Euro STOXX 50 is reported to be the least stock Granger-causally affected by the others.