A case study on using neural networks to perform technical forecasting of forex

Abstract

This paper reports empirical evidence that a neural network model is applicable to the prediction of foreign exchange rates. Time series data and technical indicators, such as moving average, are fed to neural networks to capture the underlying`rulesa of the movement in currency exchange rates. The exchange rates between American Dollar and "ve other major currencies, Japanese Yen, Deutsch Mark, British Pound, Swiss Franc and Australian Dollar are forecast by the trained neural networks. The traditional rescaled range analysis is used to test the`e$ciencya of each market before using historical data to train the neural networks. The results presented here show that without the use of extensive market data or knowledge, useful prediction can be made and signi"cant paper pro"ts can be achieved for out-of-sample data with simple technical indicators. A further research on exchange rates between Swiss Franc and American Dollar is also conducted. However, the experiments show that with e$cient market it is not easy to make pro"ts using technical indicators or time series input neural networks. This article also discusses several issues on the frequency of sampling, choice of network architecture, forecasting periods, and measures for evaluating the model's predictive power. After presenting the experimental results, a discussion on future research concludes the paper.

A neural network approach to predicting stock exchange movements using external factors

“…While this shows profitability of 9.93% per annum, it is not greater the market growth (13.03% per annum over the test period, as stated in Section 4.3). This is to be expected, because of the efficient nature of the Dow Jones, and is comparable with the results of Yao & Tao [1], where similar experiments were performed on the Japanese Yen foreign exchange markets.…”

“…Likewise, in the work of Yao & Tan [1], the effectiveness of a time series model based on the FOREX (foreign currency exchange market) with no external input parameters was evaluated. The paper discusses the concept of market efficiency, which is the time taken for asset prices to react to new information in the market.…”

“…While such charges vary between brokerage institutions, we assume a flat-rate charge of $8 per trade, which is typical of some reputable online trading services 1 . We do not deduct charges directly from the investment capital but assume they are accounted for separately.…”

“…This relies on past events in the time series repeating themselves to produce reliable predictions. Although machine learning studies using technical indicators, such as those of Yao & Tan [1] and Rodrígues et al [4], have claimed successful returns, the key limitation of these approaches is that such models do not capture the cause of the movements in the market.…”

Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

“…The partition ratios usually are no consensus and selected subjectively. However, the appropriate ratios will generate better forecasting accuracy such as 7:2:1 [26]. The partition ratios are not absolute according to different problems.…”