Foreign exchange market

About: Foreign exchange market is a research topic. Over the lifetime, 6661 publications have been published within this topic receiving 153384 citations. The topic is also known as: forex & FX.

Evaluating machine learning classification for financial trading

Abstract: Low-complexity machine learning models are used trade in the FOREX market.A six year trading simulation in USDJPY, EURGPB and EURUSD are assessed.Periodic retraining, number of attributes and retraining set size are varied and studied.Middle range accuracies are obtained with high financial returns in the long term. Technical and quantitative analysis in financial trading use mathematical and statistical tools to help investors decide on the optimum moment to initiate and close orders. While these traditional approaches have served their purpose to some extent, new techniques arising from the field of computational intelligence such as machine learning and data mining have emerged to analyse financial information. While the main financial engineering research has focused on complex computational models such as Neural Networks and Support Vector Machines, there are also simpler models that have demonstrated their usefulness in applications other than financial trading, and are worth considering to determine their advantages and inherent limitations when used as trading analysis tools. This paper analyses the role of simple machine learning models to achieve profitable trading through a series of trading simulations in the FOREX market. It assesses the performance of the models and how particular setups of the models produce systematic and consistent predictions for profitable trading. Due to the inherent complexities of financial time series the role of attribute selection, periodic retraining and training set size are discussed in order to obtain a combination of those parameters not only capable of generating positive cumulative returns for each one of the machine learning models but also to demonstrate how simple algorithms traditionally precluded from financial forecasting for trading applications presents similar performances as their more complex counterparts. The paper discusses how a combination of attributes in addition to technical indicators that has been used as inputs of the machine learning-based predictors such as price related features, seasonality features and lagged values used in classical time series analysis are used to enhance the classification capabilities that impacts directly into the final profitability.

The Microstructure of the Foreign-Exchange Market: A Selective Survey of the Literature

Abstract: though the Section sponsors the Studies, the authors are free to develop their topics as they wish. The Section welcomes the submission of manuscripts for publication in this and its other series. Please see the Notice to Contributors at the back of this Study. The microstructure of the foreign-exchange market: a selective survey of the literature All rights reserved. Except for brief quotations embodied in critical articles and reviews, no part of this publication may be reproduced in any form or by any means, including photocopy, without written permission from the publisher.

A Scapegoat Model of Exchange Rate Fluctuations

Abstract: While empirical evidence finds only a weak relationship between nominal exchange rates and macroeconomic fundamentals, forex markets participants often attribute exchange rate movements to a macroeconomic variable. The variables that matter, however, appear to change over time and some variable is typically taken as a scapegoat. For example, the current dollar weakness appears to be caused almost exclusively by the large current account deficit, while its previous strength was explained mainly by growth differentials. In this paper, we propose an explanation of this phenomenon in a simple monetary model of the exchange rate with noisy rational expectations, where investors have heterogeneous information on some structural parameter of the economy. In this context, there may be rational confusion about the true source of exchange rate fluctuations, so that if an unobservable variable affects the exchange rate, investors may attribute this movement to some current macroeconomic fundamental. We show that this effect applies only to variables with large imbalances. The model thus implies that the impact of macroeconomic variables on the exchange rate changes over time.

The Pricing of Forward Contracts for Foreign Exchange

Abstract: This paper investigates the nature of observed deviations from the unbiased expectations hypothesis in the forward foreign exchange market If these deviations are due to risk premia then the same premia should be observed in nominal bonds denominated in different currencies This condition imposes testable restrictions on the parameters of a multivariate regression model The empirical results are consistent with a world in which time-varying risk premia cause the observed deviations from unbiased expectations