An Adaptive Retraining Method for the Exchange Rate Forecasting

The paper advances an original artificial intelligence-based mechanism for specific economic predictions. The time series under discussion are non-stationary; therefore the distribution of the time series changes over time. The algorithm establishes how a viable structure of an artificial neural network (ANN) at a previous moment of time could be retrained in an efficient manner, in order to support modifications in a complex input-output function of financial forecasting. A "remembering process" for the former knowledge achieved in the previous learning phase is used to enhance the accuracy of the predictions. The results show that the first training (which includes the searching phase for the optimal architecture) always takes a relatively long time, but then the system can be very easily retrained, as there are no changes in the structure. The advantage of the retraining procedure is that some relevant aspects are preserved (remembered) not only from the immediate previous training phase, but also from the previous but one phase, and so on. A kind of slow forgetting process also occurs; thus it is much easier for the ANN to remember specific aspects of the previous training instead of the first training. The experiments reveal the high importance of the retraining phase as an upgrading/updating process and the effect of ignoring it, as well. There has been a decrease in the test error when successive retraining phases were performed.Neural Networks, Exchange Rate, Adaptive Retraining, Delay Vectors, Iterative Simulation

Can social microblogging be used to forecast intraday exchange rates?

The Efficient Market Hypothesis (EMH) is widely accepted to hold true under certain assumptions. One of its implications is that the prediction of stock prices at least in the short run cannot outperform the random walk model. Yet, recently many studies stressing the psychological and social dimension of financial behavior have challenged the validity of the EMH. Towards this aim, over the last few years, internet-based communication platforms and search engines have been used to extract early indicators of social and economic trends. Here, we used Twitter's social networking platform to model and forecast the EUR/USD exchange rate in a high-frequency intradaily trading scale. Using time series and trading simulations analysis, we provide some evidence that the information provided in social microblogging platforms such as Twitter can in certain cases enhance the forecasting efficiency regarding the very short (intradaily) forex.Comment: This is a prior version of the paper published at NETNOMICS. The final publication is available at http://www.springer.com/economics/economic+theory/journal/1106

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

An application of deep learning for exchange rate forecasting

This paper examines the performance of several state-of-the-art deep learning techniques for exchange rate forecasting (deep feedforward network, convolutional network and a long short-term memory). On the one hand, the configuration of the different architectures is clearly detailed, as well as the tuning of the parameters and the regularisation techniques used to avoid overfitting. On the other hand, we design an out-of-sample forecasting experiment and evaluate the accuracy of three different deep neural networks to predict the US/UK foreign exchange rate in the days after the Brexit took effect. Of the three configurations, we obtain the best results with the deep feedforward architecture. When comparing the deep learning networks to time-series models used as a benchmark, the obtained results are highly dependent on the specific topology used in each case. Thus, although the three architectures generate more accurate predictions than the time-series models, the results vary considerably depending on the specific topology. These results hint at the potential of deep learning techniques, but they also highlight the importance of properly configuring, implementing and selecting the different topologies

Macroeconomics modelling on UK GDP growth by neural computing

This paper presents multilayer neural networks used in UK gross domestic product estimation. These networks are trained by backpropagation and genetic algorithm based methods. Different from backpropagation guided by gradients of the performance, the genetic algorithm directly evaluates the performance of multiple sets of neural networks in parallel and then uses the analysed results to breed new networks that tend to be better suited to the problems in hand. It is shown that this guided evolution leads to globally optimal networks and more accurate results, with less adjustment of the algorithm needed

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online.Comment: Accepted by SIGIR 201

Forecasting Foreign Exchange Rates Using Recurrent Neural Networks : The Role of Political Uncertainty

In June 2016, the majority of UK citizens voted to leave the EU (Brexit). The referendum outcome took both citizens and policymakers by surprise. No other member state has ever left the EU. As a result, the global stock and currency markets collapsed. The impact of uncertainty on financial markets has been studied for decades (Garfinkel, 1999). Studies show that political instability has a significant impact on economic performance. In addition to the market fluctuation, it has been found to increase the unemployment rate and decrease consumers’ and companies’ willingness to invest. Thus, prolonged political instability may lead to a scenario in which the capital moves less, the quality of public services decreases, and economic growth slows down. (Carmignani, 2003; Canes-Wrone et al., 2014). Exchange rate forecasting is an important area of financial research that has recently received more popularity due to its dynamic nonlinear features. In the past, exchange rates have been analyzed using traditional financial models. However, recently academics have started to use artificial learning approaches alongside the traditional ones. In particular, neural networks have been used in time series modeling, and thus exchange rates have been modeled with neural networks. Machine learning aims to improve efficiency and make financial forecasting more automated. The empirical part of this analysis is carried out using a recurrent neural network architecture known as the Long Short Term Memory (LSTM). The LSTM model enables the analysis of non-linear data as well as the detection of diverse cause-and-effect relations. Therefore, it is reasonable to believe that accurate results can be obtained using this approach. The results are analyzed by comparing two different error values - the Mean Squared Error and the Absolute Mean Error. The results prove that the LSTM model is capable of modeling exchange rate values even in times of high volatility. As the Brexit-related uncertainty is higher, the predictability of the Pound to Euro and Dollar decreases. This finding is consistent with previous studies that have shown that political instability reduces the predictability of exchange rates. On the contrary, as the uncertainty surrounding Brexit increased, the predictability of the Pound to Yen improved. This result can partly be explained by the Safe Haven effect, according to which the value of the Yen rises as the values of other developed countries’ currencies fall. Finally, it can be stated that exchange rates are complex financial instruments whose volatility is influenced by a variety of factors and this study is able to produce new perspectives for further research.Kesäkuussa 2016 enemmistö Iso-Britannian kansasta äänesti EU:sta eroamisen puolesta (Brexit). Kansanäänestyksen tulos yllätti niin kansalaiset kuin vallanpitäjätkin. Mikään muu jäsenvaltio ei ole aikaisemmin eronnut EU:sta. Tämän seurauksena valuutta- sekä osake-markkinat romahtivat globaalisti. Epävarmuuden vaikutusta rahoitusmarkkinoihin on tutkittu jo vuosikausien ajan (Garfinkel, 1999). Tutkimukset todistavat, että poliittisella epävakaudella on merkittävä vaikutus taloudelliseen suorituskykyyn. Rahoitusmarkkinoiden heilunnan lisäksi sen on todettu lisäävän työttömyyttä sekä vähentävän kuluttajien ja yritysten investointihalukkuutta. Täten pitkittynyt poliittinen epävakaus voi johtaa tilanteeseen, jossa pääoma liikkuu hitaammin, julkisten palvelujen laatu heikentyy sekä talouskasvu hidastuu. (Carmignani, 2003; Canes-Wrone ym., 2014). Valuuttakurssien ennustaminen on tärkeä rahoituksen tutkimusala, joka on kasvattanut suosiotaan sen haastavien ja epälineaaristen piirteiden vuoksi. Aikaisemmin valuuttakursseja on tutkittu perinteisillä rahoituksen menetelmillä, mutta lähivuosina tutkijat ovat alkaneet hyödyntämään yhä enemmän koneoppimista perinteisten mallien rinnalla. Erityisesti neuroverkkoja on hyödynnetty aikasarjojen mallintamisessa ja täten myös valuuttakursseja on mallinnettu neuroverkoilla. Koneoppimisen malleilla pyritään tekemään rahoitusmarkkinoiden ennustamisesta tehokkaampaa ja itseohjautuvampaa. Tämä tutkimus hyödyntää empiirisessä osuudessa takaisinkytketyn neuroverkon arkkitehtuuria nimeltä pitkäkestoinen lyhytkestomuisti (Long Short Term Memory, LSTM). LSTM-arkkitehtuuri mahdollistaa epälineaarisen datan analysoinnin sekä monipuolisten syy-seurausketjujen hahmottamisen. Näin ollen on perusteellista uskoa, että tällä metodilla on mahdollista saavuttaa tarkkoja tuloksia valuuttakursseja analysoitaessa. Tulosten analysointi toteutetaan vertailemalla eri valuutoilla saatavia virhearvoja (keskihajonta sekä absoluuttinen keskivirhe). Tulokset todistavat, että LSTM-malli on kykenevä mallintamaan valuuttakurssien arvoja myös epävakaina aikoina. Euron ja dollarin ennustettavuus heikentyy tutkituilla ajanjaksoilla, kun Brexitiin liittyvä epävarmuus lisääntyy. Tämä tutkimustulos on johdonmukainen aikaisemman tutkimuksen kanssa, jonka perusteella on todettu, että valuuttakurssien ennustettavuus heikentyy poliittisen epävarmuuden seurauksena. Jenin ennustettavuus taas päinvastoin paranee ajanjaksolla, kun Brexitiin liittyvä epävarmuus lisääntyy. Tämä tulos voidaan osittain perustella turvasatamailmiöllä, jonka mukaan jenin arvo nousee, kun muiden kurssien arvot laskevat. Lopuksi todetaan, että valuuttakurssit ovat monimutkaisia rahoitusinstrumentteja, joiden heilahteluun vaikuttaa useita eri tekijöitä. Tästä huolimatta, tämä työ onnistuu tarjoamaan uusia näkökulmia tulevaisuuden tutkimukselle

Forecasting Industry-Level CPI and PPI Inflation: Does Exchange Rate Pass-Through Matter?

In this paper, we examine whether industry-level forecasts of CPI and PPI inflation can be improved using the ``exchange rate pass-through" effect, that is, when one accounts for the variability of the exchange rate and import prices. An exchange rate depreciation leading to a higher level of pass-through to import prices implies greater expenditure switching, which should be manifested, possibly with a lag, in both producer and consumer prices. We build a forecasting model based on a two or three equation system involving CPI and PPI inflation where the effects of the exchange rate and import prices are taken into account. This setup also incorporates their dynamics, lagged correlations and appropriate restrictions suggested by the theory. We compare the performance of this model with a variety of unrestricted univariate and multivariate time series models, as well as with a model that, in addition, includes standard control variables for inflation, like interest rates and unemployment. Our results indicate that improvements on the forecast accuracy can be effected when one takes into account the possible pass-through effects of exchange rates and import prices on CPI and PPI inflation.Forecasting, Vector Autoregression, Non-linear Models, Inflation, Exchange Rates, Pass-Through Effect

Does money matter in inflation forecasting?.

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation

Multi-layer feed forward neural networks for foreign exchange time series forecasting

This dissertation examines the forecasting performance of multi-layer feed forward neural networks in modeling five weekly foreign exchange rates: Australian Dollars/U.S. Dollars (AUS/USD), Euro/U.S. Dollar (EUR/USD), Swiss Franc/U.S. Dollar (CHF/USD), British Pound sterling/U.S. Dollars (GBP/USD), and Japanese Yen/U.S. Dollars (JPY/USD). There are five objectives to accomplish. The first is to determine the key modeling factors that should be considered in topology determination. The second is to compare the performances of Genetic Algorithm (GA) and Modified Tabu Search (TS) in choosing the topology for Neural Networks (NN) implementation. The third is to investigate the suitable learning algorithm for NNs for time series forecasting by comparing Back Propagation (BP) with GAs and TS. The fourth is to conduct computational studies for multi-step ahead forecasting for GBP/USD and EUR/USD, as well as to study other accuracy forecasting issues. The last is to study the implementation of multivariate time series forecasting using NNs.;The results of the experiments performed indicate that one should examine the correct topology, especially the three most important factors (number of input nodes, hidden nodes, learning rate) prior to using NNs for time series forecasting.;The comparison performance of topology suggested using GA, TS, and benchmark led to the conclusion that neither GA nor TS is guaranteed to provide better results, especially in terms of percentage of true directional changes (DIR). However, if there is no prior knowledge of the problem, GA searches for topology determination are favored and provide reasonably good performances. GA is also preferred for NN training. Compared to BP, GA guarantees better performance in terms of Mean of Absolute Percentage Error (MAPE) and, most of the time, performs better in terms of Mean of Square Error (MSE).;Caution should be taken in adopting the results, since the study of time periods indicated that the best topology for forecasting a specific foreign exchange is data specific ; hence the best for a certain period is not always the best to forecast other periods. However, the chosen topology is reasonably useful for up to three steps ahead forecasting.;The trivariate time series, which incorporate interest rates of the two countries involved, did improve the results. Multivariate time series forecasts for monthly JPY/USD, as well as for monthly EUR/USD, produced a higher level of success than the one achieved in the previous experiment. The NNs were programmed using MATLABRTM