A New Approach to Forecasting Exchange Rates

Building on purchasing power parity theory, this paper proposes a new approach to forecasting exchange rates using the Big Mac data from The Economist magazine. Our approach is attractive in three aspects. Firstly, it uses easily-available Big Mac prices as input. These prices avoid several serious problems associated with broad price indexes, such as the CPI, that are used in conventional PPP studies. Secondly, this approach provides real-time exchange-rate forecasts at any forecast horizon. Such real-time forecasts can be made on a day-to-day basis if required, so that the forecasts are based on the most up-to-date information set. These high-frequency forecasts could be particularly appealing to decision makers who want up-to-date forecasts of exchange rates. Finally, as our forecasts are obtained through Monte Carlo simulation, estimation uncertainty is made explicit in our framework which provides the entire distribution of exchange rates, not just a single point estimate. A comparison of our forecasts with the random walk model shows that although the random walk is superior for very short horizons, our approach tends to dominate over the medium to longer term.Exchange-rate forecasting, Bic Mac prices, purchasing power parity, Monte Carlo simulation

Short and long-term forecasting using artificial neural networks for stock prices in Palestine: a comparative study

To compare the forecast accuracy, Artificial Neural Networks, Autoregressive Integrated Moving Average and regression models were fit with training data sets and then used to forecast prices in a test set. Three different measures of accuracy were computed: Root Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error. To determine how the accuracy depends on sample size, models were compared between daily, monthly and quarterly time series of stock closing prices from Palestine

Challenging the Efficient Market Hypothesis with Dynamically Trained Artificial Neural Networks

A review of the literature applying Multilayer Perceptron (MLP) based Artificial Neural Networks (ANNs) to market forecasting leads to three observations: 1) It is clear that simple ANNs, like other nonlinear machine learning techniques, are capable of approximating general market trends 2) It is not clear to what extent such forecasted trends are reliably exploitable in terms of profits obtained via trading activity 3) Most research with ANNs reporting profitable trading activity relies on ANN models trained over one fixed interval which is then tested on a separate out-of-sample fixed interval, and it is not clear to what extent these results may generalize to other out-of-sample periods. Very little research has tested the profitability of ANN models over multiple out-of-sample periods, and the author knows of no pure ANN (non-hybrid) systems that do so while being dynamically retrained on new data. This thesis tests the capacity of MLP type ANNs to reliably generate profitable trading signals over rolling training and testing periods. Traditional error statistics serve as descriptive rather than performance measures in this research, as they are of limited use for assessing a system’s ability to consistently produce above-market returns. Performance is measured for the ANN system by the average returns accumulated over multiple runs over multiple periods, and these averages are compared with the traditional buy-and-hold returns for the same periods. In some cases, our models were able to produce above-market returns over many years. These returns, however, proved to be highly sensitive to variability in the training, validation and testing datasets as well as to the market dynamics at play during initial deployment. We argue that credible challenges to the Efficient Market Hypothesis (EMH) by machine learning techniques must demonstrate that returns produced by their models are not similarly susceptible to such variability

AN EMPIRICAL STUDY OF SIMPLE SUM AND DIVISIA MONETARY AGGREGATION: A COMPARISON OF THEIR PREDICTIVE POWER REGARDING PRICES AND OUTPUT IN TURKEY

If there is one thing economists agree on, probably it is that inflation is a monetary phenomenon. Money also is thought to be related to the output level of the economy. The consensus among economists, however, does not go any further, and views differ on the characteristics of the relationships between money and the other sectors of the economy. It is not only these relationships, but also the definition of money at macroeconomic level is controversial. There are different proposals on how to define and measure money. Among them are the traditionally used simple sum money and Divisia money proposed by W. Barnett. This dissertation makes an attempt to test which definition of money works better when facing the real world situations. Without going far into theoretical details, yet trying to be as rigorous as possible in applying the employed techniques, we use several models and methods to compare the performances of simple sum and Divisia aggregates in predicting Turkish inflation and output growth in last two decades both in-and out-of-sample. We used all time series approaches that allow us to incorporate money as explanatory variables. We also add an additional approach, neural networks, to these as an alternative forecasting tool. Based on our results, we confidently conclude that money provides a good amount of information in predicting inflation and output in Turkey. Divisia aggregates have superior information content in predicting output, real or nominal. In forecasting inflation, we make a distinction between high- and low-inflation environments. In high-inflation state, money appears to be more and directly related to the determination prices, while in low-inflation environment the link between money and prices get looser and more indirect. In high-inflation periods, Divisia aggregates clearly provide better information than simple sum aggregates. In low-inflation periods, on the other hand, simple sum aggregates are better predictor of inflation

Short and long-term forecasting using artificial neural networks for stock prices in Palestine: a comparative study

To compare the forecast accuracy, Artificial Neural Networks, Autoregressive Integrated Moving Average and regression models were fit with training data sets and then used to forecast prices in a test set. Three different measures of accuracy were computed: Root Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error. To determine how the accuracy depends on sample size, models were compared between daily, monthly and quarterly time series of stock closing prices from Palestine

Wavelet multiresolution analysis of financial time series

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Exchange rate forecasting: an application of radial basis function neural networks

The purpose of this research is to investigate the forecasting performance of Artificial Neural Network models applied to foreign exchange rates. The study concentrates on the behavior of forecasts of exchange rates generated from the radial basis function (RBF) network models where little previous work exists;Exchange rates examined are the German mark/US dollar, Japanese yen/US dollar, and Italian lira/US dollar. One-step-ahead forecasts from univariate and multivariate RBF models are compared with those generated from ARIMA models, random walk forecasts and the forward rates. Interest rates and the money supply (M1) are used as explanatory variables in the multivariate analyses;Out-of-sample evaluation criteria include root mean squared error, correct direction , and speculative direction . Hypothesis tests are used to assess if differences in forecast accuracy from different models are significant and to assess if models can predict the direction of change with statistical significance. The tests employed are the Modified Diebold Marino test [Harvey et al. (1997)], the Pesaran-Timmerman (1992, 1994) non-parametric market timing test, and the chi2 test of independence [see Swanson and White (1997)];The main results of the study indicate that RBF models may be a useful alternative to the other models considered for forecasting exchange rates. In particular, out-of-sample forecasting results indicate that some multivariate RBF models using interest rates as economic variables do have forecasting value for some exchange rates. In the presence of interest rates, the M1 variable does not seem to possess much explanatory power for forecasting the three exchange rates

Baltic Dry Index Estimation With NARX Neural Network Model

BDI is a global trade indicator followed by those interested in maritime trade. But it has volatility, seasonality, and uncertain cyclicality. For this reason, in this study, the BDI has been estimated to provide preliminary information to those interested in maritime trade. NARX Neural Network which performs successfully in complex and nonlinear real-life problems is used. In addition, the NARX neural network model has not been found in a previous study used for BDI estimation. Eleven independent variables are used in this study, what increases the predictive power. Independent variables are Bloomberg Commodities Index (BCOM), Twitter-Based Economic Uncertainty Index (TEU), Twitter-Based Market Uncertainty Index (TMU), S&P 500 Index, MSCI World Index, €/$ Parity, VIX (CBOE), US 10-Year Bond Yield (%), Brent Oil (USD/Barrel), Economic Uncertainty Index and World Trade Volume (USD Billion). The Twitter-Based Economic Uncertainty Index (TEU) and Twitter-Based Market Uncertainty Index (TMU), which were not used before in BDI estimation studies, were included in the analysis and contributed to the literature. The data set contains daily data for the period 9.07.2012–31.08.2020. 11-day estimate values covering 1.09.2020–15.09.2020 are calculated. MAPE, MAE and RMSE performance criteria were calculated for the estimation values. Value of MAPE (2.96%), value of MAE (36.6%) and value of RMSE (46.68) were obtained. As a result, the estimate values were compared with the actual values