G14 - Information and Market Efficiency; Event Studies; Insider TradingReturn

Using the big data of news texts and a novel, news extended exchange rate model, we investigate the impact of media news on major exchange rates. To present the impact of the U.S. Dollar related news on EUR/USD and GBP/USD, we first use a machine learning model and detect which news topics relate to U.S. Dollar. Next, we calculate the attention to the U.S. Dollar related news topics over time. Eventually, we visualize how Exchange rates react to shocks in the attention to the U.S. Dollar related news topics. The impulse response functions of U.S. Dollar bilateral rates show that exchange rates respond to the U.S. Dollar related news and to the economic uncertainty news shocks with statistical significance in several periods after the shock. Forecast error decomposition documents that 25-27% of exchange rate variation in the long run comes from the news. The results reveal, that news add valuable information to macroeconomic fundamentals for identifying exchange rates, and exchange rates are better identified when both, macroeconomic and news information are used together. These findings are important for exchange rate modeling.

Three different classes of data mining methods (k-Nearest Neighbour, Ridge Regression and Multilayer Perceptron Feed-Forward Neural Networks) are applied for the purpose of quantitative trading on 10 simulated time series, as well as real world time series of 10 currency exchange rates ranging from 1.11.1999 to 12.6.2015. Each method is tested in multiple variants. The k-NN algorithm is applied alternatively with the Euclidian, Manhattan, Mahalanobis and Maximum distance function. The Ridge Regression is applied as Linear and Quadratic, and the Feed-Forward Neural Network is applied with either 1, 2 or 3 hidden layers. In addition to that Principal Component Analysis (PCA) is eventually applied for the dimensionality reduction of the predictor set and the meta-parameters of the methods are optimized on the validation sample. In the simulation study a Stochastic-Volatility Jump-Diffusion model, extended alternatively with 10 different non-linear conditional mean patterns, is used, to simulate the asset price behaviour to which the tested methods are applied. The results show that no single method was able to profit on all of the non-linear patterns in the simulated time series, but instead different methods worked well for different patterns. Alternatively, past price movements and past returns were used as predictors. In the case when the past price movements were used, quadratic ridge regression achieved the most robust results, followed by some of the k-NN methods. In the case when past returns were used, k-NN based methods were the most consistently profitable, followed by the linear ridge regression and quadratic ridge regression. Neural networks, while being able to profit on some of the time series, did not achieve profit on most of the others. No evidence was further found of the PCA method to improve the results of the tested methods in a systematic way. In the second part of the study, the models were applied to empirical foreign exchange rate time series. Overall the profitability of the methods was rather low, with most of them ending with a loss on most of the currencies. The most profitable currency was EURUSD, followed by EURJPY, GBPJPY and EURGBP. The most successful methods were the linear ridge regression and the Manhattan distance based k-NN method which both ended with profits for most of the time series (unlike the other methods). Finally, a forward selection procedure using the linear ridge regression was applied to extend the original predictor set with some technical indicators. The selection procedure achieved limited success in improving the out-sample results for the linear ridge regression model but not the other models.