Economics

In this paper, we present a novel machine learning based forecasting system of the
EUR/USD exchange rate directional changes. Specifically, we feed an over complete
variable set to a Support Vector Machines (SVM) model and refine it through a
Sensitivity Analysis process. The dataset spans from 1/1/1999 to 30/11/2011; the data of
the last 7 months are reserved for out-of-sample testing. Results show that the proposed
scheme outperforms various other machine learning methods treating similar scenarios.

We propose an Support Vector Machine (SVM) based structural model in order to forecast the collapse of banking institutions in the U.S. using publicly disclosed information from their financial statements on a four-year rolling window. In our approach, the optimum input variable set is defined from a large dataset using an iterative relevance-based selection procedure. We train an SVM model to classify banks as solvent and insolvent. The resulting model exhibits significant ability in bank default forecasting.

The recent ceiling of U.S. federal debt and the European sovereign debt crises raised once again the interest upon balanced government budgets. The Ricardian Equivalence proposition appears as an attractive alternative for policy makers, since postponing taxes to be paid once growth is restored seems a very efficient scheme that relieves public discomfort. This paper attempts to investigate the long-run relationship between public debt and privat e consumption in order to test for the potential validity of the Ricardian equivalence proposition. We use a wide dataset of fifteen OECD countries using annual data for the period 1980–2010. For the empirical estimation we employ both a univariate timeseries and a panel cointegration approach. Our empirical findings fail to provide empirical evidence in support of the Ricardian equivalence proposition for all countries of the sample, since the assumptions proposed by theory cannot be fulfilled.

The microstructural approach to the exchange rate market claims that order flows on a currency can accurately reflect
the short-run dynamics of its exchange rate. In this paper, instead of focusing on order flows analysis we employ an alternative
microstructural approach:We focus on investors’ sentiment on a given exchange rate as a possible predictor of its future evolution.
As a proxy of investors’ sentiment we use StockTwits posts, a message board dedicated to finance. Within StockTwits investors are asked to explicitly state their market expectations. We collect daily data on the nominal exchange rate of four currencies against the U.S. dollar and the extracted market sentiment for the year 2013. Employing econometric and machine learning methodologies we develop models that forecast in out-of-sample exercise the future direction of the four exchange rates. Our empirical findings reject the Efficient Market Hypothesis even in its weak form for all four exchange rates. Overall, we find evidence that investors’ sentiment as expressed in public message boards can be an additional source of information regarding the future directional movement of the exchange rates to the ones proposed by economic theory.

The 2006 sudden and immense downturn inU.S. house prices sparked the 2007 global financial crisis and revived
the interest about forecasting such imminent threats for economic stability. In this paperwe propose a novel hybrid
forecasting methodology that combines the Ensemble Empirical Mode Decomposition (EEMD) from the
field of signal processing with the Support Vector Regression (SVR) methodology that originates from machine
learning. We test the forecasting ability of the proposed model against a Random Walk (RW), a Bayesian
Autoregressive and a Bayesian Vector Autoregressive model. The proposed methodology outperforms all the
competing models with half the error of the RW model with and without drift in out-of-sample forecasting.
Finally, we argue that this new methodology can be used as an early warning system for forecasting sudden
house price drops with direct policy implications.

We propose a Support Vector Machine (SVM)-based structural model to forecast the collapse of banking institutions in the USA using publicly disclosed information from their financial statements on a four-year rolling window. In our approach, the optimum input variable set is defined from a large data set using an iterative relevance-based selection procedure. We train an SVM model to classify banks as solvent and insolvent. The resulting model exhibits significant ability in bank default forecasting.

How to achieve the goal of automatically classifying video shots by their content is still an issue under debate. In this paper we present a novel set of low-level descriptors for the classification of TV video shots into meaningful semantic classes which can then be useful when browsing a TV stations archives. The motion features we propose consist of a modified Perceived Motion Energy Spectrum descriptor for local motion and a Normalized Dominant Motion Histogram for camera motion. Since exclusively motion-based classification has a very limited applicability, we also add three normalized local HSV histograms, extracted from particular key-frames we select with a simple yet efficient approach, as color descriptors. Our experimental implementation is tested on real-world TV video shots using a binary classifier based on Support Vector Machines and the results demonstrate that the proposed features can achieve high success rates not only on narrow and specialized classes, but also on more generic ones.