Historically, exchange rate forecasting models have exhibited poor out-of-sample performances and were inferior to the random walk model. Monthly panel data from 1973 2014 for ten currency pairs of OECD countries are used make out-of sample forecasts with artificial neural networks XGBoost models. Most approaches show significant substantial predictive power in directional forecasts. Moreover, ...

The efficient supervised training of artificial neural networks is commonly viewed as the minimization of an error function that depends on the weights of the network. This perspective gives some advantage to the development of effective training algorithms, because the problem of minimizing a function is well known in the field of numerical analysis. Typically, deterministic minimization metho...

We introduce a general method for improving the convergence rate of gradientbased optimizers that is easy to implement and works well in practice. We demonstrate the effectiveness of the method in a range of optimization problems by applying it to stochastic gradient descent, stochastic gradient descent with Nesterov momentum, and Adam, showing that it significantly reduces the need for the man...

To quantify the adaptive significance of insect learning, we documented the behavior and growth rate of grasshoppers (Schistocerca americana) in an environment containing two artificial food types, one providing a balanced diet of protein and carbohydrate, which maximizes growth, and the other being carbohydrate-deficient, which is unsuitable for growth. Grasshoppers in the Learning treatment e...

We have recently introduced an incremental learning algorithm, Learn.NSE, for Non-Stationary Environments, where the data distribution changes over time due to concept drift. Learn.NSE is an ensemble of classifiers approach, training a new classifier on each consecutive batch of data that become available, and combining them through an age-adjusted dynamic error based weighted majority voting. ...

We assume that firms actively manage risk by learning and hedging in two macroeconomic models, a simple cobweb model and a model of monopolistic competition that allows for the analysis of monetary policy and welfare. In both models, firms that learn (by paying a cost to observe the model’s stochastic shocks) face less uncertainty and produce more output than firms that hedge. Parameter or poli...

Meta learning of optimal classifier error rates allows an experimenter to empirically estimate the intrinsic ability of any estimator to discriminate between two populations, circumventing the difficult problem of estimating the optimal Bayes classifier. To this end we propose a weighted nearest neighbor (WNN) graph estimator for a tight bound on the Bayes classification error; the Henze-Penros...