The Household Finance and Consumption Survey (HFCS) provides valuable information for the monetary policy and financial stability purposes. The dataset shows, however, inconsistencies with National Account (NtlA) statistics, as the aggregated HFCS micro data do usually not match the...

We consider the basic problem of refi tting a time series over a finite period of time and formulate it as a stochastic dynamic program. By changing the underlying Markov decision process we are able to obtain a model that at optimality considers historical data as well as forecasts of future...

In this Modern era, image transmission and processing plays a major role. It would not be possible to retrieve information from satellite and medical images without the help of Image processing techniques. Image Edge Enhancement is the art of examining images for identifying objects and judging...

Calibration of financial models can have more than one local minima present, requiring the use of global optimization techniques to properly calibrate them. In general, calibrating with a global optimizer will be a slow operation. An artificial neural network, properly trained, can replicate the...

We propose a new method for solving high-dimensional dynamic programming problems and recursive competitive equilibria with a large (but finite) number of heterogeneous agents using deep learning. The „curse of dimensionality“ is avoided due to four complementary techniques: (1) exploiting...

We present a new interactive procedure for evolutionary multiobjective optimization involving the decision rule preference model in the search for the best compromise solution. As usual in the interactive multiobjective optimization, preference elicitation phases alternate with optimization...

This paper introduces the MCTS algorithm to the financial world and focuses on solving significant multi-period financial planning models by combining a Monte Carlo Tree Search algorithm with a deep neural network. The MCTS provides an advanced start for the neural network so that the combined...

In this study we tried to compare two models in order to identify optimal neural networks models in predicting bankruptcy. Multi-layered perceptron (MLP) because of easy training and high efficiency and also integrated multi-layered perceptron (most used neural network in predicting bankruptcy)...

We consider parametric portfolio policies of any complexity using deep neural networks to optimize investor utility. Risk aversion acts as an economic regularization mechanism, with higher risk aversion constraining model complexity. Empirically, Deep Parametric Portfolio Policies (DPPP)...