Democratization of the mysterious art of data science via Amazon Web Services, Google Cloud Platform, Microsoft Azure, and other machine learning (ML) service providers might make it too easy to apply ML. To underline this we present a worked-out example in R (including sources in the appendix),...

We present a worked out example in R (including sources in the appendix), where deep learning falls behind much simpler methods. It is an already published application of a LeNet style convolutional neural network (CNN) for image recognition. We show that this complex CNN is outperformed by a...

Training a Multi-Layer Perceptron (MLP) to achieve a minimum level of MSE is akin to doing Non-Linear Regression (NLR). Therefore, we use available econometric theory and the corresponding tools in R. Only if certain assumptions about the error term in the Data Generating Process are in place,...

Credit scoring models predict a person's creditworthiness. Accurate and interpretable credit scoring models help maximize a financial institution's risk-adjusted return and hence are of considerable importance. Although the XGBoost is the state-of-art classifier for this task, its complexity...

A Hidden Markov Model (HMM) is used to model the VIX (the Cboe Volatility Index). A 4- state Gaussian mixture is fitted to the VIX price history from 1990 to 2022. Using a growing window of training data, the price of the S&P500 is predicted and two trading algorithms are presented, based on the...

This paper provides a data-driven analysis of the volatility risk premium, using tools from high-frequency finance and Big Data analytics. We argue that the volatility risk premium, loosely defined as the difference between realized and implied volatility, can best be understood when viewed as a...

This paper considers the algorithmic implementation of the heteroskedasticity and autocorrelation consistent (HAC) estimation problem for covariance matrices of parameter estimators. We introduce a new algorithm, mainly based on the fast Fourier transform, and show via computer simulation that...

This paper studies the nature of volatility spillovers across countries from the per-spective of network theory and by relying on data of US-listed ETFs. I use a Lasso-related technique to estimate the International Volatility Network (IVN) where the nodes correspond to large-cap international...

The sentiment of news predicts the short-term stock market performance of individual companies. We find that this association is solely due to the idiosyncratic informational content of an article. We transparently quantify the association between news sentiment and stock market performance of...

We review heterogeneous agent-based models of financial stability and their application in stress tests. In contrast to the mainstream approach, which relies heavily on the rational expectations assumption and focuses on situations where it is possible to compute an equilibrium, this approach...