Filtering has had a profound impact as a device of perceiving information and deriving agent expectations in dynamic economic models. For an abstract economic system, this paper shows that the foundation of applying the filtering method corresponds to the existence of a conditional expectation...

Infra-monthly economic time series have become increasingly popular in official statistics in recent years. This evolution has been largely fostered by official statistics' digital transformation during the last decade. The COVID-19 pandemic outbreak in 2020 has added fuel to the fire as many...

This study examines the impact of macroeconomic variables on stock prices. We use the Databank stock index to represent the stock market and (a) inward foreign direct investments, (b) the treasury bill rate (as a measure of interest rates), (c) the consumer price index (as a measure of...

In the past decades, there has been an unprecedented increase in cross border transactions between countries in terms of goods and financial flows. This integration has been fuelled by search of lower risk investments, risk diversification, search for cost effective and more efficient factors of...

This paper describes a machine learning technique to timely identify cases of individual bank financial distress. Our work represents the first attempt in the literature to develop an early warning system specifically for small European banks. We employ a machine learning technique, and build a...

Filtering has had a profound impact as a device of perceiving information and deriving agent expectations in dynamic economic models. For an abstract economic system, this paper shows that the foundation of applying the filtering method corresponds to the existence of a conditional expectation...

Financial analysts assume that the reliability of predictions derived from regression analysis improves with sample size. This is generally true because larger samples tend to produce less noisy results than smaller samples. But this is not always the case. Some observations are more relevant...

Machine Learning algorithms have been widely used and proven effective in financial markets. In this paper, we introduced a Machine Learning model set trained on the residual factors from the Fama-French three-factor model (Fama and French, 1992) to find significant alpha factors. To include...

Infra-monthly economic time series have become increasingly popular in official statistics in recent years. This evolution has been largely fostered by official statistics’ digital transformation during the last decade. The COVID-19 pandemic outbreak in 2020 has added fuel to the fire as many...

This paper describes a machine learning technique to timely identify cases of individual bank financial distress. Our work represents the first attempt in the literature to develop an early warning system specifically for small European banks. We employ a machine learning technique, and build a...