We establish the out-of-sample predictability of monthly exchange rate changes via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To guard against overfitting, we use the elastic net to estimate a high-dimensional...

This articlemodels the dependence risk and resource allocation characteristics of two 20-stock coal–uranium and oil–gas sector portfolios fromthe Australian market in the context of the global financial crisis of 2008–2009. The modeling framework implemented consists of pair vine copulas...

The aim of this paper is to assess the effectiveness and risk in the stock exchange market in Central and Eastern Europe countries (CEE) in view of the largest stock exchanges: NYSE2‑LSE‑HKSE2. The implementation of this objective was based on an analysis of basic stock market indicators and...

This article empirically investigates the volatility spillover of stock returns from the market to disaggregated industry sectors. Seventeen sectors from the US and UK stock markets are estimated by the GARCH technique based on daily data from 1973 to 2008. The key findings are two-fold. In the...

A speculative bubble is usually defined as the difference between the market value of a security and its fundamental value. Although there are several important theoretical issues surrounding the topic of asset bubbles, the existence of bubbles is inherently an empirical issue that has not been...

This paper applies Machine learning techniques to Google Trends data to provide real-time estimates of national average subjective well-being among 38 OECD countries since 2010. We make extensive usage of large custom micro databases to enhance the training of models on carefully pre-processed...

Machine Learning models are often considered to be "black boxes" that provide only little room for the incorporation of theory (cf. e.g. Mukherjee, 2017; Veltri, 2017). This article proposes so-called Dynamic Factor Trees (DFT) and Dynamic Factor Forests (DFF) for macroeconomic forecasting, which...

Value-at-risk (VaR) is a useful risk measure broadly used by financial institutions all over the world. VaR has been extensively used to measure systematic risk exposure in developed markets like of the US, Europe and Asia. This paper analyzes the accuracy of VaR measure for Pakistan's emerging...

The experimental approach is applied to explore the value of unidentified historical information in stock-return prediction. Return sequences were randomly drawn cross section and time from historical S&P500 data. Subjects were requested to predict returns or select stocks from 12 preceding...

In this paper I examine whether one can use analyst forecasts of macroeconomic variables to improve investors ex-ante allocation of wealth between stocks and bonds. Such forecasts provide a forward-looking approach which I find improves investor's information set for the myopic stock-bond...