For stock market predictions, the essence of the problem is usually predicting the magnitude and direction of the stock price movement as accurately as possible. There are different approaches (e.g., econometrics and machine learning) for predicting stock returns. However, it is non-trivial to...

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...

With approximately 900 million observations we conduct, to our knowledge, the largest study ever of intraday stock return predictability using machine learning techniques finding consistent out-of-sample predictability across market, sector, and individual stock returns at various time horizons....

This paper examines the evidence regarding predictability in the market risk premium using artificial neural networks (ANNs), namely the Elman Network (EN) and the Higher Order Neural network (HONN), univariate ARMA and exponential smoothing techniques, such as Single Exponential Smoothing (SES)...

This paper proposes a new information share for price discovery based on Russell's (1999) autoregressive conditional intensity model. While previous studies rely on equally spaced high frequency data, we use the information conveyed by trade intensities to determine a market's contribution to...

The stock market behaviour and trend can be move according to the different internal, external, micro economic and macro economic factors. The impact of some events that definitely occurs can't be envisaged by the stock market with confidence due to their nature. A budget is an influential...

Fractal analysis is carried out on the stock market indices of seven European countries and the US. We find evidence of long-range dependence in the log return series of the Mibtel (Italy) and the PX-Glob (Czech Republic). Long-range dependence implies that predictable patterns in the log...

Pastor and Stambaugh (2012) demonstrate that from a forward-looking perspective, stocks are more volatile in the long run than they are in the short run. We investigate how the economic constraint of non-negative equity premia aspects predictive variance. When investors expect non-negative...

A rapidly growing literature has documented important improvements in volatility measurement and forecasting performance through the use of realized volatilities constructed from high-frequency returns coupled with relatively simple reduced-form time series modeling procedures. Building on...