European Stock Market Research Articles

Effects of COVID-Induced Public Anxiety on European Stock Markets: Evidence From a Fear-Based Algorithmic Trading System.

The effect of COVID-induced public anxiety on stock markets, particularly in European stock market returns, is examined in this research. The search volumes for the notion of COVID-19 gathered by Google Trends and Wikipedia were used as proxies for COVID-induced public anxiety. COVID-induced public anxiety was shown to be linked with negative returns in European stock markets when a panel data method was used to a sample of data from 14 European stock markets from January 2, 2020 to September 17, 2020. Using an automated trading system, we used this finding to suggest investment methods based on COVID-induced anxiety. The findings of back-testing indicate that these techniques have the potential to generate exceptional profits. These results have significant consequences for government officials, the media, and investors.

Assessing Financial Risk Spillover and Panic Impact of Covid-19 on European and Vietnam Stock market.

This study examined the influence of tail risks on global financial markets, which aids in better understanding of the emergence of COVID-19. This study looks at the global and Vietnamese stock markets impacted by the COVID-19 pandemic to identify systemic emergencies. Risk dependent value (CoVaR) and Delta link VaR are two important tail-related risk indicators used in Conditional Bivariate Dynamic Correlation (DCC) (CoVaR). The empirical findings demonstrate that when COVID-19's worldwide spread widens, the volatility transmission of systemic risks across the global stock market and multiple exchanges shifts and becomes more relevant over time. At the time of COVID-19, the world industrial market was larger than the Vietnamese stock market, and the Vietnamese stock market posed a lesser danger to the global market. A closer examination of the link between the Vietnam value-at-risk (VaR) range index sample and the world stock index indicates a significant degree of downside risk integration in key monetary systems, particularly during the COVID-19 era. Our study findings may help regulators, politicians, and portfolio risk managers in Vietnam and worldwide during the unique moment of uncertainty created by the COVID-19 epidemic.

The Relevance of Banks to the European Stock Market

The Relevance of Banks to the European Stock Market

New insights on the asset growth anomaly: evidence from Europe*

This study provides insights into the well-documented asset growth anomaly using an integrated European stock market sample derived from 21 countries. We assess whether the anomaly in Europe is attributable to risk or mispricing. In doing so, we examine whether the asset growth effect on stock returns is dependent on the valuation signals contained in equity financing activities. Moreover, we determine whether it is derived from firms with existing market expectation errors. Finally, we explicitly test whether asset growth is a priced risk factor using the common two-stage cross-sectional regression (2SCSR) methodology. Overall, our evidence suggests that the underlying origins of the asset growth anomaly in Europe at the aggregate level are relatively consistent with a risk-based explanation.

Empirical asset pricing via machine learning: evidence from the European stock market

This paper evaluates the predictive performance of machine learning methods in forecasting European stock returns. Compared to a linear benchmark model, interactions and nonlinear effects help improve the predictive performance. But machine learning models must be adequately trained and tuned to overcome the high dimensionality problem and to avoid overfitting. Across all machine learning methods, the most important predictors are based on price trends and fundamental signals from valuation ratios. However, the models exhibit substantial variation in statistical predictive performance that translate into pronounced differences in economic profitability. The return and risk measures of long-only trading strategies indicate that machine learning models produce sizeable gains relative to our benchmark. Neural networks perform best, also after accounting for transaction costs. A classification-based portfolio formation, utilizing a support vector machine that avoids estimating stock-level expected returns, performs even better than the neural network architecture.

Diversification opportunities in European stock markets and their impact on textile industry development based on a financial education approach

This research study explores the diversification opportunity among 18 European stock market indices for the sample period from January 2001 to December 2019. However, financial education plays an important role in the development of the textile industry, considering the dynamics of the companies listed on the European stock exchanges. The correlation matrix, pairwise cointegration and Johansen cointegration reveal that selected 18 European stock market indices do not reduces the portfolio risk because exhibit higher positive correlation among them, and their movement pulsed in tandem. Potential investors are attracted by high investment opportunities in order to maximize their return based on portfolio diversification. Financial education can effectively contribute to the sustainable growth of the textile industry in Europe. This empirical research provides an integrated perspective on the long-term evolution of certain major European stock exchange indices. The findings have significant implications for investors interested in selecting these European stock indices in order to diversify their portfolio risk. Our study also imply that selected stock indices have been strongly affected by similar political and financial belies across Europe thus, eliminating the possibility of portfolio risk diversification.

Gold as a financial instrument

This paper explores the effectiveness of gold as a hedging and safe haven instrument for a variety of market risks. Rather than confining the analysis to specific countries, we treat gold as a global asset and apply the novel Phillips, Shi and Yu (2015a) and Phillips, Shi and Yu (2015b) methodology to identify extreme price movements. This method accounts for both the level and speed of changes in price dynamics that better characterises periods of abnormally high risks. We uncover safe haven properties for the European sovereign debt crisis, stock market crash, and oil inflationary pressures. We also show that gold exhibits hedging properties when investors are faced with currency, European sovereign debt, stock market, and oil inflation risks. Finally, we demonstrate that gold was a weak safe haven for investors during the 2020 Covid-19 crisis, and highlight the importance of accounting for speed and price levels in the identification of abnormal risk periods.

Co-movement in Stock Indices and GDP During the COVID-19 Period in the Countries of the European Union

Purpose: Macroeconomic research rarely analyzes the short-run relationship between stock market indices and GDP. The study seeks to answer how close the two indicators are in the short term in times of crisis. Design/Methodology/Approach: The stock market crisis caused by the COVID-19 pandemic has also developed in the European Union member states. Following the low of the European stock market in March, there was a relatively rapid rebound. There was a decline in the third quarter and strong growth in the fourth. As a result of the fear caused by the epidemic, national economies were shut down to varying degrees by governments, leading to a decline in GDP as early as the first quarter. This decline continued in the second quarter, with a strong rebound in the third and a moderate rise in the fourth. The research methodology is correlation calculation and hierarchical cluster analysis. The research hypothesizes that in the short run, stock market indices moved along with GDP. The positive correlation between the two indicators can be assumed because investors' decisions were influenced by the decline in GDP due to COVID-19. The hypothesis was not met, only in four countries, the study confirmed the co-movement of indicators. Findings: The hypothesis of the research was not fulfilled, in the majority of the EU member states, the two indicators did not change together, they did not move in the same direction. The negative correlation coefficient for the Union as a whole (r = -0.16028) means that the indicators moved in the opposite direction. Of the 27 Member States, 23 were characterized by this opposite movement. Practical Implications: The study's empirical results draw the attention of investors, risk managers, and economic policymakers to the fact that short-term changes in GDP do not influence their decision-making. Short-term GDP data are fundamentally inadequate indicators. This means that these data cannot predict with significant accuracy either expected GDP, stock market performance, and many other macroeconomic indicators. Originality/value: The originality and value of the study are given by the fact that it examines a macroeconomic relationship that researchers rarely analyze. The study's empirical results can also be used by risk managers, investors, and economic policymakers.

On the persistence of market sentiment: A multifractal fluctuation analysis

This paper applies multifractal detrended fluctuation analysis to study the multifractal property and temporal persistence of U.S. and European stock market sentiment, providing deeper insights into investor behavior. The findings indicate that the average sentiment is anti-persistent, understood as a general tendency for investors to overreact. The multifractal spectrum has a significant width in both markets, which comes with a substantial variation in local sentiment persistence. Analyses show that the current sentiment persistence is positively related to the level of market mood. Hence, investor fear is related to overreacting, while optimism is more closely to a random walk. This result is of potential interest for investment strategies, but is also likely to affect the stability of a financial market. Conclusions are drawn from financial option and survey data.

The Effect of COVID-19 on Herding Behavior in Eastern European Stock Markets.

Unlike past health crises that were more localized, the highly contagious coronavirus disease 2019 (COVID-19) crisis is impacting the world to an unprecedented extent. This is the first study examining how and whether the COVID-19 pandemic affects herding behavior in the Eastern European stock markets. Using samples from the stock markets of Russia, Poland, the Czech Republic, Hungary, Croatia, and Slovenia from January 1, 2010 to March 10, 2021, we demonstrate that the COVID-19 pandemic has increased herding behavior in all the sample stock markets. Our results show that the COVID-19 crisis reinforces the impact of global market returns on herding behavior in these specific stock markets. We find that COVID-19 strengthens the spillover effect of regional herding on herding behavior. Thus, financial authorities should monitor investors in the stock market to avoid the increase in herding behavior as well as the reinforcement of the global market returns and regional return dispersion on herding during the period of pandemic.

The dynamic dependence of natural gas and renewable energy stock markets

We measure the dynamics dependence structure between the European renewable energy stock (ERIX) market, natural gas, and other clean energy markets from 2008 to 2021 by adopting static and time-varying copula approaches. Empirical results show strong and positive dependence between ERIX and S&P global clean energy stock markets symmetric tail dependence, which indicates S&P clean energy assets provide limited hedging condition on the ERIX market and extreme upward and downward clean prices have a similar impact on the ERIX market. Furthermore, our evidence on the co-movement mechanisms between European renewable energy and clean energy markets is helpful for policymakers and investors. Governments need to adopt fiscal policy tools for clean energy firms when renewable energy prices rise (fall) together. Investors can assess and control potential risk contagion between European renewable energy stock markets and other clean energy markets via information about co-movement mechanisms.

Impact of MiFID II on the Market Volatility—Analysis on Some Developed and Emerging European Stock Markets

The paper investigates whether the implementation of MiFID II, a packet of financial legislation applying broadly to European Union financial markets, has led to a change in the volatility of some European developed and emerging stock markets. We show that for the developed capital markets considered in the analysis, MiFID II did not lead to a decrease in the volatility of capital markets. On the contrary, for all analysis intervals considered (3 months, 6 months, 12 months, 18 months and 24 months), the impact on volatility is positive, with volatility increasing in the case of the FTSE 100, CAC40 and DAX stock indexes. There is a similar significant relationship for the Czech stock market, but only over the three-month interval. For the Polish and Romanian stock markets, which enforced MiFID II later, a negative impact of MiFID II on volatility could also be observed. In the Romanian market, MiFID II had a negative impact on volatility on the short-term horizon, while for the Polish market, the impact of MiFID II on volatility is noticeable on a longer term of 24 months.

Lessons for Euro markets from the first wave of COVID-19

Although the coronavirus pandemic hit Europe in the early days of 2020, European stock markets had signaled fluctuations in the days before. This paper assesses the observed volatility on European stock exchanges and searches for its sources during the first four months of 2020. To investigate the issue, a panel VAR model is adopted, and the generalized impulse response function and the variance decomposition methods are used. The estimations show that about 34% of the volatility in European stock markets is due to the Chinese stock market, while 7% is due to international uncertainty, as measured by VIX. The impact of pandemic cases and deaths on European stock markets is negligible, below 1%. This means that the European stock market faced two risk elements: the first is the transmission volatility from the Chinese stock market, and the second is the international uncertainty. The findings also support the view that COVID-19 is more like a systematic risk.

Risk spillovers and hedge strategies between global crude oil markets and stock markets: Do regime switching processes combining long memory and asymmetry matter?

This paper investigates risk spillovers and hedge strategies between global crude oil markets and stock markets. In the paper, we propose a multivariate long memory and asymmetry GARCH framework that integrates state-dependent regime switching in the mean process with multivariate long memory and asymmetry GARCH in the variance process. Our results first show that there are linear risk spillovers running from the US stock markets to the WTI oil market in the short term. However, the linear risk spillover effect running from the oil market to the US stock market can only exist in the long term. In addition, there is a bidirectional linear risk spillover effect between the European stock markets and the Brent oil market in the short and long terms. Furthermore, there is no linear risk spillover effect between the Dubai oil market and the Chinese stock market. Second, the nonlinear risk spillovers running from the WTI oil market to the US stock market can be found in the tranquil regime. Moreover, there is also a nonlinear risk spillover effect running from the European stock markets to the Brent oil market in the tranquil regime. In addition, the nonlinear risk spillover effect running from the Brent oil markets to the European stock market can be found in the crisis regime. Furthermore, there is bidirectional nonlinear Granger causality between the Dubai crude oil market and the Chinese stock market in the tranquil regime. Finally, dynamic hedge effectiveness shows that the regime switching process combined with long memory and asymmetry behavior seems to be a plausible and feasible way to conduct hedge strategies between the global crude oil markets and stock markets.

Cross-Border Effects of A Major Tax Reform — Evidence from the European Stock Market

We analyze the effects of the major US tax reform of 2017 on European firms. Although foreign firms that are active in the respective country should be directly affected, other foreign firms could ...

Intraday Volatility Spillovers among European Financial Markets during COVID-19

During crises, stock market volatility generally rises sharply, and as consequence, spillovers are identified across markets. This study estimates the volatility spillover among twelve European stock markets representing all four regions of Europe. The data consists of 10,990 intraday observations from 2 December 2019 to 29 May 2020. Using the methodology of Diebold and Yilmaz, we use static and rolling windows to characterize five-minute volatility spillovers. Our results show that 77.80% of intraday volatility forecast error variance in twelve European markets comes from spillovers. Furthermore, the highest gross directional volatility spillovers are found in Sweden and the Netherlands, while the minimum spillovers to other stock markets are observed in the stock markets of Poland and Ireland. However, German and Dutch markets transmit the highest net directional volatility spillovers. Splitting the whole sample in pre- and post-pandemic declaration (11 March 2020) we find more stable spillovers in the latter. The findings reveal important information about European stock market interdependence during COVID-19, which will be beneficial to both policy-makers and practitioners.

The Impact of the COVID-19 Pandemic on Persistence in the European Stock Markets

This paper analyses the impact of the Covid-19 pandemic on the degree of persistence of European stock markets. Specifically, it uses fractional integration methods to estimate persistence at the daily, weekly and monthly frequencies in the case of ten major European stock market indices; the effects of the pandemic are assessed by comparing the pre-pandemic estimates (over the period 2005-2019) to those from a sample extended until July 2021 which includes the pandemic period. The approach used is more general than the standard one based on the stationarity versus non-stationarity dichotomy and allows for a wider range of dynamic processes. Three different model specifications are considered, and these are estimated under two alternative assumptions for the disturbances (white noise and autocorrelation). The findings indicate that there has not been any significant impact of the Covid-19 pandemic on the degree of persistence of the European stock market indices, though their volatility persistence has decreased.

Boosting Agnostic Fundamental Analysis: Using Machine Learning to Identify Mispricing in European Stock Markets

Boosting Agnostic Fundamental Analysis: Using Machine Learning to Identify Mispricing in European Stock Markets

Forecasting Market Crashes via Machine Learning: Evidence from European Stock Markets

This paper uses a comprehensive set of variables from the five largest Eurozone countries to compare the performance of simple univariate and machine learning-based multivariate models in predicting stock market crashes. The statistical predictive performance of a support vector machine-based stock market crash prediction model is significantly different from zero and among the best-performing univariate benchmarks, while still being truly out-of-sample. The ability to forecast subsequent stock market crashes out-of-sample translates into value-added to investors under realistic trading assumptions (net of transaction costs). Incorporating nonlinear and interactive effects is both imperative and foundation for the predictive performance of support vector machines. This adds an economic component to the advantageousness of machine learning-based multivariate crash prediction models over their univariate counterparts. It helps identify and explain the complex relationships in the underlying economic conditions (key economic drivers) that precede substantial stock market downturns.

What is Behind Extreme Negative Returns co-movement in the South Eastern European Stock Markets?

This paper examines co-movement of extreme returns in eight South Eastern European (SEE) stock markets during the period covering both the financial crisis from 2007-2009 and the COVID-19 health crisis. The analysis is based on coexceedances which represent the number of joint occurrences of extreme returns in a group of stock market indexes. To provide a valuable insight on how persistence, asset class, and volatility effects are related with the coexceedances, we utilize a multinomial logistic regression procedure. We find evidence in favour of the continuation hypothesis. However, the factors associated with the coexceedances differ between the SEE European Union (EU) members and the SEE EU accession countries. The EU members are more dependent on signals from major EU economies, while the accession countries are mainly impacted by regional signals. The implications of our analysis may help policy makers in understanding the nature of shock transmission in SEE stock markets.JEL Codes - C25; F36; G15