European Stock Returns Research Articles

On the relationship between oil market and European stock returns

This paper investigates the dynamic relationship between the oil market and European stock market returns using monthly data from May 2007 to April 2022 for 27 European Union member countries. A novel approach is adopted by using the time-varying Granger causality test and the structural vector auto-regression model to examine the causal links. Empirical results reveal strong evidence of time-varying causation between the variables, considering the oil market from both the supply-side and demand-side perspectives. In light of these findings, numerous policy considerations emerge, including refining risk management strategies for investors, reformulating economic and energy policies, the potential impact on monetary policy decisions, the need for ad hoc market regulations, facilitating investor education initiatives, promoting international cooperation, and advancing the transition to sustainable energy sources.

New ESG rating drivers in the cross‐section of European stock returns

AbstractWe assess the performance of two quantitative signals based on ESG scores across a large, multi‐national cross‐section of European stock returns. We test whether the cost of equity capital is more influenced by the upward momentum (measured over time) of the ESG scores of the firms issuing stocks or by their stability (identified as the volatility of the scores over time), measured around a changing mean level. We find that short‐term ESG momentum over 1 month has a significant impact on the cross‐section of stock returns, lowering the anticipated cost of capital and leading to positive average abnormal returns. This suggests that short‐term ESG momentum may represent a novel, priced systematic risk factor. Furthermore, we find strong evidence that an ESG volatility spread strategy which buys low ESG score volatility stocks and sells high volatility ones, generates a substantial alpha and affects the ex‐ante cost of capital. Both quantitative ESG signals result in portfolio sorting and long‐short strategies that enhance the overall sustainability profile of the issuing firms without compromising the raw average of their ESG scores.

Robust nonparametric multiple changepoint detection for multivariate variability

Two robust, nonparametric multiple changepoint detection algorithms are introduced: DWBS and MKWP. These algorithms can detect multiple changes in the variability of a sequence of independent multivariate observations, even when the number of changepoints is unknown. The algorithms DWBS and MKWP require minimal distributional assumptions and are robust to outlying observations and heavy tails. The DWBS algorithm uses a local search method based on depth-based ranks and wild binary segmentation. The MKWP algorithm estimates changepoints globally via maximizing a penalized version of the classical Kruskal–Wallis ANOVA test statistic. It is demonstrated that this objective function can be maximized via the well-known PELT algorithm. Under mild, nonparametric assumptions, both of these algorithms are shown to be consistent for the correct number of changepoints and the correct location(s) of the changepoint(s). A data driven thresholding method for multivariate data is introduced, based on the Schwartz information criteria. The robustness and accuracy of the new methods is demonstrated with a simulation study, where the algorithms are compared to several existing algorithms. These new methods can estimate the number of changepoints and their locations accurately when the data are heavy tailed or skewed and the sample size is large. Lastly, the proposed algorithms are applied to a four-dimensional sequence of European daily stock returns.

Fear of COVID-19 Effect on Stock Markets: A Proposal for an Algorithmic Trading System Based on Fear

This study analyzes the fear of COVID-19 effect on European stock market returns. For this purpose, the search volumes (SV) collected by Google Trends (GT) and Wikipedia were used as proxies of fear of COVID-19. In a sample from 13 European stock markets, fear of COVID-19 was found to be associated with negative European stock returns. Our research employed this observation to propose an algorithmic trading system based on fear of COVID-19. Back-testing results show the possibility of extraordinary returns based on this system. These findings have important implications for political authorities, the mass media, and investors.

The relevance of banks to the European stock market

Banks have always played an ambivalent role in financial markets. On the one hand, they provide essential services for the market; on the other hand, problems in the banking sector can send shock waves through the entire economy. Given this prominent role, it is not surprising that Pereira and Rua [2018. “Asset Pricing with a Bank Risk Factor.” Journal of Money, Credit and Banking 50(5): 993–1032. doi:10.1111/jmcb.12473] found that the health of the banking sector exerts an influence on stock returns in the US. Understanding the relationship between banks and their impact on the asset prices of non-financials is essential to evaluate the risk emanating from an unhealthy banking sector and should be considered in new regulatory requirements. The aim of this study is to determine if the health of European banks is of such importance for the European stock market so that spillover effects are visible. Our results show that none of our banking-health variables have explanatory power on the cross-section of European stock returns. These findings contrast those for the US. The reasons may be manifold, from an unimportant liquidity provisioning channel over reduced room for actions due to regulatory requirements up to a moral hazard situation in Europe, where investors strongly rely on the governmental bailouts of distressed banks.

What drives US stock markets during the COVID-19 pandemic? A global sensitivity analysis

This paper identifies robust determinants of US stock price movements in the economic shadow of the COVID-19 crisis and in the presence of model uncertainty, using several influential factors highlighted in relevant research. Our investigation performs an extreme bounds analysis (EBA), a global sensitivity framework capable of handling the problem of model uncertainty. We document that excess market returns, term spread, implied volatility, oil, Twitter-based economic uncertainty, and European and Chinese stock returns are the only variables that are robust to all possible variations in the condition set of information. The results also reveal the irrelevance of newly reported COVID-19 cases and deaths as novel drivers that contribute to the formation stock prices, thus lending support to the “psychophysical numbing” phenomenon.

Practical Applications of What Portfolio in Europe Makes Sense?

In <b><i>What Portfolio in Europe Makes Sense?</i></b> from the July 2021 special issue on non-US financial markets of <b><i>The Journal of Portfolio Management</i></b>, author <b>Gueorgui Konstantinov</b> (of <b>LBBW Asset and Wealth Management</b>) analyzes European stock and bond performance. Investors have generally preferred European bonds, which have outperformed European stocks. However, eurozone economic development and demand for low-risk investments has recently driven bond yields down into negative territory. This, combined with poor long-term European stock performance, casts doubt on whether a balanced European portfolio with a large allocation to bonds can provide attractive returns. The author analyzes long- and short-term European stock and bond returns and calculates the past performance of a model set of balanced portfolios. He finds that the popular European 40/60 stock/bond balanced portfolio is unlikely to provide desirable future returns because European bonds, not stocks, are now the riskier investment. This is due to the bonds’ negative yields and high volatility, plus rising inflation. However, European emerging-market bonds have the best risk–return profile, and European stock returns are improving. The author says balanced-fund managers may wish to add emerging-market bonds and derivatives to portfolios, while investors may wish to switch to a 60/40 stock/bond allocation.

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.

What greenium matters in the stock market? The role of greenhouse gas emissions and environmental disclosures

This study provides evidence on the existence of a negative greenium, i.e. a risk premium related to the greenness of a firm, based on European individual stock returns. We define a priced ‘greenness and transparency’ factor based on companies’ greenhouse gas emissions and the quality of their environmental disclosures, and show that what is priced by the market is the combination of environmental performance and environmental transparency. Based on this factor, we offer a tool to assess the exposure of a portfolio to the risk associated with the low-carbon transition, and hedge against it. We estimate that in a stressed scenario where greener and more transparent firms very much outperform brown stocks, there would be losses at the global level, including for European large banks, should investors fail to price climate-transition risks. These results call for the introduction of climate stress tests for systemically important financial institutions.

Aggregate volatility risk: International evidence

Using a procedure analogous to that of Ang et al. (2006), this paper documents that aggregate volatility risk does not appear to be priced in European equity markets. Specifically, based on the 2002–2016 period (for which European stock return data is available), the price of aggregate volatility risk is not statistically different from zero. Analysis based on GARCH-class and high-frequency intraday data models support these results. Consequently, contrary to what has been reported in some studies that examine U.S. data, whether aggregate volatility risk is priced in equity markets is an open question.

The Greenium Matters: Evidence on the Pricing of Climate Risk

This study provides evidence on the existence of a negative Greenium, i.e. a risk premium linked to firms' greenness and environmental transparency, based on European individual stock returns. We define a priced "greenness and transparency" factor based on companies' greenhouse gas emissions and the quality of their environmental disclosures. Based on this factor, we offer a tool to assess the exposure of a portfolio to the risk associated with the low-carbon transition, and hedge against it. We estimate that in a stressed scenario where greener and more transparent firms very much outperform brown stocks, there would be losses at the global level, including for European large banks, should investors fail to price climate-transition risks. These results call for the introduction of climate stress tests for systemically important financial institutions.

The effects of oil price shocks on the prices of EU emission trading system and European stock returns

This paper examines whether oil price shocks of different origin affect the price of carbon emission allowance traded under the European Union's Emissions Trading System; leading to changes in aggregate and sector-specific European equity returns. The results show that an unexpected oil-supply disruption has an imminent but weak positive effect on carbon emission price, while a positive aggregate demand shock has a strong positive effect on carbon emission price. By contrast, a positive oil-specific (precautionary) demand shock has a negative but weak effect on carbon emission price. These findings are economically important as positive shocks on the emission allowance price trigger a decrease on the aggregate stock return of the European equity market, albeit they trigger a large and persistent increase on European equity returns of oil-related industries with the exception of the Energy sector.

The Effects of Oil Price Shocks on the Prices of EU Emission Trading System and European Stock Returns

The Effects of Oil Price Shocks on the Prices of EU Emission Trading System and European Stock Returns

ECB's unconventional monetary policies and the European bank stock returns

This paper examines the impact of the four unconventional monetary policy announcements followed by the European Central Bank on the stock price of European banks, as well as on the STOXX Europe 600 Banks index, from January 2010 to December 2016. The results show that there is a positive relation between the announced programs and the stock returns of the European banks at the same day of the announcement, while the impact is stronger for the long-term sovereign bond purchases (Securities Markets Programme). We also find that the banks of the countries which benefit most from the unconventional policies are these from Southern Europe. On the contrary, the announcement of these programs displays a limited effect on the banks of the European core countries and those with solid banking system. Finally, the announcements seem to reduce the volatility of stock prices and especially for the Covered Bond Purchase Programme (CBPP3).

Aggregate Volatility Risk: International Evidence

Using a procedure analogous to that of Ang et al. (2006), this paper documents that aggregate volatility risk does not appear to be priced in European equity markets. Specifically, based on the 2002-2016 period (for which European stock return data is available), the price of aggregate volatility risk is not statistically different from zero. Analysis based on GARCH-class and high-frequency intraday data models support these results. Consequently, contrary to what has been reported in some studies that examine U.S. data, whether aggregate volatility risk is priced in equity markets is an open question.

Covariance Versus Precision Matrix Estimation for Efficient Asset Allocation

Asset allocation constitutes one of the most crucial and most challenging tasks in financial engineering, which often requires the estimation of large covariance or precision matrices, from short time-span multivariate observations, a mandatory yet difficult step. The present contribution reviews and compares a large selection of estimators for covariance and precision matrices, organized into classes of estimation principles (direct, factor, shrinkage, sparsity). This includes the theoretical derivation of several additional estimators not available in the literature. Rather than assessing estimation performance from synthetic data based on a priori selected models of questionable practical interest, it is chosen here to evaluate practically the quality of these estimators directly from portfolio selection performance, quantified by financial criteria. Portfolio selection is conducted over two datasets of different natures: a 15-year large subset (within Stoxx Europe 600) of 244 European stock returns, and a 50-year benchmark dataset of 90 US equity portfolios. This large scale comparative study addresses issues such as the relative benefits and difficulties of using robust versus direct estimates, of choosing precision or covariance estimates, of quantifying the impacts of constraints.

Cross-autocorrelations in European stock returns

This paper examines lead-lag relationships between monthly index returns from 18 European industries. Several interesting and clear relationships are found that call into question the efficiency of European stock markets. While the Automobiles & Parts sector lags more than half of the other sectors, the Financial Services, Technology, and Telecommunications sectors lead many others. In particular, the leadership of the Technology sector has strengthened in recent years.

Discovering and Disentangling Effects of US Macro-Announcements for European Stocks

In this study, we analyze the effects of US macroeconomic announcements on European stock returns, return volatility and bid-ask spreads using intraday data. While an index-based analysis provides expected outcomes of differing importance of macro-economic announcements, we provide first evidence on stock-specific reactions. The study further contributes by disentangling stock-specific impacts from overall market reactions. A spread analysis reveals that return volatility affects the spread size positively, and that spreads are systematically higher directly after news releases. This is followed by structurally lower spreads, indicating quickly decreasing asymmetric information in the market after announcements. Additionally, spreads tend to react to announcements even if the returns or the volatility of the underlying stock is not significantly affected. This points at the importance of the analysis of news events beyond return and volatility analyses.

Do macroeconomic factors matter for stock returns? Evidence from the European automotive industry

This paper applies the multi-index model to explore the sensitivity of the stock return of European automotive companies to some macroeconomic variables. The analysis involves the monthly returns of nine European companies operating in the automotive industry from January 2003 to April 2012 using 15 macroeconomic variables and the market index represented by S&P 350. The study finds that the S&P 350 positively affects stock returns, supporting the single index model. Furthermore, exchange rate, exports and platinum positively affect stock return, while aluminium and unemployment rate negatively affect stock return. The results suggest that a multi-index model using selected macroeconomics variables provides additional power in explaining the variability of the European stock returns in the automobile industry over a single index model using the market index alone.

European business cycles and stock return predictability

This paper finds that the European leading economic indicator, a prime business cycle indicator for the European economies published by the OECD, can strongly predict European stock returns and generate utility gains. Importantly, the predictive power of the European indicator is above and beyond that contained in the country-specific leading indicator. Furthermore, we find that the predictive power of the European indicator is stable.