Abstract

Purpose – This study examines how oil market volatility and clean energy trends impact the stock performance of automakers, specifically comparing traditional manufacturers with electric vehicle (EV) producers such as BYD and Tesla. The objective is to assess the extent to which traditional automakers are sensitive to oil market fluctuations, while EV manufacturers align more closely with clean energy dynamics, particularly during global market crises.

Design/methodology/approach – Using daily data from January 2013 to December 2023, we conduct linear regressions, GARCH, DCC-GARCH and the Diebold–Yilmaz connectedness approaches in the analysis. We use these econometric models to capture volatility patterns, correlations and cross-market spillovers.

Findings – Traditional manufacturers are affected by both oil prices and clean energy development. While traditional automakers remain more vulnerable to oil price volatility, global leading EV manufacturers BYD and Tesla are less sensitive to oil price shocks and show strong alignment with clean energy indices. Significant volatility spillovers are observed during global crises, such as the COVID-19 pandemic and the Russia–Ukraine conflict.

Originality/value – The paper uniquely integrates clean energy indices into the analysis of oil price impacts on automaker stocks. By comparing traditional and EV manufacturers using advanced econometric models, it sheds light on the literature of energy markets and sustainable financial markets.

Abstract

Purpose – This paper represents the first attempt to examine investor behaviour for green stocks through the lens of return co-movement, and provides evidence indicating that green investment practices have gained traction after 2012.

Design/methodology/approach – We empirically test the hypotheses that the stock returns of firms with similar carbon dioxide emissions levels move together and, if so, whether this co-movement has increased over time as people become more “carbon-conscious.” Our baseline sample, based on carbon emissions data from public company disclosures, suffers from limited coverage, particularly before 2016, leading to low statistical power and sample selection bias. To address this, we employ machine learning methodologies to forecast the carbon emissions of firms that do not disclose such information, nearly quadrupling the sample size. Our findings indicate that stocks with similar carbon emissions exhibit higher co-movement in stock returns in both the baseline and augmented data samples. Furthermore, this co-movement has increased during the 2012–2020 period compared to the 2004–2011 period, suggesting that green investment has gained traction over time.

Findings – We find that stocks with similar carbon emissions exhibit higher co-movement in stock returns in both the baseline sample and the augmented data sample, and the co-movement has increased in the 2012–2020 period compared to the 2004–2011 years, suggesting that green investment has gained traction over time.

Originality/value – (1) We use machine learning methodology to augment carbon emissions sample which goes back to 2004. Our approach almost quadruples the original data, enabling large-sample testing. (2) We are the first paper to examine how green companies’ stock returns co-move and thus provide complementary results on the research on expected returns and carbon emissions.

Abstract

Purpose – Investigation of the anomalies associated with crashes and jackpots in the Chinese stock market.

Design/methodology/approach – We propose a logit model to predict the events of crashes and jackpots in the Chinese stock market. The model introduces a new variable of the price-to-sales ratio and takes into account the market states, Up and Down.

Findings – The anomalies associated with crashes and jackpots are not related to variations in economic conditions, but are associated with limits to arbitrage. High-liquidity stocks have strong mispricing effects. The institutions’ speculative trading will push liquid stock prices further away from their fundamentals but avoid buying illiquid stocks with a higher probability of price crashes and jackpots.

Originality/value – We propose a logit model to predict the extreme events of both crash and jackpot in the Chinese stock market. Our model effectively disentangles from CRASHP and JACKP. Compared with the traditional model, it substantially enhances in-sample and out-sample predictions. Based on the predictions of the extreme events, we find two strong and robust pricing effects associated with ex ante CRASH and JACKP in the Chinese stock market.

Combustion instability, which causes dangerous pressure oscillations in combustors, arises from complex feedback between heat release, acoustics, and flow. Now, researchers from Japan have applied network science to spray combustion instability, shedding light on the dynamics of ‘turbulence networks.’ By identifying critical regions, they found a way to suppress combustion instability. This method offers a novel mathematical approach to stabilizing the combustion state in various combustors.

A research team led by Dr. Dirk Henkensmeier at the Hydrogen and Fuel Cell Research Center of the Korea Institute of Science and Technology (KIST, President Sang-Rok Oh) has developed a novel membrane material for water electrolysis that operates stably and has significantly higher conductivity under low alkalinity conditions than existing systems. The newly developed membrane maintains high hydrogen production performance even in low-concentration alkaline environments, providing a technological foundation for low-alkalinity water electrolysis.

Abstract

Purpose – Clean energy stocks have recently received significant attention from both investors and researchers, reflecting their growing importance in financial markets. This paper forecasts clean energy stock (CES) returns using many predictors, including technical, macroeconomic, climate risk and financial predictors. The goal is to reveal how different predictor groups work and their time-varying patterns.

Design/methodology/approach – This study establishes a robust forecasting framework using monthly data from the Wilder Hill Clean Energy Index, spanning January 2009 to December 2023, and integrates 56 predictors across four categories. To address multicollinearity and identify key drivers, the framework applies advanced shrinkage methods, regularization, quantile regression and model combination. This offers a dynamic solution for forecasting CES returns.

Findings – The study identifies macroeconomic predictors as the most stable and powerful drivers of CES returns; the Chicago Fed National Activity Index (CFNAI) is a particularly important indicator. Climate predictors show temporal variability, while technical and financial predictors are more important during market volatility. A group-level analysis highlights macroeconomic variables as key to forecasting accuracy. Climate predictors play critical roles in specific periods. Medium-term dynamics (2–4 months) associated with macroeconomic predictors have the strongest impact on performance.

Originality/value – This paper introduces a novel approach to forecasting CES returns by integrating 56 diverse predictors. This addresses research gaps, given the previous focus on traditional predictors or single-model frameworks. The study further examines the roles of predictor grouping, component selection, rolling windows and forecasting horizons in increasing prediction accuracy and in describing the dynamic interactions driving CES returns.

A research team at the Korea University College of Medicine has uncovered a genetic mechanism responsible for delayed tooth development and impaired mineralization.