Cambridge researchers have become the first to combine multiple image types – RGB (that mimics human vision), depth and infra-red – in a 3D camera set up to monitor premature babies in neonatal intensive care. The aim is to provide a second set of eyes for nurses through continuous visual monitoring of the babies’ behaviour.

A new seismic study reveals that Earth’s largest volcanic event fundamentally transformed the oceanic plate beneath the Ontong Java Plateau, the world’s largest oceanic plateau. By analyzing high-frequency seismic waves traveling through the plate, researchers discovered that massive volcanic activity not only built the plateau itself but also chemically and structurally modified the underlying oceanic plate.

The results show that the plate has a complex internal structure composed of layered formations intersected by extensive dike swarms, created as magma rose from a deep thermochemical mantle plume. Unusually slow seismic velocities indicate that this magma chemically altered the plate through a process known as refertilization. These findings demonstrate that large-scale volcanism can significantly reshape pre-existing oceanic plates, offering new insights into how tectonic plates form and evolve.

Abstract

Purpose – Stock markets are essential for households for wealth creation and for firms for raising financial resources for capacity expansion and growth. Market participants, therefore, need an understanding of stock price movements. Stock market indices and individual stock prices reflect the macroeconomic environment and are subject to external and internal shocks. It is important to disentangle the impact of macroeconomic shocks, market uncertainty and speculative elements and examine them separately for prediction. To aid households, firms and policymakers, the paper proposes a granular decomposition-based prediction framework for different time periods in India, characterized by different market states with varying degrees of uncertainty.

Design/methodology/approach – Ensemble empirical mode decomposition (EEMD) and fuzzy-C-means (FCM) clustering algorithms are used to decompose stock prices into short, medium and long-run components. Multiverse optimization (MVO) is used to combine extreme gradient boosting regression (XGBR), Facebook Prophet and support vector regression (SVR) for forecasting. Application of explainable artificial intelligence (XAI) helps identify feature contributions.

Findings – We find that historic volatility, expected market uncertainty, oscillators and macroeconomic variables explain different components of stock prices and their impact varies with the industry and the market state. The proposed framework yields efficient predictions even during the COVID-19 pandemic and the Russia–Ukraine war period. Efficiency measures indicate the robustness of the approach. Findings suggest that large-cap stocks are relatively more predictable.

Research limitations/implications – The paper is on Indian stock markets. Future work will extend it to other stock markets and other financial products.

Practical implications– The proposed methodology will be of practical use for traders, fund managers and financial advisors. Policymakers may find it useful for assessing the impact of macroeconomic shocks and reducing market volatility.

Originality/value – Development of a granular decomposition-based forecasting framework and separating the effects of explanatory variables in different time scales and macroeconomic periods.

Abstract

Purpose – This study examines the stock market efficiency in China to offer trading strategy guidance to investors and efficiency evaluation insight to policymakers.

Design/methodology/approach – This study examines the stock market efficiency in China with a new combined liquidity trading strategy by blending technical analysis into a liquidity buy-and-hold strategy.

Findings – Our results show that the combined strategy generates significant excess returns in the whole sample period, suggesting that the Chinese stock market is not consistent with the weak form efficient hypothesis. In addition, the combined strategy yields more significant risk-adjusted excess returns after the 2004 split-share reform, indicating the stock market efficiency in China does not exhibit a distinct upgrade after the reform. Our further test results reinforce the main conclusions after taking transaction costs, market states, short-selling reform and other issues into consideration.

Originality/value – Our study contributes to the literature in two ways: First, we shed light on the mixed documented results about the market efficiency form in China. Second, we contribute to the mixed relation between the 2004 split-share reform and market efficiency in China.

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.