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 – 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.

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.

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 – Our study investigates how board co-option influences solvency risk in Australian and New Zealand banks. Board governance is considered one of the most critical variables impacting bank risk management practices and policies.

Design/methodology/approach – Our sample consists of commercial banks from both countries and data from 2011 to 2021. The results obtained were based on fixed-effect, 2SLS and GMM Models. Our results are robust to the other two measures of Board Co-option, Tenure-Weighted Co-Option and Residual Co-option, showing the applicability of our econometric model.

Findings – Results reveal that an increased proportion of co-opted directors on the board is associated with a notably reduced Z-Score ratio value, signifying an elevated level of solvency risk for banks. The evidence is consistent with the notion that co-opted directors bring about less effective board governance, escalating agency problems and enhancing solvency risk.

Research limitations/implications – The banks in these two countries must carefully establish a risk management framework under the Basel Accords to avoid risks like solvency risk. The regulators in the financial services industry may also devise mechanisms and regulate the banks under the second pillar of Basel-II and III, “Supervisory Review Process,” to avoid solvency risk management issues. Future researchers and scholars can extend the limits of future studies from two countries to various geographic locations, such as Europe, China and Southeast Asian regions.

Practical implications – Our study establishes the fact that banks in Australia and New Zealand are more exposed to solvency risk due to increasing board co-option phenomena at the board level.

Social implications – The unique measure of board co-option reveals the significance of board governance for bank risk management. To properly develop and implement bank risk management policies, the appointment and performance of board members must be actively monitored in Australian and New Zealand banks through a sensitive measure of board co-option.