Purpose To test whether the long-run reversal of the 2021 meme stocks reflects a meme-specific effect, or simply the reversal that follows any extreme short-run appreciation. The cohort is defined by realized event-window returns, not by any observed measure of social-media activity or retail order flow, so the study characterizes long-run return behavior and makes no causal claim about retail coordination. Design/methodology/approach In a long-horizon event study, we track 44 CRSP common stocks with event-window cumulative abnormal returns above 100% during the January 2021 squeeze through December 2025 (48- and 58-month horizons), via matched-firm buy-and-hold abnormal returns (BHAR) and calendar-time factor models. The primary test is a within-2021 regression of abnormal return on event-window CAR and a meme indicator, with a same-window benchmark, long-short portfolio, squeeze/attention proxies and a cross-decade placebo as robustness. Findings The cohort reverted sharply (median 48-month abnormal return −27.4%; 81.8% underperformed their matched controls). Its multi-year decline is consistent with that of the extreme winners used as comparison groups: the within-2021 meme-threshold coefficient is insignificant (−51 points, t = −1.76), the long-short five-factor-plus-momentum alpha is insignificant (−0.31% per month, t = −0.37) and neither short interest nor turnover predicts reversal. The raw same-window gap is marginal (Mann–Whitney p = 0.07) and does not survive risk adjustment. We report a bounded failure to reject, not equivalence. Originality/value Among the first multi-year cross-sectional studies of the return-defined 2021 meme cohort, combining matched long-run returns, same-window benchmarks, measured squeeze and attention proxies, and explicit construct-validity and BHAR-tail caveats.
Purpose This study aims to examine whether investor emotions embedded in financial news can predict next-day (t+1) stock market movements in Pakistan. Specifically, it assesses whether incorporating emotion-based indicators, operationalized using the NRC Emotion Lexicon (v0.92), which maps tokenized words to eight primary emotions, into deep learning models improves the accuracy of stock price forecasts for the KSE-100 index on the Pakistan Stock Exchange (PSX). Design/methodology/approach Financial news articles published between January 2013 and August 2025 were collected from five leading Pakistani English-language news outlets. Investor emotions were quantified by applying the NRC Emotion Lexicon to preprocessed article text, producing normalized emotion scores for eight categories (anger, anticipation, disgust, fear, joy, sadness, surprise and trust) at the article level. These scores were aggregated into daily emotion indices and integrated with historical stock market data as input features for four deep learning architectures: long short-term memory (LSTM) networks, deep neural networks (DNN), convolutional neural networks (CNN) and gated recurrent units (GRU). Model performance was evaluated across multiple forecast horizons (t+1, t+20, and t+50 days) using a rolling-window validation approach, with mean absolute error (MAE) and mean absolute percentage error (MAPE) as the primary evaluation metrics. Granger causality tests were additionally employed to investigate the causal ordering between emotion indices and stock returns. Findings The results reveal a dominance of negative emotions in financial news, with fear and sadness exerting a significant influence on adverse market movements. Emotion-augmented models outperform baseline models that rely solely on historical price data, with LSTM achieving the highest predictive accuracy (MAPE = 1.58%) at the t+1 horizon. Rolling window validation confirms model robustness across different market regimes. Granger causality tests reveal that disgust, sadness and joy significantly Granger-cause stock returns, while returns in turn Granger-cause anticipation and disgust, indicating bidirectional feedback loops. These findings constitute evidence against the semi-strong form of the efficient market hypothesis (EMH) in the Pakistani market. Originality/value This study contributes novel evidence from Pakistan's emerging market by integrating emotion-level (rather than binary positive/negative) sentiment measures with deep learning models for stock prediction. It provides the first systematic test of whether emotion signals in financial news predict stock returns on the PSX, offering evidence on the semi-strong-form efficiency of the Pakistani equity market. The multi-horizon and rolling window evaluation framework offers methodological improvements over prior single-split approaches.
Purpose This study examines the relationship between firms' ESG performance and analyst forecast errors, and the moderating role of executive gender diversity, within European financial institutions. The analysis distinguishes between optimistic and pessimistic analyst forecast biases. Design/methodology/approach Using a Feasible Generalised Least Squares (FGLS) estimator, we analyse a panel of 200 European financial institutions over 2015–2024. We address endogeneity concerns through one-period lagged specifications and an instrumental-variable (2SLS) approach, using the country-year Global Gender Gap Index as an instrument for executive gender diversity. Findings ESG performance increases analyst forecast errors, consistent with a cognitive bias interpretation. Executive gender diversity significantly moderates this relationship. The effect is concentrated among optimistic forecasts, where gender-diverse leadership curbs ESG-driven over-optimism. Pessimistic forecasts remain largely unresponsive to ESG signals. Disaggregating ESG into its pillars extends this moderation to pessimistic forecasts as well. Results are robust to lagged and instrumental-variable specifications, to a two-year horizon, and to the inclusion of country fixed effects. Practical implications The findings offer guidance for regulators, directors and shareholders by highlighting executive gender diversity as a governance mechanism that enhances the credibility of ESG information for financial markets, with implications for forecast accuracy, analyst relations and investment decision-making. Originality/value This research contributes to the literature by examining the moderating role of executive gender diversity in the ESG–analyst forecast accuracy relationship, drawing on diversity theory and the resource-based view. It further reveals that this moderation operates asymmetrically across optimistic and pessimistic analyst biases within the underexplored context of European financial institutions.
Purpose This study compares the market-specific safe-haven performance of gold, the US dollar, and Bitcoin across advanced and emerging equity markets and examines whether daily changes in geopolitical risk are associated with conditional volatility in equity markets. Design/methodology/approach Using daily data from 5th January, 2016 to 9th March, 2026, the study applies market-specific exponential generalized autoregressive conditional heteroskedasticity models with exogenous variables (EGARCH-X) and dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC-GARCH) models, as well as downside safe-haven regressions, across eight equity markets. The downside models evaluate asset performance under bottom-10% and bottom-5% equity-market conditions. Findings Standardized daily changes in geopolitical risk are statistically insignificant at the 5% level across all EGARCH-X variance specifications. The DCC-GARCH estimates reveal predominantly negative correlations between the US dollar and equity markets, correlations around zero for gold, and mostly positive correlations for Bitcoin. Under bottom-5% conditions, the US dollar provides the broadest protection, servin g as a strong safe haven in two markets and a weak one in three. Gold provides weak protection in one market, whereas Bitcoin exhibits limited safe-haven performance. Originality/value The study distinguishes time-varying diversification characteristics from actual downside protection, demonstrating that predominantly negative dynamic correlations do not necessarily translate into safe-haven effectiveness during severe equity-market declines.
Purpose The primary purpose of the present study is to explore the role of generational differences in the money scripts of Indian baby boomers and millennials while integrating filial piety through the family financial socialization process. Design/methodology/approach The study adopted an empirical-phenomenological approach and analyzed interviews using Colaizzi's phenomenological method (Haase's adaptation). Semi-structured interviews were conducted with a purposive sample of 54 participants, comprising 26 Millennials and 28 Baby Boomers, to capture the lived experiences of financial socialization and cultural obligations. Findings The study finds that while millennials feel empowered by easy access to information and digital finance, they struggle with economic uncertainty and with balancing filial duties with economic burdens. The baby boomers, conversely, are more financially stable, with asset ownership and financial freedom forming a significant part of their legacy. However, their struggle lies in maintaining their financial independence while relying on the next generation's filial duties. Practical implications The study provides actionable insights for financial therapists and advisors working with South Asian clients. By recognizing the role of Sewa, practitioners can avoid mis-pathologizing intergenerational financial interdependence and instead develop culturally competent interventions that improve family financial well-being. Originality/value The study is among the first to conduct a qualitative investigation of money scripts in a collectivist culture across different generations. The study moves beyond a descriptive analysis of existing money script inventories and explores how family structure and filial piety shape beliefs about money across generations. It also links these culturally embedded money beliefs to core behavioral finance constructs, including experience-based risk conditioning, intertemporal choice, and financial well-being.
