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This study aims to predict stock market crashes identified by the CMAX approach (current index level relative to historical maximum) during periods of global and local events, namely the subprime crisis of 2008, the political and social instability of 2011 and the COVID-19 pandemic.

Over the period 2004–2020, a log-periodic power law model (LPPL) has been employed which describes the price dynamics preceding the beginning dates of the crisis. In order to adjust the LPPL model, the Global Search algorithm was developed using the “fmincon” function.

By minimizing the sum of square errors between the observed logarithmic indices and the LPPL predicted values, the authors find that the estimated parameters satisfy all the constraints imposed in the literature. Moreover, the adjustment line of the LPPL models to the logarithms of the indices closely corresponds to the observed trend of the logarithms of the indices, which was overall bullish before the crashes. The most predicted dates correspond to the start dates of the stock market crashes identified by the CMAX approach. Therefore, the forecasted stock market crashes are the results of the bursting of speculative bubbles and, consequently, of the price deviation from their fundamental values.

The adoption of the LPPL model might be very beneficial for financial market participants in reducing their financial crash risk exposure and managing their equity portfolio risk.

This study differs from previous research in several ways. First of all, to the best of the authors' knowledge, the authors' paper is among the first to show stock market crises detection and prediction, specifically in African countries, since they generate recessionary economic and social dynamics on a large extent and on multiple regional and global scales. Second, in this manuscript, the authors employ the LPPL model, which can expect the most probable day of the beginning of the crash by analyzing excessive stock price volatility.

The education services sector faces ever-changing global market dynamics with creative disruptions. Building knowledge brands can push the higher education sector beyond its geographical boundaries into the global arena. This study aims to identify key constructs, their theoretical background and dimensions that aid in building a global knowledge brand. The authors' research focuses on adapting and validating scales for global knowledge and education services brands from well-established academic literature.

The authors have adopted a mixed methodology approach and a systematic literature review. Authors interviewed 18 subject matter experts as part of content and face validity to arrive at select constructs, dimensions and items. Quantitative methods with random sampling were adopted as the primary methodology. Initially, the survey was administered to 390 students to test preliminary results. The survey was also administered to 5,112 students at a later part of this study. Valid responses stood at 3,244 with a 63% response rate. Further, the authors conducted confirmatory factor analysis, exploratory factor analysis and structural equation modeling to test the reliability and validity of scales. This study analyzed composite reliability, convergent validity and discriminant validity to finalize items for scales. The authors also validated the hypotheses based on the discriminant validity assessment scores.

Authors' key research findings are that academic stimulus, campus infrastructure and student intent play a significant role in campus culture and events design and experience at campus. Authors were able to bring out 16 key constructs and 55 critical dimensions vital to global education services brand building. This study also adapted and validated 99 items that meet construct validity and composite reliability criteria. This study also highlights that constructs such as student intent, academic stimulus, campus infrastructure scalability, selection mechanism, pedagogical content knowledge, brand identity, events experience and campus culture play a vital role in global brand recognition.

The authors' work is fairly generalizable to education services and the higher education sector. However, this study must be extrapolated and empirically validated in other industry sectors. The research implications of this study are that it aided the authors in building theoretical background for student brand loyalty theory, student expectation theory and study loyalty theory. This study adds to the body of knowledge by contributing to theoretical concepts on students, knowledge culture, events, infrastructure and branding. Researchers can adopt the scales proposed in this study to build research models in higher education branding. This study acts as a catalyst for building theories in education services areas. Researchers can delve deep into proposed research aspects of campus infrastructure, knowledge infrastructure, campus knowledge culture, events design and events experience.

This study aids educators and brand managers to develop global education services and optimize their effort and budget. Administrators in the education services sector must focus on practical aspects of student perception, campus infrastructure, culture and events experience. Practically administrators can reorient their efforts based on this study to achieve global brand recognition.

This study highlights that students are not customers but are co-creators of value in the education sector. This study provides scales and dimensions needed to build co-creation frameworks and models.

Most research in higher education branding has not covered wider aspects of global brand building. Existing theories proposed in higher education and education services articles cover only narrower aspects of campus infrastructure, culture, events design and branding. This study presents a comprehensive list of critical factors that play a vital role in global knowledge brand building. This study highlights the constructs and scales integral to building a global education services brand.

Over the last few years, the European Central Bank (ECB) has adopted unconventional monetary policies. These measures aim to boost economic growth and increase inflation through the bond market. The purpose of this paper is to study the impact of the ECB’s quantitative easing (QE) on the investor’s behavior in the stock market.

First, the authors theoretically identify the transmission channels of the QE shocks to the stock market. Then, the authors empirically assess the financial market’s responses to QE shocks in a data-rich environment using a factor augmented VAR (FAVAR).

The results show that the ECB’s unconventional monetary policy positively affects the stock market. A QE shock leads to an increase in stock prices and a drop in the realized volatility and the implied risk premium. The authors also suggest that the ECB’s QE is transmitted to the stock market through five main channels: the liquidity, the expectation, the portfolio reallocation, the interest rates and the risk premium channels.

The findings help to better understand the behavior of stock market assets in a data-rich economic context and guide investors and policymakers in the presence of unconventional monetary tools. For instance, decision-makers and investors should consider the short-term effect of the QE interventions and the changing behavior of the financial actors over time. In addition, high stock market returns can increase risk appetite. This can lead investors to underestimate the market risk. Decision-makers and market participants should take into consideration the impact of the large injection of money through the QE, which may raise the risk of a speculative bubble in the financial market.

To the best of the authors’ knowledge, this is the first study that incorporates a theoretical and empirical analysis to explore QE transmission to the stock market in the European context. Unlike previous studies, the authors use the shadow rate proposed by Wu and Xia (2017) to quantify the effect of the ECB’s QE in a data-rich environment. The authors also include two key risk indicators – the stock market risk premium and the realized volatility – to capture investors’ behavior in the stock market following QE shocks.

This paper seeks to examine the effect of monetary policy rate (MPR) on the lending rates of commercial banks in Ghana.

The paper employed the autoregressive distributed lag (ARDL) model as well as the non-linear autoregressive distributed lag (NARDL) model econometric techniques on a quarterly time series data from 2002 to 2018.

The ARDL results revealed that, MPR has a positive and significant effect on lending rate in the long and short run. Although there exists a direct relationship between MPR and lending rate, from the NARDL revealed an asymmetric effect of MPR on lending rate to the effect that, lending rate in Ghana responds more to positive shock (a rise in MPR) compared to a negative shock (a decrease in MPR) both in the long and short run.

The paper contributes to policy and literature in Ghana by providing empirical evidence on the asymmetric effect that MPR has on lending rates in Ghana. The paper recommends among others, the establishment of a rating system of banks according to their monetary policy compliance, where highly rated banks could have for instance a reduction on borrowed reserves from the central bank.

The study intends to evaluate the impact of inflation expectation on the performance of listed commercial banks in India during 2005–2021. Inflation expectation is considered as a direct policy tool by the policymakers for stability of the economy. The study explores how inflation expectation affects the performance indicators of the Indian banking industry while controlling for a wide range of bank-specific factors.

The study applies the generalized method of moments (GMM) on a panel sample of 27 listed bank to analyse the impact of inflation expectation on banking sector performance. The data on inflation expectation are obtained from the household inflation expectation survey introduced in India by the Reserve Bank of India in 2005. Return on assets (ROA), return on equity (ROE) and Tobin's Q have been considered as the banking performance indicators in this study.

Empirical results exhibit that inflation expectation is instrumental in deciding the banking sector's performance. Inflation expectation has been found to have a significant and positive impact on accounting-based measures of banking performance. At the same time, it shows negative impact on the marketing-based measure.

The study gives a clear picture about how inflation expectation affects the banking performance and the monetary policy of the country. The study provides crucial insights to develop strategic decisions for the Indian banking sector. The adoption of proper macroeconomic policies, taking into account inflation expectation levels, is instrumental in enhancing bank's performance and in achieving economic growth.

This study contributes to the growing body of literature on the impact of inflationary conditions on banking performance. The originality lies in capturing the role of inflation expectation solely in determining banking sector performance.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.

The intention of the empirics is to contribute to the general understanding of investor responses to market price shocks. The authors review assumptions about investor behavior in response to price shocks and investigate alternative rebalancing heuristics.

The authors use market data over 40 years to define market shocks. Portfolio rebalancing implements constrained Markowitz mean-variance (MV) heuristics.

Momentum rebalancing in portfolio management outperforms contrarian rebalancing in the study interval. Sensitivity analysis by decade, sector constraints and proportion of security holdings bought or sold continue to support momentum rebalancing.

The results are consistent with under-responding to price shocks at consensus levels in financial markets. The theoretical background provides a basis for experimental lab studies of shocks of different magnitudes under conditions in which participants have information on the levels of other participants and a condition in which they can only observe their previous estimates.

Managing portfolios in the face of price disturbances of different magnitudes is informed by empirical studies and their implications for investor behavior.

This is the first study the authors can locate that uses market data with alternative rebalancing heuristics to estimate price returns from the respective heuristics over a time interval of 40 years. The authors support the results with sensitivity estimates and consider implications for the underlying agent heuristics in light of background studies.

This study delves into the nuanced implications of short-sale constraints on stock prices within the context of stock market efficiency. While existing research has explored this relationship, inconsistencies persist in their findings. The purpose of this study is to conduct a comprehensive review of literature to elucidate the reasons behind these disparities.

A systematic review of existing theoretical and empirical studies was conducted following the PRISMA method. The analysis centered on discerning the factors contributing to the divergence in projected stock prices due to these constraints. Key areas explored included assumptions related to expectations homogeneity, revisions, information uncertainty, trading motivations and fluctuations in supply and demand of risky assets.

The review uncovered multifaceted reasons for the disparities in findings regarding the influence of short-sale constraints on stock prices. Variations in assumptions related to market expectations, coupled with fluctuations in perceived information uncertainty and trading motivations, were identified as pivotal factors contributing to differing projections. Empirical evidence disparities stemmed from the use of proxies for short-sale constraints, varied sample periods, market structure nuances, regulatory changes and the presence of option trading.

This study emphasizes the significance of not oversimplifying the impact of short-sale constraints on stock prices. It highlights the need to understand these effects within the broader context of market structure and methodological considerations. By delineating the intricate interplay of factors affecting stock prices under short-sale constraints, this review provides a nuanced perspective, contributing to a more comprehensive understanding in the field.

From these data, the authors construct an uncertainty index through the use of a vector autoregressive (VAR) model to measure the impact of uncertainty on GDP, controlling for inflation, which may affect macroeconomic performance. Results indicate that uncertainty is negatively correlated with the economic cycle and the inter-annual variation of the biannual average product.

This study empirically explores the dynamics of expectations of the Uruguayan manufacturing firms about industrial economic growth. This study explores the dynamics of the industrial economic growth expectations of Uruguayan manufacturing firms. The empirical research is based on firms' expectations data collected through a monthly survey carried out by the Chamber of Industries of Uruguay (CIU) in 2003–2018.

Granger causality tests show that uncertainty Granger-causes industrial production growth and a one standard deviation shock on uncertainty generates a contraction in the industrial production growth rate. Finally, the authors use statistical and network tools to identify groups of firms with similar performance on expectations. Results show that higher uncertainty is associated with smaller, more interconnected groups of firms, and that the number of homogeneous groups and the distance between groups increases with uncertainty. These findings suggest that policies focused on the coordination of expectations can lead to the development of stable opinion groups.

The paper introduces new data and new methodologies to analyze the dynamics of expectations of manufacturing firms about industrial economic growth.

1. An empirical approach to compare expectations of firms is introduced.

2. The occurrence of groups of opinion is tested.

3. Central companies in the network of expectations are detected.

4. More uncertainty implies a higher degree of discrepancy between the overall firm’s opinions and more compact opinion groups.

An empirical approach to compare expectations of firms is introduced.

The occurrence of groups of opinion is tested.

Central companies in the network of expectations are detected.

More uncertainty implies a higher degree of discrepancy between the overall firm’s opinions and more compact opinion groups.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.