Publication activity
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
Research across participating journals · All insights follow your collection filters.
Publication activity alongside incoming citations.
Articles grouped by publication year
Hover over a year or focus the chart and use arrow keys to inspect values.
2026 is year to date. 0 undated publications excluded.
OpenAlex · year of the citing work
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Leading affiliations and themes in this collection.
Full distributions & methodology ↓Independent sources. Clear time scales.
Crossref and OpenAlex · separate totals for the same publication cohorts
Hover over a year or focus the chart and use arrow keys to inspect values.
Latest lifetime totals, grouped by the cited article’s year. These are not citations received in that year.
OpenAlex · incoming citation links grouped by citing-work year
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
Select journals to compare the same collection filters. Source totals stay separate. These measures describe research activity, not journal quality.
Articles by publication year
Latest lifetime totals · grouped by publication year
Latest lifetime totals · grouped by publication year
By year of the citing work · 2017–2026
2026 is year to date. Crossref and OpenAlex lifetime charts share a citation scale. Unknown observations appear as gaps; known zero stays zero. Annual receipts use available OpenAlex histories and may not sum to lifetime totals. 0 undated publications excluded from publication-year charts.
Crossref citation coverage: 6 / 7 publications (85.7%). Latest observation: 21 Oct 2026, 07:22 UTC. Means and percentages use records with known counts.
DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.
Observation range: 21 Oct 2026 07:23 UTC — 22 Oct 2026 12:49 UTC. Missing matches are not zero citations. Previous observations may remain if a refresh fails or a DOI no longer matches.
OpenAlex affiliations and primary-topic classification. Each publication counts once per institution or country; multi-affiliated publications appear in multiple groups, so percentages can sum above 100%. Countries describe author affiliations, not nationality. Topics use one primary topic per publication.
6 / 7 with data · 1 missing or unmatched. Percentages use all 7 selected publications.
6 / 7 with data · 1 missing or unmatched. Percentages use all 7 selected publications.
7 / 7 with data · 0 missing or unmatched. Percentages use all 7 selected publications.
Citations are the latest recorded totals for articles published in each year, not citations received during that year. Crossref and OpenAlex remain separate and share the same citation scale. 2026 is incomplete; newer articles have had less time to receive citations.
0 undated publications excluded. Unknown citation counts are shown as —, never as zero. Partial coverage can understate totals; expand each table for known / total records.
| Publication year | Journal / scope | Publications | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2026 | All selected publications | 7 | 7 | 6/7 | 7/7 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2026 | All selected publications | 0 | 7 | 6/7 | 7/7 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2026 | All selected publications | 0 | 7 | 6/7 | 7/7 |
Incoming citations to the selected publications, grouped by the year of the citing work. This is separate from lifetime citation totals grouped by the cited article’s publication year.
The publication filters above define which articles receive the citations. The citation-year range below only controls this chart and the incoming-citation comparison. Removing publication-year filters includes older articles’ incoming citations too.
Annual history available for 7 / 7 selected publications. Supported display window: 2017–2026; 2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Missing histories and years beyond a saved observation’s window remain unknown (—); partial coverage can understate totals.
Annual-history observations (UTC): 2026-10-21T07:23:04.610884+00:00 — 2026-10-22T12:49:09.393200+00:00. Older observations may be retained after failed or incomplete refreshes.
| Citation year | Journal / scope | OpenAlex · Citations received by year | Publications | Annual history known |
|---|---|---|---|---|
| 2017 | All selected publications | 0 | 7 | 7/7 |
| 2018 | All selected publications | 0 | 7 | 7/7 |
| 2019 | All selected publications | 0 | 7 | 7/7 |
| 2020 | All selected publications | 0 | 7 | 7/7 |
| 2021 | All selected publications | 0 | 7 | 7/7 |
| 2022 | All selected publications | 0 | 7 | 7/7 |
| 2023 | All selected publications | 0 | 7 | 7/7 |
| 2024 | All selected publications | 0 | 7 | 7/7 |
| 2025 | All selected publications | 0 | 7 | 7/7 |
| 2026 | All selected publications | 0 | 7 | 7/7 |
Counts represent citation links, not unique citing papers across a journal: one paper citing two selected articles contributes two links. Older citations outside this window are excluded, so annual sums need not equal lifetime totals. OpenAlex methodology ↗
Publications by publication year. 2026 is an incomplete year.
| Period | Publications |
|---|---|
| 2026 | 7 |
Latest Crossref totals for the selected publications; not citations received in the selected years.
Unknown counts are excluded from the mean, median and cited-publication rate.
Same filters, source and counting rules across journals. Descriptive statistics, not a quality ranking.
| Journal / publisher | Publications | Crossref citations | OpenAlex citations | OpenAlex coverage | Crossref mean | Crossref median | Crossref cited %* | Crossref coverage |
|---|---|---|---|---|---|---|---|---|
| International Journal of Business Ecosystem & Strategy Bussecon International Academy | 2 | 0 | 0 | 2/2 | 0.0 | 0.0 | 0.0 | 2/2 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 1 | — | 0 | 1/1 | — | — | — | 0/1 · 0.0% |
| International Journal of Research in Business and Social Science SSBFNET | 4 | 0 | 0 | 4/4 | 0.0 | 0.0 | 0.0 | 4/4 · 100.0% |
* Percentage among publications with a known citation count.
Market participants usually seek exposure to global beta which are best achieved usually through large capitalization equities where higher idiosyncratic risk represents a potential source of alpha. This study empirically explored the persistence and drivers of idiosyncratic risk across the S&P 500, CAC 40, FTSE 100, DAX, and Nikkei 225 for a period of 20 years (2004 – 2024). Utilizing a quantitative risk decomposition framework, the analysis revealed a stark dichotomy where the S&P 500 exhibits minimal idiosyncratic risk, functioning as a proxy for global systematic factors. The Nikkei 225 was dominated by idiosyncratic risk which can be attributed to the unique domestic monetary policy. The European counterparts occupied a middle ground, with idiosyncratic risk amplified by sectoral concentrations. The correlation analysis further suggested a deep regional integration within the European markets and a diversification potential with the Japanese equity market. The findings challenged the notion that globalization has harmonized global financial market returns. These findings also demonstrates that idiosyncratic risk remains a significant and potentially compensable component of the concept of international investing, necessitating a strategic shift towards factor aware asset allocation.
Traditional asset pricing models assume rational investors and informational efficiency. However, persistent anomalies, and recurrent financial crises challenge these assumptions. This study synthesizes findings from leading international research to evaluate how cognitive biases shape asset prices and provides a comprehensive review and synthesis of behavioral asset pricing in international financial markets over a period from 2004 to 2024. Evidence from the extensive stock market data, cross-market behavioral heterogeneity and survey-based risk premia consistently demonstrates that behavioral effects are time-varying and regime-dependent. The findings indicate that behavioral heterogeneity intensifies during boom and bubble periods, diminishes during crashes, and generates predictable patterns in returns across asset classes and countries. Overall, the study re-iterates support for a dynamic behavioral asset pricing framework that integrates psychological factors into international financial market analysis.
This research investigates how the overall mood of investors influences the profits of the major commercial banks traded on the JSE. Although behavioural factors are increasingly acknowledged in financial markets, there is very little empirical evidence that investor sentiment has any impact on the South African banking sector, despite this sector being the main driver of financial stability and economic growth. This study investigated whether investor sentiment has a significant impact on bank stock returns in both the short and long term. The research period ranged from September 2008 to March 2025 and used monthly return data. The paper implements the Autoregressive Distributed Lag (ARDL) approach to illustrate immediate and long-term relationships between sentiment, macroeconomic variables, and bank performance. The findings demonstrate that investor sentiment is one of the significant factors influencing bank stock returns, but the effect varies between different banks. Sentiment, on average, tends to have a negative effect on the majority of banks in the long run, which means that in times of extreme optimistimism are followed by those of overvaluation and subsequent correction. However, one bank revealed a positive return-sentiment relationship which suggests that characteristics unique to a specific bank can have a moderating effect on behavioural factors. The short-run outcomes indicate that bank stock returns are very reactive to changes in sentiment and macroeconomic shocks and at the same time, diagnostic tests for model reliability support these results. The paper contributes to behavioural finance literature by delivering fresh data derived from an emerging market and providing an answer to the problem of bank-level disaggregated analysis that has been neglected. In addition to the theoretical contributions, the results can be turned into actionable insights by investors, policymakers, and portfolio managers who will be reminded of the importance of including sentiment indicators in their bank performance assessment and risk management activities in the South African financial sector.
The Johannesburg Stock Exchange (JSE) is considered a cornerstone of the South African economy, with its size-based indices serving as critical benchmarks for passive investment and portfolio construction. Although macroeconomic variables affect stock returns, the existing literature has primarily relied on linear models which yield heterogeneous results and do not allow for the dynamic and changing structure of financial markets. Consequently, this study investigates the nonlinear, regime-dependent effects of key macroeconomic variables such as inflation, money supply, short- and long-term interest rates, GDP, and the real effective exchange rate (REER) on the returns of the JSE Fledgling, Small-Cap, and Mid-Cap indices. Utilizing monthly data from 2002 to 2024, the study employs a two-state Markov Regime-Switching which allows the study to endogenously address the changes in bull and bear market regimes. The most pressing finding is that the Real Effective Exchange Rate (REER) is a consistently positive driver of returns across all market conditions, while the influences of other variables, such as interest rates and GDP growth, are highly dependent on the market regime (bull or bear) and the specific index size. Finally, these findings lead us to conclude that the relationship between macroeconomic fundamentals and stock returns is not a constant phenomenon but rather is fundamentally dependent on the regime, lending strong support for the Adaptive Market Hypothesis. This insight is significant for investors and portfolio managers, underscoring the necessity of adaptive, regime-sensitive strategies for effective risk management and portfolio allocation on the JSE.
Herding behaviour has been shown to significantly influence market performance over the years, often leading to mispricing of assets due to asset bubbles. This study aimed to analyse herding behaviour on the Johannesburg Stock Exchange (JSE) before, during and after COVID-19. A cross-sectional absolute deviation (CSAD) model was used to determine the presence of herding during the specified periods, from the 1st of January 2018 to the 31st of January 2024, while causes and implications were explored further to offer a better understanding of herding behaviour. Herding behaviour was detected in all observed periods, which was not anticipated, which raised the need to discuss the policies in place to curb irrational investor behaviour. The evidence suggests that portfolio managers should understand that herding behaviour can cause market movements that are not necessarily rational or based on fundamentals. Instead, the results indicate that prices can be influenced by collective sentiment. As such, portfolio managers can assess the risks associated with blindly following the crowd and formulate strategies to avoid it. Portfolio managers can also educate their clients on the risks associated with herding behaviour to set realistic expectations and prioritise long-term stability over short-term trends.
Market participants usually seek exposure to global beta which are best achieved usually through large capitalization equities where higher idiosyncratic risk represents a potential source of alpha. This study empirically explored the persistence and drivers of idiosyncratic risk across the S&P 500, CAC 40, FTSE 100, DAX, and Nikkei 225 for a period of 20 years (2004 – 2024). Utilizing a quantitative risk decomposition framework, the analysis revealed a stark dichotomy where the S&P 500 exhibits minimal idiosyncratic risk, functioning as a proxy for global systematic factors. The Nikkei 225 was dominated by idiosyncratic risk which can be attributed to the unique domestic monetary policy. The European counterparts occupied a middle ground, with idiosyncratic risk amplified by sectoral concentrations. The correlation analysis further suggested a deep regional integration within the European markets and a diversification potential with the Japanese equity market. The findings challenged the notion that globalization has harmonized global financial market returns. These findings also demonstrates that idiosyncratic risk remains a significant and potentially compensable component of the concept of international investing, necessitating a strategic shift towards factor aware asset allocation.
Traditional asset pricing models assume rational investors and informational efficiency. However, persistent anomalies, and recurrent financial crises challenge these assumptions. This study synthesizes findings from leading international research to evaluate how cognitive biases shape asset prices and provides a comprehensive review and synthesis of behavioral asset pricing in international financial markets over a period from 2004 to 2024. Evidence from the extensive stock market data, cross-market behavioral heterogeneity and survey-based risk premia consistently demonstrates that behavioral effects are time-varying and regime-dependent. The findings indicate that behavioral heterogeneity intensifies during boom and bubble periods, diminishes during crashes, and generates predictable patterns in returns across asset classes and countries. Overall, the study re-iterates support for a dynamic behavioral asset pricing framework that integrates psychological factors into international financial market analysis.
Herding behaviour has been shown to significantly influence market performance over the years, often leading to mispricing of assets due to asset bubbles. This study aimed to analyse herding behaviour on the Johannesburg Stock Exchange (JSE) before, during and after COVID-19. A cross-sectional absolute deviation (CSAD) model was used to determine the presence of herding during the specified periods, from the 1st of January 2018 to the 31st of January 2024, while causes and implications were explored further to offer a better understanding of herding behaviour. Herding behaviour was detected in all observed periods, which was not anticipated, which raised the need to discuss the policies in place to curb irrational investor behaviour. The evidence suggests that portfolio managers should understand that herding behaviour can cause market movements that are not necessarily rational or based on fundamentals. Instead, the results indicate that prices can be influenced by collective sentiment. As such, portfolio managers can assess the risks associated with blindly following the crowd and formulate strategies to avoid it. Portfolio managers can also educate their clients on the risks associated with herding behaviour to set realistic expectations and prioritise long-term stability over short-term trends.
This research investigates how the overall mood of investors influences the profits of the major commercial banks traded on the JSE. Although behavioural factors are increasingly acknowledged in financial markets, there is very little empirical evidence that investor sentiment has any impact on the South African banking sector, despite this sector being the main driver of financial stability and economic growth. This study investigated whether investor sentiment has a significant impact on bank stock returns in both the short and long term. The research period ranged from September 2008 to March 2025 and used monthly return data. The paper implements the Autoregressive Distributed Lag (ARDL) approach to illustrate immediate and long-term relationships between sentiment, macroeconomic variables, and bank performance. The findings demonstrate that investor sentiment is one of the significant factors influencing bank stock returns, but the effect varies between different banks. Sentiment, on average, tends to have a negative effect on the majority of banks in the long run, which means that in times of extreme optimistimism are followed by those of overvaluation and subsequent correction. However, one bank revealed a positive return-sentiment relationship which suggests that characteristics unique to a specific bank can have a moderating effect on behavioural factors. The short-run outcomes indicate that bank stock returns are very reactive to changes in sentiment and macroeconomic shocks and at the same time, diagnostic tests for model reliability support these results. The paper contributes to behavioural finance literature by delivering fresh data derived from an emerging market and providing an answer to the problem of bank-level disaggregated analysis that has been neglected. In addition to the theoretical contributions, the results can be turned into actionable insights by investors, policymakers, and portfolio managers who will be reminded of the importance of including sentiment indicators in their bank performance assessment and risk management activities in the South African financial sector.
This paper investigates whether personality traits, affects, and anticipated emotions measured before a stock market simulation explain participants’ trading style. The study relies on a sample of 133 undergraduate students from the University of Mons, Belgium, who participated in a four-hour CAC 40 trading simulation with a virtual portfolio of EUR 100,000. Regressions were estimated on several dimensions of trading style: number of transactions, minutes per order, average transaction size, average cash holdings, and portfolio volatility. The results show that psychological variables explain little of trading frequency or pacing but are informative about the structure of market exposure. Negative affect is associated with higher cash retention, whereas positive anticipated emotions are associated with lower cash holdings. Gender and openness also predict position size, while gender, age, and openness are significant predictors of portfolio volatility. Overall, the findings suggest that psychology explains less how much individuals trade than how they structure their market exposure.
Source coverage refers to this result set; a publication may occur in several sources. Sync times refer to contributing journals.
Missing abstracts: 0 · Missing authors: 0 · Missing publication dates: 0 · Dates without day precision: 0
| Source | Most recent successful journal sync | Latest attempt |
|---|---|---|
| crossref | 21 Oct 2026 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |
| ojs-rest | 21 Oct 2026 07:22 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 06:46 UTC |
| oai-pmh | 20 Oct 2026 19:56 UTC · ijfbs | complete · ijfbs · 20 Oct 2026 19:55 UTC |
| openalex-enrichment | 21 Oct 2026 07:23 UTC · ijrbs | complete · ijrbs · 21 Oct 2026 07:22 UTC |