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: 4 / 5 publications (80.0%). 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 00:22 UTC — 21 Oct 2026 07:23 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.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 selected publications.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 selected publications.
5 / 5 with data · 0 missing or unmatched. Percentages use all 5 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 |
|---|---|---|---|---|---|
| 2025 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2026 | All selected publications | 2 | 2 | 1/2 | 2/2 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2025 | All selected publications | 0 | 3 | 3/3 | 3/3 |
| 2026 | All selected publications | 0 | 2 | 1/2 | 2/2 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2025 | All selected publications | 0 | 3 | 3/3 | 3/3 |
| 2026 | All selected publications | 0 | 2 | 1/2 | 2/2 |
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 5 / 5 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): 2026-10-21T00:22:06.120304+00:00 — 2026-10-21T07:23:08.991145+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 | 5 | 5/5 |
| 2018 | All selected publications | 0 | 5 | 5/5 |
| 2019 | All selected publications | 0 | 5 | 5/5 |
| 2020 | All selected publications | 0 | 5 | 5/5 |
| 2021 | All selected publications | 0 | 5 | 5/5 |
| 2022 | All selected publications | 0 | 5 | 5/5 |
| 2023 | All selected publications | 0 | 5 | 5/5 |
| 2024 | All selected publications | 0 | 5 | 5/5 |
| 2025 | All selected publications | 0 | 5 | 5/5 |
| 2026 | All selected publications | 0 | 5 | 5/5 |
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 |
|---|---|
| 2025 | 3 |
| 2026 | 2 |
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 Finance & Banking Studies SSBFNET | 2 | 0 | 0 | 2/2 | 0.0 | 0 | 0.0 | 1/2 · 50.0% |
| International Journal of Research in Business and Social Science SSBFNET | 3 | 0 | 0 | 3/3 | 0.0 | 0 | 0.0 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
Through a thematic analysis, we analyze the evolution of cognitive biases among eight participants (students) during a three-day trading simulation in a market that was perceived as unfavorable. The biases studied include overconfidence, representativeness, anchoring, herd behavior, availability and prospect theory. Prospect Theory, particularly through loss aversion, is strongly present and dominates the experience of most participants. It is perceived as ‘difficult’ even with virtual money. Some manage loss aversion with strict stop losses, while others, when confronted with losses, may take more risks in order to try to ‘recover’. Gains generate moderate satisfaction rather than euphoria. Virtual money seems to reduce the emotions connected with gains and losses for some participants. The Availability Bias is widespread. The participants focus on easily accessible information such as graphs, well-known company names, basic news and widely used indicators. Fundamental analysis is often rejected as too complex, time-consuming or irrelevant. Anchoring Bias is variable. Some participants use specific limits as reference points. Representativeness bias is generally stable throughout the experiment. It involves using familiarity with companies as a decision-making criterion, or applying principles perceived as effective, such as using well-known technical indicators. Overconfidence varies considerably from one participant to another, influenced by previous experience and results achieved. It can be very low at the beginning, grow with confirmed intuition, or decrease in response to an unfavorable market. In the context of the study, Herd Behavior corresponds to a desire to find comfort in the group of participants in order to overcome a feeling of isolation, rather than a desire to copy behavior. The key findings of our research demonstrate a strong aversion to loss (prospect theory).
This study examines the determinants of post-simulation regret in a stock market exercise involving 133 second-year undergraduate management students. Using data from a four-hour trading simulation, it relates post-simulation regret to anticipated regret, portfolio return, trading-style indicators and personality traits. Five regression models are estimated with a common specification, each including a different proxy for trading style. The results show that anticipated regret is positively associated with post-simulation regret, while portfolio return is negatively related to regret and is significant or marginally significant across models. Conscientiousness also emerges as a significant positive predictor. By contrast, none of the trading-style proxies reaches standard significance levels. The findings suggest that regret in simulated trading is explained more by prior regret disposition, portfolio performance, and personality than by trading style.
Using a methodology from experimental finance based on qualitative analysis tools, this article aims at analyzing the potential existence of the ‘all that glitters attracts’ bias in order to explain the behavior of individual investors. For this purpose, we observed the investment strategies and behaviors of eight students in a continuous trading situation over three days. Using a fictional portfolio of 100,000 euros, the participants were given the opportunity to trade shares in companies from the CAC40 index (the 40 largest market capitalizations on the French market). Regarding the size of the companies in the index, we observe a high volume of information disclosed to the markets which could, to a certain extent, complicate the interpretation of the information signals sent. Based on trading journals - and more specifically the number of transactions carried out on specific stocks - we find that participants are strongly focused on companies that are closely followed by financial analysts and receive the most media coverage. To support our findings, we have also used a process of participative observation to collect first-hand experiences from participants during the experiment. The different elements collected suggest that the participants’ decision-making processes were largely influenced by the intensity of the companies’ communication and their presence in the information flows provided by the stock market websites. This conclusion leads us to believe that, in the mind of individual investors, marketing linked to the stock market life of companies seems to prevail over any informational content. In any case, in their decision-making process, the participants clearly selected shares that were in the spotlight, and so, depending on the context of analysis and the investigative tools used, we confirm the presence of an ‘anything that glitters attracts’ bias, a strong focus on available information and some herd effects. The contribution of this article is threefold: the analysis of a bias largely neglected by scientific studies in the financial field, the use of an innovative experimental protocol, and the use of methodological tools that provide an assessment of individuals' personal perceptions.
Using a qualitative experimental design underpinned by elements of experimental finance, we analyze decision-making processes on stock markets, using a student population. The various results obtained, based on the tools used (open and closed questionnaires as well as trading journals), demonstrate the presence of certain biases (representativeness bias, rank bias, presence of heuristics and “all that glitters attracts”) and the influence of specific emotions (anger, fear, anxiety and sadness) in decision-making. The conclusions drawn from the analysis allow us to suggest that the development of biases and emotional pressure are strongly conditioned by the general context in which decision-making takes place, as well as by some socio-demographic characteristics of the participants (in particular, gender).
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.
This study examines the determinants of post-simulation regret in a stock market exercise involving 133 second-year undergraduate management students. Using data from a four-hour trading simulation, it relates post-simulation regret to anticipated regret, portfolio return, trading-style indicators and personality traits. Five regression models are estimated with a common specification, each including a different proxy for trading style. The results show that anticipated regret is positively associated with post-simulation regret, while portfolio return is negatively related to regret and is significant or marginally significant across models. Conscientiousness also emerges as a significant positive predictor. By contrast, none of the trading-style proxies reaches standard significance levels. The findings suggest that regret in simulated trading is explained more by prior regret disposition, portfolio performance, and personality than by trading style.
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.
Using a methodology from experimental finance based on qualitative analysis tools, this article aims at analyzing the potential existence of the ‘all that glitters attracts’ bias in order to explain the behavior of individual investors. For this purpose, we observed the investment strategies and behaviors of eight students in a continuous trading situation over three days. Using a fictional portfolio of 100,000 euros, the participants were given the opportunity to trade shares in companies from the CAC40 index (the 40 largest market capitalizations on the French market). Regarding the size of the companies in the index, we observe a high volume of information disclosed to the markets which could, to a certain extent, complicate the interpretation of the information signals sent. Based on trading journals - and more specifically the number of transactions carried out on specific stocks - we find that participants are strongly focused on companies that are closely followed by financial analysts and receive the most media coverage. To support our findings, we have also used a process of participative observation to collect first-hand experiences from participants during the experiment. The different elements collected suggest that the participants’ decision-making processes were largely influenced by the intensity of the companies’ communication and their presence in the information flows provided by the stock market websites. This conclusion leads us to believe that, in the mind of individual investors, marketing linked to the stock market life of companies seems to prevail over any informational content. In any case, in their decision-making process, the participants clearly selected shares that were in the spotlight, and so, depending on the context of analysis and the investigative tools used, we confirm the presence of an ‘anything that glitters attracts’ bias, a strong focus on available information and some herd effects. The contribution of this article is threefold: the analysis of a bias largely neglected by scientific studies in the financial field, the use of an innovative experimental protocol, and the use of methodological tools that provide an assessment of individuals' personal perceptions.
Through a thematic analysis, we analyze the evolution of cognitive biases among eight participants (students) during a three-day trading simulation in a market that was perceived as unfavorable. The biases studied include overconfidence, representativeness, anchoring, herd behavior, availability and prospect theory. Prospect Theory, particularly through loss aversion, is strongly present and dominates the experience of most participants. It is perceived as ‘difficult’ even with virtual money. Some manage loss aversion with strict stop losses, while others, when confronted with losses, may take more risks in order to try to ‘recover’. Gains generate moderate satisfaction rather than euphoria. Virtual money seems to reduce the emotions connected with gains and losses for some participants. The Availability Bias is widespread. The participants focus on easily accessible information such as graphs, well-known company names, basic news and widely used indicators. Fundamental analysis is often rejected as too complex, time-consuming or irrelevant. Anchoring Bias is variable. Some participants use specific limits as reference points. Representativeness bias is generally stable throughout the experiment. It involves using familiarity with companies as a decision-making criterion, or applying principles perceived as effective, such as using well-known technical indicators. Overconfidence varies considerably from one participant to another, influenced by previous experience and results achieved. It can be very low at the beginning, grow with confirmed intuition, or decrease in response to an unfavorable market. In the context of the study, Herd Behavior corresponds to a desire to find comfort in the group of participants in order to overcome a feeling of isolation, rather than a desire to copy behavior. The key findings of our research demonstrate a strong aversion to loss (prospect theory).
Using a qualitative experimental design underpinned by elements of experimental finance, we analyze decision-making processes on stock markets, using a student population. The various results obtained, based on the tools used (open and closed questionnaires as well as trading journals), demonstrate the presence of certain biases (representativeness bias, rank bias, presence of heuristics and “all that glitters attracts”) and the influence of specific emotions (anger, fear, anxiety and sadness) in decision-making. The conclusions drawn from the analysis allow us to suggest that the development of biases and emotional pressure are strongly conditioned by the general context in which decision-making takes place, as well as by some socio-demographic characteristics of the participants (in particular, gender).
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 |