CiteNScope — Scholarly Index & Analytics
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Research across participating journals · All insights follow your collection filters.

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OpenAlex coverage 100.0% · 3/3 recordsLast OpenAlex observation 21 Oct 2026, 07:23 UTC
PUBLICATIONS3In the selected collection
CROSSREF CITATIONS03/3 with known counts
OPENALEX CITATIONS03/3 with known counts
OPENALEX MEAN0.0Per article with a known count
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topic: Decision-Making and Behavioral Economics Remove ×
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Output and reach

Publication activity alongside incoming citations.

Publication activity

Articles grouped by publication year

Publications

Hover over a year or focus the chart and use arrow keys to inspect values.

2026 is year to date. 0 undated publications excluded.

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Citations received

OpenAlex · year of the citing work

OpenAlex

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.

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Research landscape

Leading affiliations and themes in this collection.

Full distributions & methodology ↓

Institutions

University of Mons33/3 records with data

Countries

Belgium33/3 records with data

Primary topics

Decision-Making and Behavioral Economics33/3 records with data
Two perspectives on citations

Follow the research impact

Independent sources. Clear time scales.

Lifetime citations by publication year

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.

View data & coverage

Citations received each year

OpenAlex · incoming citation links grouped by citing-work year

OpenAlex

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.

View data & coverage
Annual history: 3/3 articles. Latest history observation: 2026-10-21T07:23:08.991145+00:00. Source totals are never added together.
Research side by side

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Select journals to compare the same collection filters. Source totals stay separate. These measures describe research activity, not journal quality.

Leave all unchecked to include every matching journal. Selection affects only comparison charts.
Display range
International Journal of Finance & Banking StudiesInternational Journal of Research in Business and Social Science

Publications

Articles by publication year

View data & coverage

Crossref citations

Latest lifetime totals · grouped by publication year

View data & coverage

OpenAlex citations

Latest lifetime totals · grouped by publication year

View data & coverage

OpenAlex · Citations received

By year of the citing work · 2017–2026

View data & coverage

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.

Full analytics report All distributions, publications, comparisons & data provenance
3 MATCHING PUBLICATIONSExplore results →Export CSV ↓
INDEXED PUBLICATIONS3
CROSSREF CITATIONS0
CROSSREF · CITED PUBLICATIONS0.0%
CROSSREF · MEAN CITATIONS0.0

Crossref citation coverage: 3 / 3 publications (100.0%). Latest observation: 21 Oct 2026, 07:22 UTC. Means and percentages use records with known counts.

OpenAlex / Separate citation source

A broader view of this research

DOI-matched OpenAlex observations for the same selected publications. These counts are separate from Crossref and are never added together.

OPENALEX CITATIONS0
DOI MATCH COVERAGE100.0%3 / 3 publications
MEAN · KNOWN COUNTS ONLY0.03 known · 0 unknown
MEDIAN · KNOWN COUNTS ONLY0

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 citation distributionNo citations31–506–20021+0Unknown0

Institutions, countries & topics

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.

Institutions

3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.

Countries

3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.

Primary topics

3 / 3 with data · 0 missing or unmatched. Percentages use all 3 selected publications.

Citation year / OpenAlex

Citations received over time

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 3 / 3 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.

OpenAlex · Citations received by year

10.50 2017 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 3/32018 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 3/32019 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 3/32020 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 3/32021 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 3/32022 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 3/32023 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 3/32024 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 3/32025 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 3/32026 · All selected publications · OpenAlex · Citations received by year: 0 · Annual history coverage 3/3 2017201820192020202120222023202420252026
View openalex · citations received by year data & coverage
Citation yearJournal / scopeOpenAlex · Citations received by yearPublicationsAnnual history known
2017All selected publications033/3
2018All selected publications033/3
2019All selected publications033/3
2020All selected publications033/3
2021All selected publications033/3
2022All selected publications033/3
2023All selected publications033/3
2024All selected publications033/3
2025All selected publications033/3
2026All selected publications033/3

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 ↗

Crossref citation distribution

Latest Crossref totals for the selected publications; not citations received in the selected years.

Mean0.0
Median0
No citations31–506–20021+0Unknown0

Unknown counts are excluded from the mean, median and cited-publication rate.

Journal comparison

Same filters, source and counting rules across journals. Descriptive statistics, not a quality ranking.

Journal / publisherPublicationsCrossref citationsOpenAlex citationsOpenAlex coverageCrossref meanCrossref medianCrossref cited %*Crossref coverage
International Journal of Finance & Banking Studies
SSBFNET
1001/10.000.01/1 · 100.0%
International Journal of Research in Business and Social Science
SSBFNET
2002/20.00.00.02/2 · 100.0%

* Percentage among publications with a known citation count.

Explore publications

View all 3 →

Most cited · Crossref

JOURNAL ARTICLE

Trading Psychology Under Pressure

Alain Finet · Kevin Kristoforidis · Julie Laznicka

International Journal of Finance & Banking Studies · 2025

Read abstract

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).   

DOI 10.20525/ijfbs.v14i3.43730 Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

From anticipated regret to experienced regret: Evidence from a stock market simulation

Alain Finet · Kevin Kristoforidis · Julie Laznicka

International Journal of Research in Business and Social Science · 2026

Read abstract

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. 

DOI 10.20525/ijrbs.v15i4.50980 Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

Qualitative analysis of the influence of biases and emotions on decision-making in stock markets: the case of individual investors

Alain Finet · Julie Laznicka · Hugo Palumbo

International Journal of Research in Business and Social Science · 2025

Read abstract

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).

DOI 10.20525/ijrbs.v14i3.39920 Crossref citations0 OpenAlex citations

Selected results

JOURNAL ARTICLE

From anticipated regret to experienced regret: Evidence from a stock market simulation

Alain Finet · Kevin Kristoforidis · Julie Laznicka

International Journal of Research in Business and Social Science · 2026

Read abstract

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. 

DOI 10.20525/ijrbs.v15i4.50980 Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

Trading Psychology Under Pressure

Alain Finet · Kevin Kristoforidis · Julie Laznicka

International Journal of Finance & Banking Studies · 2025

Read abstract

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).   

DOI 10.20525/ijfbs.v14i3.43730 Crossref citations0 OpenAlex citations
JOURNAL ARTICLE

Qualitative analysis of the influence of biases and emotions on decision-making in stock markets: the case of individual investors

Alain Finet · Julie Laznicka · Hugo Palumbo

International Journal of Research in Business and Social Science · 2025

Read abstract

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).

DOI 10.20525/ijrbs.v14i3.39920 Crossref citations0 OpenAlex citations

Data coverage & freshness

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

SourceMost recent successful journal syncLatest attempt
crossref21 Oct 2026 07:22 UTC · ijrbscomplete · ijrbs · 21 Oct 2026 07:22 UTC
ojs-rest21 Oct 2026 07:22 UTC · ijrbscomplete · ijrbs · 21 Oct 2026 06:46 UTC
oai-pmh20 Oct 2026 19:56 UTC · ijfbscomplete · ijfbs · 20 Oct 2026 19:55 UTC
openalex-enrichment21 Oct 2026 07:23 UTC · ijrbscomplete · ijrbs · 21 Oct 2026 07:22 UTC
Full import history & methodology →