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 / 6 publications (100.0%). 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 07:22 UTC — 22 Oct 2026 01:52 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 / 6 with data · 0 missing or unmatched. Percentages use all 6 selected publications.
6 / 6 with data · 0 missing or unmatched. Percentages use all 6 selected publications.
6 / 6 with data · 0 missing or unmatched. Percentages use all 6 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 |
|---|---|---|---|---|---|
| 2020 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | 0 | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | Crossref citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 6 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 1 | 3 | 3/3 | 3/3 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 0 | 1 | 1/1 | 1/1 |
| Publication year | Journal / scope | OpenAlex citations | Publications | Crossref known | OpenAlex known |
|---|---|---|---|---|---|
| 2020 | All selected publications | 5 | 1 | 1/1 | 1/1 |
| 2021 | All selected publications | — | 0 | 0/0 | 0/0 |
| 2022 | All selected publications | 3 | 3 | 3/3 | 3/3 |
| 2023 | All selected publications | 1 | 1 | 1/1 | 1/1 |
| 2024 | All selected publications | 0 | 1 | 1/1 | 1/1 |
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 6 / 6 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): 2026-10-21T07:22:43.959444+00:00 — 2026-10-22T01:52:38.081305+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 | 6 | 6/6 |
| 2018 | All selected publications | 0 | 6 | 6/6 |
| 2019 | All selected publications | 0 | 6 | 6/6 |
| 2020 | All selected publications | 0 | 6 | 6/6 |
| 2021 | All selected publications | 0 | 6 | 6/6 |
| 2022 | All selected publications | 1 | 6 | 6/6 |
| 2023 | All selected publications | 2 | 6 | 6/6 |
| 2024 | All selected publications | 1 | 6 | 6/6 |
| 2025 | All selected publications | 5 | 6 | 6/6 |
| 2026 | All selected publications | 0 | 6 | 6/6 |
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 |
|---|---|
| 2020 | 1 |
| 2022 | 3 |
| 2023 | 1 |
| 2024 | 1 |
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 | 1 | 0 | 0 | 1/1 | 0.0 | 0 | 0.0 | 1/1 · 100.0% |
| International Journal of Finance & Banking Studies SSBFNET | 2 | 1 | 1 | 2/2 | 0.5 | 0.5 | 50.0 | 2/2 · 100.0% |
| International Journal of Research in Business and Social Science SSBFNET | 3 | 7 | 8 | 3/3 | 2.33 | 1 | 66.7 | 3/3 · 100.0% |
* Percentage among publications with a known citation count.
This study aims to determine the effect of six-factor (market return, firm size, value, profitability, investment, human capital) on excess return in companies with shares listed in LQ45 for the 2015-2019 period. The population of this study consists of 45 LQ45 companies listed on the Indonesia Stock Exchange (IDX). The purposive sampling method is the sample selection method that is used in this study. As a result, it shows that there are 26 companies that fit the criteria and taken as samples. Furthermore, the data analysis method that is used in this study is the panel data regression model. Based on the results of this study, it is showed that partially, market return (X1), firm size (X2), profitability (X4), and investment (X5) have a significant positive effect on the excess return of companies listed in LQ45 for the 2015-2019 period. Whereas, value (X3) and human capital (X6) do not have a significant effect on the excess return of companies listed in LQ45 for the 2015-2019 period partially. Another result also shows that simultaneously or as a whole, six-factors (market return, firm size, value, profitability, investment, human capital) have a significant effect on the excess return of companies listed in LQ45 for the 2015-2019 period.
Mutual fund is one of the investment instruments widely used as an investment option. There are two essential things to be considered by the Investment Manager as a mutual fund manager: the stock selection and the market timing ability. The Covid-19 pandemic has caused many investors to experience potential investment losses. Understanding stock selection and market timing will be needed for determining which Investment Managers perform well in managing equity mutual fund, especially while dealing with the Covid-19 pandemic. The objective of this study is to analyze the ability of Investment Managers to perform stock selection and market timing, the performance of equity mutual fund, and the effect of stock selection and market timing ability on the performance of equity mutual fund in Indonesia. This study applies the Fama net selectivity methods to assess stock selection ability and Treynor-Mazuy and Henriksson-Merton models to analyze market timing ability. Sharpe, Treynor, and Jensen's alpha methods are also used for measuring mutual fund performance. The data is processed per mutual fund to recognize each mutual fund's stock selection and market timing ability. Furthermore, the research spans two time periods: before and following the Covid-19 pandemic. A dataset of 104 equity mutual funds from 2016-2021 has been analyzed. The findings show that only 9 and 12 Investment Managers have stock selection ability prior to and following the Covid-19 pandemic, respectively. According to Treynor Mazuy model, there are only two Investment Managers with market timing ability prior to the Covid-19 pandemic, and only twelve Investment Managers have market timing ability during the Covid-19 pandemic, according to the Henriksson-Merton model. Every mutual fund with stock selection ability outperformed the benchmark. However, not all equity mutual fund with market timing ability outperformed the benchmark.
This study aims to (i) test the behavior bias of gamblers fallacy occurs at the time of uptrend and downtrend conditions; (ii) test the behavior bias of halo effect occurs at the time of uptrend and downtrend conditions; and (iii) test the behavior bias of familiarity effect occurs at the time of uptrend and downtrend conditions. The number of samples in the study was as many as 41 people. The test equipment used is One-Sample t-Test and Paired t-Test by using statistical package for social scientists (SPSS) as a static test tool. The results of this study show that: (i) Gamblers' fallacy that occurs when the uptrend condition is greater than when the condition is downtrend; (ii) Halo effect that occurs when the uptrend condition is greater than when the downtrend condition; (iii) Familiarity effect that occurs when the uptrend condition is greater than when the downtrend condition.
This study delves into the performance of Indonesian state-owned enterprises (BUMN) stocks from February 2021 to October 2023, attracting investor attention due to their strategic position in the national economy. However, the stock performance during this period exhibited suboptimal movements, failing to surpass the returns of the Indonesia Stock Exchange Composite Index (IHSG). To address this, a prudent investment strategy is essential, encompassing portfolio construction and management. In portfolio construction, the study employs proven smart beta strategies, known for delivering superior returns compared to traditional portfolios. Portfolio management involves testing two strategies: a passive approach utilizing the buy-and-hold technique and an active approach employing portfolio recomposition. This quantitative descriptive study utilizes historical closing price data, forming portfolios based on selected beta groups. Weighting is adjusted, with higher beta stocks receiving a larger allocation in the portfolio. The research compares two beta groups (high and low) and two investment strategies (active and passive) using the Sharpe, Treynor, and Jensen indices. The results indicate that the high-beta group and active strategy outperform, aligning with the investment adage "high risk, high return." The high-beta group yields higher returns due to its increased risk profile, consistent with the notion of high-risk, high-reward investments. The active strategy provides opportunities to discard underperforming stocks and enhance the weighting of well-performing ones during recomposition. These findings are valuable for investors seeking to capitalize on BUMN stocks at lower prices. The study aids in minimizing negative returns (losses) by identifying the most suitable strategy for BUMN portfolios. Investors can leverage this research to make informed decisions and navigate the challenges associated with investing in state-owned enterprises.
The Indonesia Stock Exchange has experienced high growth in its 32 years. The Composite Stock Price Index rose 1.012% in that period, or an average of 31.63% per year. This study analyzes the risk premium on the Indonesia Stock Exchange (IDX) in the last 32 years. In this study, we found the following facts. First, the risk premium at IDX is 2.24% per year if using annual data and 5.54% when using annualized monthly data. It seems that the risk premium in IDX is sensitive to the return calculation horizon used. Second, in the 1997-1998 economic and 2008 financial crises, the risk premium experienced a sharp decline but was followed by a sharp increase in the following months. Third, the macroeconomic variables of inflation and currency exchange rates significantly affect the risk premium, but the interest rate does not. The effect of macroeconomic variables on the risk premium is also sensitive to the horizon of the return calculation used. The results above are when using annual data. The three macroeconomic variables do not affect the risk premium when using monthly data.
This study delves into the performance of Indonesian state-owned enterprises (BUMN) stocks from February 2021 to October 2023, attracting investor attention due to their strategic position in the national economy. However, the stock performance during this period exhibited suboptimal movements, failing to surpass the returns of the Indonesia Stock Exchange Composite Index (IHSG). To address this, a prudent investment strategy is essential, encompassing portfolio construction and management. In portfolio construction, the study employs proven smart beta strategies, known for delivering superior returns compared to traditional portfolios. Portfolio management involves testing two strategies: a passive approach utilizing the buy-and-hold technique and an active approach employing portfolio recomposition. This quantitative descriptive study utilizes historical closing price data, forming portfolios based on selected beta groups. Weighting is adjusted, with higher beta stocks receiving a larger allocation in the portfolio. The research compares two beta groups (high and low) and two investment strategies (active and passive) using the Sharpe, Treynor, and Jensen indices. The results indicate that the high-beta group and active strategy outperform, aligning with the investment adage "high risk, high return." The high-beta group yields higher returns due to its increased risk profile, consistent with the notion of high-risk, high-reward investments. The active strategy provides opportunities to discard underperforming stocks and enhance the weighting of well-performing ones during recomposition. These findings are valuable for investors seeking to capitalize on BUMN stocks at lower prices. The study aids in minimizing negative returns (losses) by identifying the most suitable strategy for BUMN portfolios. Investors can leverage this research to make informed decisions and navigate the challenges associated with investing in state-owned enterprises.
Mutual fund is one of the investment instruments widely used as an investment option. There are two essential things to be considered by the Investment Manager as a mutual fund manager: the stock selection and the market timing ability. The Covid-19 pandemic has caused many investors to experience potential investment losses. Understanding stock selection and market timing will be needed for determining which Investment Managers perform well in managing equity mutual fund, especially while dealing with the Covid-19 pandemic. The objective of this study is to analyze the ability of Investment Managers to perform stock selection and market timing, the performance of equity mutual fund, and the effect of stock selection and market timing ability on the performance of equity mutual fund in Indonesia. This study applies the Fama net selectivity methods to assess stock selection ability and Treynor-Mazuy and Henriksson-Merton models to analyze market timing ability. Sharpe, Treynor, and Jensen's alpha methods are also used for measuring mutual fund performance. The data is processed per mutual fund to recognize each mutual fund's stock selection and market timing ability. Furthermore, the research spans two time periods: before and following the Covid-19 pandemic. A dataset of 104 equity mutual funds from 2016-2021 has been analyzed. The findings show that only 9 and 12 Investment Managers have stock selection ability prior to and following the Covid-19 pandemic, respectively. According to Treynor Mazuy model, there are only two Investment Managers with market timing ability prior to the Covid-19 pandemic, and only twelve Investment Managers have market timing ability during the Covid-19 pandemic, according to the Henriksson-Merton model. Every mutual fund with stock selection ability outperformed the benchmark. However, not all equity mutual fund with market timing ability outperformed the benchmark.
The Indonesia Stock Exchange has experienced high growth in its 32 years. The Composite Stock Price Index rose 1.012% in that period, or an average of 31.63% per year. This study analyzes the risk premium on the Indonesia Stock Exchange (IDX) in the last 32 years. In this study, we found the following facts. First, the risk premium at IDX is 2.24% per year if using annual data and 5.54% when using annualized monthly data. It seems that the risk premium in IDX is sensitive to the return calculation horizon used. Second, in the 1997-1998 economic and 2008 financial crises, the risk premium experienced a sharp decline but was followed by a sharp increase in the following months. Third, the macroeconomic variables of inflation and currency exchange rates significantly affect the risk premium, but the interest rate does not. The effect of macroeconomic variables on the risk premium is also sensitive to the horizon of the return calculation used. The results above are when using annual data. The three macroeconomic variables do not affect the risk premium when using monthly data.
Market anomaly is an occurring phenomenon in the market. Supposedly, an anomaly does not exist in markets that are considered efficient. An anomaly is an aberration in the efficient market theory where existing information does not reflect stock prices; therefore, investors can earn abnormal returns. This study examines how The Day Of The Week Effect and The Month Of The Year Effect affect stock returns on the Indonesian Stock Price Index and the Global Stock Price Index. Samples in this study are daily stock return data and return data on stocks of IHSG, DJIA, SSEC, and N225. The Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is used to analyze data. The results show that in IHSG and SSEC, there was no The Day Of The Week Effect. The DJIA and N225 were found in The Day of the Week Effect. The Month of the Year Effect was found in IHSG, DJIA, SSEC, and N225.
This study aims to (i) test the behavior bias of gamblers fallacy occurs at the time of uptrend and downtrend conditions; (ii) test the behavior bias of halo effect occurs at the time of uptrend and downtrend conditions; and (iii) test the behavior bias of familiarity effect occurs at the time of uptrend and downtrend conditions. The number of samples in the study was as many as 41 people. The test equipment used is One-Sample t-Test and Paired t-Test by using statistical package for social scientists (SPSS) as a static test tool. The results of this study show that: (i) Gamblers' fallacy that occurs when the uptrend condition is greater than when the condition is downtrend; (ii) Halo effect that occurs when the uptrend condition is greater than when the downtrend condition; (iii) Familiarity effect that occurs when the uptrend condition is greater than when the downtrend condition.
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 |