Analysts versus time-series forecasts of quarterly earnings: A maintained hypothesis revisited
We re-examine the maintained hypothesis of analysts' quarterly earnings per share (EPS) superiority versus ARIMA time-series forecasts. While our empirical results are consistent with overall analysts' dominance, they suggest a more contextual interpretation of this important relationship....
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Pagach, Donald P. [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
|---|
| Übergeordnetes Werk: |
Enthalten in: Development and implementation of a contemplative-based intervention for systemic lupus erythematosus - Penberthy, J. Kim ELSEVIER, 2021, a research annual, New York, NY [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:51 ; year:2020 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.adiac.2020.100497 |
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| 520 | |a We re-examine the maintained hypothesis of analysts' quarterly earnings per share (EPS) superiority versus ARIMA time-series forecasts. While our empirical results are consistent with overall analysts' dominance, they suggest a more contextual interpretation of this important relationship. Specifically, we find that for a relatively large number of cases (approximately 40%) ARIMA time-series forecasts of quarterly EPS are equal to or more accurate than consensus analysts' forecasts. Moreover, the percentage of time-series superiority increases: (1) for longer forecast horizons, (2) as firm size decreases, and (3) for high-technology firms. Due to the data demands that ARIMA forecasting requires we also examine using a seasonal random walk (SRW) model that requires only one year of data to create quarterly forecasts. Although the ARIMA time-series model results in a significant reduction in sample size it dominates the SRW model. Our findings support the analyst dominance over time series models but suggest that ARIMA time-series models may provide useful input to researchers seeking quarterly EPS expectation models for certain types of firms. | ||
| 520 | |a We re-examine the maintained hypothesis of analysts' quarterly earnings per share (EPS) superiority versus ARIMA time-series forecasts. While our empirical results are consistent with overall analysts' dominance, they suggest a more contextual interpretation of this important relationship. Specifically, we find that for a relatively large number of cases (approximately 40%) ARIMA time-series forecasts of quarterly EPS are equal to or more accurate than consensus analysts' forecasts. Moreover, the percentage of time-series superiority increases: (1) for longer forecast horizons, (2) as firm size decreases, and (3) for high-technology firms. Due to the data demands that ARIMA forecasting requires we also examine using a seasonal random walk (SRW) model that requires only one year of data to create quarterly forecasts. Although the ARIMA time-series model results in a significant reduction in sample size it dominates the SRW model. Our findings support the analyst dominance over time series models but suggest that ARIMA time-series models may provide useful input to researchers seeking quarterly EPS expectation models for certain types of firms. | ||
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10.1016/j.adiac.2020.100497 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001224.pica (DE-627)ELV052306208 (ELSEVIER)S0882-6110(20)30067-5 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Pagach, Donald P. verfasserin aut Analysts versus time-series forecasts of quarterly earnings: A maintained hypothesis revisited 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We re-examine the maintained hypothesis of analysts' quarterly earnings per share (EPS) superiority versus ARIMA time-series forecasts. While our empirical results are consistent with overall analysts' dominance, they suggest a more contextual interpretation of this important relationship. Specifically, we find that for a relatively large number of cases (approximately 40%) ARIMA time-series forecasts of quarterly EPS are equal to or more accurate than consensus analysts' forecasts. Moreover, the percentage of time-series superiority increases: (1) for longer forecast horizons, (2) as firm size decreases, and (3) for high-technology firms. Due to the data demands that ARIMA forecasting requires we also examine using a seasonal random walk (SRW) model that requires only one year of data to create quarterly forecasts. Although the ARIMA time-series model results in a significant reduction in sample size it dominates the SRW model. Our findings support the analyst dominance over time series models but suggest that ARIMA time-series models may provide useful input to researchers seeking quarterly EPS expectation models for certain types of firms. We re-examine the maintained hypothesis of analysts' quarterly earnings per share (EPS) superiority versus ARIMA time-series forecasts. While our empirical results are consistent with overall analysts' dominance, they suggest a more contextual interpretation of this important relationship. Specifically, we find that for a relatively large number of cases (approximately 40%) ARIMA time-series forecasts of quarterly EPS are equal to or more accurate than consensus analysts' forecasts. Moreover, the percentage of time-series superiority increases: (1) for longer forecast horizons, (2) as firm size decreases, and (3) for high-technology firms. Due to the data demands that ARIMA forecasting requires we also examine using a seasonal random walk (SRW) model that requires only one year of data to create quarterly forecasts. Although the ARIMA time-series model results in a significant reduction in sample size it dominates the SRW model. Our findings support the analyst dominance over time series models but suggest that ARIMA time-series models may provide useful input to researchers seeking quarterly EPS expectation models for certain types of firms. Warr, Richard S. oth Enthalten in Elsevier Penberthy, J. Kim ELSEVIER Development and implementation of a contemplative-based intervention for systemic lupus erythematosus 2021 a research annual New York, NY [u.a.] (DE-627)ELV009904336 volume:51 year:2020 pages:0 https://doi.org/10.1016/j.adiac.2020.100497 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 51 2020 0 |
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10.1016/j.adiac.2020.100497 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001224.pica (DE-627)ELV052306208 (ELSEVIER)S0882-6110(20)30067-5 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Pagach, Donald P. verfasserin aut Analysts versus time-series forecasts of quarterly earnings: A maintained hypothesis revisited 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We re-examine the maintained hypothesis of analysts' quarterly earnings per share (EPS) superiority versus ARIMA time-series forecasts. While our empirical results are consistent with overall analysts' dominance, they suggest a more contextual interpretation of this important relationship. Specifically, we find that for a relatively large number of cases (approximately 40%) ARIMA time-series forecasts of quarterly EPS are equal to or more accurate than consensus analysts' forecasts. Moreover, the percentage of time-series superiority increases: (1) for longer forecast horizons, (2) as firm size decreases, and (3) for high-technology firms. Due to the data demands that ARIMA forecasting requires we also examine using a seasonal random walk (SRW) model that requires only one year of data to create quarterly forecasts. Although the ARIMA time-series model results in a significant reduction in sample size it dominates the SRW model. Our findings support the analyst dominance over time series models but suggest that ARIMA time-series models may provide useful input to researchers seeking quarterly EPS expectation models for certain types of firms. We re-examine the maintained hypothesis of analysts' quarterly earnings per share (EPS) superiority versus ARIMA time-series forecasts. While our empirical results are consistent with overall analysts' dominance, they suggest a more contextual interpretation of this important relationship. Specifically, we find that for a relatively large number of cases (approximately 40%) ARIMA time-series forecasts of quarterly EPS are equal to or more accurate than consensus analysts' forecasts. Moreover, the percentage of time-series superiority increases: (1) for longer forecast horizons, (2) as firm size decreases, and (3) for high-technology firms. Due to the data demands that ARIMA forecasting requires we also examine using a seasonal random walk (SRW) model that requires only one year of data to create quarterly forecasts. Although the ARIMA time-series model results in a significant reduction in sample size it dominates the SRW model. Our findings support the analyst dominance over time series models but suggest that ARIMA time-series models may provide useful input to researchers seeking quarterly EPS expectation models for certain types of firms. Warr, Richard S. oth Enthalten in Elsevier Penberthy, J. Kim ELSEVIER Development and implementation of a contemplative-based intervention for systemic lupus erythematosus 2021 a research annual New York, NY [u.a.] (DE-627)ELV009904336 volume:51 year:2020 pages:0 https://doi.org/10.1016/j.adiac.2020.100497 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 51 2020 0 |
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10.1016/j.adiac.2020.100497 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001224.pica (DE-627)ELV052306208 (ELSEVIER)S0882-6110(20)30067-5 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Pagach, Donald P. verfasserin aut Analysts versus time-series forecasts of quarterly earnings: A maintained hypothesis revisited 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We re-examine the maintained hypothesis of analysts' quarterly earnings per share (EPS) superiority versus ARIMA time-series forecasts. While our empirical results are consistent with overall analysts' dominance, they suggest a more contextual interpretation of this important relationship. Specifically, we find that for a relatively large number of cases (approximately 40%) ARIMA time-series forecasts of quarterly EPS are equal to or more accurate than consensus analysts' forecasts. Moreover, the percentage of time-series superiority increases: (1) for longer forecast horizons, (2) as firm size decreases, and (3) for high-technology firms. Due to the data demands that ARIMA forecasting requires we also examine using a seasonal random walk (SRW) model that requires only one year of data to create quarterly forecasts. Although the ARIMA time-series model results in a significant reduction in sample size it dominates the SRW model. Our findings support the analyst dominance over time series models but suggest that ARIMA time-series models may provide useful input to researchers seeking quarterly EPS expectation models for certain types of firms. We re-examine the maintained hypothesis of analysts' quarterly earnings per share (EPS) superiority versus ARIMA time-series forecasts. While our empirical results are consistent with overall analysts' dominance, they suggest a more contextual interpretation of this important relationship. Specifically, we find that for a relatively large number of cases (approximately 40%) ARIMA time-series forecasts of quarterly EPS are equal to or more accurate than consensus analysts' forecasts. Moreover, the percentage of time-series superiority increases: (1) for longer forecast horizons, (2) as firm size decreases, and (3) for high-technology firms. Due to the data demands that ARIMA forecasting requires we also examine using a seasonal random walk (SRW) model that requires only one year of data to create quarterly forecasts. Although the ARIMA time-series model results in a significant reduction in sample size it dominates the SRW model. Our findings support the analyst dominance over time series models but suggest that ARIMA time-series models may provide useful input to researchers seeking quarterly EPS expectation models for certain types of firms. Warr, Richard S. oth Enthalten in Elsevier Penberthy, J. Kim ELSEVIER Development and implementation of a contemplative-based intervention for systemic lupus erythematosus 2021 a research annual New York, NY [u.a.] (DE-627)ELV009904336 volume:51 year:2020 pages:0 https://doi.org/10.1016/j.adiac.2020.100497 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 51 2020 0 |
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10.1016/j.adiac.2020.100497 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001224.pica (DE-627)ELV052306208 (ELSEVIER)S0882-6110(20)30067-5 DE-627 ger DE-627 rakwb eng 610 VZ 44.91 bkl Pagach, Donald P. verfasserin aut Analysts versus time-series forecasts of quarterly earnings: A maintained hypothesis revisited 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We re-examine the maintained hypothesis of analysts' quarterly earnings per share (EPS) superiority versus ARIMA time-series forecasts. While our empirical results are consistent with overall analysts' dominance, they suggest a more contextual interpretation of this important relationship. Specifically, we find that for a relatively large number of cases (approximately 40%) ARIMA time-series forecasts of quarterly EPS are equal to or more accurate than consensus analysts' forecasts. Moreover, the percentage of time-series superiority increases: (1) for longer forecast horizons, (2) as firm size decreases, and (3) for high-technology firms. Due to the data demands that ARIMA forecasting requires we also examine using a seasonal random walk (SRW) model that requires only one year of data to create quarterly forecasts. Although the ARIMA time-series model results in a significant reduction in sample size it dominates the SRW model. Our findings support the analyst dominance over time series models but suggest that ARIMA time-series models may provide useful input to researchers seeking quarterly EPS expectation models for certain types of firms. We re-examine the maintained hypothesis of analysts' quarterly earnings per share (EPS) superiority versus ARIMA time-series forecasts. While our empirical results are consistent with overall analysts' dominance, they suggest a more contextual interpretation of this important relationship. Specifically, we find that for a relatively large number of cases (approximately 40%) ARIMA time-series forecasts of quarterly EPS are equal to or more accurate than consensus analysts' forecasts. Moreover, the percentage of time-series superiority increases: (1) for longer forecast horizons, (2) as firm size decreases, and (3) for high-technology firms. Due to the data demands that ARIMA forecasting requires we also examine using a seasonal random walk (SRW) model that requires only one year of data to create quarterly forecasts. Although the ARIMA time-series model results in a significant reduction in sample size it dominates the SRW model. Our findings support the analyst dominance over time series models but suggest that ARIMA time-series models may provide useful input to researchers seeking quarterly EPS expectation models for certain types of firms. Warr, Richard S. oth Enthalten in Elsevier Penberthy, J. Kim ELSEVIER Development and implementation of a contemplative-based intervention for systemic lupus erythematosus 2021 a research annual New York, NY [u.a.] (DE-627)ELV009904336 volume:51 year:2020 pages:0 https://doi.org/10.1016/j.adiac.2020.100497 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.91 Psychiatrie Psychopathologie VZ AR 51 2020 0 |
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Analysts versus time-series forecasts of quarterly earnings: A maintained hypothesis revisited |
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Analysts versus time-series forecasts of quarterly earnings: A maintained hypothesis revisited |
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Pagach, Donald P. |
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Development and implementation of a contemplative-based intervention for systemic lupus erythematosus |
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Development and implementation of a contemplative-based intervention for systemic lupus erythematosus |
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Pagach, Donald P. |
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Pagach, Donald P. |
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10.1016/j.adiac.2020.100497 |
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| title_sort |
analysts versus time-series forecasts of quarterly earnings: a maintained hypothesis revisited |
| title_auth |
Analysts versus time-series forecasts of quarterly earnings: A maintained hypothesis revisited |
| abstract |
We re-examine the maintained hypothesis of analysts' quarterly earnings per share (EPS) superiority versus ARIMA time-series forecasts. While our empirical results are consistent with overall analysts' dominance, they suggest a more contextual interpretation of this important relationship. Specifically, we find that for a relatively large number of cases (approximately 40%) ARIMA time-series forecasts of quarterly EPS are equal to or more accurate than consensus analysts' forecasts. Moreover, the percentage of time-series superiority increases: (1) for longer forecast horizons, (2) as firm size decreases, and (3) for high-technology firms. Due to the data demands that ARIMA forecasting requires we also examine using a seasonal random walk (SRW) model that requires only one year of data to create quarterly forecasts. Although the ARIMA time-series model results in a significant reduction in sample size it dominates the SRW model. Our findings support the analyst dominance over time series models but suggest that ARIMA time-series models may provide useful input to researchers seeking quarterly EPS expectation models for certain types of firms. |
| abstractGer |
We re-examine the maintained hypothesis of analysts' quarterly earnings per share (EPS) superiority versus ARIMA time-series forecasts. While our empirical results are consistent with overall analysts' dominance, they suggest a more contextual interpretation of this important relationship. Specifically, we find that for a relatively large number of cases (approximately 40%) ARIMA time-series forecasts of quarterly EPS are equal to or more accurate than consensus analysts' forecasts. Moreover, the percentage of time-series superiority increases: (1) for longer forecast horizons, (2) as firm size decreases, and (3) for high-technology firms. Due to the data demands that ARIMA forecasting requires we also examine using a seasonal random walk (SRW) model that requires only one year of data to create quarterly forecasts. Although the ARIMA time-series model results in a significant reduction in sample size it dominates the SRW model. Our findings support the analyst dominance over time series models but suggest that ARIMA time-series models may provide useful input to researchers seeking quarterly EPS expectation models for certain types of firms. |
| abstract_unstemmed |
We re-examine the maintained hypothesis of analysts' quarterly earnings per share (EPS) superiority versus ARIMA time-series forecasts. While our empirical results are consistent with overall analysts' dominance, they suggest a more contextual interpretation of this important relationship. Specifically, we find that for a relatively large number of cases (approximately 40%) ARIMA time-series forecasts of quarterly EPS are equal to or more accurate than consensus analysts' forecasts. Moreover, the percentage of time-series superiority increases: (1) for longer forecast horizons, (2) as firm size decreases, and (3) for high-technology firms. Due to the data demands that ARIMA forecasting requires we also examine using a seasonal random walk (SRW) model that requires only one year of data to create quarterly forecasts. Although the ARIMA time-series model results in a significant reduction in sample size it dominates the SRW model. Our findings support the analyst dominance over time series models but suggest that ARIMA time-series models may provide useful input to researchers seeking quarterly EPS expectation models for certain types of firms. |
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Analysts versus time-series forecasts of quarterly earnings: A maintained hypothesis revisited |
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https://doi.org/10.1016/j.adiac.2020.100497 |
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Warr, Richard S. |
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