Analysts’ forecast accuracy in an unlisted company
Pihlajaniemi, Riikka (2023-06-02)
Pihlajaniemi, Riikka
R. Pihlajaniemi
02.06.2023
© 2023 Riikka Pihlajaniemi. Ellei toisin mainita, uudelleenkäyttö on sallittu Creative Commons Attribution 4.0 International (CC-BY 4.0) -lisenssillä (https://creativecommons.org/licenses/by/4.0/). Uudelleenkäyttö on sallittua edellyttäen, että lähde mainitaan asianmukaisesti ja mahdolliset muutokset merkitään. Sellaisten osien käyttö tai jäljentäminen, jotka eivät ole tekijän tai tekijöiden omaisuutta, saattaa edellyttää lupaa suoraan asianomaisilta oikeudenhaltijoilta.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202306022085
https://urn.fi/URN:NBN:fi:oulu-202306022085
Tiivistelmä
The purpose of this study is to investigate the optimism of analysts and the impact of the forecast period length on the accuracy of forecasts in unlisted companies. Previous research shows an optimistic bias towards forecasts by analysts in listed companies, and forecast error is positively correlated with the length of the forecast period. However, there is a lack of previous research on forecast optimism and accuracy for unlisted companies. This study aims to bridge this research gap and contribute to the understanding of forecast behavior in this context.
The research questions and hypotheses of this study are derived from previous studies. Hypothesis 1 states that analysts make optimistic forecasts at the beginning of the fiscal year, while Hypothesis 2 proposes a positive correlation between forecast errors and the length of the forecast period. The data used in the study includes analysts’ one-year-ahead forecasts and the actual earnings and EBITDA figures for the corresponding period.
The findings of this study reveal that analysts’ earnings forecasts in unlisted companies are also optimistic. Furthermore, the accuracy of these forecasts diminishes as the forecasted period extends. The accuracy of earnings forecasting significantly decreases from the seventh month onward, indicating that analysts can effectively forecast the first seven months. In contrast, there is no observed significant deterioration in the accuracy of EBITDA forecasting over time within the fiscal year. Thus, while the hypothesis regarding earnings is supported, the hypothesis concerning EBITDA is not supported by the results.
In conclusion, this thesis contributes to the understanding of analysts’ forecasting behavior in unlisted companies. The results highlight the importance of considering the forecasted periods’ length and the positive bias when evaluating the analysis in unlisted companies. It is recommended to be meticulous for investors and financiers when relying on analysts’ long-term forecasts for unlisted companies.
Further investigation is required to explore the accuracy and optimism of forecasts with a greater sample size. The study confirms the hypothesis that analysts tend to be optimistic when forecasting earnings for unlisted companies. This finding suggests the need for further research to explore the sources of this optimism and understand the underlying reasons, especially regarding the generalizable optimism.
The research questions and hypotheses of this study are derived from previous studies. Hypothesis 1 states that analysts make optimistic forecasts at the beginning of the fiscal year, while Hypothesis 2 proposes a positive correlation between forecast errors and the length of the forecast period. The data used in the study includes analysts’ one-year-ahead forecasts and the actual earnings and EBITDA figures for the corresponding period.
The findings of this study reveal that analysts’ earnings forecasts in unlisted companies are also optimistic. Furthermore, the accuracy of these forecasts diminishes as the forecasted period extends. The accuracy of earnings forecasting significantly decreases from the seventh month onward, indicating that analysts can effectively forecast the first seven months. In contrast, there is no observed significant deterioration in the accuracy of EBITDA forecasting over time within the fiscal year. Thus, while the hypothesis regarding earnings is supported, the hypothesis concerning EBITDA is not supported by the results.
In conclusion, this thesis contributes to the understanding of analysts’ forecasting behavior in unlisted companies. The results highlight the importance of considering the forecasted periods’ length and the positive bias when evaluating the analysis in unlisted companies. It is recommended to be meticulous for investors and financiers when relying on analysts’ long-term forecasts for unlisted companies.
Further investigation is required to explore the accuracy and optimism of forecasts with a greater sample size. The study confirms the hypothesis that analysts tend to be optimistic when forecasting earnings for unlisted companies. This finding suggests the need for further research to explore the sources of this optimism and understand the underlying reasons, especially regarding the generalizable optimism.
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