@techreport{20869,
  abstract     = {{This study provides evidence of significant biases in multi-year management forecasts by analyzing a proprietary dataset on venture-backed start-ups in Germany. We find that revenues and expenses are highly overestimated in each of the investigated one- to five-year-ahead planning periods. Furthermore, entrepreneurs underestimate one-year-ahead profit forecasts but clearly overestimate their profit forecasts for all longer-term forecast horizons. Additional analyses reveal that teams with prior management experience issue even more overestimated forecasts and misrepresent their forward-looking information. In contrast, greater asset verifiability and corporate lead investors are associated with lower levels of forecast errors. All key results hold if bias is either measured by traditionally comparing forecasts to ex-post realizations or by using a cross-sectional projection approach based on historical accounting data developed by prior research.}},
  author       = {{Sievers, Sönke and Mokwa, Christopher Frederik}},
  keywords     = {{Management forecasts, Forecasting biases, Venture-backed start-ups, Projection methods}},
  pages        = {{42}},
  title        = {{{Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors}}},
  doi          = {{10.2139/ssrn.1714399}},
  year         = {{2012}},
}

@techreport{20870,
  abstract     = {{This study shows how venture capital investors can identify potential biases in multi-year management forecasts before an investment decision and derive significantly more accurate failure predictions. By advancing a cross-sectional projection method developed by prior research and using firm-specific information in financial statements and business plans, we derive benchmarks for management revenue forecasts. With these benchmarks, we estimate forecast errors as an a priori measure of biased expectations. Using this measure for our proprietary dataset on venture-backed start-ups in Germany, we find evidence of substantial upward forecast biases. We uncover that firms with large forecast errors fail significantly more often than do less biased entrepreneurs in years following the investment. Overall, our results highlight the implications of excessive optimism and overconfidence in entrepreneurial environments and emphasize the relevance of accounting information and business plans for venture capital investment decisions.}},
  author       = {{Sievers, Sönke and Mokwa, Christopher Frederik}},
  keywords     = {{Management forecast biases, cross-sectional projection models, venture-backed start-ups, failure prediction, overoptimism, overconfidence}},
  pages        = {{31}},
  title        = {{{The Relevance of Biases in Management Forecasts for Failure Prediction in Venture Capital Investments}}},
  doi          = {{10.2139/ssrn.2100501}},
  year         = {{2012}},
}

@article{5196,
  abstract     = {{This study shows how venture capital investors can identify potential biases in multi-year management forecasts before an investment decision and derive significantly more accurate failure predictions. By advancing a cross-sectional projection method developed by prior research and using firm-specific information in financial statements and business plans, we derive benchmarks for management revenue forecasts. With these benchmarks, we estimate forecast errors as an a priori measure of biased expectations. Using this measure for our proprietary dataset on venture-backed start-ups in Germany, we find evidence of substantial upward forecast biases. We uncover that firms with large forecast errors fail significantly more often than do less biased entrepreneurs in years following the investment. Overall, our results highlight the implications of excessive optimism and overconfidence in entrepreneurial environments and emphasize the relevance of accounting information and business plans for venture capital investment decisions. }},
  author       = {{Mokwa, Christopher Frederik and Sievers, Sönke}},
  journal      = {{SSRN Electronic Journal}},
  keywords     = {{Management forecast biases, cross-sectional projection models, venture-backed start-ups, failure prediction, overoptimism, overconfidence}},
  title        = {{{The Relevance of Biases in Management Forecasts for Failure Prediction in Venture Capital Investments}}},
  doi          = {{10.2139/ssrn.2100501}},
  year         = {{2012}},
}

@article{5198,
  abstract     = {{This study provides evidence of significant biases in multi-year management forecasts by analyzing a proprietary dataset on venture-backed start-ups in Germany. We find that revenues and expenses are highly overestimated in each of the investigated one- to five-year-ahead planning periods. Furthermore, entrepreneurs underestimate one-year-ahead profit forecasts but clearly overestimate their profit forecasts for all longer-term forecast horizons. Additional analyses reveal that teams with prior management experience issue even more overestimated forecasts and misrepresent their forward-looking information. In contrast, greater asset verifiability and corporate lead investors are associated with lower levels of forecast errors. All key results hold if bias is either measured by traditionally comparing forecasts to ex-post realizations or by using a cross-sectional projection approach based on historical accounting data developed by prior research. }},
  author       = {{Mokwa, Christopher and Sievers, Sönke}},
  journal      = {{SSRN Electronic Journal}},
  keywords     = {{Management forecasts, Forecasting biases, Venture-backed start-ups, Projection methods}},
  title        = {{{Biases in Management Forecasts of Venture-Backed Start-Ups: Evidence from Internal Due Diligence Documents of VC Investors}}},
  doi          = {{10.2139/ssrn.1714399}},
  year         = {{2012}},
}