I am CEO of HEGERS HOME, our fourth-generation family business specializing in custom-made kitchens, tables, and staircases.
Alongside my role in the family business, I am a Visiting Fellow and formerly Assistant Professor at Vrije Universiteit Amsterdam, specializing in IT & Accounting. My research uses survey and archival data to contribute to our understanding of data analytics, forecasting, and the quality of financial and non-financial information.
by Oliver Hegers
Abstract. This paper investigates the relation between a business unit (BU) controller’s mindset, role conflict, and financial misreporting. ‘Mindset’ is based on implicit person theories and ranges from the deeply held belief on whether, in general, people can learn, develop and change throughout their lives (growth mindset), or whether, for example, one’s abilities and character are inherited and thus unmalleable (fixed mindset). While having a growth mindset is beneficial for learning and overcoming challenges (e.g., facing competing duties), it leads to a more dynamic view on breaking rules. Using survey data from 180 BU controllers, I find that a growth mindset is negatively associated with the BU controller’s perceived level of role conflict, which is positively related to financial misreporting. However, a growth mindset is positively associated with misreporting and strengthens the relation between role conflict and misreporting. The total effect is positive and significant when role conflict is high.
by Oliver Hegers and Matthias D. Mahlendorf
Abstract. Online platforms such as Amazon.com, Google, and Glassdoor have to design policies for submitting ratings. We investigate how lowering barriers to rate affects the informativeness of the rating consensus – a crowd-sourced performance measure. In 2020, Amazon.com introduced a new one-tap rating system, whereas before, a written text review was required to rate a product. Changing the cost of submitting ratings may affect the underlying distribution of raters (i.e., satisfied and unsatisfied customers, sellers of managed ratings). Our analyses show that after the policy change, the average rating increases, and the effect is stronger for lower-rated products. Thus, the rating consensus becomes less informative for platform users to discriminate between products. A potential explanation is that lowering the barriers makes it cheaper to provide managed ratings which may outweigh a potential increase in authentic ratings. Alternatively, relatively more satisfied customers make use of the new option. The results of several additional analyses are consistent with both explanations.
by Marie Dutordoir, Oliver Hegers, Joao Quariguasi Frota Neto, and Frank Verbeeten
Abstract. We evaluate the extent to which brand value can help forecast cash flows and earnings. We rely on brand values of publicly-listed U.S. firms, as estimated by brand consultancy firms Interbrand, Brand Finance and BrandZ between 2006 and 2021. While in-sample regressions suggest a positive incremental impact of brand value estimates (BVE) on future cash flows and earnings, out-of-sample predictions based on linear regressions and Machine Learning methods show that BVE have no additional forecasting power over standard accounting information. An analysis of abnormal returns on zero-cost portfolios based on forecasted cash flow and earnings performance supports this conclusion. Our findings contribute to discussions on the balance sheet recognition and managerial relevance of brand values. Moreover, our results demonstrate the importance of relying on out-of-sample predictions when judging the incremental information content of intangible assets.
by Oliver Hegers, Frank Verbeeten, and Klaus Möller
Abstract. Prior literature suggests the existence of two opposing effects in disaggregated internal financial forecasts (IFFs). While disaggregated random errors (mistakes) offset each other, disaggregated non-random errors (biases) accumulate when they are combined. We argue that environmental uncertainty interacts with the level of disaggregation. Uncertainty may reduce non-random errors (as it is more difficult to consistently bias results) yet may also increase random errors in the forecast (due to unpredictability of results). Using survey data from 167 controllers, we theoretically predict and empirically show that forecast disaggregation increases (reduces) forecast accuracy under high (low) environmental uncertainty. Moreover, our findings suggest that the joint effect of disaggregation and uncertainty on forecast accuracy disappears when the ability to manipulate earnings is high. Our results imply that investments in more sophisticated forecasting tools may not provide the expected benefits when non-random errors in forecasting or a weak internal control environment are a key concern in firms.
by Oliver Hegers, Peter Kroos, Jeroen van Raak, and Frank Verbeeten