ABSTRACT In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Using high-frequency price and volume data from several large exchanges, we show that FOMC and CPI announcements have a massive impact on Bitcoin's realized volatility and volume. However, this effect is a recent phenomenon, which started as inflation rose at the beginning of 2021. We also document large abnormal returns on FOMC announcement days, but contrary to equity markets, the initial FOMC price reaction tends to reverse during the following four hours. Our findings have implications both for theoretical asset pricing models dealing with the resolution of uncertainty and pricing models specific to cryptocurrency.

Historically, companies have communicated taxes as a burden, as the benefits provided by governments are not perceived to be in proportion to their payments. With sustainable development becoming central in many policy areas, new discourses including 'fair' or 'sustainable' tax have become omnipresent in public talk on taxes. This paper analyzes tax discourses in corporate annual reports within this changing context to examine the use of language in the construction of the meaning of tax. By analyzing tax reporting by a state-owned multinational company over two decades, we observe that the communication of taxes increased in both number and types of discourses. Importantly, this highlights the shift in tax discourses from one dominated by codified accounting discourse reinforcing the monolithic representation of tax as an unfair expense, to one where tax is given a multidimensional meaning within a broader discursive context, where tax is a meaningful corporate responsibility to society.

We analyze to what extent the contribution of banks to systemic risk depends on their centrality in financial networks. We find that centrality is an important determinant of systemic risk, but not primarily by its direct effect. Its main influence is to make other risk measures, such as probability of default, more important for highly connected banks. Neglecting the indirect effect of centrality may severely underestimate or overestimate the systemic risk of banks. We also show that, even though size and centrality are related, the inclusion of centrality provides valuable information when assessing the systemic importance of banks.

We propose a simple theoretical model for how a company with both private and state shareholders decides on its optimal tax policy. The model predicts that even in the absence of state shareholding, a company will not always pick a tax policy that minimizes taxes. Conversely, majority state ownership will generally not result in zero tax avoidance. Using panel regressions on the entire population of state-owned as well as publicly listed Swedish companies from 2000-2019, we find that a one standard deviation increase in state ownership increases corporate tax payments by around 14%.

This paper is the first to use loan level data to investigate the relationship between equity volatility and financial leverage on the firm level. We use a comprehensive dataset of large syndicated loans with a total loan amount in excess of 12 trillion USD. This allows us to precisely identify when a company experiences a large change in leverage. In contrast to several previous studies that have relied only on accounting data, we find very clear results that increased financial leverage increases equity volatility. Our findings are robust to controlling for time trends in variance as well as the type and purpose of the loan.

During good economic times, the likelihood of obtaining a loan from a foreign bank increases in the borrower firm's opacity. During bad economic times (recessions), this relation reverses as the probability of obtaining a foreign loan decreases for all firms, but drops disproportionately more for opaque borrowers. Independently of the business cycle, firms with a higher share of foreign sales are more likely to obtain a foreign loan. We derive these predictions in a formal theoretical framework and confirm them empirically using a loan-bank-firm level dataset covering forty countries during the 1999-2016 period.

I propose a new model-free method for estimating long-run changes in expected volatility using VIX futures contracts. The method is applied to measure the effect on stock market volatility of scheduled macroeconomic news announcements. I find that looking at long-run changes gives qualitatively different results compared to previous studies that only look at realized variance and the VIX. I further find that FOMC announcements on average resolve uncertainty, but only during times when policy uncertainty is higher than average. Real side macro announcements increase long-run volatility during times of low policy uncertainty, but the effect is reversed during times of high policy uncertainty.

We use Swedish ownership data to explore whether a large and diversified shareholder base leads to lower volatility by improving the information content of stock prices. We find that volatility increases in the number of shareholders with respect to both the number of relatively large shareholders and the fraction of shares held by investors with stakes below 0.1%. Volatility is also positively related to the number of institutional owners but negatively related to the number of large and underdiversified institutional owners. Foreign investors have no impact. Our results suggest that a large shareholder base does not lower volatility.

We analyze the heterogeneity of foreign bank loans in a newly constructed global dataset that explicitly distinguishes in a disaggregated loan-bank-firm setting between domestic loans and three categories of foreign loans: loans by subsidiaries of foreign banks, loans by foreign bank branches, and direct cross-border loans. We find that borrower characteristics and loan conditions often significantly differ across different foreign loan categories, with loans by foreign bank subsidiaries in many respects resembling domestic loans rather than other foreign loan categories. We also find pronounced non-monotonicities in loan conditions and borrower characteristics when moving from "less foreign" to "more foreign" bank loans.

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A conjecture in the literature holds that a large and diversified investor base leads to lower volatility by improving the quality of the price signal. In this paper this hypothesis is examined using unique Swedish ownership data. The data does not support the conjecture. Instead, volatility increases in the number of investors and in the size of the firm’s micro-float (the fraction of shares held by investors with stakes below 0.1%). In separate regressions we show that trading volume increases in the size of the investor base, suggesting a trading channel explanation. We also show that proxies for the portfolio concentration of the largest owners are important. We conclude that ownership structure has major implications for stock return volatility.

Enterprise risk management (ERM) has emerged as a framework for more holistic and integrated risk management with an emphasis on enhanced governance of the risk management system. ERM should theoretically reduce the volatility of cash flows, agency risk, and information riskultimately reducing a firm's default risk. We empirically investigate the relationship between the degree of ERM implementation and default risk in a panel data set covering 78 of the world's largest banks. We create a novel measure of the degree of ERM implementation. We find that a higher degree of ERM implementation is negatively related to the credit default swap (CDS) spread of a bank. When a rich set of control variables and fixed effects are included, a one-standard-deviation increase in the degree of ERM implementation decreases CDS spreads by 21 basis points. The degree of ERM implementation is, however, not a significant determinant of credit ratings when controls for corporate governance are included.

This article examines the way in which classification of financial instruments as debt or equity has developed in the Swedish income taxation system over the past 25 years. Although the structure of the tax system is based on the assumption that debt instruments are financial instruments with low risk, legal developments have not shared that assumption, resulting in several types of high-risk derivative instruments being covered by the definition of legal debt. This article illustrates how those developments, which can be recognized in most income-tax systems within OECD countries, seriously threatens the fundament of the tax system: equal taxation for capital income and income from labor. The article concludes by illustrating how the standard solution to the problem of classifying financial instruments as debt and equity – by treating them alike – does not fulfill the challenged principle of equal taxation, but actually intensifies the development towards unequal taxation.

A conjecture in the literature holds that a large and diversified investor base leads to lower volatility by improving the quality of the price signal. In this paper this hypothesis is examined using unique Swedish ownership data. The data does not support the conjecture. Instead, volatility increases in the number of investors and in the size of the firm’s micro-float (the fraction of shares held by investors with stakes below 0.1%). In separate regressions we show that trading volume increases in the size of the investor base, suggesting a trading channel explanation. We also show that proxies for the portfolio concentration of the largest owners are important. We conclude that ownership structure has major implications for stock return volatility.

In the literature, large family owners are widely assumed to have undiversified portfolios and long-run horizons in the firms in which they invest. Consequently, these families are expected to take less risk in corporate financial decisions. Using a novel Swedish ownership dataset, we question these two assumptions and investigate whether family owners’ investment horizon and family-portfolio diversification level have any impact on corporate investment. Our data suggest that there is heterogeneity in both families’ investment horizon and family-portfolio diversification level. We first show that family firms seem to avoid long-run, so-called risky, investments. This is consistent with the literature’s conclusion that family firms are risk averse. Yet, exploiting the variations in our data, we find that longinvestment-horizon family owners invest in long-run corporate projects and families with diversified portfolios prefer long-run investments. However, diversified family owners choose less risky capital expenditures relative to more risky R&D. Moreover, we find that a lower level of investment in family firms is valued negatively by outside shareholders. The results are robust to a number of additional tests, including alternative measurements of investment, family ownership, horizon, and diversification, as well as sample-splits and endogeneity. Version: December 15, 2013

This paper proposes a new model for computing value-at-risk forecasts. The model is fully nonparametric and easy to implement. Further, it incorporates information about the market's perceived uncertainty about the future. The forward-looking information is obtained from the option market via the Chicago Board Options Exchange's implied volatility index (VIX). Using S&P 500 data from 1990 to 2010 we find that the use of option implied volatility compares favorably with generalized autoregressive conditional heteroscedasticity (GARCH)-type models in terms of forecast performance. By comparing the model primarily used in the banking sector to our new model, we find that a financial institution using our model has on average a lower market induced capital requirement (MCR). However, during the time period leading up to the financial crisis our model gives a 40% higher MCR.