In recent years, several accounting standards, including IFRS 3, issued by the IASB, substitute historical cost with fair value measures and so provide managers with increased discretion to determine fair value without an actual market for the asset. Using Swedish data, we document the accounting consequences of the adoption of IFRS 3 and the stock market's reaction. After the adoption of this standard in January 2005 the amount of capitalized goodwill increased substantially. Goodwill impairments under IFRS are considerably lower than goodwill amortizations and impairments made under Swedish GAAP. Consequently, the adoption of IFRS 3 increased reported earnings. An analysis of economic incentives influencing the impairment decision at the initial adoption of IFRS 3 shows that tenured management is negatively associated with the impairment decision. However, most firms did not reclassify goodwill or make additional impairments. Firms with substantial amounts of goodwill yielded abnormally high returns despite abnormally low earnings. Investors seem to, correctly or incorrectly, have viewed the accrual-based increase in earnings stemming from IFRS 3 as an indication of higher future cash flows.

We present a novel approach for measuring executive personality traits. Relying on recent developments in machine learning and artificial intelligence, we utilize the IBM Watson Personality Insights service to measure executive personalities based on CEOs' and CFOs' responses to questions raised by analysts during conference calls.We obtain the Big Five personality traits -openness, conscientiousness, extraversion, agreeableness and neuroticism -based on which we estimate risk tolerance. To validate these traits, we first demonstrate that our risk-tolerance measure varies with existing inherent and behavioural-based measures (gender, age, sensitivity of executive compensation to stock return volatility, and executive unexercised-vested options) in predictable ways. Second, we show that variation in firm-year level personality trait measures, including risk tolerance, is largely explained by manager characteristics, as opposed to firm characteristics and firm performance. Finally, we find that executive inherent risk tolerance helps explain the positive relationship between client risk and audit fees documented in the prior literature. Specifically, the effect of CEO risk-tolerance -as an innate personality trait -on audit fees is incremental to the effect of increased risk appetite from equity risk-taking incentives (Vega).Measuring executive personality using machine-learning algorithms will thus allow researchers to pursue studies that were previously difficult to conduct. K E Y W O R D Sbig five, machine learning, personality, risk tolerance

A key theoretical prediction in financial economics is that under risk neutrality and rational expectations a currency's forward rates should form unbiased predictors of future spot rates. Yet scores of empirical studies report negative slope coefficients from regressions of spot rates on forward rates, which is inconsistent with the forward rate unbiasedness hypothesis. We collect 3,643 estimates from 91 research articles and using recently developed techniques investigate the effect of publication and misspecification biases on the reported results. Correcting for these biases we estimate the slope coefficients of 0.31 and 0.98 for developed and emerging currencies respectively, which implies that empirical evidence is in line with the theoretical prediction for emerging economies and less puzzling than commonly thought for developed economies. Our results also suggest that the coefficients are systematically influenced by the choice of data, numeraire currencies, and estimation methods.

Firm size is commonly used in numerous empirical asset pricing models as a determinant of expected stock returns. Yet there is little consensus over the magnitude and stability of the size premium. In fact, some researchers even question whether firm size should be used as a pricing factor. We collect 1746 estimates of the slope coefficients capturing the association between firm size and stock returns reported in 102 published studies and conduct the first meta‐analysis on the size premium. We find evidence of a strong bias toward publishing statistically significant negative slope coefficients. After correcting for the bias, we find that the literature implies a difference in annual stock returns on the smallest and the largest New York Stock Exchange (NYSE) market capitalization quintiles of 1.72%. For the time periods covered in the sampled articles, we find that the size premium was larger in earlier years and that the intensity of publication bias has been decreasing over time.

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