Diverse Beliefs and Time Variability of Risk Premia

Why do risk premia vary over time? We examine this problem theoretically and empirically by studying the effect of market belief on risk premia. Individual belief is taken as a fundamental primitive state variable. Market belief is observable; it is central to the empirical evaluation and we show how to measure it. Our asset pricing model is familiar from the noisy REE literature but we adapt it to an economy with diverse beliefs. We derive equilibrium asset prices and implied risk premium. Our approach permits a closed form solution of prices; hence we trace the exact effect of market belief on the time variability of asset prices and risk premia. We test empirically the theoretical conclusions. Our main result is that, above the effect of business cycles on risk premia, fluctuations in market belief have significant independent effect on the time variability of risk premia. We study the premia on long positions in Federal Funds Futures, 3- and 6-month Treasury Bills (T-Bills). The annual mean risk premium on holding such assets for 1-12 months is about 40-60 basis points and we find that, on average, the component of market belief in the risk premium exceeds 50% of the mean. Since time variability of market belief is large, this component frequently exceeds 50% of the mean premium. This component is larger the shorter is the holding period of an asset and it dominates the premium for very short holding returns of less than 2 months. As to the structure of the premium we show that when the market holds abnormally favorable belief about the future payoff of an asset the market views the long position as less risky hence the risk premium on that asset declines. More generally, periods of market optimism (i.e. "bull" markets) are shown to be periods when the market risk premium is low while in periods of pessimism (i.e. "bear" markets) the market's risk premium is high. Fluctuations in risk premia are thus inversely related to the degree of market optimism about future prospects of asset payoffs. This effect is strong and economically very significant

Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus

A locus at 19p13 is associated with breast cancer (BC) and ovarian cancer (OC) risk. Here we analyse 438 SNPs in this region in 46,451 BC and 15,438 OC cases, 15,252 BRCA1 mutation carriers and 73,444 controls and identify 13 candidate causal SNPs associated with serous OC (P=9.2 × 10-20), ER-negative BC (P=1.1 × 10-13), BRCA1-associated BC (P=7.7 × 10-16) and triple negative BC (P-diff=2 × 10-5). Genotype-gene expression associations are identified for candidate target genes ANKLE1 (P=2 × 10-3) and ABHD8 (P<2 × 10-3). Chromosome conformation capture identifies interactions between four candidate SNPs and ABHD8, and luciferase assays indicate six risk alleles increased transactivation of the ADHD8 promoter. Targeted deletion of a region containing risk SNP rs56069439 in a putative enhancer induces ANKLE1 downregulation; and mRNA stability assays indicate functional effects for an ANKLE1 3′-UTR SNP. Altogether, these data suggest that multiple SNPs at 19p13 regulate ABHD8 and perhaps ANKLE1 expression, and indicate common mechanisms underlying breast and ovarian cancer risk

The outperformance of family firms: the role of variance in earnings per share and analyst forecast dispersion on the Swiss market

Recent studies provide empirical evidence that family firms are outperforming their non-family counterparts in terms of stock market performance. For the Swiss stock market we find that family firms indeed outperform their non-family counterparts after controlling for firm size and beta. In addition, our data shows that family firms display more stable earnings per share in contrast to their non-family counterparts. Furthermore we find that the variance of earnings per share positively affects analyst forecast dispersion. According to anomaly literature, lower analyst forecast dispersion has been found to induce higher excess return, which our data supports for the Swiss stock market. By linking variance of earnings per share, analyst forecast dispersion and stock performance we provide an insightful explanation for the excess stock market returns of family firms. In addition, our text extends the theory of dispersion effect with an additional empirical element, the variance of earnings per share. Copyright Swiss Society for Financial Market Research 2007Family firms, Analyst forecast, Dispersion, Earnings per share, G14 (information and market efficiency, event studies), G15 (international financial markets), G11 (portfolio choice, investment decisions),

Board monitoring, firm risk, and external regulation

Regulation, Board monitoring, Firm risk, Board independence, G34,

Overconfidence and trading volume

Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors. Approximately 3,000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, volatility estimates, better than average effect). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance (but who did not have above average performance in the past) trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. In connection with other recent findings, we conclude that the usual way of motivating and modeling overconfidence which is mainly based on the calibration literature has to be treated with caution. Moreover, our way of empirically evaluating behavioral finance models—the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data—seems to be a promising way to better understand which psychological phenomena actually drive economic behavior. Copyright The Geneva Association 2007Overconfidence, Differences of opinion, Trading volume, Individual investors, Investor behavior, Correlation of economic and psychological variables, Combination of psychometric measures of judgment biases and field data, D8, G1,

BRCA2 Polymorphic Stop Codon K3326X and the Risk of Breast, Prostate, and Ovarian Cancers

Background: The K3326X variant in BRCA2 (BRCA2∗c.9976A>T p.Lys3326∗rs11571833) has been found to be associated with small increased risks of breast cancer. However, it is not clear to what extent linkage disequilibrium with fully pathogenic mutations might account for this association. There is scant information about the effect of K3326X in other hormonerelated cancers. Methods: Using weighted logistic regression, we analyzed data from the large iCOGS study including 76637 cancer case patients and 83796 control patients to estimate odds ratios (ORw) and 95% confidence intervals (CIs) for K3326X variant carriers in relation to breast, ovarian, and prostate cancer risks, with weights defined as probability of not having a pathogenic BRCA2 variant. Using Cox proportional hazards modeling, we also examined the associations of K3326X with breast and ovarian cancer risks among 7183 BRCA1 variant carriers. All statistical tests were two-sided. Results: The K3326X variant was associated with breast (ORw = 1.28, 95% CI = 1.17 to 1.40, P = 5.9×10-6) and invasive ovarian cancer (ORw = 1.26, 95% CI = 1.10 to 1.43, P = 3.8×10-3). These associations were stronger for serous ovarian cancer and for estrogen receptor-negative breast cancer (ORw = 1.46, 95% CI = 1.2 to 1.70, P = 3.4×10-5 and ORw = 1.50, 95% CI = 1.28 t