The Volatility of Liquidity and Expected Stock Returns

The pricing of total liquidity risk is studied in the cross-section of stock returns. The study suggests that there is a positive relation between total volatility of liquidity and expected returns. Our measure of liquidity is based on Amihud (2002) and its volatility is measured using daily data. Furthermore, we document that total volatility of liquidity is priced in the presence of systematic liquidity risk: the covariance of stock returns with aggregate liquidity, the covariance of stock liquidity with aggregate liquidity, and the covariance of stock liquidity with the market return. The separate pricing of total volatility of liquidity indicates that idiosyncratic liquidity risk is important in the cross section of returns. This result is puzzling in light of Acharya and Pedersen (2005) who develop a model in which only systematic liquidity risk affects returns. The positive correlation between the volatility of liquidity and expected returns suggests that risk averse investors require a risk premium for holding stocks that have high variation in liquidity. Higher variation in liquidity implies that a stock may become illiquid with higher probability at a time when it is traded. This is important for investors who face an immediate liquidity need and are not able to wait for periods of high liquidity to sell

Director networks and informed traders

We provide evidence that sophisticated investors like short sellers, option traders, and financial institutions are more informed when trading stocks of companies with more connected board members. For firms with large director networks, the annualized return difference between the highest and lowest quintile of informed trading ranges from 4% to 7.2% compared to the same return difference in firms with less connected directors. Sophisticated investors better predict outcomes of upcoming earnings surprises and firm-specific news sentiment for companies with more connected directors. Changes in board connectedness are positively associated with changes in measures of adverse selection

A radiomic signature based on magnetic resonance imaging to determine adrenal Cushing's syndrome

Purpose: The aim of this study was to develop radiomics signature-based magnetic resonance imaging (MRI) to determine adrenal Cushing’s syndrome (ACS) in adrenal incidentalomas (AI). Material and methods: A total of 50 patients with AI were included in this study. The patients were grouped as nonfunctional adrenal incidentaloma (NFAI) and ACS. The lesions were segmented on unenhanced T1-weighted (T1W) in-phase (IP) and opposed-phase (OP) as well as on T2-weighted (T2-W) 3-Tesla MRIs. The LASSO regression model was used for the selection of potential predictors from 111 texture features for each sequence. The radiomics scores were compared between the groups. Results: The median radiomics score in T1W-Op for the NFAI and ACS were -1.17 and -0.17, respectively (p < 0.001). Patients with ACS had significantly higher radiomics scores than NFAI patients in all phases (p < 0.001 for all). The AUCs for radiomics scores in T1W-Op, T1W-Ip, and T2W were 0.862 (95% CI: 0.742-0.983), 0.892 (95% CI: 0.774-0.999), and 0.994 (95% CI: 0.982-0.999), respectively. Conclusion: The developed MRI-based radiomic scores can yield high AUCs for prediction of ACS

Do Mutual Fund Investors Overweight the Probability of Extreme Payoffs in the Return Distribution?

We investigate the role of extreme positive payoffs in the distribution of monthly fund returns on investors’ mutual fund preferences. We document a positive and significant relation between the maximum style-adjusted monthly return (MAX) over the previous year’s monthly returns and future fund flows. Our findings are consistent with the idea that fund investors overweight the probability of high payoff states in past return distribution. MAX as a measure of average past fund performance, increase in fund visibility, convex performance-flow relation, underlying stocks’ extreme returns, and MAX as a metric of future performance do not explain our results

Determinants and consequences of information processing delay: Evidence from the Thomson Reuters Institutional Brokers’ Estimate System

We present new evidence that highlights the role of information intermediaries in the distribution and processing of earnings estimates in capital markets. We find that the time taken to activate an analyst's earnings forecast in the Thomson Reuters Institutional Brokers’ Estimate System is related to measures of investor demand for timely information processing, processing difficulty, and limited attention. Furthermore, we find that forecast announcement returns are muted and post-announcement drift is magnified for forecasts with longer unexpected activation delay and that market inefficiency is concentrated in neglected stocks and potentially exploitable. Finally, analyzing intraday returns, we find that activations facilitate price discovery

Determinants and Consequences of Information Processing Delay: Evidence from the Thomson Reuters Institutional Brokers’ Estimate System

We present new evidence that highlights the role of information intermediaries in the distribution and processing of earnings estimates in capital markets. We find that the time taken to activate an analyst’s earnings forecast in the Thomson Reuters Institutional Brokers’ Estimate System is related to measures of investor demand for timely information processing, processing difficulty, and limited attention. Furthermore, we find that forecast announcement returns are muted and post-announcement drift is magnified for forecasts with longer unexpected activation delay and that market inefficiency is concentrated in neglected stocks and is potentially exploitable. Finally, analyzing intra-day returns, we find that activations facilitate price discovery