The aim of this study was to retrospectively analyze 18F-FDG positron emission tomography/computed tomography (18F-FDG PET/CT) metabolic variables, programmed death-ligand 1 (PD-L1) and phosphorylated signal transducer and activator of transcription 3 (p-STAT3) tumor expression, and other factors as predictors of disease-free survival (DFS) in patients with lung adenocarcinoma (LUAD) (stage IA-IIIA) who underwent surgical resection. We still lack predictor of immune checkpoint (programmed cell death-1 [PD-1]/PD-L1) inhibitors. Herein, we investigated the correlation between metabolic parameters from 18F-FDG PET/CT and PD-L1 expression in patients with surgically resected LUAD.Seventy-four patients who underwent 18F-FDG PET/CT prior to treatment were consecutively enrolled. The main 18F-FDG PET/CT-derived variables were primary tumor maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). Surgical tumor specimens were analyzed for PD-L1 and p-STAT3 expression using immunohistochemistry. Correlations between immunohistochemistry results and 18F-FDG PET/CT-derived variables were compared. Associations of PD-L1 and p-STAT3 tumor expression, 18F-FDG PET/CT-derived variables, and other factors with DFS in resected LUAD were evaluated.All tumors were FDG-avid. The cutoff values of low and high SUVmax, MTV, and TLG were 12.60, 14.87, and 90.85, respectively. The results indicated that TNM stage, PD-L1 positivity, and high 18F-FDG PET/CT metabolic volume parameters (TLG ≥90.85 or MTV ≥14.87) were independent predictors of worse DFS in resected LUAD. No 18F-FDG metabolic parameters associated with PD-L1 expression were observed (chi-square test), but we found that patients with positive PD-L1 expression have significantly higher SUVmax (P = .01), MTV (P = .00), and TLG (P = .00) than patients with negative PD-L1 expression.18F-FDG PET/CT metabolic volume parameters (TLG ≥90.85 or MTV ≥14.87) were more helpful in prognostication than the conventional parameter (SUVmax), PD-L1 expression was an independent predictor of DFS in patients with resected LUAD. Metabolic parameters on 18F-FDG PET/CT have a potential role for 18F-FDG PET/CT in selecting candidate LUAD for treatment with checkpoint inhibitors.

Postcrisis regulations apply stricter liquidity rules to both money market funds (MMFs) and banks, requiring MMFs to do more overnight lending and banks to borrow longer-term. MMFs and banks resolve this dilemma by developing a “bundling” strategy across overnight and longer term markets. In particular, MMFs increase longer term funding and charge a lower rate to banks that have recently accommodated MMFs’ overnight depositing needs. Such cross-market reciprocity is stronger between MMFs and foreign banks, which depend on MMFs for dollar funding more than U.S. banks do. MMFs with lower liquidity buffers and higher flow volatility are more likely to engage in bundling.

Using a structural model, we estimate the liquidity multiplier of an interbank network and banks’ contributions to systemic risk. To provide payment services, banks hold reserves. Their equilibrium holdings can be strategic complements or substitutes. The former arises when payment velocity and multiplier are high. The latter prevails when the opportunity cost of liquidity is large, incentivising banks to borrow neighbors’ reserves instead of holding their own. Consequently, the network can amplify or dampen shocks to individual banks. Empirically, network topology explains cross-sectional heterogeneity in banks’ systemic-risk contributions while changes in the equilibrium type drive time-series variation.

We model competition for liquidity provision between high-frequency traders (HFTs) and slower execution algorithms (EAs) designed to minimize investors’ transaction costs. Under continuous pricing, EAs dominate liquidity provision by using aggressive limit orders to stimulate HFTs’ market orders. Under discrete pricing, HFTs dominate liquidity provision if the bid-ask spread is binding at one tick. If the tick size (minimum price variation) is not binding, EAs choose between stimulating HFTs and providing liquidity to non-HFTs. Transaction costs increase with the tick size but can be negatively correlated with the bid-ask spread when all traders can provide liquidity.

Disagreement measures are known to predict cross-sectional stock returns but fail to predict market returns. This paper proposes a partial least squares disagreement index by aggregating information across individual disagreement measures and shows that this index significantly predicts market returns both in- and out-of-sample. Consistent with the theory in Atmaz and Basak (2018), the disagreement index asymmetrically predicts market returns with greater power in high-sentiment periods, is positively associated with investor expectations of market returns, predicts market returns through a cash flow channel, and can explain the positive volume-volatility relationship.

We propose a novel high-frequency decomposition of daily stock returns into news- and non-news-driven components, and uncover evidence of pervasive stock market underreaction to firm news. Prices tend to drift in the same direction as the initial market response for several days after the news arrival without reversals. A trading strategy exploiting the return drift generates high abnormal returns and remains profitable after transaction costs. To understand the economic mechanism, we find that the return drift is stronger when investors are distracted. Analysts’ slow adjustments of market expectations following firm news also contribute to the market underreaction.

We identify the broker each corporate insider trades through, and find that analysts and mutual fund managers affiliated with such “inside brokers” have a substantial information advantage on the insider’s firm. Affiliated analysts issue more accurate earnings forecasts, and affiliated mutual funds trade the insider’s stock more profitably than their peers, following insider trades through their brokerage. Notably, this advantage persists well after these insider trades are publicly disclosed. Our results challenge the prevalent perception that information asymmetry arising from insider trading is acute only before trade disclosure, and suggest that brokers facilitating these trades are in a position to exploit this asymmetry.

We argue that the pervasive practice of evaluating portfolio managers relative to a benchmark has real effects. Benchmarking generates additional, inelastic demand for assets inside the benchmark. This leads to a “benchmark inclusion subsidy:” a firm inside the benchmark values an investment project more than the one outside. The same wedge arises for valuing M&A, spinoffs, and IPOs. This overturns the proposition that an investment’s value is independent of the entity considering it. We describe the characteristics that determine the subsidy, quantify its size (which could be large), and identify empirical work supporting our model’s predictions.

Inspired by the recent health science findings that air pollution affects mental health and cognition, we examine whether air pollution can intensify the cognitive bias observed in the financial markets. Based on a proprietary data set obtained from a large Chinese mutual fund family consisting of complete trading information for more than 773,198 accounts in 247 cities, we find that air pollution significantly increases investors’ disposition effects. Analysis based on two plausible exogenous variations in air quality (the vast dissipation of air pollution caused by strong winds and the Huai River policy) supports a causal interpretation. Mood regulation provides a potential mechanism.