We demonstrate that while Federal Reserve decisions may not introduce new information for professional forecasters and rational investors, they act as "information shocks" for a significant segment of the market. Our findings suggest that overconfident equity investors who rely on the Fed's economic assessments hesitate to trade in the absence of positive signals from the Fed. Stocks sensitive to this hesitation show increased trading volume, higher liquidity, and elevated abnormal returns during a short window after the FOMC meeting. Notably, these effects are primarily driven by retail investors rather than institutional investors, becoming more pronounced after the 2008 financial crisis, but diminishing when market scrutiny intensifies.

We construct multiple horizon density forecasts using a novel optimisation which calibrates the beta distribution in order to transform option-implied risk-neutral density, into real-world density forecasts. We find that the calibrated densities account for investors' subjective attitude to risks by reducing the distance between the true unknown density and the empirical ones as, measured by the Kullback-Liebler information criterion (KLIC). Our results reinforce the stylised fact stipulating that returns are non-Gaussian and establish why simpler models of past information designed to capture Gaussian distributions may outperform option-implied information based on the KLIC divergence. The methodology we propose demonstrates significant forecasting ability improvements, evidenced by both parametric and non-parametric option pricing models. The results indicate the superiority of non-parametric models of option-implied information. In addition, these results are contrasted with models of conditional volatility with an underlying Gaussian distribution and indicates that historical density forecasts provide better results than real-world calibrated density forecasts regardless of the forecast horizon.

Based on companies' financial statements, this article analyzes how costs are passed on in companies during periods of high inflation The COVID-19 pandemic and Russo-Ukrainian war severely disrupted the global economy since 2020. This has driven up commodity including energy costs, causing an inflation spike in 2022. Using panel linear regression, we demonstrate inflation on agricultural goods, industrial metals and energy in 2021-2022 have a significant impact on cost pass-through estimated by the variation in the ratio of value added to net sales. Our analysis demonstrates that firms with lower turnover growth, greater export intensity, higher profitability, higher productivity, higher leverage, higher liquidity and higher intangibility are better positioned to pass through cost increases. Furthermore, companies in the food and beverage sectors demonstrate excess pass-through of agricultural product cost increases. We demonstrate that, despite significant commodity shocks during the inflationary period, sectoral responses vary widely, highlighting the importance of sector-specific factors and individual firm characteristics within the sector.

We define a simple and efficient “push-and-solve” algorithm to compute the best execution or “optimal split” of a market order given a finite set of automated market makers (AMMs). Each AMM has to be decomposable into basic building blocks which we call price-parametrised AMMs. This has a practical application to the optimal splitting of orders among Uniswap v3 clones (the dominant form of AMM in decentralised finance). Indeed our algorithm is query-optimal (information on sources is queried on a call-by-need basis) and therefore of low enough complexity to be implemented as a smart contract. We also find a sufficient condition for AMMs based on price parametrisations to be aggregatable which is of independent interest as it allows one to build novel AMMs with concentrated liquidity, families of which can also be optimally executed on-chain.

I exploit the exogenous liquidity windfalls from the 2003 oil and natural gas shale discoveries to examine how positive deposit supply shocks affect bank dividend payout policy. Using a sample of 568 U.S. Bank Holding Companies from 1999-2006, I find that banks exposed to shale windfalls increase dividends and that shale exposure is economically meaningful, with a one standard deviation increase in shale-well exposure increasing banks’ dividend payout ratio by 1.5%. Through cross-sectional tests, I show that the decision to increase dividend payouts is driven by signaling incentives rather than risk-shifting tendencies. These findings are robust to various matched sampling methods, model specifications, and estimation techniques.