In this paper, I construct a trading volume model that explicitly incorporates the impact of ambiguity surrounding public information announcements. I derive a closed form expression that illustrates how ambiguity influences trading volume through two channels: expectations and volatility. The empirical results highlight the significant role played by the expectations channel of ambiguity in driving trading activity. Specifically, on a monthly basis, I observe that a one-standard-deviation increase in ambiguity results in approximately a 19% to 58%-standard-deviation increase in trading volume. Additionally, by employing this model, I demonstrate that a substantial portion of the positive returns of a standard U.S. equity turnover sorted portfolio, approximately 80% of the returns, traditionally attributed to liquidity, are actually driven by information ambiguity.

We use UK transaction-level data on nominal bond, inflation-linked bond and inflation swap markets to study trading behaviour and prices in inflation markets. Our empirical analysis yields five main results: (i) there is persistent inflation mispricing over the 2018–22 period, with nominal gilts on average 135 basis points more expensive (per £100 notional) than their synthetic counterparts constructed from inflation swaps and inflation-linked bonds; (ii) hedge funds respond to changes in mispricing but their response does not constitute arbitrage – they adjust their bond portfolios appropriately, but do not hedge these trades in the inflation swap market; (iii) inflation markets are largely segmented with liability-driven investors and pension funds (LDI-P) dominating the inflation swap market, and many clients that are active in bond markets are absent in the inflation swap market; (iv) LDI-P activity is a key driver of inflation mispricing – the sector’s orderflows in inflation-linked bonds and (to lesser extent) nominal bonds and inflation swaps contribute significantly to day-to-day variations in mispricing; (v) the generally weak link between market-based measures of inflation expectations and survey-based measures is strengthened once we clean market prices from the effect of LDI-P trading activity. 

In this paper, I uncover a significant pattern in the behavior of G10 currencies and equity implied volatility, which explains a substantial portion of their net returns from 1994 to 2019. The vast majority of the positive returns from a long-short position in G10 currencies and a substantial 90% of the cumulative negative returns in implied volatility are concentrated on the even weeks around U.S. FOMC meetings. In contrast, odd weeks, encompass on average nearly all the negative returns from such a long-short position in G10 currencies and all the increases in implied volatility. This interplay between these opposing dynamics explains most of the net returns of these assets over the 26-year period. U.S. equities show a similar pattern. Furthermore, I establish that this recurring even-odd weeks cycle is explained by a combination of repetitive calendar events at the beginning of the month (start of the month effect) and on the day-16 (day-16 effect), along with sporadic shocks primarily related to political news. This explanation also extends to equities, with evidence dating back to 1955. Recurring patterns in liquidity, tied to U.S. Treasury instrument cashflows and the interbank market, play a pivotal role in driving this phenomenon.

We study the wealth effects of peer-following behavior of pension fund managers. We structure our analysis around a stylized model where overestimating peers' information quality leads to investment losses. Empirically, we first confirm that funds follow peers in portfolio allocations. We then quantify the economic damage of peer-effects through a counterfactual fund that is not peer-influenced. We find evidence of underperformance in funds with high levels of foreign investment, in which the managers have less expertise and rely more on their competitors' information. The economic impact of peer effects is meaningful, translating into a 30%-48% loss in workers' pension wealth.