Centralized Trading, Transparency, and Interest Rate Swap Market Liquidity: Evidence from the Implementation of the Dodd–Frank Act

We use proprietary transaction data on interest rate swaps to assess the effects of centralized trading, as mandated by Dodd-Frank, on market quality. Contracts with the most extensive centralized trading see liquidity metrics improve by between 12% and 19% relative to those of a control group. This is driven by a clear increase in competition between dealers, particularly in US markets. Additionally, centralized trading has caused inter-dealer trading in EUR swap markets to migrate from the US to Europe. This is consistent with swap dealers attempting to avoid being captured by the trade mandate in order to maintain market power

Profiting from Mimicking Strategies in Non-Anonymous Markets

We explore the information content of counterparty identities and how their disclosure can be exploited by other investors in a post-trade transparent market. Using data from the Helsinki Stock Exchange, we form dynamic mean-variance strategies with daily rebalancing which condition on the net flow of individual brokers. We find that investors can benefit greatly, up to 36% in annualized risk adjusted returns, from knowing who has been trading. We demonstrate a link between the information content of broker order flow and the sophistication of their clients. Brokers who have clients that trade with a momentum style or who are predominantly institutions or foreign investors have much more informative flow than do others. In the Finnish setting, this means that brokers with large market share have uninformative flows

Profiting from Mimicking Strategies in Non-Anonymous Markets

We explore the information content of counterparty identities and how their disclosure can be exploited by other investors in a post-trade transparent market. Using data from the Helsinki Stock Exchange, we form dynamic mean-variance strategies with daily rebalancing which condition on the net flow of individual brokers. We find that investors can benefit greatly, up to 36% in annualized risk adjusted returns, from knowing who has been trading. We demonstrate a link between the information content of broker order flow and the sophistication of their clients. Brokers who have clients that trade with a momentum style or who are predominantly institutions or foreign investors have much more informative flow than do others. In the Finnish setting, this means that brokers with large market share have uninformative flows

The Cost of Clearing Fragmentation

Fragmenting clearing across multiple central counterparties (CCPs) is costly because global dealers cannot net positions across CCPs. They have to collateralize both the short position in one CCP and an offsetting long position in another CCP. This, coupled with a structural net order imbalance across CCPs, can cause prices to persistently differ across them ("the CCP basis"). Tests based on unique CCP data for interest-rate derivatives (IRDs), yield broad empirical support for this intuition and suggest that the clearing friction costs sellers clearing in LCH, the largest European CCP for IRDs, $80 million daily

Can investors benefit from market transparency? : an asset allocation perspective

A current debate in finance concerns transparency in financial markets and the disclosure of counterparty identity information. We use a simple mean-variance framework and data from Helsinki Stock Exchange to explore the asset allocation implications of post-trade market transparency. We find that broker identity conveys information that is economically significant. A mean-variance investor can benefit remarkably, up to 36% (annualized) percentage points for the most parsimonious forecasting model, from knowing who trades. A second result is the substantial variation in the information content of order flow at the broker level. We show that the predictive power of broker customer order flow can be attributed to observable broker-specific characteristics: market share, daily volume, investment style and degree of sophistication

A Least Squares Regression Realised Covariation Estimation

We propose a least squares regression framework for the estimation of the realized covariation matrix using high frequency data. The new estimator is robust to market microstructure noise (MMS) and non-synchronous trading. Comprehensive simulation and empirical analysis show that our estimator performs as well as a set of popular estimators in the literature. More importantly, our framework allows for the unique identification of MMS noise moments. We find that these noise moments are related to measures of liquidity and contain predictive information that helps to significantly improve out-of-sample asset allocation

Weighted Least Squares Realized Covariation Estimation

We introduce a novel weighted least squares approach to estimate daily realized covariation and microstructure noise variance using high-frequency data. We provide an asymptotic theory and conduct a comprehensive Monte Carlo simulation to demonstrate the desirable statistical properties of the new estimator, compared with existing estimators in the literature. Using high-frequency data of 27 DJIA constituting stocks over a period from 2014 to 2020, we confirm that the new estimator performs well in comparison with existing estimators. We also show that the noise variance extracted based on our method can be used to improve volatility forecasting and asset allocation performance

Sell-side analysts' career concerns during banking stresses

We propose a new approach to examine sell-side analysts’ career concerns by relating their forecast boldness to their employers’ news flows. Specifically, we use banking sector news to proxy for the severity of career concerns. Analysts follow more closely the consensus forecast when the prospects of the banking sector are negative (and vice versa). The effect is both economically and statistically significant after controlling for various firm, analyst, brokerage house, and forecasting characteristics, as well as sector and economy wide effects. The more established analysts, in terms of reputation and experience, are generally unaffected by banking sector news. In contrast, their less established peers tend to cluster their forecasts near the consensus after a sequence of negative news flows for banks. Collectively, our results support the notion that during banking stresses when job security is low analysts’ tendency to imitate others increases