Editor’s Letter

The Summer issue of the journal begins with an examination by Carlens and Higgins of the impact of MiFID II on European equity market liquidity. They investigate the changes in the market in the lead-up to the January 3, 2018 implementation date and the early evidence supporting the expected liquidity shift toward block networks, periodic auctions, and systematic internalizers.

In August 2012, the New York Stock Exchange (NYSE) launched the Retail Liquidity Program (RLP). The RLP enables market makers to quote dark (nondisplayed) limit orders that can be filled only by market orders that originate from retail traders. Garriott and Walton study the informational and market-quality impacts of segmentation using Trade and Quote (TAQ) data from the NYSE. They analyze the mechanism by which segmentation affects market quality by computing the information share of each component of the order flow using the techniques of Hasbrouck (The Journal of Finance, 1991).

Next, Cole, Van Ness, and Van Ness study municipal bond market activity before, during, and after natural disasters (tornadoes, wildfires, and hurricanes/tropical storms). Using a sample of municipal bond trades from 2010 to 2013, they find that natural disasters influence municipal bond trading. They also determine that linkages exist between the bonds affected by natural disasters and related bonds.

To continue, Kakushadze and Yu provide an explicit formulaic algorithm and source code for building long-only benchmark portfolios and then using these benchmarks in long-only market outperformance strategies. They use a multifactor risk model (which utilizes multilevel industry classification or clustering) specifically tailored to long-only benchmark portfolios to compute their weights, which are explicitly positive in their construction.

To conclude this issue, Graf Plessen and Bemporad present a simple method for a posteriori (historical) multivariate, multistage optimal trading under transaction costs and a diversification constraint. The developed methods are based on efficient graph generation and consequent graph search and are evaluated quantitatively on real-world data. The fundamental motivation of this work is preparatory labeling of financial time-series data for supervised machine learning.

As always, we welcome your submissions. We value your comments and suggestions, so please email us at journals{at}investmentresearch.org.

TOPICS: Big data/machine learning, interest-rate and currency swaps, portfolio construction, factor-based models

Brian Bruce

Editor-in-Chief

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