Ryan J. Davies, Ph.D.
Associate Professor of Finance, Babson College
Chair, Finance DivisionFinance Division, Babson College, 220 Tomasso Hall, Babson Park, MA 02457-0310 Tel: 781-239-5345; Fax: 781-239-5004
This paper documents order submission strategies during the Toronto Stock Exchange preopening session. I examine the role of the designated market maker, known as a registered trader (RT), in the price discovery process of a transparent automated opening call auction. I find that the RT actively participates in the market opening, even though he cannot set the opening price directly, and has no apparent informational advantage. RT opening trades are profitable, moderate overnight price changes, and appear to be motivated, in part, by inventory adjustment concerns. I examine interlisted stocks that simultaneously open for trading under two different mechanisms and show how the comparative levels of pre-trade market transparency of each exchange impacts RT profits and participation.
This study is largely motivated by the ongoing process to revise the Investment Services Directive (ISD). Perhaps the most important aspect of this process are the consequences of the repeal of Article 14(3) which allows national authorities to stipulate that retail investor orders be executed only on a "regulated market" (the so-called "concentration rule"). In the absence of a concentration rule, trades may be executed away from the main market centre. Fragmentation occurs as orders are executed through preferencing arrangements and through in-house matching. This paper focuses on a specific type of preferencing - the internalisation of order flow. This paper examines the extent to which order flow is internalised in European financial markets. In doing so, it formally defines internalisation and explores its relationship with issues such as market fragmentation and price discovery. It illustrates how internalisation practices differ across each of the major European financial centres and presents new empirical evidence of the possible effects of internalisation on price discovery.
We document systematic patterns in daily aggregate market returns around end-of-quarters consistent with strategic fund manager behavior and the growing presence of mutual funds in the market.
How much capital should investors allocate to different hedge fund strategies? The answer is elusive. Hedge fund exhibit complex, non-normal return distributions. In this context, it is difficult to use standard mean-variance portfolio theory and performance measures based on it (e.g. the Sharpe ratio). An allocation technique based on Polynomial Goal Programming (PGP) appears to be a suitable alternative.
This paper explores strategic trade in short-lived securities by agents who have private information that is potentially long-term, but do not know how long their information will remain private. Trading short-lived securities is profitable only if enough of the private information becomes public prior to contract expiration; otherwise the security will worthlessly expire. We highlight how this results in trading behavior fundamentally different from that observed in standard models of informed trading in equity. Specifically, we show that informed speculators are more reluctant to trade shorter-term securities too far in advance of when their information will necessarily be made public, and that existing positions in a shorter-term security make future purchases more attractive. Because informed speculators prefer longer-term securities, this can make trading shorter-term contracts more attractive for liquidity traders. We characterize the conditions under which liquidity traders choose to incur extra costs to roll over short-term positions rather than trade in distant contracts, providing an explanation for why most longer-term derivative security markets have little liquidity and large bid-ask spreads. [Final working paper version available here. Supplemental derivations for section 4.2 available here.]
This study evaluates the efficiency of cross hedging with single stock futures (SSF) contracts. We propose a new technique for hedging exposure to an individual stock that does not have options or exchange-traded SSF contracts written on it. Our method selects as a hedging instrument a portfolio of SSF contracts which are selected based on how closely matched their underlying firm characteristics are with the characteristics of the individual stock we are attempting to hedge. We investigate whether using cross-sectional characteristics to construct our hedge can provide hedging efficiency gains over that of constructing the hedge based on return correlations alone. Overall, we find that the best hedging performance is achieved through a portfolio that is hedged with market index futures and a SSF matched by both historical return correlation and cross-sectional matching characteristics. We also find it preferable to retain the chosen SSF contracts for the whole out-of-sample period while re-estimating the optimal hedge ratio at each rolling window.
This paper provides guidance on how to use matched samples to test for differences in trade execution costs (e.g. quoted and effective spreads). Based on extensive simulation results, we conclude that the best practice is to match firms one-to-one based on market capitalization and share price, and to test for differences between the matched pairs using a Wilcoxon signed rank test. We demonstrate that pre-sorting by industry groups or discarding apparent poor matches may reduce test power. We show that, in general, tests based on one-to-one nearest-neighbor matching have comparable power and less size distortion than alternatives that place more weight on distant rms. We find that matching without replacement can reduce size distortion when the control sample is relatively small. We highlight conditions under which matched sample estimation may be preferred to the corresponding event study.
We develop a model of mutual fund manager investment decisions near the end of quarters. We show that when investors reward better performing funds with higher cash flows, near quarter-ends a mutual fund manager has an incentive to distort new investment toward stocks in which his fund holds a large existing position. The short-term price impact of these trades increase the fund's reported returns. Higher returns are rewarded by greater subsequent fund inflows which, in turn, allow for more investment distortion the next quarter. Because the price impact of trades is short-term, each subsequent quarter begins with a larger return deficit. Eventually, the deficit cannot be overcome. Thus, our model leads to the empirically observed short-run persistence and long-run reversal in fund performance. In doing so, our model provides a consistent explanation of many other seemingly contradictory empirical features of mutual fund performance.
This paper develops a technique for fund of hedge funds to allocate capital across different hedge fund strategies and traditional asset classes. Our adaptation of the polynomial goal programming optimisation method incorporates investor preferences for higher return moments, such as skewness and kurtosis, and provides computational advantages over rival methods. We show how optimal allocations depend on the interaction between strategies, as measured by covariance, co-skewness and co-kurtosis. We also demonstrate the importance of constructing `like for like' representative portfolios that reflect the investment opportunities available to different-sized funds. Our empirical results reveal the importance of equity market neutral funds as volatility and kurtosis reducers and of global macro funds as portfolio skewness enhancers. (May 2006 working paper version available here.)
We examine a fund manager's alleged manipulation of platinum and palladium futures settlement prices. Using benchmarks from parallel electronic markets, we find that the manager’s market-on-close trading causes significant settlement price artificiality. Defying predictions that competition among floor traders should limit any artificiality, the artificiality increases in the second half of the alleged manipulation period. Between 35% and 52% of the latter-period artificiality is directly attributable to noncompetitive floor prices. Inflated floor volume contributes a similar proportion to artificiality via the exchange’s trade-weighted settlement price formula. We estimate that floor counterparties reaped more than $6.0 million in excess profits.
In 2004, the European Council and Parliament adopted a new directive on markets in financial instruments (MiFID). The implementation of the directive in EU member states is still ongoing. We provide a framework for understanding some of the main features of this new regulatory structure. We summarize the economics of fragmentation and internalisation -- issues that have been at the forefront of discussion throughout the MiFID consultation, approval and implementation process. We also provide lessons from the US regulatory experience, with a special focus on US trade reporting rules. We highlight relevant features of the largest European equity markets, describe the main features of the MiFID and provide recommendations on its implementation.
We provide a simple method to predict how the higher order return moments of a single strategy fund of hedge funds will vary as one or more funds are added to, or removed from, the portfolio. Our model-free approach uses average co-moments obtained from the universe of available funds to develop a functional relationship between portfolio return distributions and the number of funds in the portfolio.
We examine current areas of concern in the regulation of secondary trading markets, including questions concerning market fragmentation, protected orders, minimum tick size, maker-taker pricing models, transparency and dark liquidity, algorithmic and high-frequency trading, the duties and obligations of broker-dealers who handle customer orders, system robustness, flash crashes and episodic liquidity, exchange traded funds, distributed ledger technology, the ownership and usage of market data, and the governance of market data plans. We also discuss potential ways to reduce transaction costs and improve transparency in fixed income markets. To the extent possible, we summarize key findings from the existing academic literature, describe directions for future research, and offer suggestions for future rulemaking.
In November 2007, the Markets in Financial Instruments Directive (MiFID) came into full effect. MiFID is the most significant European Union legislation for investment intermediaries and financial markets ever introduced. In general terms, MiFID is designed to provide a common, harmonized set of rules for the provision of investment services in each of the EU member states. A key feature of this legislation is the concept of a passport by which firms (e.g., investment banks, broker dealers, stock exchanges and alternative trading systems) are regulated primarily by their home state but can operate in other EU host states. MiFID also removes the so-called concentration rule, allowing greater competition for order flow across trading venues. This paper shows how new requirements concerning best execution, client classification, systematic internalisers, pre- and post-trade transparency, and the ownership of market data have created enormous new business opportunities. This paper argues that MiFID has already succeeded in its goal of introducing greater competition, as already made evident by the early success of new trading venues such as Instinet Chi-X and new market data providers such as Markit BOAT. Further exchange consolidation, more sophisticated smart order routing, and new entrants such as BATS Europe, Nasdaq OMX Europe, and Project Turquoise suggest that European financial markets are on the cusp of further major (and unpredictable) changes. In addition to exploring developments in Europe, the paper explores the impact of international exchange linkages, such as NYSE Euronext, and contrasts the principles-based approach of MiFID with the rules-based approach of the SEC's RegNMS.
It has been widely debated how much nonsynchronous trading drives asymmetric portfolio cross-autocorrelations: lagged returns on a portfolio of larger-capitalization stocks are far more heavily correlated with current returns on a portfolio of smaller-capitalization stocks than the converse. This paper proposes a new method to generate precise estimates of the extent to which nonsynchronous trading underlies these differences. By contrasting cross-autocorrelations using 24-hour portfolio returns based on trade prices before an arbitrary point in the trading day with those using returns based on prices after, our difference-in-differences approach isolates the impact of nonsynchronous trading on differences in portfolio cross-autocorrelations.
We develop measures of stock-specific trading activity based on durations of sequences of consecutive trades with fixed cumulative values. Trade sizes and signed-trade imbalances rise with activity, while price impacts generally fall, but not always, due to endogenous variation in liquidity provision and consumption. After controlling for this endogenity, price impacts uniformly fall with predicted activity. The sensitivity of price impacts to changes in activity becomes more similar across stocks post-RegNMS, but price impacts diverge, falling (rising) for liquid (illiquid) stocks. In asset pricing models, our illiquidity measure outperforms standard measures and we find illiquidity premia rise as trading costs diverge.
Multi-year regulatory rate plans are becoming increasingly common. An agreement to "stay out" of the rate revision process takes away an option for the utility company or regulatory commission to request a modification to rates in response to changes in benchmark interest rates. This paper develops an approach to adjust the required cost of equity of the utility to account for these lost options. The paper also shows how to account for the ability of the utility to pay a fixed monetary penalty to enter into a new rate agreement prior to the end of the stay-out period.
This page was last updated on 06/20/2016.