Articles

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. 

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. 

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.

Dramatic microstructure changes in equity markets have made standard liquidity measures less accurate proxies of trading costs. We develop trade-time based liquidity measures that reflect per-dollar price impacts of fixed-dollar volumes. Our measures (i) better capture institutional trading costs; and (ii) better explain the cross-section of returns than do standard measures, e.g., quoted and effective spreads, estimates of Kyle's lambda, and Amihud's (2002) measure, especially in more recent years. Despite improvements in measures of market quality, expected trading costs still have explanatory power for the cross-section of expected returns for NYSE-listed stocks: we obtain monthly liquidity premium estimates of 5.3bp for expected returns and 2.4bp for risk-adjusted returns. Estimated liquidity premia rise after the financial crisis and remain consistently high thereafter.

Multi-year regulatory rate plans are becoming more common. An agreement to "stay out" of the rate revision process removes an option for the utility company or regulatory commission to request a modification to tariff rates in response to changes in interest rates. We develop an objective approach to adjust the utility's required return on equity to account for these lost options.

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.