High-frequency trading in the stock market and the costs of option market making

Using a comprehensive panel of 2,969,829 stock-day data provided by the Securities and Exchange Commission (MIDAS), we find that HFT activity in the stock market increases market-making costs in the options markets. We consider two potential channels - the hedging channel and the arbitrage channel - and find that HFTs' liquidity-demanding orders increase the hedging costs due to a higher stock bid-ask spread and a higher price impact for larger hedging demand. The arbitrage channel subjects the options market-maker to the risk of trading at stale prices. We show that the hedging (arbitrage) channel is dominant for ATM (ITM) options. Given the significant growth in options trading, we believe that our study highlights the need to better understand the costs/risks due to HFT activities in equity markets on derivative markets

The 2007-09 financial crisis and bank opaqueness

Doubts about the accuracy with which outside investors can assess a banking firm’s value motivate many government interventions in the banking market. The recent financial crisis has reinforced concerns about the possibility that banks are unusually opaque. Yet the empirical evidence, thus far, is mixed. This paper examines the trading characteristics of bank shares over the period from January 1990 through September 2009. We find that bank share trading exhibits sharply different features before vs. during the crisis. Until mid-2007, large (NYSE-traded) banking firms appear to be no more opaque than a set of control firms, and smaller (NASD-traded) banks are, at most, slightly more opaque. During the crisis, however, both large and small banking firms exhibit a sharp increase in opacity, consistent with the policy interventions implemented at the time. Although portfolio composition is significantly related to market microstructure variables, no specific asset category(s) stand out as particularly important in determining bank opacity.Banks and banking ; Stock market ; Financial crises

Non-Standard Errors

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

Information and noise in speculative markets: Two essays.

The study makes three major contributions towards understanding the role of asymmetric information and noise in speculative markets. First, a model of informed speculation is developed in which the informed agent trades strategically so as to withhold part of the information. This leads to a model of imperfect competition which avoids the unsatisfactory "schizophrenia" problem associated with the competitive models in which each trader takes the equilibrium price as given despite the fact that he influences the price. The model yields predictions about the adverse selection component of the bid-ask spread, volatility of prices and the autocorrelation structure of returns. The determinants of the bid-ask spread is shown to depend on the fundamental parameters governing the information process and the noise in the system. Second, an empirical methodology to test the predictions of the model is developed. This is achieved by postulating that the stock price dynamics follow a jump diffusion process where it is assumed that the diffusion component is induced by noise, and the jump component is due to the impact of significant information. Parameter estimates of the stochastic model are used as surrogates to test the predictions. The empirical results confirm the predictions of the model. Finally, an event-study methodology is introduced that is based on the generalized Poisson jump diffusion model for the stock price dynamics. The model consists of a Weiner process which captures the normal fluctuations in stock prices, and an independent compound event process which models the price reaction to events. The structure added by separating the event process from the non-event process leads to a cumulative event return estimator that is more efficient and has higher power than the traditional multi-day abnormal return estimator for multiple events with event day uncertainty. A maximum likelihood technique is implemented to estimate the parameters of the model, and simulations confirm the higher power and efficiency of the estimator. The method is applied to study the impact of greenmail on stockholders and it is found that shareholders earn a positive return for the overall period including the blockholding and repurchase.Ph.D.Business AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/104027/1/9034489.pdfDescription of 9034489.pdf : Restricted to UM users only