We investigate the effects of market making on market performance, focusing on allocative efficiency as well as gains from trade accrued by background traders. We employ empirical simulation-based methods to evaluate heuristic strategies for market makers as well as background investors in a variety of complex trading environments. Our market model incorporates private and common valuation elements, with dynamic fundamental value and asymmetric information. In this context, we compare the surplus achieved by background traders in strategic equilibrium, with and without a market maker. Our findings indicate that the presence of the market maker strongly tends to increase total welfare across various environments. Market-maker profit may or may not exceed the welfare gain, thus the effect on background-investor surplus is ambiguous. We find that market making tends to benefit investors in relatively thin markets, and situations where background traders are impatient, due to limited trading opportunities. The presence of additional market makers increases these benefits, as competition drives the market makers to provide liquidity at lower price spreads. A thorough sensitivity analysis indicates that these results are robust to reasonable changes in model parameters.

We study the effect of latency arbitrage on allocative efficiency and liquidity in fragmented financial markets. We propose a simple model of latency arbitrage in which a single security is traded on two exchanges, with aggregate information available to regular traders only after some delay. An infinitely fast arbitrageur profits from market fragmentation by reaping the surplus when the two markets diverge due to this latency in cross-market communication. We develop a discrete-event simulation system to capture this processing and information transfer delay, and using an agent-based approach, we simulate the interactions between high-frequency and zero-intelligence trading agents at the millisecond level. We then evaluate allocative efficiency and market liquidity arising from the simulated order streams, and we find that market fragmentation and the presence of a latency arbitrageur reduces total surplus and negatively impacts liquidity. By replacing continuous-time markets with periodic call markets, we eliminate latency arbitrage opportunities and achieve further efficiency gains through the aggregation of orders over short time periods.

In this paper, we present a novel portfolio optimization method that aims to generalize the delta changes of future returns, based on historical delta changes of returns learned in a past window of time. Our method addresses two issues in portfolio optimization. First, we observe that daily returns of stock prices are very noisy and often non-stationary and dependent. In addition, they do not follow certain well-defined distribution functions, such as the Gaussian distribution. To address this issue, we first aggregate the return values over a multi-day period into an average return in order to reduce the noise of daily returns. We further propose a pre-selection scheme based on stationarity, normality and independence tests in order to select a subset of stocks that have promising statistical properties. Second, we have found that optimizing the average risk in a past window does not typically generalize to future returns with minimal risks. To this end, we develop a portfolio optimization method that uses the delta changes of aggregated returns in a past window to optimize the delta changes of future expected returns. Our experimental studies show that data conditioning and aggregation in our proposed method is an effective means of improving the generalizability while simultaneously minimizing the risk of the portfolio.

The computing resources of the TeraGrid are a powerful tool for research by graduate students throughout the world. In this paper we analyze the usage of the TeraGrid by a sample of 876 graduate students from the institutions that form the Committee on Institutional Cooperation. We investigate demographic information and usage of TeraGrid resources and present visualizations. We also analyze the sample using data mining algorithms, specifically rule association learning and hierarchical clustering, and create interactive visualizations. This work reveals interesting patterns about the research done by graduate students on the TeraGrid and the resources they use.

With the development of petascale computing systems, a long-term effort is needed to educate and train the next generation of researchers. As part of its graduate education component, the Virtual School of Computational Science and Engineering held a summer school in August 2008 entitled "Accelerators for Science and Engineering Applications," providing participants with knowledge and hands-on experience with graphics processing units (GPUs). In this paper, we present visualizations exploring the broad spectrum of summer school applicants and participants. We examine demographic information of the overall applicant pool, accepted and attending applicants, and remote participants, as well as apply hierarchical clustering and rule association techniques to all applicant data. These statistical and data mining analyses demonstrate the wide range of fields of study where research applications can be readily accelerated through the use of massively parallel computing resources.