Risk arbitrage trading systems

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (leaf 42).The risk arbitrage investment process involves betting on the outcome of announced mergers and acquisitions. We analyzed a sample of 1309 stock and cash mergers from 1996 to 2004 Q2 and developed insights into the risk arbitrage process. We found share price reactions for both the acquirer and target companies as a result of the merger announcement and compared these to factors such as type of merger, premium paid by the acquirer for the target, relative size of the deal to the size of the acquirer and target, and deal consummation time. We utilized this information to develop a merger return prediction model that predicts a merger's return given various deal characteristics. We constructed several portfolios, one using a trading strategy in which we invest equally in every announced deal, one where we invest only in deals that have a predicted return higher than two times the T-Bills rate, one where we invest in deals that have a predicted return higher than 0, and one where we invest in deals with a predicted return higher than one standard deviation of the predicted returns. A subsequent out of sample analysis of' generating a predicted return model using data from 1996 to 1999 and predicting returns from 2000 to 2004 Q2 produces returns of 4.96%, 3.14%, and 5.87% for our three portfolios compared with 1.74% generated from investing in all deals from 2000 to 20)4 Q2.(cont.) Our study shows that our strategy focuses mainly on cash deals but our strategy still makes improvements in the Sharpe Ratio despite this limitation. Our analysis provides insights into mergers and how the market prices such deals. Furthermore, the trading strategies employed can be used as a basis for constructing a profitable risk arbitrage trading platform.by Jiong J. Want.M.Eng

Approximate Quantile Computation over Sensor Networks

Sensor networks have been deployed in various environments, from battle field surveillance to weather monitoring. The amount of data generated by the sensors can be large. One way to analyze such large data set is to capture the essential statistics of the data. Thus the quantile computation in the large scale sensor network becomes an important but challenging problem. The data may be widely distributed, e.g., there may be thousands of sensors. In addition, the memory and bandwidth among sensors could be quite limited. Most previous quantile computation methods assume that the data is either stored or streaming in a centralized site, which could not be directly applied in the sensor environment. In this paper, we propose a novel algorithm to compute the quantile for sensor network data, which dynamically adapts to the memory limitations. Moreover, since sensors may update their values at any time, an incremental maintenance algorithm is developed to reduce the number of times that a global recomputation is needed upon updates. The performance and complexity of our algorithms are analyzed both theoretically and empirically on various large data sets, which demonstrate the high promise of our method

Direct observation of high-speed plasma outflows produced by magnetic reconnection in solar impulsive events

Spectroscopic observations of a solar limb flare recorded by SUMER on SOHO reveal, for the first time, hot fast magnetic reconnection outflows in the corona. As the reconnection site rises across the SUMER spectrometer slit, significant blue- and red-shift signatures are observed in sequence in the Fe XIX line, reflecting upflows and downflows of hot plasma jets, respectively. With the projection effect corrected, the measured outflow speed is between 900-3500 km/s, consistent with theoretical predictions of the Alfvenic outflows in magnetic reconnection region in solar impulsive events. Based on theoretic models, the magnetic field strength near the reconnection region is estimated to be 19-37 Gauss.Comment: 5 pages, 6 color figures, 1 animation onlin

Effects of post-abortion family planning services on contraceptive practices in China : protocol for a clustered randomized controlled trial

Study objectives: To determine whether integrating post-abortion services in hospital settings in China will increase the contraceptive use and decrease repeat abortion rates. Study design: Three-arms cluster randomised controlled trial in which the unit of randomisation is hospital. Participants: Women seeking induced abortion within 12 weeks of gestation age. Sites: Ninety hospitals from 30 provinces in China will be randomised to the three arms of the study stratified by province. In each province, eligible hospitals will be matched on the characteristics of abortion departments, especially the volume of abortions in the 2 months in the situation survey. Length of follow up: Six months. Intervention: Multiple interventions that aim to increase the use of more effective contraceptive methods, improve user adherence to reduce the unintended pregnancies and repeat induced abortions. Data collection: Data will be collected at four time points, one at baseline(month 0 at the time of enrolment) and twice during intervention (1st 3rd and 6th month after enrolment, respectively). Primary outcome: Unintended pregnancies or repeated induced abortions; immediate contraceptive uptake and the use of modern effective contraceptive methods