In this paper, a high frequency and dynamic pairs trading system is proposed, based on a market-neutral statistical arbitrage strategy using a two-stage correlation and cointegration approach. The proposed pairs trading system was applied to equity trading in U.S. equity markets in any type of market cycle condition to capture statistical mispricing between the prices of each stock pair based on its residuals and to model the stock pairs naturally as a mean-reversion process. The proposed pairs trading system was tested for out-of-sample testing periods with high frequency stock data from 2012 and 2013. Our trading strategy yields cumulative returns up to 56.58% for portfolios of stock pairs, well exceeding the S&P 500 index performance by 34.35% over a 12-month trading period. The proposed trading strategy achieved a monthly 2.67 Sharpe ratio and an annual 9.25 Sharpe ratio. Furthermore, the proposed pairs trading system performed well during the two months in which the S&P 500 index had negative returns. Thus, the trading system might be especially more profitable at times when the U.S. stock market performed poorly. Therefore, the performance returns of the proposed pairs trading system were relatively market-neutral and were positive regardless of the performance of the S&P 500 index.

Abstract-Globally, heart disease is the leading cause of death for both men and women. One in every four people is afflicted with and dies of heart disease. Early and accurate diagnoses of heart disease thus are crucial in improving the chances of longterm survival for patients and saving millions of lives. In this research, an advanced ensemble machine learning technology, utilizing an adaptive Boosting algorithm, is developed for accurate coronary heart disease diagnosis and outcome predictions. The developed ensemble learning classification and prediction models were applied to 4 different data sets for coronary heart disease diagnosis, including patients diagnosed with heart disease from Cleveland Clinic Foundation (CCF), Hungarian Institute of Cardiology (HIC), Long Beach Medical Center (LBMC), and Switzerland University Hospital (SUH).The testing results showed that the developed ensemble learning classification and prediction models achieved model accuracies of 80.14% for CCF, 89.12% for HIC, 77.78% for LBMC, and 96.72% for SUH, exceeding the accuracies of previously published research. Therefore, coronary heart disease diagnoses derived from the developed ensemble learning classification and prediction models are reliable and clinically useful, and can aid patients globally, especially those from developing countries and areas where there are few heart disease diagnostic specialists.