נתן בגרוב, הרצאה סמינריונית למגיסטר
יום רביעי, 29.9.2021, 10:30
Statistical Arbitrage is one of the pillars of quantitative trading, and has long been used by hedge funds. Historically, statistical arbitrage evolved out of the simpler pairs trade strategy, in which stocks are put into pairs (a portfolio of two stocks) by fundamental or market-based similarities. When one stock in a pair outperforms the other, the under performing stock is bought long and the outperforming stock is sold short with the expectation that under performing stock will climb towards its outperforming partner.
Mathematically speaking, such portfolios are typically selected using tools fromcointegration theory, whose aim is to detect combinations of assets that are stationary,and therefore mean-reverting.
However, having a stationary (hence mean-reverting) portfolio, may not be enough to ensure profitability. This portfolio might show very small volatility, which require significant leverage to be profitable, and also incurs trade and borrow costs (commission).
As a step toward bridging this gap, we present a framework, which consist of three main stages, which are (i) constructing time-series of mean-reverting portfolios, (ii) ranking these portfolios and balancing the reversion rate together with profitability potential, and (iii) trading these portfolios efficiently, using risk-averse decision-making.