First article in the open literature to document the short term return-reversal effect that early StatArb funds exploited. By closing out its positions quickly, the fund put pressure on the prices of the stocks it was long and short. Nevertheless, the results thus far have been quite encouraging. They then place their bets on stocks that are most likely to revert to their historical mean. This thesis deals with the statistical arbitrage in shares and Exchange traded funds (ETFs) markets. It is a noteworthy point of contention, that the common reduction in portfolio value could also be attributed to a causal mechanism. The secret to maximize your profits is to focus on one or two strategies.The more strategies you use, the more mistakes you are likely to do. To answer the question in the introduction, I do think that this could form the basis of a trading strategy and hope that over the next few weeks I will have a chance to investigate further. As a trading strategy, statistical arbitrage is a heavily quantitative and computational approach to securities trading. As more competitors enter the market, and funds diversify their trades across more platforms than StatArb, a point can be made that there should be no reason to expect the platform models to behave anything like each other. One gives money to a manager, who in return issues a share of the ETF to represent the ownership and invests that money in a basket of assets. © Robert Andrew Martin 2021. In a liquidation event, we will not receive the full NAV because of the transaction costs, which may be rather significant if we are dealing with assets that are hard to sell. So, what is SA? However, there is no common definition in the literature while investors use the expression SA for a variety of different strategies. We can take this a step further and go long the basket and short the individual stock, which results in a risk-free profit once other market participants realise that the basket and the two stocks should be priced identically. All rights reserved. The existence of the investment based upon model itself may change the underlying relationship, particularly if enough entrants invest with similar principles. Negative arbitrage – Negative arbitrage refers to the opportunity lost when the interest rate that a borrower pays on its debt (a bond issuer, for example) is higher than the interest rate at which those funds are invested. Jamie Morgan (Faculty of Business & Law, Leeds Metropolitan University, Leeds, UK) critical perspectives on international business. It involves the simultaneous buying and selling of security portfolios according to … The standard example is Coca-Cola and Pepsi. [8] While the reasons are not yet fully understood, several published accounts blame the emergency liquidation of a fund that experienced capital withdrawals or margin calls. The fair price of this basket is then $\$289 + \$202 = \$491$. It may be possible to work-around this by computing the beta of the NAV with respect to a comparable asset/ETF then trading the correct amount of that comparable. This systematic fund dynamically combines factor and statistical arbitrage models to exploit trending and mean reverting behaviour in stocks. Furthermore, the fund managers are free to enter and exit positions as they desire, so an arbitrageur would have to constantly make changes to adequately replicate the portfolio. Factors, which the model may not be aware of having exposure to, could become the significant drivers of price action in the markets, and the inverse applies also. For example, if we were dealing with a CEF that invests in high yield bonds, we could compute the beta of the NAV to a high yield bond ETF (e.g HYG). As of the time of writing, AAPL has a price of \$289/share and FB has a price of \$202/share. Various statistical tools have been used in the context of pairs trading ranging from simple distance-based approaches to more complex tools such as cointegration and copula concepts. Conversely, CEFs issue a fixed number of shares when they first IPO, and there is no subsequent creation or redemption. To be specific, we shall use the Spearman rank correlation coefficient, sometimes referred to in finance as the information coefficient (IC), which is more robust to nonlinearity than the Pearson product-moment correlation coefficient. Over a finite period of time, a low probability market movement may impose heavy short-term losses. This is a logical requirement – if the statistical properties were changing, nothing stops the spread from widening forever. Stat arb is most effective when the spread is stationary, a technical term which describes the fact that the data-generating process for the time series does not change (its statistical properties are constant in time). Rather than doing any kind of proper backtest, we will just compute the correlation between the current spread and the subsequent change in the spread. Many bank proprietary operations now center to varying degrees around statistical arbitrage trading. Statistical Arbitrage When a manager places trades on stocks based on quantitative methods, they use sophisticated algorithms to analyze historical data and expose price discrepancies in the given data. LinkedIn Statistical arbitrage faces different regulatory situations in different countries or markets. For both ETFs and CEFs, an important point is that the share price is not “naturally” guaranteed to be equal to the value of all of the securities that the fund owns, i.e the net asset value (NAV). Limits of Arbitrage 3. This phase often uses commercially available risk models like MSCI/Barra, APT, Northfield, Risk Infotech, and Axioma to constrain or eliminate various risk factors.[5]. Furthermore, we explore whether this could be the basis for an algorithmic trading strategy. However, the AP’s role goes far beyond the initial purchase of the securities to set up the ETF. The Sabre Style Arbitrage Fund was launched in August 2002. If such short-term losses are greater than the investor's funding to meet interim margin calls, its positions may need to be liquidated at a loss even when its strategy's modeled forecasts ultimately turn out to be correct. The association of observed losses at hedge funds using statistical arbitrage is not necessarily indicative of dependence. Statistical arbitrage, also known as stat arb, refers to any trading strategy that uses statistical and econometric techniques to profit with an element of market risk reduction. Below is a plot of the spread, i.e $\text{price}/\text{NAV} - 1$ (positive is premium, negative is discount). Statistical arbitrage, also known as stat arb, is a type of algorithmic trading strategy that uses mathematical modelling to determine price inefficiencies between securities, and then buys and sells, according to preset thresholds or adaptive statistical models. Most of us are probably familiar with exchange-traded funds (ETFs) – … Github The 2007-2008 financial crisis also occurred at this time. In addition to the reasonably high information coefficient, we have a clear economic hypothesis about why the discount to NAV may be a persistent predictive factor. The 1998 default of Long-Term Capital Management was a widely publicized example of a fund that failed due to its inability to post collateral to cover adverse market fluctuations.[7]. These events showed that StatArb has developed to a point where it is a significant factor in the marketplace, that existing funds have similar positions and are in effect competing for the same returns. In many countries where the trading security or derivatives are not fully developed, investors find it infeasible or unprofitable to implement statistical arbitrage in local markets. Mean-reverting strategies 3.4. If you have been investing in arbitrage funds, here is the update on our recommended arbitrage mutual funds for December. This doesn’t completely preclude the development of a trading strategy, but it wouldn’t be a proper pairs trade since we would only be acting on one of the legs. Arbitrage Strategy: Strategies to Maximize returns. As for statistical arbitrage on cryptocurrency markets, a coin is bought on an ... Arbinox makes trading strategies based on the statistic arbitrage and used, until recently, only by hedge funds available to everyone. Statistical arbitrage is also subject to model weakness as well as stock- or security-specific risk. However, ETFs have an interesting mechanism for ensuring that these deviations between the price and NAV are very short-lived. Statistical arbitrage trading strategies 3.1. The fund manager then issues shares of the ETF to the AP, in return for the securities that were bought. If the basket was instead trading at \$400, we would be correct in thinking that the basket is “cheap” – we’d certainly rather buy the basket than the two stocks individually. Volatility arbitrage is a statistical arbitrage strategy used to profit by exploiting the difference between the implied volatility of options and predicted volatility of the underlying asset. Risks. Simulations of simple StatArb strategies by Khandani and Lo show that the returns to such strategies have been reduced considerably from 1998 to 2007, presumably because of competition. Thus, even without APs, the price of a CEF should track its NAV. Subscribe. Using an appropriate colour scheme, we can plot the price and NAV: From 2000 to 2009, the CEF alternates between trading at a discount/premium to NAV, before settling into a persistent discount from 2009 onwards. Description. It involves data mining and statistical methods, as well as the use of automated trading systems. Most of us are probably familiar with exchange-traded funds (ETFs) – baskets of securities where the basket itself is tradable. states "the widespread use of standardized factor risk models such as those from MSCI/BARRA or North-field Information Systems ... will almost certainly create common exposures among those managers to the risk factors contained in such platforms", "Risk and Portfolio Management; Statistical Arbitrage", Courant Institute of Mathematical Sciences, "Statistical arbitrage in the US equities market", Ed Thorp: A Perspective on Quantitative Finance – Models for Beating the Market, Statistical Arbitrage in the U.S. Equities Market, Statistical Arbitrage Based on No-Arbitrage Models, Taxation of private equity and hedge funds, Alternative investment management companies, https://en.wikipedia.org/w/index.php?title=Statistical_arbitrage&oldid=997509774, Articles with specifically marked weasel-worded phrases from June 2011, Creative Commons Attribution-ShareAlike License. The exploitation of arbitrage opportunities themselves increases the efficiency of the market, thereby reducing the scope for arbitrage, so continual updating of models is necessary. Statistical Arbitrage (SA) is a common financial term. The only wrinkle is that the NAV isn’t actually a tradable security. [9], It has also been argued that the events during August 2007 were linked to reduction of liquidity, possibly due to risk reduction by high-frequency market makers during that time.[11]. It is recomended to find two securities that are in the same sector / industry, they should have similar market capitalization and average volume traded. In this post, we will instead be discussing closed-end funds (CEFs). Definition 2. The same Spearman rank calculation gives a correlation of $-0.07$. Statistical arbitrage is a trading strategy that allows one to trade two highly correlated assets for a high probability mean reversion. Disclaimer. This was chosen because it is large and liquid (and has a memorable ticker, USA). [10], In a sense, the fact of a stock being heavily involved in StatArb is itself a risk factor, one that is relatively new and thus was not taken into account by the StatArb models. Statistical arbitrage trading techniques (sometimes knows as convergence or pairs trading) are based on the concept of mean reversion. After giving a short survey of the hedge fund industry and the existent hedge fund strategies, statistical arbitrage is explained in further detail. as a multi-factor approach to StatArb. Signals are often generated through a contrarian mean reversion principle but can also be designed using such factors as lead/lag effects, corporate activity, short-term momentum, etc. All the recommended schemes have managed to retain their spots in the list this month. This sort of strategy resembles market making and is sometimes considered a form of statistical arbitrage. It involves data mining and statistical methods, as well as the use of automated trading systems. a history of being a hugely profitable algorithmic trading strategy for many big investment banks and hedge funds. This is usually referred to[by whom?] However, as we discussed previously, the NAV isn’t tradable so we instead want to find out the correlation between the current spread and the price return of the CEF. statistical arbitrage opportunities. Statistical arbitrage Index: 1. During July and August 2007, a number of StatArb (and other Quant type) hedge funds experienced significant losses at the same time, which is difficult to explain unless there was a common risk factor. Statistical arbitrage (stat arb) is a fancy term describing the process of buying assets that are statistically cheap and selling assets that are statistically expensive, hoping to lock in the difference. 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