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QuantInsti EPAT: Final Project on Statistical Arbitrage, [By Jacques Joubert] (https://za.linkedin.com/in/jacquesjoubert). 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. Step 1: Find 2 related securities Although CEFs don’t have the same creation and redemption mechanism as ETFs, there is still a fundamental link between the NAV and the price. Like all trading strategies, statistical arbitrage has its risk. For starters, an ETF is a financial asset that tracks a basket of financial assets like stocks and bonds. 1 Introduction ... written on a basket of bonds with maturities ranging from 15 to 25 years. The statistical arbitrage can be traced back to the famous pairs trading [4] strategy, a.k.a. Statistical Arbitrage (SA) is a common financial term. This talk was given by Max Margenot at the Quantopian Meetup in Santa Clara on July 17th, 2017. taking a short position in the 2Year treasury futures - contract, gives rise to a statistical arbitrage. 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. ETF fund managers are seldom the people who buy and sell the securities. In the case of Coca-Cola and Pepsi, the argument goes that because they are essentially substitute goods, any widening of the spread should be temporary. Most of us are probably familiar with exchange-traded funds (ETFs) – baskets of securities where the basket itself is tradable. Arbitrage trading is based on trading the same instrument or similar ones. Statistical Arbitrage Statistical arbitrage exploits the pricing inefficiency between mean-reverting pairs of assets or buckets of assets in a market. Statistical Arbitrage (SA) is build to gain profit on simultaneously buying and selling two shares of two correlated instruments. Rank them based on … Cost of transacting – we already touched on this. It does this by compiling a basket of over-performing currency pairs and a basket … A synthetic asset based on the cointegration relationship of the stocks with Index was constructed. Investors identify the arbitrage situation through mathematical modeling techniques. Like all trading strategies, statistical arbitrage has its risk. A methodology to create statistical arbitrage in stock Index S&P500 is presented. This is quite a decent IC – according to a paper by JP Morgan Equity Research, investors can “achieve significant risk adjusted excess returns with information coefficients between 0.05 and 0.15” (the sign doesn’t matter here). It is comforting that there is a negative sign because it supports the hypothesis that the spread mean-reverts: premiums are correlated with decreasing premiums, discounts are correlated with narrowing discounts. A very common, and particularly attractive to investors, statistical arbitrage strategy is called market-neutral strategy. The payoffs of those options are not linear, i.e. Say for example that you have entered positions for a pair and then the spread picks up a trend rather than mean reverting. by dropprod Multifactor models This is the bread-and-butter for quant funds. This is an advanced form of pair trading where more sophisticated mathematical models are used to determine the historical relationship between two or more instruments (which must be co-integrated rather than merely correlated). JEL Classification C12, G13, G14. In particular, we study in some detail the performance of the strategies during the liquidity crisis of the summer of 2007. ETFs are a little bit more complicated. It is the idea that a co-integrated pair is mean reverting in nature. Statistical Arbitrage, Mean Reverting, Pair Trading, Kalman Filter, Trading Algorithms 1. But since you can’t trade the NAV, although you are still able to make bets on convergence (by going long the CEF when it is trading at a discount), such bets would not be pure bets on the spread. The value of +1 means there exists a perfect positive correlation between the two variables, -1 means there is a perfect negative correlation and 0 means there is no correlation. An example of this is Anglo Gold and Harmony Gold. To make arbitrage, trading is python algorithmic-trading cointegration pairs-trading statistical-arbitrage Updated Mar 14, … Jonathan Tuck, Raphael Abbou, Vin SachidanandaMS&E 448 Group 3: Statistical Arbitrage Strategy June 20202/20 Therefore, the discrepancy between the Statistical arbitrage, or statarb, is just a fancy word for betting on historical relationships remaining consistent...using statistics. It is the idea that a co-integrated pair is mean reverting in nature. QuantInsti-Final-Project-Statistical-Arbitrage, https://za.linkedin.com/in/jacquesjoubert, First developed and used in the mid 1980s by Nunzio Tartaglia’s quantitative group at Morgan Stanly, Pair Trading is a “contrarian strategy” designed to harness mean-reverting behavior of the pair ratio, David Shaw, founder of D.E Shaw & Co, left Morgan Stanley and started his own “Quant” trading firm in the late 1980s dealing mainly in pair trading. Step 4: Test for co-integration Another popular statistical arbitrage trading strategy is in ETFs. Quantopian Academia and Data Science Lead Max Margenot presents, "Basic Statistical Arbitrage: Understanding the Math Behind Pairs Trading." Praise for Statistical Arbitrage "In this lucid, intelligent, and highly readable book, Andrew Pole presents the insights of an experienced and successful exponent of statistical arbitrage, with an uncommon mixture of flair, accessibility, and academic precision. A pedant might point out that by definition, a CEF is an ETF since it is a fund that trades on an exchange – this is unfortunately not correct, as there is a true conceptual distinction. Statistical Arbitrage Statistical arbitrage aims to profit from a perceived discrepancy in price between two securities. Let’s take a very simple case where we have a “basket” of two stocks, say AAPL and FB. i.e, buy 1 lot eur/usd + 1 lot usd/chf. basket of stocks is likely to drop and produce negative (positive) returns, and we want to short (long) the basket. Each instrument is generally taken into calculation multiplied by a weight coefficient. You need to understand that prices are constructed in terms of statistical principles like the "expected value principle." Let’s say $\beta = 2$, in which case we could bet on the spread converging by going long \$10k in the CEF and short \$5k of HYG. One classic example of technological arbitrage is ETF arbitrage.… You signed in with another tab or window. 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. This process is known as creation because shares of the ETF are being issued. These strategies vary depending on number, types, and weights of instruments in a portfolio and its risk taking capacity. Statistical arbitrage traders are typically long underperforming stocks and short overperformers. This is another application area in which Python is becoming popular. Another popular statistical arbitrage trading strategy is in ETFs. Program trading is a type of trading in securities, usually consisting of baskets of fifteen stocks or more that are executed by a computer program simultaneously based on predetermined conditions. Introduction Financial markets are based on the general trading rulebuy with : low price and sell with high price. There are several responses to the closed-end fund puzzle, though I would refer interested readers to Lee, Shleifer and Thaler (1991) for a detailed investigation: The rest of this post will investigate the hypothesis that the spread between the CEF price and its NAV is mean-reverting, or more intuitively, that larger discounts are predictive of a future price increase and larger premiums are predictive of a future price decrease. Pairs trading is a type of statistical arbitrage that attempts to take advantage of mis-priced assets in the market place. ETF arbitrage brings the market price of ETFs back in line with net asset values when divergence happens. 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. FX Statistical Arbitrage. 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. However, empirically, this is not the case. spread trading, where only two assets are considered. These strategies vary depending on number, types, and weights of instruments in a portfolio and its risk taking capacity. It involves data mining and statistical methods, as well as the use of automated trading systems.. Step 3: Calculate the mean, standard deviation, and z-score of the pair ratio / spread. A Complete Training Manual This "Art of the Arb" training manual covers statistical & risk arbitrage, basket trading, pair investing, money management, pyschology of spread trading, relative strength and anti-correlated pair trading, enveloping for gaps and sector strategies, tape reading and technical analysis. Fortunately, many ETFs track similar financial assets. A methodology to create statistical arbitrage in stock Index S&P500 is presented. 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. I set up three tests, each with a different number of observations (120, 90, 60), all three tests have to reject the null hypothesis that the pair is not co-integrated. The key is that these arbitrage opportunities are not at all risk-free, they are only really arbitrage opportunities in the sense that if we make a sufficiently large number of such bets, we will have a positive expected value (the law of large numbers). Statistical arbitrage courses Commercial Content. AQR has pioneered this space the most. they have convexities. This has gone pretty mainstream. So if Coca-Cola stock appreciates while Pepsi’s stays flat, the statistical arbitrageur may long Pepsi and short Coca-Cola. CEFs have a much lower fraction of institutional ownership than the underlying – it is possible that the pricing of CEFs is less rational as a result, with price movement driven by liquidity and sentiment rather than value. As of the time of writing, AAPL has a price of \$289/share and FB has a price of \$202/share. They play a critical role in ensuring that the ETF share price never strays too far from the NAV. Statistical arbitrage seeks to profit from statistical mispricing of one or more assets based on the expected value of these assets. There is an incredibly strong economic link between the NAV and the price; we even know that at liquidation they must converge (less fees). All rights reserved. Market neutral quantitative trading using statistical arbitrage methods is discussed. To make arbitrage, trading is Now, we can perform an Augmented Dickey-Fuller test to determine whether the spread is indeed stationary. This is a logical requirement – if the statistical properties were changing, nothing stops the spread from widening forever. The existence of this spread (typically a discount) between the price and the NAV is known as the Closed-End Fund Puzzle. A synthetic asset based on the cointegration relationship of the stocks with Index was constructed. Program trading is often used by hedge funds and other institutional investors pursuing index arbitrage or other arbitrage strategies.   statistical arbitrage in the US equity market by proposing a factor model with mean-reverting residuals and a threshold-based bang-bang strategy. Rather than making a bet on the overall direction of either stock, we can eliminate idiosyncratic risk by trading the difference between the price of Coca-Cola shares and Pepsi shares. Therefore it incorporates the skew effect to some degree. Statistical arbitrage methodologies refer to investment strategies that have high probability of outperforming the market (see notes in chapter 4 of (33)). Modeling the Dynamics of Statistical Arbitrage • Russell Wojcik, Pairs Trading: A Professional Approach • Daniel Herlemont, Pairs trading, convergence trading, cointegration _ • Paul Teetor, Using R to Test Pairs of Securities for So, what is SA? We present statistical arbitrage market neutral techniques. Sometimes, it is cheaper to buy a basket of assets than it is to buy the assets in the basket. A very common, and particularly attractive to investors, statistical arbitrage strategy is called market-neutral strategy. Program trading is often used by hedge funds and other institutional investors pursuing index arbitrage or other arbitrage strategies. This important caveat notwithstanding, we shall now examine the stationarity of the price-NAV spread and investigate whether the spread is predictive of future returns. Program trading is a type of trading in securities, usually consisting of baskets of fifteen stocks or more that are executed by a computer program simultaneously based on predetermined conditions. QuantChart.io Long 100 BAC Short 22 PNC 4. 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). In many ways, the spread between the price and NAV is the ideal candidate for a stat arb strategy. And that different assets have different levels of risk. The term mean-reversion means the assumption that the stock price tends to converge to the mean price over time. spread trading, where only two assets are considered. Below is a plot of the spread, i.e $\text{price}/\text{NAV} - 1$ (positive is premium, negative is discount). There is a spread between the instruments and the further it deviates from its mean, the greater the probability of a reversal. Find two securities that are in the same sector / industry, they should have similar market capitalization and average volume traded. Statistical arbitrage on Forex Another interesting Forex arbitrage trading system is statistical arbitrage. (I would have preferred a z-score of 2, as it better matches the literature, however it is less profitable), Step 6: Process transactions based on signals. Statistical arbitrage (pairs, basket trading): mostly involves equities and other instruments whose payoffs are linear. 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. Funds Step 5: Generate trading signals Thus, even without APs, the price of a CEF should track its NAV. Statistical arbitrage, or statarb, is just a fancy word for betting on historical relationships remaining consistent...using statistics. We will look at a single CEF, namely, the Liberty All-Star Equity Fund. ... Statistical arbitrage. The paper also relates the performance of mean-reversion statistical arbitrage strategies with the stock market cycle. The ETF industry has grown rapidly over the past few decades because ETFs provide a low-cost way to get exposure to a certain market, sector, or strategy. Furthermore, we explore whether this could be the basis for an algorithmic trading strategy. taking a short position in the 2Year treasury futures - contract, gives rise to a statistical arbitrage. As a trading strategy, statistical arbitrage is a heavily quantitative and computational approach to securities trading. Note however that statistical arbitrage is not a risk free strategy. It involves quantitative modelling techniques to find price inefficiencies between assets. Statistical Arbitrage In finance, statistical arbitrage refers to automated trading strategies that are typical of a short-term and involve a large number of securities. 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. Equity CEFs typically have a smaller discount compared with bond CEFs, because equities are easier to sell. In a recent MacroVoices podcast, Eric Peters shared a great insight (emphasis is mine): Our approach to anything that we ever do in markets, is to first ask the question: why do you get paid to do something? On the surface, these may seem similar to ETFs: CEFs are also baskets of securities, and they can also be listed and traded on an exchange. We can visualise the relationship with a scatterplot: There is clearly a lot more exploration that needs to be done – we have only looked at the spread for a single fund, and have chosen a simplistic information coefficient analysis rather than conducting a full backtest. Stationarity aside, let’s press on with a somewhat crude estimate of the predictive value of the spread. Disclaimer. The fund manager then issues shares of the ETF to the AP, in return for the securities that were bought. Web Development. Multifactor models are models designed to extract cross-sectional risk premia in markets. The portfolio should have duration close to zero No systematic interest rate risk exposure. I uploaded everything to GitHub in order to welcome readers to contribute, improve, use, or work on this project. So, what is SA? Statistical Arbitrage includes different types of strategies such as pairs trading, index arbitrage, basket trading or delta neutral strategies. The term mean-reversion means the assumption that the stock price tends to converge to the mean price over time. In order to answer this question, we investigate SA strategies across equity, fixed income and commodity. Without this economic understanding, it’s much more likely that any pattern you’ve discovered is a statistical spectre that haunts the historical data but will disappear in the daylight of future data. Statistical arbitrage in ETFs. But again, this is not a perfect bet on the spread. 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). It’s been a journey and this article serves as a report on my final project focusing on statistical arbitrage, coded in R. This article is a combination of my class notes and my source code. Statistical arbitrage in ETFs. It is recomended to find two securities that are in the same sector / industry, they should have similar market capitalization and average volume traded. Some of the real-world applications of machine learning include medical diagnosis, statistical arbitrage, basket analysis, sales prediction, etc. Correlation is quantified by the correlation coefficient ρ, which ranges from -1 to +1. Buy Statistical Arbitrage: Algorithmic Trading Insights and Techniques: 411 (Wiley Finance) 1 by Pole, Andrew (ISBN: 9780470138441) from Amazon's Book Store. Github In addition it has questions from current traders and real life trading journals. Put simply you look at all the stocks in a market. LinkedIn The fair value of the portfolio should be relatively at over time. However, ETFs have an interesting mechanism for ensuring that these deviations between the price and NAV are very short-lived. At such a time, the price of the shares must be equal to the NAV, less the cost/slippage from selling the securities. We find that the correlation between the current spread and the change in the spread over the following week is $-0.06$ (statistically significant at the 5% level). Nevertheless, the results thus far have been quite encouraging. Any momentary supply-demand imbalances can be arbitraged away – for example, if there is suddenly less demand for the ETF, and it trades at a discount to the NAV, the AP can buy ETF shares on the open market and redeem them from the ETF fund manager. We often like to think of ETFs as upscaled equivalents of a personal trading portfolio. Historically, StatArb evolved out of the simpler pairs trade strategy, in which stocks are put into pairs by fundamental or market-based similarities. Type of statistical principles like the `` expected value principle. the people who buy sell! 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Risk taking capacity Pepsi and short Coca-Cola models designed to extract cross-sectional premia! Then free to hold these shares, or more assets based on the cointegration relationship of the ETF price. We often like to think of ETFs back in line with net asset when! And that different assets have different levels of risk Max Margenot presents, `` Basic statistical strategy. The stocks with index was constructed closed-end fund Puzzle are very short-lived standard deviation, and attractive... Arbitrage situation statistical arbitrage basket mathematical modeling techniques, ETFs have an interesting mechanism ensuring!

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