by Haksun Li | Jan 6, 2015 | AlgoQuant, Algorithmic Trading, Seminars

This blog article is taken from our book [1]. In most entry-level materials on pairs trading such as in [2], a mean reverting basket is usually constructed by this relationship: , where is the price of asset at time t, the price of asset at time t, and the price of the mean reverting asset to trade. One way to find is to use cointegration. There are numerous problems in this approach as detailed in [1]. To mention a few: the identified portfolios are dense; executions involve considerable transaction costs; the resultant portfolios behave like insignificant and non-tradable noise; cointegration is too stringent and often unnecessary a requirement to satisfy. This article highlights one important problem: it is much better to work in the space of (log) returns than in the space of prices. Therefore, we would like to build a mean reverting portfolio using a similar relationship to (eq. 1) but in returns rather than in prices. The Benefits of Using Log Returns When we compare the prices of two assets, [… TODO …] A Model for a Mean Reverting Synthetic Asset Let’s assume prices are log-normally distributed, which is a popular assumption in quantitative finance, esp. in options pricing. Then, prices are always positive, satisfying the condition of “limited liability” of stocks. The upside is unlimited and may go to infinity. [5] We have: is the return for asset between times 0 and t; likewise for asset . Instead of applying a relationship, e.g., cointegration (possible but not a very good way), to the pair on prices, we can do it on returns. This is possible because, just like prices, the returns at...
by Ken Yiu | Jun 19, 2013 | AlgoQuant, Algorithmic Trading, Investment

Many portfolio optimization methods (e.g., Markowitz/Modern Portfolio Theory in 1952) face the well-known predicament called the “corner portfolio problem”. When short selling is allowed, they usually give efficient allocation weighting that is highly concentrated in only a few assets in the portfolio. This means that the portfolio is not as diversified as we would like, which makes the optimized portfolio less practically useful. In [Corvalan, 2005], the author suggests to look for instead an “almost efficient” but “more diversified” portfolio within the close neighborhood of the Mean-Variance (MV) optimal solution. The paper shows that there are many eligible portfolios around the MV optimal solution on the efficient frontier. Specificially, given the MV optimal solution, those “more diversified” portfolios can be computed by relaxing the requirements for the portfolio return and risk in an additional optimization problem: where , is the Markowitz MV optimal weights, are the relaxation tolerance parameters, and is a diversification measure for the portfolio (for example, , ). In other words, the new optimization problem looks for a portfolio with the maximal diversification around the optimal solution. Corvalan’s approach can be extended to create an approximate, sufficiently optimal and well diversified portfolio from the optimal portfolio. The approximate portfolio keeps the constraints from the original optimization problem. References: SuanShu Javadoc Alejandro Corvalan (2005). Well Diversified Efficient Portfolios. Documentos de trabajo del Banco Central, no....
by Ken Yiu | Feb 16, 2013 | AlgoQuant, Algorithmic Trading

[Lai, Xing and Chen, 2010], in the paper “Mean-Variance Portfolio Optimization When Means And Covariances Are Unknown”, proposed a ground breaking method to do portfolio optimization. In what follows we summarize their idea and use it to implement a periodic rebalancing strategy based on the AlgoQuant framework. Harry Markowitz won the Nobel prize for his work in mean-variance (MV) portfolio optimization in 1950s. The theory is widely regarded as fundamental in financial economics. It says, given a target return of a portfolio of m assets, the optimal (in terms of information ratio) weighting is given by where is the expected future returns, and is the expected covariance matrix of future returns. This problem is readily solved by quadratic programming. Nonetheless, the assumption that and are known in advance is very dubious. This has been referred to as the “Markowitz optimization engima”. The attempts made so far are to better forecast these estimators, namely and , as accurately as possible. The maximum likelihood estimate (MLE) from the training sample is an example. It turns out, however, that MLE performs poorly because the estimators are quite different from the realized values [Michaud, 1989]. Since then, three approaches have been proposed to address the difficulty. The first approach uses a multi-factor model to reduce the dimensionality in estimating [Fan, Fan and Lv, 2008]. The second approach uses Bayes or other shrinkage estimates of [Ledoit and Wolf, 2004]. Both approaches attempt to use improved estimates of for the plug-in efficient frontier. They have also been modified to provide better estimates of , for example, in the quasi-Bayesian approach of [Black and Litterman, 1990]. The third approach uses bootstrapping...
by Haksun Li | Sep 3, 2012 | Algorithmic Trading, Investment, Seminars

Here is the synopsis of my presentation at HKSFA, September 2012. The presentation can be downloaded from here. 1. Many people lose money playing the stock market. The strategies they use are nothing but superstitions. There is no scientific reason why, for example, buying on a breakout of the 250-day-moving average, would make money. Trading profits do not come from wishful thinking, ad-hoc decision, gambling, and hearsay, but diligent systematic study. • Moving average as a superstitious trading strategy. 2. Many professionals make money playing the stock market. One approach to investment decision or trading strategy is to treat it as a science. Before we make the first trade, we want to know how much money we expect to make. We want to know in what situations the strategy will make (or lose) money and how much. • Moving average as a scientific trading strategy 3. There are many mathematical tools and theories that we can use to quantify, analyse, and verify a trading strategy. We will show case some popular ones. • Markov chain (a trend-following strategy) • Cointegration (a mean-revision strategy) • Stochastic differential equations (the best trading strategy, ever!) • Extreme value theory (risk management, stop-loss) • Monte Carlo simulation (what are the success factors in a trading...
by Haksun Li | Feb 12, 2012 | Algorithmic Trading

The good quant trading models reveal the nature of the market; the bad ones are merely statistical artifacts. One most popular way to create spurious trading model is data snooping or data mining. Suppose we want to create a model to trade AAPL daily. We download some data of, e.g., 100 days of AAPL, from Yahoo. If we work hard enough with the data, we will find a curve (model) that explains the data very well. For example, the following curve perfectly fits the data. Suppose the prices are Of course, most of us are judicious enough to avoid this obvious over-fitting formula. Unfortunately, some may fall into the trap of it in disguise. Let’s say we want to understand what factors contribute to the AAPL price movements or returns. (We now have 99 returns.) We come up with a list of 99 possible factors, such as PE, capitalization, dividends, etc. One very popular method to find significant factors is linear regression. So, we have Guess how well this fits? The goodness-of-fit (R-squared) turns out be 100% – a perfect fit! It can be proved that this regression is a complete nonsense. Even if we throw in random values for those 99 factors, we will also end up with a perfect fit regression. Consequently, the coefficients and t-stats mean nothing. Could we do a “smaller” regression on a small subset of factors, e.g., one factor at a time, and hope to identify the most significant factor? This step-wise regression turns out to be spurious as well. For a pool of large enough factors, there is big probability of finding (the...
by Haksun Li | Oct 27, 2011 | AlgoQuant, Algorithmic Trading, Programming

I posted my presentation titled “The Role of Technology in Quantitative Trading Research” presented in HKU-HKUST-Stanford Conference in Quantitative Finance. Dec 9, 2011. Workshop On Recent Developments Of Financial Mathematics (REDFIN2011). Dec 13, 2011. You can find the powerpoint here. Abstract: There needs a technology to streamline the quantitative trading research process. Typically, quants/traders, from idea generation to strategy deployment, may take weeks if not months. This means not only loss of trading opportunity, but also a lengthy, tedious, erroneous process marred with ad-hoc decisions and primitive tools. From the organization’s perspective, comparing the paper performances of different traders is like comparing apples to oranges. The success of the firm relies on hiring the right geniuses. Our solution is a technological process that standardizes and automates most of the mechanical steps in quantitative trading research. Creating a new trading strategy should be as easy and fun as playing Legos by assembling together simpler ideas. Consequently, traders can focus their attention on what they are supposed to be best at – imagining new trading ideas/strategies. Excerpts: In reality, the research process for a quantitative trading strategy, from conceptual design to actual execution, is very time consuming, e.g., months. The backtesting step, in the broadest sense, takes the longest time. There are too many details that we can include in the backtesting code. To just name a few, data cleaning and preparation, mathematics algorithms, mock market simulation, execution and slippage assumptions, parameter calibration, sensitivity analysis, and worst of all, debugging. In practice, most people will ignore many details and make unfortunate “approximation”. This is one major reason why real and paper...
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