Publications by systematicinvestor
Minimizing Downside Risk
In the Maximum Loss and Mean-Absolute Deviation risk measures, and Expected shortfall (CVaR) and Conditional Drawdown at Risk (CDaR) posts I started the discussion about alternative risk measures we can use to construct efficient frontier. Another alternative risk measure I want to discuss is Downside Risk. In the traditional mean-variance optimi...
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Maximizing Omega Ratio
The Omega Ratio was introduced by Keating and Shadwick in 2002. It measures the ratio of average portfolio wins over average portfolio losses for a given target return L. Let x.i, i= 1,…,n be weights of instruments in the portfolio. We suppose that j= 1,…,T scenarios of returns with equal probabilities are available. I will use historical ass...
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Geometric Efficient Frontier
What is important for an investor? The rate of return is at the top of the list. Does the expected rate of return shown on the mean-variance efficient frontier paints the full picture? If investor’s investment horizon is longer than one period, for example 5 years, than the true measure of portfolio performance is Geometric return, which is alw...
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Resampling and Shrinkage : Solutions to Instability of mean-variance efficient portfolios
Small changes in the input assumptions often lead to very different efficient portfolios constructed with mean-variance optimization. I will discuss Resampling and Covariance Shrinkage Estimator – two common techniques to make portfolios in the mean-variance efficient frontier more diversified and immune to small changes in the input assumption...
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Black-Litterman Model
The Black-Litterman Model was created by Fisher Black and Robert Litterman in 1992 to resolve shortcomings of traditional Markovitz mean-variance asset allocation model. It addresses following two items: Lack of diversification of portfolios on the mean-variance efficient frontier. Instability of portfolios on the mean-variance efficient frontie...
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Style Analysis
During the final stage of asset allocation process we have to decide how to implement our desired allocation. In many cases we will allocate capital to the mutual fund managers who will invest money according to their fund’s mandate. Usually there is no perfect relationship between asset classes and fund managers. To determine the true style of...
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Introduction to Backtesting library in the Systematic Investor Toolbox
I wrote a simple Backtesting library to evaluate and analyze Trading Strategies. I will use this library to present the performance of trading strategies that I will study in the next series of posts. It is very easy to write a simple Backtesting routine in R, for example: bt.simple <- function(data, signal) { # lag singal signal = Lag(signal,...
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Trading Strategy Sensitivity Analysis
When designing a trading strategy, I want to make sure that small changes in the strategy parameters will not transform the profitable strategy into the loosing one. I will study the strategy robustness and profitability under different parameter scenarios using a sample strategy presented by David Varadi in the Improving Trend-Following Strategi...
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Backtesting with Short positions
I want to illustrate Backtesting with Short positions using an interesting strategy introduced by Woodshedder in the Simple, Long-Term Indicator Near to Giving Short Signal post. This strategy was also analyzed in details by MarketSci in Woodshedder’s Long-Term Indicator post. The strategy uses the 5 day rate of change (ROC5) and the 252 day ra...
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Multi-Asset Backtest : Rotational Trading Strategies
I want to discuss the implementation of Rotational Trading Strategies using the backtesting library in the Systematic Investor Toolbox.The Rotational Trading strategy switches investment allocations throughout the time, betting on few top ranked assets. For example, the ranking can be based on relative strength or momentum. A few examples of the ...
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