Hedge Fund with Traditional and Alternative Investment
Project GitHub | LinkedIn: Niki Moritz Hao-Wei Matthew Oren
The skills we demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Introduction
During the 2008 financial crisis, institutional investors such as pension funds, insurance companies and banks suffered large losses. Hedge funds in particular had failed to provide financial strategies and returns uncorrelated to markets at a time when they were most needed.
Over 10 years since then, the natural question remains whether the financial industry is well-equipped for another crisis. The inherent complexity of financial products makes it challenging to effectively monitor the underlying risks of a portfolio, yet it is an essential task. In that spirit, we aim to create a simple tool that decomposes portfolio returns into investment strategies and explains the returns of hedge funds. The prototype of this tool can be found on ShinyApps.io, and the source code on GitHub.
Portfolio Decomposition
Portfolio decomposition to identify underlying risk factors is a standard approach for monitoring portfolio risk. This approach expresses our portfolio returns as a linear combination of factor returns with coefficients, a constant term and a residual vector. Coefficients (denoted beta) are exposures of portfolio to risk factors. The constant term (denoted alpha) shows whether the portfolio outperforms a basket of risk factors. Alpha > 0 indicates an outperforming portfolio while alpha < 0 indicates an underperforming portfolio.
Optimal coefficients can be found by minimizing the standard deviation between portfolio return and a risk factor basket – the concept known in finance as a tracking error. In this way, we decompose the variance of the portfolio into variances of individual factors and a remaining idiosyncratic (unexplained) variance component.
Data
We apply traditional and alternative investment strategies across multiple asset classes: equities, fixed income, FX, commodities, and volatility. Traditional strategies are defined as long-only investments (as involving the S&P500, US 10-year sovereign bonds, and so on), while alternative strategies are defined as a combination of long and short positions. The alternative strategies are further sub-divided into carry, value and momentum investment styles. Carry is earned when holding a high-yielding asset and financing it with a low-yielding asset. Value is associated with buying undervalued assets and selling overvalued assets based off a predefined fair value metric. Momentum involves buying assets that appreciate, and selling assets that depreciate, over a specified time horizon (e.g. 12-month).
Hedge funds currently account for nearly $3 trillion in assets globally. They can be classified into four different styles: equity long/short, event-driven, global macro, and relative value. In this analysis, we use Credit Suisse hedge fund indices that aggregate performances of underlying monthly hedge fund returns within each style. We also study the daily returns of the Credit Suisse Liquid Alternative Beta indices.
Visualization with ShinyApp
As with recipes and their ingredients, hedge funds can be broken down into the building blocks of their underlying investment strategies. The portfolio decomposition tab in our app partitions the traditional and alternative investment strategies of each portfolio and allocates percentages to them. The chart below shows one example of factor exposures generated by the app for a Credit Suisse Lab daily hedge fund index.
The variance decomposition chart shows the percentages of portfolio risk attributed to each strategy and a remaining (idiosyncratic) component that cannot be explained by our factors.
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