Beta Explorer App
When constructing or evaluating an investment portfolio, it is important to develop a point of view of its risk characteristics. One area of risk involves selection; questions that can help frame this risk include: is a manager choosing his/her investments wisely? Are fundamental drivers properly understood? Is the asset priced well enough on the market to afford an attractive rate of return? A second category of risk, known as systemic risk, relates to the performance of the asset class as a whole, independent of a manager’s selection capability. Addressing this risk involves an understanding of how a particular investment asset, or portfolio of assets, is likely to behave in response to market gyration and changes in broader macro-economic forecasts. The performance of even the most carefully selected portfolio will be subject to the vicissitude of the market at large.
Beta is an important statistical tool used to evaluate systemic risk, and is defined as the covariance of an asset with a benchmark, divided by the variance of the benchmark. An asset with a beta of 1 is expected to behave similarly to the benchmark index; assets with betas of less than 1 should move less than the market, and betas greater than 1 should move more than the market. Two investment portfolios with comparable return profiles can be distinguished, in part, by their betas; the portfolio that can produce a similar return with a lower overall beta (i.e. less systemic risk) ought to be more desirable. But what if two portfolios had similar return profiles and similar beta levels... could an understanding of what may be driving beta in each portfolio help manage systemic risk?
The Beta explorer app is a tool that allows users to visualize sources and causes of beta in a particular stock and/or sector. Currently, the app examines Beta through the lenses of five popular market measures – Revenue Growth, Price to Earnings ratio, Dividend Yield, Market Capitalization, and Financial Leverage. Data is pooled from 3 sources: DataHub.io, Portfolio 123, and the ‘stocks’ library in R. Though these 5 variables were the measures most readily available to me, this study could easily be extended to accommodate additional metrics in the future.
An investor in AAPL stock, for example, may open the app and enter its ticker symbol into the app dashboard. The Beta explorer app shows the user that AAPL has a beta that is both greater than 1 and greater than the Information Technology in which it is classified.
A correlation matrix provides a visualization of which variables most/least influence the beta of the information technology sector. From here, the user can view a data table with all members of the sector, sortable across the key five metrics. In the data table below, the user has asked for lower beta stocks in the Information Technology sector.
Based on its high beta level, an investor in AAPL stock may seek to reduce his/her overall exposure level by purchasing stocks with lower betas. For this, he/she may turn to sectors with low beta, such as the consumer products, healthcare, energy, or utilities sectors, for new ideas. The beta app quickly shows its user that while the high beta of AAPL stock is correlated with its revenue growth and P/E ratio, the lower beta sectors involve additional relationships, such as to dividend yield and market capitalization.
Depending on one's investment objectives, hedging beta exposure could take different forms. An investor could buy a large capitalization stock in the utilities sector, whose size, P/E ratio and dividend yield all contribute to a lower beta profile. Alternatively, he/she could buy a healthcare stock with a low beta, that may have lower revenue growth and P/E ratio, and/or a high dividend yield. Another option could be to find a lower beta stock within the information technology sector, though sector concentration may become an issue. Each of these options creates new risk dimensions and sensitivities. With the beta app, one becomes more aware of a stock’s beta, what is behind it, and how other stocks behave relative to it. This should yield a more insightful risk management process.
It could be helpful to investors to visualize how stocks are grouped relative to each other and the market. Histograms and scatterplots are presented and allow the users to observe relationships and cluster the sector (or the market as a whole) based on a specified range of betas. A correlation tab provides full tables with both variable-specific correlation coefficients and P-values.
Down the road, I’d like to include more variables into my beta app, as well as add additional functionality, such as a recommendation engine, that could offer users specific suggestions on how to best increase or reduce beta in a portfolio, given a set of optimization parameters.
Shiny App: https://marcsprojects.shinyapps.io/shinyproject/