Here I explain how to make sense of the econometric factor model widget.

The post explains the Econometric Factor Model widget that I have built. The factors used in the econometric factor model are: unemployment, consumer sentiment, market, size and value/growth. A brief explanation of the factors are as follows.

  • The unemployment factor is a proxy for the state of economy, and exposure to this factor measures the impact the economy can have on the performance of this stock.
  • The consumer sentiment is a voice of consumers behaviour and sentiment, and this factor can help understand the impact of consumer confidence on the stock's performance.
  • The exposure to market factor captures how the stock's performance is correlated with that of the market.
  • The size factor (or SML - Small Minus Large ) is a measure of the excess returns that the small cap has over the large cap stocks. The exposure to this factor is meant to capture the impact size has on the performance of this stock. Small (large) cap stock will typically have positive (negative) exposures to this factor.
  • The value/growth (or HML - High book-to-value Minus Low) is meant to identify the value or growth aspect of a stock.

As a use case, let us try a few examples and see if it makes sense. The following table is based on the factor model run using 5 years of historical returns as of 09-30-2014.

Ticker Unemployment Consumer Sentiment Market Size Value
{{item.Ticker}} {{item.Unemployment}} {{item.ConsumerSentiment}} {{item.Market}} {{item.Size}} {{item.Value}}

United States Steel Corporation (X), Delta Airlines (DAL), and MicroSoft (MSFT) have negative exposure to unemployment, meaning increase in unemployment (or a detiorating economy) hurts the returns of this company. Mc Donalds (MCD) has a positive exposure to unemployment, in a hurting economy MCD has a better business.

Delta Airlines has the most significant exposure to the consumer sentiment factor. This makes sense because travel industry benefits from a positive consumer sentiment.

The companies on the higher end of the large cap spectrum have a negative exposure to the size factor. The strong positive exposure of DAL to size is a little puzzling, and its history of mergers with Northwest might explain. This is also highlights that factor models do need more than intuition sometimes to make sense and make correct use of.

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I am Goutham Balaraman, and I explore topics in quantitative finance, programming, and data science. You can follow me @gsbalaraman.

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