Tag: quant

Models don’t lie, incorrect assumptions do

Snarl

An engineer, a physicist and an economist are stranded on a deserted island with nothing to eat. A crate containing many cans of soup washes ashore and the three ponder how to open the cans.
Engineer: Let’s climb that tree and drop the cans on the rocks.
Physicist: Let’s heat each can over our campfire until the increase in internal
pressure causes it to open.
Economist: Let’s assume we have a can opener.

In a recent paper titled Chameleons: The Misuse of Theoretical Models in Finance and Economics Paul Pfleiderer of Stanford University lays out how some models, built on assumptions with dubious connections to the real world, end up being used to inform policy and other decision making. He terms these models “Chameleons.”

Notice how similar these two abstracts are:

To establish that high bank leverage is the natural (distortion-free) result of intermediation focused on liquid-claim production, the model rules out agency problems, deposit insurance, taxes, and all other distortionary factors. By positing these idealized conditions, the model obviously ignores some important determinants of bank capital structure in the real world. However, in contrast to the MM framework – and generalizations that include only leverage-related distortions – it allows a meaningful role for banks as producers of liquidity and shows clearly that, if one extends the MM model to take that role into account, it is optimal for banks to have high leverage.

– “Why High Leverage is Optimal for Banks” by Harry DeAngelo and René Stulz.

To establish that high intake of alcohol is the natural (distortion free) result of human liquid-drink consumption, the model rules out liver disease, DUIs, health benefits, spousal abuse, job loss and all other distortionary factors. By positing these idealized conditions, the model obviously ignores some important determinants of human alcohol consumption in the real world. However, in contrast to the alcohol neutral framework – and generalizations that include only overconsumption-related distortions – it allows a meaningful role for humans as producers of that pleasant “buzz” one gets by consuming alcohol, and shows clearly that if one extends the alcohol neutral model to take that role into account, it is optimal for humans to be drinking all of their waking hours.

– “Why High Alcohol Consumption is Optimal for Humans” by Bacardi and Mondavi 😉

These are a good illustration that one can generally develop a theoretical model to produce any result within a wide range.

Read the whole thing at your leisure: Chameleons: The Misuse of Theoretical Models in Finance and Economics

Machine Learning Stocks, Part II

We had previously rolled out the “similar stocks” feature that grouped stocks based on risk and technical parameters. The idea of grouping/clustering stocks is that it allows you to find better alternatives to your favorite stocks. We combined machine learning with our Fundamental Quantitative Scores to cluster stocks based on fundamental metrics.

For example, here’s what Glenmark’s looks like:

Glenmark

 

Related:

Machine Learning Stocks
Quantitative Value Series
StockViz Trading/demat Account
Fundamental Quantitative Scores for Stocks

Fundamental Quantitative Scores for Stocks

We are big fans of quantitative investment strategies, here at StockViz. The primary reason driving our obsession is that they work! They work because they impose discipline and gives us a framework to measure risk-adjusted returns. As a sign of our unwavering focus for providing our clients with an investment edge, we are now making our Fundamental Quantitative Scores for Stocks accessible to our trading/demat clients.

Fundamental Quantitative Scores rank each stock based on a single metric: for example, Return on Capital (ROC), Leverage, Total Accruals To Total Assets, etc. These ranks provide a snapshot of how the company is doing vs. all the other investment options out there.

Here’s a screenshot for Glenmark:

quantitative fundamental scores for glenmark

A couple of these metrics (Earnings Yield and Book to Market) are price based (and hence the blue highlight). The scores also indicate the total number of stocks that were analyzed on that metric. For example, Glenmark is ranked 748 out of 957 on Sales Growth Index. We’ll be discussing each of these metrics over the next couple of weeks.

 

Related:

Machine Learning Stocks
Quantitative Value Series
StockViz Trading/demat Account

Machine Learning Stocks

There are no “new new” things in finance, there are no “unique” opportunities and the stock that you labored so hard to pick is no “diamond in the rough.”

The perils of static classification

In the olden days, stocks were classified either based on the industry they operated in (i.e., sectors) or based on their market cap (i.e. cap-weighted indices.) But neither of them do justice to the underlying risk or technical behavior of the stocks themselves. Not all stocks in an index are similar, nor do they have a 100% overlap with other stocks within the same sector. Buy say you really liked a stock, like ACCELYA for example, but found its valuation unattractive, how do you go about finding a substitute?

Finding substitutes using clustering

This is where machine learning can help. Using clustering algorithms, stocks can be grouped based on parameters different from just industry or market-cap. What if stocks are grouped based on risk metrics (alpha, beta, Sharpe…) and technicals (RSI, ADX…)?

This was the question we set out to answer and we are now proud to present the results in the “Quant” tab in the equities page. Here’s how the Quant section looks like for Accelya:

ACCELYA Quantitative Analysis

It shows that Accelya, although a retail value favorite, is not alone when it comes to its risk profile and technicals. There are other fish in the sea that is worth a gander.

This might be a scary thought for most investors: their favorite stocks are not alone, and there might be cheaper substitutes to the stocks that they like.

Geek Note

  • All factors are equally weighted and normalized.
  • A lot depends on whether the underlying data is actually clustered or not. For example, if you only cluster based on risk metrics, then by definition, most stocks will cluster around Alpha=0 and Beta=1.
  • Risk metrics look back 365 calendar days whereas technical metrics look back over shorter time-frames.
  • In case you are wondering, there is no information content in observing “cluster migration.”

Related:
Alpha, Beta, Sharpe and Information Ratio
Risk Adjusted Returns