Tag: quant

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