Category: News

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.



Machine Learning Stocks
Quantitative Value Series
StockViz Trading/demat Account

Making your portfolio more efficient

We spend way too much time picking stocks and too little time figuring out how to allocate our capital between them. Even if you end up picking the “right” stocks, poor allocation will drag down the performance of your portfolio.

For the first time in India, you can now find out the appropriate weights that individual stocks should have within your portfolio and constantly monitor your portfolio as the market changes – automatically.

Users who have uploaded their portfolio can have a look at these statistics under the “Analytics” section:

efficient portfolio analysis

And if you just want to upload an adhoc set of stocks and just see what the most efficient allocation is, you can do that using our Marko tool, named after Harry Markowitz who introduced Modern Portfolio Theory.

Be efficient, be smart!


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.”

Alpha, Beta, Sharpe and Information Ratio
Risk Adjusted Returns

Keeping track of brokerage, etc…

Investing is more than just buy low, sell high. Its buy low, sell high, pay the broker, the exchange and then the government. Although these fees are lumped under “transaction charges”, the biggest chunk of it goes to the broker as commissions.

For our trading account clients, we have now made it easier to keep track of total commissions paid since the time the account was opened with us. This, along with the cumulative mark-to-market and realized P&L, will give you a truer picture of your returns.

StockViz cash and brokerage

Also, we have included ledger balance, aka cash balance in your account, as part of your position dashboard.


For our non-brokerage accounts, we have reintroduced the ability to reset portfolios and start from scratch.

Note: once you reset your portfolio, there is no going back!

Please let us know if there are any new features you would like to see on StockViz.


Intraday Scanner

StockViz tracks more than a thousand different equity based variables and metrics on a daily basis. Our Intra-day scanner is based on our Anomaly Detection Algorithms that are constantly scouring stock-market action to surface trade ideas.

Average True Range (ATR) Anomalies

Stocks trade within a daily range. The range, per se, is dependent on liquidity and intrinsic volatility of the stock. However, occasionally, a stock starts trading outside of its typical daily range, as measured by ATR. This gives an opportunity to traders to either bet on mean-reversion or continuance. Our ATR Anomaly Detection Algorithm surfaces stocks that break-out both on the upside and on the downside.

Bollinger Band Anomalies

Bollinger bands are volatility bands placed above and below a moving average of a stock’s daily close. When stocks break-out above its upper band or below its lower band, it signifies that “something” is afoot. Savvy traders can now step in and either bet on mean-reversion or continuance. Our Bollinger Anomaly Detection Algorithm surfaces stocks that break-out both on the upside and on the downside.

You can see these algorithms in action at stockviz/Scan


Do you use a scan that we can help you automate? Let us know in the comments below!