The NSE site describes Strategy Indices as “designed based on quantitative models/investment strategies to provide a single value for the aggregate performance of a number of companies.” That is close to how we think about “Factor Investing”. A more straightforward way of thinking about factor or quantitative investing is
Factor Investing is about defining a characteristic (also called factor or set of factors), and consistently applying a set of criteria to buy a diversified portfolio of stocks that share that characteristic.
What is factor investing?
The no-jargon version:
Let’s say you were tasked with constructing a high-quality basketball team.
One way you might go about it is to ask around for the best basketball players, the folks with pedigree colleges with track records of producing great players, those who have scored the most points or been in the most winning games. Then look at recorded footage, observe them in live practice. You might even meet them in person, talk to their trainers. And only then decide whether to have them on your team.
You might be thinking, “Wait, I don’t know anything about basketball.” The approach will be similar for any other team sport. In fact, it holds for other fields like how big companies recruit, how VCs fund start-ups
An alternate approach in our basketball team hypothetical would be to come up with a set of characteristics that might represent good basketball players. Thinking intuitively, you start with height. You’d go back into the past, and create teams of the tallest available players. Then you’d see how many points those teams scored and whether they outperformed the average team. If the data says a team of the tallest available players consistently scored 3 extra points per game compared to the average team, you could conclude that it might continue to work in the future.
Note how both approaches are rooted in pattern recognition. Relying on the past to tell you what might work in the future. The first is largely subjective but considers a broad set of hard and soft aspects. The second is objective and only considers what can be quantified and more importantly, can be tested with historical data.
It’s probably apparent, the first approach is the investing equivalent of fundamental stock selection, and the second approach is the analogy to factor investing.
Factor Investing is about defining a characteristic (also called factor), and consistently applying a set of criteria to buy a diversified portfolio of stocks that share that characteristic.
What factors offer the potential to beat the market?
Bangladesh Butter Production and the S&P 500
We picked height as the “factor” for exploration in our illustration above. But what if there were other factors more effective at predicting outperformance?
Speed for instance. Or Age. How about Place of birth? Relationship status? You quickly realize the possibilities are endless. After all, all kinds of things could impact the performance of professional athletes.
For example, if you were considering NBA data from the 1990s, picking players from teams with domesticated agricultural animals in their names would deliver many points in excess of the average team. Wait, what?
Fact: The Chicago Bulls won the NBA championship 6 years out of the 10 between 1991 and 2000.
This brings us to the Bangladesh Butter Production problem. In 1995, David Leinweber, now the head of the Center for Innovative Financial Technology, Lawrence Berkley National Lab, examined over 150 macro-economic indicators to find statistically significant relationships with the S&P500.
His intent was to identify any indicators that could predict the market. It was butter production in our neighbouring country that showed the tightest relationship with the US Equity Index. Leinweber published this finding as a joke which has since been part of most “correlation is not causation” lessons in courses the world over.
The simple takeaway for factor investors, if a factor does not make intuitive sense, it probably is a chance relationship applicable only to the historical data in question. Therefore unlikely to deliver excess return in the future.
In their book, Your Complete Guide to Factor-Based Investing, the authors lay out five tests to determine whether a factor offers the promise of superior returns. To merit consideration, a factor should be:
- Persistent – over long periods of time and regimes
- Pervasive – across countries, sectors and asset classes
- Robust – for various definitions (e.g. there is a value premium whether measured by Price-to-Book, Earnings Yield, Price-to-CashFlow etc)
- Investable – not just on paper, but after considering actual implementation issues
- Intuitive – has logical risk-based or behavioural-based explanations on why it might continue to exist
The more of the above five conditions a factor checks, the more likely it is to offer potential for outperformance in the future.
This page summarises the performance of the various NSE single-factor indices since their inception.
Nifty 200 Momentum 30 Index
Nifty200 Momentum 30 Index aims to track the performance of the top 30 companies within the Nifty 200 selected based on their Normalised Momentum Score. The Normalised Momentum Score for each company is determined based on its 6-month and 12-month price return, adjusted for volatility. Stock weights are based on a combination of the stock’s Normalised Momentum Score and its free-float market capitalisation.
Nifty Midcap150 Momentum 50 Index
Nifty Midcap150 Momentum 50 Index aims to track the performance of the top 50 companies within the Nifty Midcap 150 selected based on their Normalised Momentum Score. The Normalised Momentum Score for each company is determined based on its 6-month and 12-month price return, adjusted for volatility. Stock weights are based on a combination of the stock’s Normalised Momentum Score and its free-float market capitalisation.
Nifty MidSmallcap400 Momentum Quality 100
The Nifty MidSmallcap400 Momentum Quality 100 Index tracks the performance of the large and mid cap stocks which are selected based on the combination of momentum and quality factors from the Nifty MidSmallcap 400 index.
Nifty200 Quality 30 Index
The Nifty200 Quality 30 index includes the top 30 companies from its parent Nifty 200 index, selected based on their ‘quality’ scores. The quality score for each company is determined based on return on equity (ROE), financial leverage (Debt/Equity Ratio) and earning (EPS) growth variability analysed during the previous five years.
Nifty500 Value 50 Index
The Nifty500 Value 50 index consists of 50 companies from its parent Nifty 500 index, selected based on their ‘value’ scores. The value score of each company is determined based on the Earnings to Price ratio (E/P), Book Value to Price ratio (B/P), Sales to Price ratio (S/P) and Dividend Yield.
Nifty 50 Value 20 Index
The Nifty50 Value 20 Index reflects the behaviour and performance of a diversified portfolio of value companies forming a part of the Nifty 50 Index. It consists of the most liquid value blue chip companies. The Nifty50 Value 20 Index comprises 20 National Stock Exchange (NSE) companies. Value companies are generally perceived as companies with low PE (Price to Earning), low PB (Price to Book) and high DY (Dividend Yield).
Nifty100 Low Volatility 30 Index
Nifty100 Low Volatility 30 Index aims to measure the performance of the low volatile securities in the large market capitalisation segment. The selection of securities and their weights in Nifty100 Low Volatility 30 is based on volatility.
Nifty Low Volatility 50 Index
The index measures the performance of the least volatile securities listed on the NSE. To make the 50 stocks index investible and replicable, criteria such as turnover and market capitalisation are applied when selecting securities. Weights of securities in the index are assigned based on the volatility values. The least volatile security in the index gets the highest weight. To derive the volatility of the securities, the standard deviation of daily price returns (log-normal) for the last year is considered.
Nifty200 Alpha 30 Index
Nifty200 Alpha 30 index consists of 30 stocks selected from its parent Nifty 200 based on ‘Jensen’s Alpha’. Stock weights are based on their alpha scores. The alpha score for each company is determined based on Jensen’s alpha computed using 1-year trailing prices.
Nifty High Beta 50
The index aims to measure the performance of the stocks listed on the NSE with High Beta. Beta can be referred to as a measure of the sensitivity of stock returns to market returns. The market is represented by the performance of the Nifty 50 index. To make the 50 stocks index investible and replicable, criteria such as turnover and market capitalisation are applied when selecting securities. Weights of securities in the index are assigned based on the beta values. Security with the highest Beta in the index gets the highest weight.
Nifty Dividend Opportunities 50
The Nifty Dividend Opportunities 50 Index is designed to provide exposure to high-yielding companies listed on NSE while meeting stability and tradability requirements. The index comprises 50 companies. A key feature of the index is the methodology of selection of stocks, i.e. the method employs a yield-driven selection criterion that aims to maximise yield while providing stability and tradability.
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