Dalal Street Rules – Going Quant

Background: Efficient Market Hypothesis Vs Factor Investing

Market participants in search of inefficiencies to profit from

Efficient Market Hypothesis (EMH) and Bill Sharpe and his Capital Asset Pricing Model (CAPM) model were the accepted framework for evaluating expected returns from stocks for almost 3 decades between 1960 and 1990. The most important assumption of EMH was that prices adjusted quickly to new information, making the stock market efficient. Picture a tank of hungry fish in which fish food is dropped. The hungry fish are quickly going to gobble up the food and the tank is clean again. Similarly, market participants were like these hungry fish, who moved quickly to factor new information into prices by buying or selling. And since now all the new information was factored in, the next move the stock made would essentially be random – hence the random walk theory by Burton Malkiel. And market cannot be beaten was the hypothesis of the EMH proponents.

I’m convinced that there is much inefficiency in the market. When the price of a stock can be influenced by a “herd” on Wall Street with prices set at the margin by the most emotional person, or the greediest person, or the most depressed person, it is hard to argue that the market always prices rationally. In fact, market prices are frequently nonsensical.”

– Warren Buffett

However, the EMH process had some interesting consequences. For the market to be efficient, there had to be those fishes who gobble up the food a.k.a market participants who play the “Active” role in making the market efficient by their actions. And for them to do this, there have to be enough incentive or profits. So if there are no “Active” investors, there is no one to make the market efficient, because the “Passive” investors are price takers. This is a paradox that till date continues to be a conundrum for the EMH gang. And is also at the heart of the “Active” vs “Passive” debate. But that is a piece for another day.

Therefore by the above argument, the market is always in the process of moving towards equilibrium or efficiency. So the market is “partly efficient”. Ironically the strongest proponents of the EMH, Fama And French, in 1992 came and threw grenade to bust the EMH. They used the massive resources and super computers at Chicago and ran tests on database of stock returns from 1963 – 1990. Their finding was that the all important beta has NO impact on stock returns. Hence market beta, the only factor considered in the CAPM model until then was not how investors set prices. There were other effects. Their conclusion was that there are other risk premiums other than the market risk premium that exist and can be harvested – value and size. Their findings were that the following two factors accounted for gains not explained by the market beta:
1. Value Factor: Cheaper companies out perform expensive companies over time.
2. Size Factor: Smaller companies tend to out perform larger companies over time.

Along comes Momentum

In 1990, Cliff Asness (who was doing his PhD under Eugene Fama) entered his room and told Fama that he was about to write a paper that was pro-momentum. In Fama’s world, momentum or a trend in prices didn’t exist. But he allowed Asness to pursue his thesis and “if it’s in the data, write the paper.” Another duo, Jagadeesh and Titman were also doing their own research on momentum and in 1993, published their seminal paper –Returns to buying winners and selling losers – Implications for stock market efficiency . Sure there were value and size factor but momentum till date and very reluctantly, is admitted as an “anomaly” that should not exist as per Fama. Value or cheap stocks are perceived to have embedded risk premia. Also smaller companies are riskier than larger companies. The narrative is easier to build. But momentum has no such narrative – except that investors under react to good news. And in that, it is the opposite of the Value factor where investors over react to bad news. And this has been backed by data. Value and Momentum Everywhere by AQR has documented this effect across asset classes and geographies. If the only free lunch in investing is diversification, then for dessert you can have “value and momentum” factors, which are negatively correlated.

No Pain, No Premium.

About 300 odd factors have been discovered – but only 5 have made it till date. Low Volatility is still waiting for the 3rd Umpires video review. But on the screen it is evident that it is a robust factor. Now remember, these are not the holy grail that will work every day, week, month or even every year. They don’t work all the time because they work over time. Because markets can remain irrational, longer than most investors can remain solvent as said Keynes. Cheap companies can remain cheap or get cheaper for a long time. So can small companies under perform large companies in an environment that favors large companies. So you still need patient capital if one is to exploit these factors over the longer term.

Back to Aks Capital

Aks Capital’s team had put together their set of rules. The strategy sought to combine two of the most robust factors – Momentum and Low Volatility. Over short and time frames, Momentum had the lowest odds of under performance. In fact over a 20 year period the odds of under performance are almost nil. This is followed by market beta which the odds of under performance of just 3% over the same time period.

1. Momentum: The tendency for assets with a positive trend to continue to do well, and those that are falling continue to slide. This “anomaly” is rooted in investor behavior where it has been empirically observed over decades that investors under react to good news.
2. Volatility: Assets with regular muted performance tend to out perform more volatile ones over time. This again runs contrary to the CAPM view, that more volatile the asset, higher the risk compensation for it.

The Rules

1. The initial universe would the top 500 companies, by market capitalization, listed on the National Stock Exchange (NSE)
2. Momentum would be measured as the average daily return of each security during the previous twelve months. The latest month was to be excluded for reversal effect. The securities would then be ranked in order of returns, from high to low.
3. Volatility would be measured using the inverse of standard deviation based on 12 month look back. The securities would then be ranked in order of volatility from low to high.
4. The ranks would be combined and the securities would be ranked again. The top 30 securities with the combined rank on Momentum and Volatility would be part of the portfolio.
5. The weight of each stock in the portfolio would be based on its volatility when it entered the portfolio with a maximum cap of 10%. Lesser the historical volatility, higher the weight in the portfolio.
6. The portfolio would be re-balanced on a monthly basis and any stock which fell below the top 50 in terms of combined rankings would be replaced with the stock having the next best rank.
7. Any stock having a pending corporate event like a merger or takeover would be removed from the portfolio.

Since this was meant for institutional and UHNWI who had a a longer time horizon like 3-5 years, Aks Capital had planned to market it to those for whom capital appreciation with lower risk was priority. However, Aks Capital still had the dna of a hedge fund and it was decided to put a more aggressive version of this strategy and trade it with proprietary capital to begin with. A team of a trader and an associate were formed to implement this in the prop book. They were told to keep away from news, reading fundamental sell side reports and focus more on execution with minimum deviation from the rules.

Cut short your losses, and let your profits run on.

David Ricardo, British Economist, (1772-1823)

The additions made to the rules above were as follows:

1. The portfolio would be re-balanced on a weekly basis instead of a monthly basis.
2. An additional Entry criteria here would be measure of magnitude of breakout from a mean value. For simplicity sake it can be thought of a 2 SD Bollinger Band Breakout with the mean as 40 week moving average.
3. An additional exit criteria would be to remove the stock if it closed 1 standard deviation from the 40 week moving average
4. A market regime filter was added for buying stocks. No stock would be added to the portfolio unless the benchmark was trading above its 40 week moving average on the day the signal was generated.

(With inputs from Aayushi Shah)

Disclaimer: Nothing in this blog should be construed as investment advice. This is purely for educational purposes only.  Please consult an investment advisor before investing