Momentum + Volatility Investing

Aditya Bakde | June 7, 2021


One of the promising factors in the emerging countries have been the momentum among the  other factors as the growth is rampant and the developed market see it as a potential money  multiplying opportunity. Same is the fundamental idea behind momentum investing, which  proposes to invest in assets which have consistently been growing since a particular time  frame. Here in this assignment the momentum factor have been used as the primary factor  of investment and the volatility is been utilised to impact the weight of a stock in the portfolio.  Including volatility has been quite the conservative practise as it helps in consistency in  returns and to have an overall control on the max drawdown.  

For momentum and volatility too there have been two time frames which they have been  calculated viz. Momentum_longTerm, Momentum_shortTerm, Volatility_longTerm,  Volatility_shortTerm. The long and short refers to a general time frame of 12 and 6 months  respectively, stating that the data used for the calculation of the factor for a stock is of past  12 or 6 months. But since in this strategy we are using volatility as a weight determining  parameter no such parameters were taken into consideration. 

Firstly, the momentum indicator, which is built on the basis of momentum score, the white  paper by the NSE India included this method of momentum calculation also used for the ETF  NIFTY 200 Momentum 30. 

Momentum Ratio = Price returns / Standard deviation price 

For, 12 month momentum ratio, 

12 months price returns = [P(M-1) / P(M-13)] – 1 

Standard Deviation 12 = Annualised standard deviation of a stock’s logarithmic daily returns  for the last one year. Similarly for the 6 months. 

This momentum score can be used to filter, top stocks with maximum scores can be  selected and then weighted accordingly. 

But for the NSE index where a mixed score of both 6 months and 12 months is aggregated, a  normalisation is done which is:  

Zscore = (Momentum Ratio12 - Mean Momentum Ratios)/ Standard Deviation Momentum Ratios Where,  

Mean Momentum Ratios = Mean of all the momentum ratios in case of NIFTY 200, the 200 stocks. Standard Deviation Momentum Ratios = Standard deviation of the momentum ratios of 200 stock Similarly for the 6 months. 

The weighted average Zscore of the eligible stock would be:

Weighted average Zscore = [50% * (Zscore for 12 month’s momentum) + 50% * (Zscore for 6  month’s momentum)] 

Normalised momentumscore = (1 + Weighted Avg. Zscore) : If weighted avg. Zscore >= 0  (1 - Weighted Avg. Zscore) : If weighted avg. Zscore < 0 

For volatility in NIFTY low volatility 50 the volatility is calculated by standard deviation of the  daily logarithmic price returns adjusted for any corporate actions. The weight also adjusted  are in the inverse proportion to the volatility. 

Methodology: 

The aim is to surpass the benchmark of momentum strategy for all industry included having  arguments for the parameters as follows:  

Frequency: Monthly 

Indicator: Momentum_longTerm 

position sizing: inverse volatility  

Ann_returns 

Ann_risk 

Max_drawdown 

Sharpe ratio

20.60% 

10.54% 

21.37% 

1.39



To accomplish this for each particular industry the results have to be extracted from the  simulator, among the industries Diversified outstand in returns. (Quarterly,  Momentum_shortTerm, Sector_cross sectional) 

Ann_returns 

Ann_risk 

Max_drawdown 

Sharpe ratio

40.14% 

23.72% 

29.28% 

1.44



Since to diversify and focus on high returning sectors we included Automobiles and Textiles,  producing results as follows: (Quarterly, Momentum_shortTerm, industry cross section,  inverse_vol). 

Ann_returns 

Ann_risk 

Max_drawdown 

Sharpe ratio

32.86% 

14.15% 

28.65% 

1.90



To further diversify we have constructed portfolio of by adding FMCG and Others sector  (sector_count=5) and another portfolio including 7 sectors by adding banking and chemical  sector to it, results as follows:  

(Quarterly, momentum_shortTerm, sectional cross_sectional, 5 sector) 

Ann_returns 

Ann_risk 

Max_drawdown 

Sharpe ratio

27.32% 

11.63% 

23.86% 

1.83



(Quarterly, momentum_shortTerm, sectional cross_sectional, 7 sectors)

Ann_returns 

Ann_risk 

Max_drawdown 

Sharpe ratio

24.20% 

10.65% 

22.29% 

1.71



Note that above results are excluding any kind of charges or tax deductions, also the  frequency of any portfolio adjustment is quarterly which provides a trade-off between  changes by the indicator and the transaction cost unlike the monthly or yearly frequency.  Hence considering these factors an overall 2.5-5% deduction can be projected in te above  results. 

Future Scope:  

Similar work on the momentum investing is done by Gary Anotonacci where he has described  GEM (Global Equity Momentum), which included set of rules for multiple asset class along  with equities. Based on GEM there were two strategies know as dual momentum and dual  momentum plus. In this technique the absolute momentum was calculated which is one year  momentum of the stock minus money momentum (EUR-USD rates in paper). The quantile  whose momentum was negative was divested and the money divested remained in cash. For  dual momentum plus the same technique but the cash left wash re-distributed among the  positive quantiles. The study showed that the first (dual momentum) strategy reduced the fall  of the portfolio whereas the second (dual momentum plus) showed an improved rebound  after a fall in comparison to the momentum based investing.



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