Momentum Investing

Hrituraj Kalsekar & Snehal Patil | June 7, 2021

Your Investment strategy is like your game plan to building your portfolio. But it is very important that  you find the one that's right for your objectives and situation in life. A 25-year-old should have a different  strategy then a 65 year old. 

We generally spend a decent amount of time planning for our workday, a vacation, and buying a car, but  we often forget (or choose to ignore) the most important plan of all: mapping out our investment  strategy and plan for growing old and retiring. 

Investing your money without an investment strategy is like a football team going into a game without a  playbook. Although they are not required, they significantly improve your chances of winning. 

Having an investment strategy is like having an instruction booklet guiding you through the investment  process. It will help you discard many potential investments that may perform poorly overtime or that  are not right for the investment goals you are looking to achieve. When creating an investment strategy,  it is important to quantitatively figure out what you are seeking to accomplish. Stating that you simply  want to make money or become wealthy is not helpful. The more specific the objective, the better. There  are many different strategies that apply to different investment objectives, the key is pairing the right  strategy with the right objective. 

For this project we have considered the Momentum – Short Term Strategies. Momentum investing is a  system of buying stocks or other securities that have had high returns over the past three to twelve  months and selling those that have had poor returns over the same period. Momentum strategies can be  developed on a single stock basis wherein the idea is to measure momentum across all the stocks in the  tracking universe and trade the ones which showcase the highest momentum. Do note, momentum can  be either way – long or short, so a trader following single stock momentum strategy will get both long  and short trading opportunities. 

Traders also develop momentum strategies on a sector-specific basis and set up sector-specific trades.  The idea here is to identify sector which exhibits strong momentum, this can be done by checking  momentum in sector-specific indices. Once the sector is identified, further look for the stocks within the  sector which display maximum strength in terms of momentum. 

Momentum can also be applied on a portfolio basis. This involves the concept of portfolio creation with  say ‘n’ number of stocks, with each stock in the portfolio showcasing momentum. In my opinion, this is a  great strategy as it is not just plain vanilla momentum strategy but also offers safety in terms of  diversification. 

In this project we have used the simulator of Wright Research to determine how the strategy has  performed over the last decade. We have tweaked the parameters and have tried to deduce the best  performing strategy across different time frames. All the iterations are done on Nifty 500 and are  performed on all sectors. The results of the iterations are mentioned hereon.

Summary Of All The Iterations: 

All the iterations are done on Nifty 500 and are performed on all sectors. 

Monthly Iterations 

Normalization Position Sizing Annualized Return Annualized Risk Max Drawdown Sharpe Ratio None None 15.64 13.01 29.48 0.74 Cross Sectional None 17.27 12.91 29.65 0.87 Sector Cross Sectional None 14.55 13.01 35.11 0.66 Industry Cross sectional None 14.23 13.48 38.14 0.61 None Inverse Vol 16.92 11.95 25.61 0.91 Industry Cross sectional Inverse Vol 15.76 12.24 34.04 0.8 Sector Cross Sectional Inverse Vol 15.96 11.81 31.22 0.84 Cross Sectional Inverse Vol 18.53 11.77 25.79 1.07 

Quarterly Iterations 

Normalization Position Sizing Annualized Return Annualized Risk Max Drawdown Sharpe Ratio None None 15.78 13.15 30.34 0.74 Cross Sectional None 15.34 12.14 29.54 0.77 Sector Cross Sectional None 12.72 11.99 32.25 0.56 Industry Cross Sectional None 11.47 12.72 38.11 0.43 None Inverse Vol 17.1 12.09 26.72 0.92 Cross Sectional Inverse Vol 16.75 10.91 25.52 0.99 Sector Cross Sectional Inverse Vol 14.22 10.94 28.33 0.75 Industry Cross Sectional Inverse Vol 13.19 11.6 34.16 0.62 

Yearly Iterations

Normalization Position Sizing Annualized Return Annualized Risk Max Drawdown Sharpe Ratio None None 14.79 11.43 30.7 0.77 Cross Sectional None 14.49 11.14 30.59 0.76 Sectoral Cross Sectional None 14.21 11.38 28.29 0.72 Industry Cross Sectional None 12.00 12.34 34.37 0.49 None Inverse Vol 16.10 10.87 26.93 0.93 Cross Sectional Inverse Vol 16.05 10.55 26.71 0.95 Sectoral Cross Sectional Inverse Vol 15.72 10.58 25.34 0.92 Industry Cross Sectional Inverse Vol 13.44 11.43 31.53 0.65 

Iterations Considering Monthly Time Frame: Tweaking Normalization 

Theme: Momentum_ShortTerm 

Normalization: None 

Position sizing = None 

Cost: 0 

Tags technical 

Frequency: Monthly 

Theme: Momentum_ShortTerm 

Normalization: None 

Position sizing = Inverse Vol Cost: 0 Tags: technical

Frequency: Monthly 

Theme: Momentum_ShortTerm 

Normalization: Cross Sectional 

Position sizing = None 

Cost: 0 

Tags: technical 

Frequency: Monthly 

Theme: Momentum_ShortTerm 

Normalization: Sector Cross Sectional 

Position sizing = None 

Cost: 0

Tags: technical 

Frequency: Monthly 

Theme: Momentum_ShortTerm 

Normalization: Industry Cross Sectional 

Position sizing = None 

Cost: 0 

Tags: technical

Tweaking Position Sizing 

Frequency: Monthly 

Theme: Momentum_ShortTerm 

Normalization: Industry Cross Sectional 

Position sizing = Inverse Vol 

Cost: 0 

Tags: technical 

Frequency: Monthly 

Theme: Momentum_ShortTerm 

Normalization: Sector Cross Sectional 

Position sizing = Inverse Vol 

Cost: 0 

Tags: technical

Frequency: Monthly 

Theme: Momentum_ShortTerm 

Normalization: Cross Sectional 

Position sizing = Inverse Vol 

Cost: 0 

Tags: technical

Iterations Considering Quarterly Time Frame:: 

Tweaking Normalization 

Theme: Momentum_ShortTerm 

Normalization: None 

Position sizing = None 

Cost: 0 

Tags: technical 

Theme: Momentum_ShortTerm 

Normalization: Cross Sectional 

Position sizing = None 

Cost: 0 

Tags: technical

Theme: Momentum_ShortTerm 

Normalization: Sector Cross Sectional 

Position sizing = None 

Cost: 0 

Tags: technical 

Theme: Momentum_ShortTerm 

Normalization: Industry Cross Sectional 

Position sizing = None 

Cost: 0 

Tags: technical

Tweaking Position Sizing 

Theme: Momentum_ShortTerm 

Normalization: None 

Position sizing = Inverse Vol 

Cost: 0 

Tags: technical 

Theme: Momentum_ShortTerm 

Normalization: Cross Sectional 

Position sizing = Inverse Vol

Cost: 0 

Tags: technical 

Theme: Momentum_ShortTerm 

Normalization: Sector Cross Sectional 

Position sizing = Inverse Vol 

Cost: 0 

Tags: technical 

Theme: Momentum_ShortTerm 

Normalization: Industry Cross Sectional 

Position sizing = Inverse Vol 

Cost: 0 

Tags: technical

Iterations Considering Yearly Time Frame: 

Tweaking Normalization 

Theme: Momentum_ShortTerm 

Normalization: None 

Position sizing = None 

Cost: 0 

Tags: technical 

Theme: Momentum_ShortTerm

Normalization: Cross sectional 

Position sizing = None 

Cost: 0 

Tags: technical 

Theme: Momentum_ShortTerm 

Normalization: Sector Cross sectional 

Position sizing = None 

Cost: 0 

Tags: technical 

Theme: Momentum_ShortTerm 

Normalization: Industry Cross sectional

Position sizing = None 

Cost: 0 

Tags: technical 

Tweaking Position Sizing 

Theme: Momentum_ShortTerm 

Normalization: None 

Position sizing = Inverse Vol 

Cost: 0 

Tags: technical 

Theme: Momentum_ShortTerm

Normalization: Cross Sectional 

Position sizing = Inverse Vol 

Cost: 0 

Tags: technical 

Theme: Momentum_ShortTerm 

Normalization: Sector Cross Sectional 

Position sizing = Inverse Vol 

Cost: 0 

Tags: technical

Theme: Momentum_ShortTerm 

Normalization: Industry Cross Sectional 

Position sizing = Inverse Vol 

Cost: 0 

Tags: technical 

Conclusion 

The best performing strategy of all has to be the one which was done on Monthly Time Frame, Inverse  Volatility Position Sizing and cross sectional normalization. This strategy produced a 18.53% Annualized  return, with 11.77% Annualized Risk, 25% of Max Drawdown with 1.07 Sharpe Ratio. 

It's important to understand that momentum trading involves a good deal of risk. In essence, you're  deciding to invest in a stock or ETF based on recent buying by other market participants. There's no  guarantee that buying pressures will continue to push the price higher. For example, a news  development may impact investor market perception and lead to widespread selling. Or, with many  investors already holding a long position in the ETF or stock, it's possible that profit-taking on existing  positions will overpower new buyers coming into the market, forcing prices down.

INDIVIDUAL WRITE UP 

Momentum Investing 

Momentum Investing is a strategy of buying stocks that have had high returns over the past three to  twelve months and selling those that have had poor returns over the same period. Where buy-low, sell high is investing in companies that look cheap, and selling them if and when they get expensive,  momentum investing is the opposite. 

Momentum investors don’t value a company on the fundamentals - they don’t look at a company’s  profits, losses, assets. Instead, they base their decisions on observations of stock market trends; they buy  stocks that are going up and sell or avoid those that are going down. 

To see which trend is worth hopping on, momentum investors use technical analysis. This involves  looking at charts of share prices and using various pattern-spotting methods to find potential entry and  exit points, as well as gauging the strength of the trend. The goal is to work with volatility by finding  opportunities in short-term uptrends and then sell when they start to lose momentum. 

Technical analysis is also used to analyse, understand, and predict the behaviour of other investors in the  market. Even the most experienced investor can have biases and let emotions cloud their judgement, so  momentum investors are keyed into this. 

Overall, investing biases fall into two main categories: emotional and cognitive. Emotional biases are  spontaneous and are based on the personal feelings of an individual at the time a decision is made. This  could be related to a personal experience or based just on a feeling they have. Cognitive biases involve  decision making based on established concepts that may or may not be accurate - decisions are made  based on a subjective reality. 

Momentum investing is somewhat academically inconvenient, as it contradicts the market efficiency  hypothesis. This widely held hypothesis states that asset prices reflect all available information. In theory,  shrewd investors shouldn’t buy a stock just to get on the bandwagon - they shouldn’t react to prices  emotionally… but sometimes they do! When a share price rises, investors’ fear of missing out on a trend  can often attract a growing stream of buyers, and conversely a growing stream of sellers if a share price  drops, even if there is no new information available on a company. 

There have been several studies on momentum investing because of its academic inconvenience, and  they have shown that it works! Because of this, momentum investing has been described as an investing  anomaly. A potentially profitable, well regarded and highly adopted anomaly. 

As with any kind of investing, momentum investing has its risks. No one makes money on everything all  the time. But momentum investing involves extra dedication and patience, understanding market trends,  investor biases and deep knowledge of stock markets. 

Momentum trading is a bit different than the usual value investing paradigm of “buying low and selling  high”. Over the years momentum trading strategies have proved to be profitable in the financial markets.

In practice, momentum trading is seen to be more popular than “buying low and selling high”. This is  because you buy an asset which is already moving up. You do not have to buy an undervalued asset and  wait for the market to reassess that stock so that your investment finally turns profitable. 

Another advantage of using momentum trading is that there is a potential for high profits over a short  period. Since you are leveraging the market's volatility to your advantage, the momentum trading  ultimately boils down to chasing the market performance to maximize your investment. 

The momentum trading strategies find opportunities in short term asset price movement. The  assumption is that if the price of an asset is increasing, it will continue to increase in absence of other  factors. 

Think of momentum trading as a moving car. The speed is slow as you start moving forward. This is when  you identify a stock which is increasing in price. 

As the car accelerates, the speed increases. If you have identified the stock and purchased it, your  investment now starts to grow. 

On seeing a red traffic signal, the car decelerates, and the speed reduces. This is similar to when you exit  your position at a profit on seeing a momentum loss in the asset price. 

Best Way To Model The Factors 

For the strategy that we have chosen, the best one performing was for monthly iterations, cross sectional  normalization and Inverse Volatility position sizing. Let us look at every single factor and the best way to  model it. 

1. Time Frame: With the iterations that we have performed, it is evident that shorter  timeframes gave better returns. This could be because monthly timeframes kept churning  the portfolio of stocks and kept weeding out the losers as early as possible. But longer  timeframes such as quarterly or yearly held onto losing stocks for a significantly higher  timeframe, causing the strategy to miss out on other opportunities. For this factor, the best  timeframe would be to choose anywhere between a month to a quarter. 

2. Normalization: For normalization, the best factor should be cross sectional. What this means  is, the data will be normalized between all sectors and industries. We have other parameters  which will normalize data within the industry or sector. But these factors have grossly  underperformed cross sectional normalization across all sectors. 

3. Position Sizing: Inverse volatility position sizing has outperformed most of the other  strategies by a significant margin. Volatility based position sizing strategy uses a measure of  volatility to determine the position size. Market volatility varies with time, and with higher  volatility comes greater swings, which needs to be taken into account when sizing your trade  For inverse volatility position sizing the basic idea is that the more volatile positions have  smaller size while the less volatile ones get a larger size. What is interesting, is the fact that  in every single time frame, Inverse volatility tends to outperform other strategy. In fact, 

there is not a single strategy which has given higher returns which do not have inverse  volatility, compared to those who do, irrespective of time frame, normalization. This can  only be considered as an anomaly because generally higher volatility is generally associated  with higher returns. But in our case the results are stark opposite.


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