Posted on: August 13, 2019 | By: Vikas Gupta
Behavioural bias is one of the biggest challenges in running a sustainably successful investment operation. What helps here is to have an extremely robust framework to manage the investments. There are three important layers to this framework. First layer is an investment philosophy which provides a strong foundation to the framework. Built on top of that layer is the second layer, i.e. core principles, and then, finally, on top of that is the last layer, i.e. a set of rules that will guide you through the turmoil of markets. One should remember that the investment philosophy is the most important overarching entity that is timeless and remains constant. Principles help you put the philosophy in action and can evolve over a period as the investment environment evolves. Rules are the final implementation of the principles. A rules-based framework helps you avoid several behavioural biases and keeps you focused. Though remember that the rules are not to be followed blindly. Rules are for less sophisticated investors to help them follow a set path, but the rules have to be continuously re-evaluated to keep them in tandem with the principles and philosophy.
Let us understand the above with an example. OmniScience has developed a Scientific Alpha investment framework from the first principles of Investing. With respect to investing one understands the concept of Risk and Reward. However, it has been interpreted many times wrongly. A layman definition is that high risk is high return. Or if you need high returns then you need to take higher risk. We believe this is a wrong interpretation. Conceptually, one must be compensated for taking higher risk with higher reward but, the question is does it actually happen. The correct interpretation is that when you are investing, and especially in a high-return asset class such as equities, you are exposed to various risks and if you are able to mitigate risks you can have higher rewards. The academic world links risk with fluctuation or volatility and measures it by standard deviation or beta. Here again, a security with high standard deviation is clearly risky but it is wrong to conclude that any high volatility security will give higher returns.
What one should understand is that since you are investing in a risky asset class you need to minimize your risk. This is the investment philosophy of the Scientific alpha framework – Risk Averseness. The concept of risk averseness was well understood by Benjamin Graham and it was implemented through the requirement of a Margin of Safety. He also defined an investment operation as one which involves thorough analysis, provides safety of capital and an adequate return. This formulates our guiding principles of the Scientific Alpha process – Safety of principal with adequate returns. This further helps define the rules and implementation which primarily focus on the risk mitigation process.
Scientific Alpha can be described as a structured value investing framework that focuses on risk mitigation to generate alpha. For risk mitigation one first needs to understand what risks are present and then understand how to mitigate it. Scientific Alpha framework classifies risk in three categories. First category is of the company specific risks and where these risks are mitigated, we call them SuperNormal Companies. Second category is of the investment related risk and where these risks are mitigated, we call them SuperNormal Price. This category is of the portfolio related risks and where the all three categories of risks are mitigated i.e., minimising chances of losing capital and generating adequate returns, we call it SuperNormal Portfolio. Let us understand each risk category in more detail.
Indian IT services sector over the last few years is an interesting case study to understand the strengths of Scientific Alpha process and how it works. For more than two years, IT service company have formed a majority and significant part of the Scientific Alpha portfolios and it continues to be so. Let us see how IT Service companies fit the scientific alpha framework.
On cost arbitrage, the persistent advantage still remains. India has the largest pool of digital talent globally. Globally there is a shortfall of data scientists, analysts and digital professionals. Indian IT firms have a significant competitive advantage in terms of recruiting 100,000s of professionals, train them and put them on the job. Even for the global majors such as Accenture, IBM and others have almost 1/3rd of their work force in India.
Our investment thesis is based on the view that each IT services company is having two businesses – a low growth and stagnant legacy business and a vibrant, high growth Digital business. The digital business is showing significant traction and growing at a fast pace. As the digital business has gained significant size, we have now started seeing the rise in the overall growth numbers of the companies. The legacy business was masking the attractive digital business. In our view, if the IT companies were to spin off their digital businesses and list them as separate business, they would have gotten much better valuation, at times higher than the valuation of full company, including both, digital and legacy business.
We have been tracking the digital business revenues, growth rates, deal wins and partnerships by the IT services companies. The chart below shows that the DX business is almost 30% of the overall business and growing at around 30%+ rate
Chart1: IT firms digital business revenue share and growth rate; Source: OmniScience estimates, Company reports.
Finally, let us also evaluate the current situation of the IT services pack. We will test the IT services pack through the Scientific Alpha framework of SuperNormal Companies @ SuperNormal Price. Refer to the Key Fundamentals table given below which presents the various fundamental parameters for the IT portfolio firms and the broader market (Nifty 500). On all parameters the IT portfolio numbers are significantly better whether it is ROE, Asset utilization, Debt to Equity or Margins).
|Key Fundamentals (Feb 28, 2019)||IT Portfolio||Nifty 500|
|5Yr Average ROCE||28.1%||13.3%|
|Sales to Asset||1.20||0.73|
|Gross Debt to Equity||9.5%||80.4%|
|Net Debt to Equity||-33.9%||65.9%|
The table below presents various valuation metrics. The test is to see if the SuperNormal companies (IT Portfolio) are also available at a SuperNormal price. Across various price multiples the IT portfolio is at a significant discount.
|Key Valuation Metrics (Feb 28, 2019)||IT Portfolio||Nifty 500|
Indian IT Service companies are part of a technological breakthrough that is disrupting the world around us. With 5G implementation we will see a lot more traction towards the expected multi-trillion-dollar economic impact of both AI and IoT. The IT services companies play a significantly critical role of implementing these new age technologies at scale.
The above case of the IT sector companies shows how even for a well-researched sector such as IT which has 100s of Indian and Global analysts tracking it, the market can be making a mistake and Scientific Alpha framework is able to create a SuperNormal Portfolio of SuperNormal Companies @ SuperNormal Prices. The thesis has already played out delivering higher returns than the market in 2018 and continues to perform well. All of this, while actually exposing the portfolio to quantifiably lower risks.