We speak with Sandeep Tandon, CIO & Founder, quant Mutual Fund, to understand the VLRT framework followed at the AMC, his unconventional investment views and how they bring together the best of man and machine together
We understand that you employ the multi-dimensional VLRT investment framework at quant Mutual Fund. How does it help you select stocks?
VLRT is a very scalable framework and forms a superior stock-selection methodology. We can use VLRT for analysing any asset classes and we can use it from a global macro point of view as well.
Unlike the traditional approach, we give only one-third weightage to the Valuation Analytics when it comes to selecting stocks. We give another one-third weightage to Liquidity Analytics, which focuses on various macro data derived from a number of economic parameters. Then, we give one-third weightage to Risk Appetite Analytics, which measures sentiment data that measures the conviction or confidence of market participants.
The 'T' part of it refers to Timing. A lot of people are allergic to timing. However, timing for us is the biggest risk-management technique. If you can time the market better, your risk reduces. If you come in at the peak of the cycle for a stock or a sector, you are running the highest risk. Therefore, when our Liquidity, Risk Appetite and Valuation Analytics are skewed on one side, it gives us better Timing. Here, it is all driven by a pure mathematical model where everything is quantified. Thus, the word 'quant' comes from our background since we quantify everything, including sentiments.
So, what can go wrong here? Conventional wisdom suggests that the inflection points about the market risk or fear factor climb suddenly and come in the most unanticipated way. So, do you agree with that?
I agree with you. We can only quantify known risk and not unknown risk. What we can do is quantify whether fear is peaking out or not. Such parameters are well captured in our VLRT framework - when multiple data points are skewed on one side, it results in better timing, which ultimately reduces risk.
For example, in March 2020, we had fear because of COVID and today we have 'Russia fear'. So, when COVID happened, the market collapsed and then it rallied. Since a lot of macro data comes with some lag, what we spotted sometime around mid-April 2020 is that risk appetite, not just for India, but globally, was at a multi-decade low. Liquidity was at an all-time high. It was a brilliant opportunity since the fear intensity, or what we measure through our 'quant fear index', was at a multi-decade high, even higher than the 2008 financial crisis.
So, it's not about conventional wisdom. It's all about changing with time, adapting to new technologies which we talked about and understanding new things. Since we live in a global and dynamic world, how can your investment style be conventional or static? That's the reason we brought the concept of a dynamic style of money management and the edge this has given us has been demonstrated by our outperformance.
Can you talk more about how the VLRT framework and dynamic money management enabled your outperformance since the COVID lows in March 2020?
Coming back to the COVID crisis when risk appetite was at a multi-decade low and liquidity was at an all time high. Based on our VLRT Analytics, this is a lethal combination signalling a massive bull run, which is what played out. At the time, we spotted something unique, so we started deploying our cash completely. Then, somewhere in the month of May or June 2020, in terms of the Money-Flow Analysis, we spotted that it was the best time to buy mid and small caps. So, we increased our exposure to that market segment when the timing was right and that's the reason you saw the outperformance by around 80 to 180 per cent over the last two years. It was the data points that were supporting me and hence, we took a conscious call of having higher exposure towards mid and small caps till September 2021.
Given that you do run a lot of analytics and base your decisions on the objective inputs you get from the data, can you help us understand where the role of the fund manager comes in?
First of all, we don't use quantitative techniques in the conventional way like doing high-frequency, algorithm-based trading, factor investing, etc. We did employ these in the past and realised that it's not very effective.
What we work on is the concept of 'quantamental', where we try to capture the best of man and machine together. Our core belief is 'Fundamental is the Atman, Liquidity the Prana, Sentiments the Maya'. So, we capture qualitative, quantitative and behaviour research and that is where the multi-dimensional research comes in.
These data analytics support money managers with their decisions. The machine doesn't take any decision but acts as a support function similar to that of a smart and experienced research analyst. We spend a substantial amount of money on technology that helps in faster data analytics.
Apart from numbers, do you have any other checks on the 'look and feel' kind of factors? How do your processes and your investment approach factor in qualitative things which may not be accurately quantifiable?
The look and feel of factors is best captured via our Perception Analytics, it is a combination of a lot of things and it captures the quality aspect quite well. For example, what drives the prices is the demand and supply. But what drives the demand and supply, it is the perception.
To give you an example of the relationship between behaviour research and valuation multiple, most analysts use P/E or any other valuation multiple as a balancing figure to increase or downgrade a target, thereby changing the price target. But we built Perception Analytics, which is quite a complex thing but easy to talk about. When Perception Analytics peaks out, that is a sign that valuation multiples have also peaked out or trading at the upper end of the spectrum. For example, in September 2021, we said the Perception Analytics of global technology or growth stocks was at a multi-decade high. We predicted it three months beforehand. Predictive Analytics is all about connecting the dots on multiple data points and seeing what the picture looks like.
We are actually a behaviour-based research organisation, which tracks participants' behaviour-based data. So, we look at human psychology via behavioural finance, which is still an evolving concept globally. Thus, we keep on evolving a lot of new things to figure out the perspective for various horizons and accordingly take our call.
Are there any pockets or market segments or sectors to which you are generally averse? Are there any negative lists?
Our investment philosophy is very simple - 'Active, Absolute, Unconstrained'. 'Active' is all about the dynamic style. 'Absolute' is our endeavour to give you absolute returns. With regard to the third component, we are a completely unconstrained in our approach to investing. Our opinions change as our data changes; we are a very process-driven organisation. Thus, we are agnostic to any particular style, sector, market cap, etc. This enables superior risk-management and removes biases and emotions from our investment process.
What kind of metrics or triggers do you have to identify mistakes in your portfolio and rectify them? Also, how do you decide the time to exit and move on?
For us, exits are easy. It is a function of three parameters. When my Perception and Liquidity Analytics are at a lifetime high or in the upper-most band for a stock or sector and risk appetite has started declining, that's the time I'll exit it. And the reverse is also true. We work on the entire thesis of Money-Flow Analysis based on these parameters for better stock/sector rotation.
This is the first part of the interview. You can read the second and the final part here.
This interview was conducted in February, 2022.