As the stock market becomes more mature, the amount of human versus regulations in investing increases dramatically. In India, the last two years have caused our markets to mature faster than the entire decade before that. This means, passives are now mainstream. Typically, while passives track an index, active funds try to outperform the index by taking stock/asset level calls.
On the proactive side, most funds operate on human judgment. Active Volume Mutual funds are still a small category (less than 1% of the total assets under management of the mutual fund). These are recent funds that still need to have a proven track record.
Generally, ‘volume’ in India mostly refers to trading tools like high frequency trading (HFT) or technical analysis.
At the institutional level, quant mutual funds use a mix of fundamental filters and technical analysis to build portfolios and sometimes combine human decision making. At higher ticket sizes, there are very few portfolio management services (PMS) or alternative investment funds (AIFs) that use volume to invest.
This is different in the US where one in three hedge funds claim to use some sort of volume to invest. This is because the quant model has proven to be more dynamic. If the right models are created, they should learn and change with market conditions. Quant allows a fund to be a different kind of investor in different markets. Therefore, the two primary pillars on which any good quantity shop should rest are systematic investing and dynamic rules.
main question
Any machine is only as good as its manufacturer. Thus, it is important to ask the right questions to evaluate whether a quant model is built on strong pillars.
Data quality: is it clean, complete and accurate? You should ask or read the documents to understand what the source of the data is, how the fund cleans up for missing, incorrect, nonstandard data, what process the fund follows to verify accuracy, and how much data Happens frequently and update process.
Quality of Models Built: Like human investors, quantity models can be of varying quality. Ask questions about logic, is it dynamic or static, is it rigorously tested, what risk factors are built into the model. For example, if the model assumes a limited downside break in bear markets; To test this, ask for performance data in March 2020 or 2008.
Quality of Back Tests: Since the live history of the model is short, you should ask how the back tests are performed, the companies/time periods considered and the techniques used. For example, whether the tests are blind, that is, when the model is testing in 2019, the system should not contain any information after 2019.
Team Quality: It is important to understand whether the team is stable or has high churn. Also, ‘What is the background of the founding team in machine learning? Have they outsourced the technology? What happens to the technology if the team leaves? ‘ are important.
Qualitative issues: how the machine solves for corporate governance (ie, fraud, operator-operated stock, etc.), management quality, etc. Perhaps the answer is that they rely only on numbers and no qualitative factors – understanding their reasoning, what happens in extreme events, and their experience dealing with fraud at the companies they buy.
Portfolio fit: Whether the fund’s stocks suit your portfolio and risk profile. For example, if it only buys large caps, are you better off with an exchange traded fund?
Time frame: Ask yourself what is your investment horizon. Like any equity strategy, you need to have a 3-5 year horizon for the volume to work in different market cycles.
There may be other challenges with volume investing. For example, in 2010, algos caused US markets to drop 9% within minutes in a “flash crash”. Twenty years ago, LTCM, a quant fund started by Nobel laureates, was shut down after its algo broke during the Asian Games. Financial Crisis. These stories are more high profile because the computer caused these problems.
Nevertheless, quantitative investing will help cover the human blind spots of bias and emotion. Over the next few years, we will see more rules-based products find a place in investor portfolios.
Kanika Agarwal is the co-founder of Upside AI.