when the model is testing in 2019, it must have no information post-2019 in the system. Quality of back tests: Since models have shorter live history, you should ask how the back tests are performed, the companies/ time period considered and the techniques used. For example, if the model is supposed to have limited downside in bear markets to check this, ask for data of performance in March 2020 or 2008. Ask questions about the logic, is it dynamic or static, is it robustly tested, what risk factors are built into the model. Quality of the model built: Just like human investors, quant models can be of differing quality. Thus, it is important to ask the right questions to evaluate whether the quant model is built on strong pillars.ĭata 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, inaccurate, non-standard data, what is the process the fund follows to verify accuracy and how often is the data and process updated. Therefore, the two primary pillars on which any good quant shop should rest are systemized investing and dynamic rules.Īny machine is as good as its maker. Quant allows a fund to be a different kind of investor in different markets. If the right models are built, they should learn and change with market conditions. This is because the quant model has proven to be more dynamic. This is different in the US where one in three hedge funds claim to use some sort of quant to invest. At higher ticket sizes, there are very few portfolio management services (PMS) or Alternative Investment Funds (AIF) that use quant to invest. Generally, ‘quant’ in India mostly means trading tools like high-frequency trading (HFT) or technical analysis.Īt an institutional level, quant mutual funds use a mix of fundamental filters and technical analysis to build portfolios and sometimes add human decision-making.