We await SEBI's findings, yet the notion of a top Indian MF engaging in frontrunning is hardly shocking. The industry still struggles to look beyond mere performance figures, presenting the regulator with the formidable task of uncovering the truth. Several months ago, an emerging fund manager in India approached me after successful meetings with family offices, praising the analytics we developed for him. He saw it as an opportunity to present his figures through a statistical, rather than a purely performance-based, lens. We agreed to apply our statistical methods, used in our own indexes and models, to evaluate Indian mutual funds, starting with the Small Cap category—his area of focus. Among the ten funds we analyzed, Quant Small Cap was included. Given our emphasis on dissecting performance to delve into risk and attribution, Quant did not emerge as a top-rated fund, despite its stellar performance.
Upon hearing of the SEBI investigation, I felt compelled to explain why we rated the fund poorly despite its impressive numbers. True irregularities are revealed when performance figures are stress-tested statistically. For our analysis, we examine fund performance since its inception, breaking it down into smaller segments to mitigate starting point bias and provide cross-period insights. This segmentation allowed us to examine thousands of fund NAV segments, revealing several startling findings in the Quant Fund that are not typically observed in our analytics. Below we have carried some visuals and also contrasted Quant’s metrics vs. SBI’s Small Cap, the top-rated fund in the small cap category.
The reasons for our lower rating were twofold: the fund failed the three-year rolling returns test, and its since-inception figures were not the category’s strongest. Notably, most of its performance skew dates from 2021, not extending back to 2016. The fund abruptly moved from a period of underperformance to exceptional outperformance. Performance spikes draw attention both from naive investors and smart regulators.
3 Year Rolling Returns vs. Benchmark
The most astounding observation, which I had never encountered in fund analysis, was a fund achieving high excess returns while maintaining a low tracking error relative to its benchmark. Initially, I attributed this to clustered returns over several years. However, upon closer inspection, another unique attribute of Quant Small Cap emerged: 56.41% of its performance segments exhibited annualized excess negative volatility. This suggests that Quant’s managers possess an uncanny ability—not just to pick excellent stocks, but to choose them at their most dormant. Essentially, Quant’s fund managers might predict risk, a feat akin to solving Maxwell’s Demon’s dilemma by preemptively knowing whether a stock’s volatility will fall asleep in the upcoming period.
Quant Small Cap Performance Segments had 56.41% of data in negative excess volatility
While SBI Small Cap Performance Segments had 13.27% [6.15%+7.12%] data in negative excess volatility
The Annualized Excess Return distributions for Quant defy conventional statistics. They are so flat compared to SBI Small cap that the only way to describe them is non-normal.
Quant Small Cap Annualized Excess Distributions was negatively fat tailed
SBI Small Cap Annualized Excess Distributions was more normal.
Our codebase is open. We invite you to rerun our analysis and correct us if we are mistaken. However, it appears that SEBI's statistical research team is onto what might be a groundbreaking scientific discovery at Quant or maybe something hidden in the plain statistical distributions.
India’s $11 Billion Quant Mutual Fund Faces Regulator Inquiry
AlphaBlock's Sandbox on GitHub
https://github.com/alphablockorg
SBI Small Cap