Quant Funds 101 – Introduction

Who would be known as the greatest investor in Human history?

Many investors would have one name, Warren Buffett! He has a cult-like following across the globe.

Many hedge fund investors would call out George Soros! His highly profitable & contrarian currency bets have placed him at Mount Everest for his followers.

However, it would be interesting to note that neither of them qualifies to be the greatest investor (if percentage returns were the only criteria). Instead, the pride belongs to a mathematician named Jim Simons. The hedge fund of Jim Simons known as Renaissance Technologies is also known as “the greatest money-making machine of wall-street”. Since 1988, the Medallion Fund of Jim Simons has generated a return of 66% (pre-fees). In absolute terms, Medallion Fund has earned a trading profit of more than $100bn since 1988.

Not just Warren Buffet and George Soros, even investors like Peter Lynch, Ray Dalio, and Steve Cohen do not even come close to beating percentage returns generated by Jim Simons.

How has Jim Simons achieved such a feat?

Simons, a mathematician, saw patterns where other fund managers saw randomness. Jim Simons, along with a fellow mathematician Rene Carmona translated their investment decisions into an algorithm. Further, a team of computer scientists in Berkeley found repeatable & reliable short-term patterns in the market.

Simons started transacting thousands of smaller bets just for a few days before the transactions moved in a favorable direction. The narrower the time frame, the greater the inefficiencies in the market they were able to expose. Eventually, he was able to generate a return of 55.9% in his breakthrough year of 1990.

Simons’ approach was also an outlier in the world of asset and hedge fund management. His approach was so successful that he was also known as the “Quant King”. He attributed his success to starting the most successful Quant fund, Renaissance Technologies. (aka Rentech)

What is a Quant / Quantitative Fund?

Quantitative Fund (or Quant Fund) is an investment fund wherein 1) asset allocation and 2) securities selection is based on statistical and mathematical models. Such mathematical models consider numerical data wide-ranging from historical investments, sentiment analysis to develop trading algorithms.

There are two distinct parts to quantitative investing; research and implementation. Research undertaken may be based on proprietary research or by using published academic papers. Fund managers use research papers to construct a model that will more likely outperform the benchmark index. Moreover, fund managers implement the model by assigning weights to each criterion or characteristic. As per the characteristics, quant fund filters top-ranked securities. Further, similar tests are performed at regular intervals to rebalance the portfolio of securities.

For instance, consider a flexi-cap mutual fund consisting of securities from the NIFTY50 Index. When volatility rises the fund would move towards a higher cash-based portfolio. When the volatility decreases, it could shift the assets back to securities. This is merely an example but mathematical models can be more complex involving bonds, currencies, commodities, stocks, etc.

How different is it from Qualitative or Fundamental Investing?

Fundamental or qualitative investing is generally based on bottom-up analysis based on their forecasts, financial and economic performance. It involves meeting the management, assessing the asset quality, understanding the balance sheet, etc. Qualitative investing also requires fund managers to understand the business prospects and the moats of the company.

Quantitative investing, unlike fundamental investing, does not require fund managers to meet the management, assess the products, and identify the moats. It also often does not care about the balance sheet of the company. Instead, it purely relies its decision on math and data. Fund managers are only looking for parameters that have reliably proven to beat the benchmark returns. Therefore, rather than making decisions on economic forecasts, decisions are made using empirical evidence.

Qualitative investing is primarily based on the discretion of the fund manager. However, a mathematical model buys and sells securities with little discretion of the fund manager.

Types of Quant Investment Strategies

There may be an overlap in the following major strategies of quantitative investment strategies but the core of each strategy is different:

1. Factor investing

Fund managers use factor investing to select one or more factors whose dominance led to certain stocks outperforming the markets in the past. Such factors can either be general or specific factors. General factors include characteristics like growth, momentum, value, market capitalization, etc. Specific factors include ratios like P/E, P/B, return on equity, EV / FCF, etc. Quantitative investing models evaluate securities on various factors (either general or specific). Scores are assigned to the factors. Based on the score assigned to each factor, a stock portfolio of best-performing stocks is selected.

2. Event-driven strategies

Such strategies take advantage of price movement before various events. Such events include earnings release, economic data announcements, regulatory changes, or corporate actions. The event-driven strategy creates portfolios built with particular stocks if the price action of the securities follows a similar pattern.

3. Systematic macro global

As per the Macro global strategy, quantitative funds allocate capital to country, regions, sectors, and asset classes based on the quantitative analysis of a country or a region. Such a strategy is based on data indicating conditions when the geography would perform well.

4. Risk Parity

Risk parity funds balance the risk of the portfolio based on how the asset behaves in different situations. This strategy may not beat the returns generated by equity funds, but it provides a better risk-adjusted return.

5. Statistical arbitrage

Statistical arbitrage is a mean reversion approach where the model looks for mispricing between relationships of securities. Appropriate long and short positions are entered to book profit when the prices revert to mean. Fund managers also use financial ratios to identify under-priced assets.

6. Smart Beta

The investing world uses Smart Beta to manage passive investing vehicles like Mutual Funds & ETFs. Rather than using market capitalization to weight stocks, other factors can be used to improve the risk-adjusted returns of the portfolio.

7. Managed Futures

Managed futures, also known as CTAs, commodity trading advisors, and trend-following hedge funds, use a systematic method to follow major market trends. Traditionally, managed futures funds have focussed on futures trading, but increasingly they are also active in the stock market.

8. Quantitative value funds

Quant value funds use a systematic approach of going through each and every entry in the balance sheet & income and expenditure statement of each company. Based on the reading of the balance sheet & income and expenditure statement, an aggregated value score is then calculated. This score is used to rank and evaluate stocks. This approach of systematic value investing can be very effective, but involves a long-term investment horizon. However, this investing method assumes that the values in the balance sheet and income statement are always representative of the company.

Now, that we know the basics of quantitative investing, the differences between fundamental investing, and various strategies of the quantitative funds, we will understand more about the quant funds in the later articles of the quant fund series.

Drop a comment or write us an email at info@pirimidtech.com for any feedback about this article. Do check out our portfolio in building Robo advisory, Large Scale Trading Systems, Algo Trading, Stock Sentiments, Price Trends Forecasting, Backtesting frameworks, Credit Model, Open Banking, etc. on our website. Connect with us to discover how our Fintech expertise can help you build cutting-edge solutions powered by AI/ML.

This is a guest post by Yash Surana, who is a CA and an MBA (Finance) from SPJIMR, Mumbai, and loves writing about finance, strategy, startups & Personal Finance.













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