01110001 01110101 01100001 01101110 01110100 00100000 01100110 01110101 01101110 01100100 01110011 a.k.a. Quant Funds

This installment is the last in my series on alternative investments. While this series has been nowhere near exhaustive on the subject, it has highlighted a few of the more common investment types in the alternatives arena. You can read the previous issue on private credit funds here and the original overview on alternative investments here.

Origins of Quant Investing, a Historical Context

The beginnings of quantitative analysis, and therefore quant funds, can be traced back to the 1934 book Security Analysis. Written by renowned value investors Benjamin Graham and David Dodd, the book promoted concepts and systematic analysis of securities based on the measurement of specific criteria related to stocks. This analysis was all performed manually, of course, and was time consuming.

Reuters provides some key events in the evolution of quantitative analysis:

1952: Harry Markowitz, an economist at the University of Chicago, develops the Modern Portfolio Theory, which holds that diversification can reduce risk.

1964: William Sharpe publishes a paper outlining the Capital Asset Pricing Model, which separates systemic risk, which affects all securities, from asset-specific risk.

1973: Robert Merton publishes a paper setting a framework for an options pricing model, later called "Black-Scholes" after Fischer Black and Myron Scholes who developed the original formulas.

1987: Some blame computerized "program trading" for exacerbating the severity and speed of the market's fall during the October 19 crash.

1994: Hedge fund Askin Capital Management loses $420 million on bad bets on collateralized mortgage obligations (CMOs).

1997: Merton and Myron Scholes win Nobel Prize in Economics.

1998: Long Term Capital Management, a hedge fund founded by John Merriwether that has Scholes as a partner, loses $4.6 billion in derivatives after the Russian financial crisis.

2007: Some quant funds, including Goldman Sachs' Global Alpha Fund and Renaissance Technologies Corp, suffer large drops as the credit crisis begins to worsen.

2008: Financial crisis and recession strike the world economy, with quants being singled out for the blame.

And that only gets us to 17 years ago. There are advantages to the quant style of investing, but there are risks – as the above illustrates.

What Are Quant Funds?

Quantitative funds, often referred to as quant funds, are investment funds that use mathematical and statistical models to make investment decisions. These funds rely on quantitative analysis, which involves using numerical data and algorithms to identify investment opportunities. The quant fund structure is simple on the surface. Consider this infographic:

Of course, the devil is in the details. While this structure and process is straightforward in appearance, each of the three steps shown above are complex. Over the years their complexity has increased with the rise in computing power.

In basic terms, each step does the following:

• Input System: Required data inputs, such as market and individual stock performance histories, which are analyzed by statistical techniques to identify trends and patterns.
• Forecasting System: Generates estimates of prices, returns and risk parameters based on one or more algorithms.
• Portfolio Construction: Uses advanced systems to construct a portfolio based on predictions made by the forecasting system.

Types of Quant Funds

There are several types of quant funds. Some of them are very esoteric in approach, underlying investment or both. Here are the six most common types of quant funds according to Investopedia and Aurum.

• Single Factor Model: This model filters the investment universe based on a single factor. Examples are valuation ratios (price-to-earnings, price-to-book), quality metrics (return on equity, return on capital employed), and volatility measures (standard deviation, beta). The fund selects the top companies based on the strength of the single factor.
• Multi-Factor Model: These models combine two or more factors to filter the investment universe. Besides those already mentioned, factors can also include valuation, quality, volatility, and other statistical measures. The combination of factors helps in creating a more diversified and robust portfolio.
• Statistical Arbitrage: This strategy involves using price data and its derivatives, such as correlation, volatility, and volume, to identify patterns and mispricings in the market. These funds aim to generate returns regardless of the broad market direction.
• Quantitative Equity Market Neutral: This type of fund uses both technical and fundamental data to identify attractive long and short positions. The primary focus is on relative value rather than market direction, aiming to profit from relative mispricings.
• Quantitative Macro: These funds use macroeconomic indicators and other data to make investment decisions across various asset classes, including equities, fixed income, foreign exchange, and commodities. They tend to perform well in periods of economic uncertainty.
• Quantitative Volatility Arbitrage: This strategy involves capturing shifts in volatility. Funds trading volatility can do so using quantitative processes, and if combined with other strategies, they are typically classified as statistical arbitrage.

The Advantages and Risks of Quant Funds

There is a lot of money allocated to quant trading – well over $1 trillion. Here is a nice infographic showing some of the top quant firms.

According to the Corporate Finance Institute, here are some of the advantages and risks of quant funds:

Advantages

• Quant funds eliminate human judgment, take on neutral bias, and remove prejudice.
• Quant funds charge lower management fees, making them cost-effective due to their passive and consistent strategy.
• Risk control is superior due to a consistent investment model regardless of changing market conditions.
• Fast decision making due to automation of a model that can place orders quickly and exploit gains from thin price differentials more effectively.
• The occurrence of errors is not rampant as in traditional investing.
• Quant funds make use of superior algorithms and the best minds in quantitative analysis to exploit market inefficiencies and achieve alpha.
• Machine learning capabilities of quant models draw insights by analyzing large amounts of data in real-time.

Risks

• Quant funds use historical data, but sometimes history does not repeat itself.
• Quant models need rigorous and continuous back-testing to ensure they continue working as expected.
• Some models do not factor in unexpected circumstances, which can result in undesirable results in the event of a catastrophic event, e.g., a pandemic.
• Too many assumptions are inputted into the model. Some assumptions may not hold if the environment changes, resulting in undesirable buy/sell orders.

Do Quant Funds Outperform Human Run Funds?

Quant funds have outperformed non-quant funds at times. The graph below shows how quant funds comparatively fared from 2010 to 2019:

But 2020 was a different story. Many quant funds were stung by the sudden COVID reversal and ensuing ‘V’ bottom in the markets. As a result, many non-quant funds outperformed them. However, in 2022 when the non-quants generally struggled, many quant funds had strong performance.

So, the answer to if quant funds outperform humans is – it depends.

You knew it was inevitable. I had MS Co-Pilot render an image using the prompt, “The Quant Golden Age Dawns.”

 

Forecasts & Trends is published by Halbert Wealth Management, Inc., a Registered Investment Adviser under the Investment Advisers Act of 1940. Information contained herein is taken from sources believed to be reliable but cannot be guaranteed as to its accuracy. Opinions and recommendations herein generally reflect the judgement of the named author and may change at any time without written notice. Market opinions contained herein are intended as general observations and are not intended as specific advice. Readers are urged to check with their financial counselors before making any decisions. This does not constitute an offer of sale of any securities. Halbert Wealth Management, Inc., and its affiliated companies, its officers, directors and/or employees may or may not have their own money in markets or programs mentioned herein. Past results are not necessarily indicative of future results. All investments have a risk of loss. Be sure to read all offering materials and disclosures before making a decision to invest. Reprinting for family or friends is allowed with proper credit. However, republishing (written or electronically) in its entirety or through the use of extensive quotes is prohibited without prior written consent.