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I have worked in quantitative trading for a couple of years so I know what that space is about. I am curious to know what quantitative investing is all about. Based on what I have read and talked to people about, it seems like quant investors tend to build factor portfolios (based on either fundamental or pricing data) and there is a lot of emphasis on portfolio construction and a decent risk model. People who typically work at quant asset managers are econometricians or statisticians as opposed to electrical engineers or physicists in the quant trading world.

Based on my naive interpretation as an outsider, it seems like quant asset managers build long-short portfolios on fundamental factors. Since there is a finite amount of information that a financial statement can provide, how does a quant manager generate alpha uniquely as a lot of these fundamental factors can be easily data mined (to see which ones work )? Not looking for any secret sauce here, I am just trying to understanding how the guys on the quant investment side justify the fee they charge their clients.

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Most of the quant AUM is long-only (even if they build long-short factor models internally). In my view the quant managers have better risk control than many other active managers. The good ones can achieve comparable alpha with lower tracking error, giving them higher information ratios. Also, my understanding is that the fees are typically lower for quant managers than other active managers. – John Dec 12 '13 at 21:45
up vote 4 down vote accepted

Quant investing has the same basic problem as any approach to asset management: capacity for capital invested. Unlike quant trading, quant investing deals with large assets. For this reason, the type of arbitrage opportunities pursued by quant traders are not feasible for investing - those strategies simply do not have the capacity necessary for asset management. Factor models are popular in the field because they have high capacity. The capacity comes from the core of factor modeling; you typically start by ranking all securities by a given factor for inclusion in the long/short side of a portfolio.

So, if all factors are based on fundamentals, how can you differentiate your model from anyone else? Firstly, new and interesting data emerges all the time. One recent example is estimize, which has crowd-sourced buy-side earnings estimates. Secondly, factors can be deconstructed into multiple sub-factors, combined to form composite factors, sliced by time, by industry, by season, or filtered using learning techniques. Or you could look at non-corporate data like the fed beige book, or census data, or social data, or industry specific measures. In short, the range of possible approaches is huge.

In fact, the difficulty in quant investing with factors is that the search space is so large that it is very difficult to find a signal that works without falling prey to over-fitting.

Looking at an example analysis and implementation is often helpful - here is a presentation and sample python implementation for a short-interest factor model (disclosure I work at Quantopian).

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I would like to say the Portfolio Construction thing is weak and quite fake. Too much assumption which is too unrealistic are out there for these econometrician to play around. Think about how the Credit Crisis happened:every body was so happy about using model to combine the risky asset and assumed unrisky assets together to sell it. After the 2008, the model is the one who got blamed. It is ridiculous. Fundamental, long term investment needs more talent and bottom to top way to analysis. If you see it as a math game, you will lose for sure. They just play the limited number and lazy enough to get result quick without thinking. Warren Buffent never has any magic regression, right?

I prefer the quant trading more. They take the risk and make the hard money.

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Quantitative investing is using primarily quantitative information to justify the investment decision. This is different from an investment process which relies primarily on expert judgement.

Example: when assessing a companies financial reporting quality a quantitative investor may rather run a fraud detection model where a non-quantitative investor may rather judge the reliability, character, opportunity and other factors of the CFO.

Quantitative decision making can be applied not only to trading but also to research, portfolio construction, execution and other investment disciplines.

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