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What if, we could combine the tools and strategies from quantitative trading, with the fundamental knowledge of certain assets with the traditional finance methods, to take advantage of new and developing technologies to improve our investing?

In Chapter 3 of “Security Analysis” by Benjamin Graham and David Dodd, the authors advocated for a systematic, detailed approach for analyzing financial data. Clearly, even since the 1930’s when the book was published, professional investors were cognizant of the importance of having a well-developed framework for financial analysis. Every investor has some sort of system or strategy, whether it’s scrubbing through public filings and attending trade shows to get a feel for what’s going on in a specific industry, to keeping up to date on geopolitical happenings to develop a macroeconomic view.

Quantitative Trading

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Quantitative Trading is an umbrella term used to capture any trading system that is used to make trading decisions given some input (Data). Already, we can see some parallels to the systematic trading framework. There are several implementations of quantitative trading, well beyond the scope of this article, and I encourage you to do some more reading (I love QuantStart). I will be going over a simple example, just so we can get a feel for how the implementation of some of these strategies works, but this is by no means an exhaustive list at the myriad of strategies that fall under quantitative trading.

A common factor of many quantitative strategies is exploiting some statistical inefficiency within a specific asset. I have attached a link to a mean-reverting algorithm. At the surface level, this algorithm detects stocks within the US Equities that have deviated from their historic moving average by a predetermined amount and will buy/short the stock within certain risk parameters. Systematic implementation of the age-old adage “What goes up, must come down”.

25% CAGR, not bad for a relatively simple idea

The Merger of the Two

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Take something as simple as writing far out-of-the-money options. Investors already do this to some extent. A quantemental strategy looking at making money off OTM options, could scrub through thousands of equities and their respective options chains, calculate a select few with the highest probability of expiring OTM, filter to those with a favourable risk/reward payoff, and write a large amount of options, before a human investor could even load the option chain. This is one demonstration of the real power behind quanta mental strategies, building upon our finance knowledge and leveraging new tech to capitalize on investment opportunities.

Bottoms-Up Fundamental

Data providers like Thomson Reuters are already providing large datasets from which several fundamental ratios can be calculated. As our datasets become more sophisticated, it is not unreasonable to assume that we could very well one day be able to filter public companies via DCF valuations or relations to peers. Combining a keen eye for statistics and a solid understanding of the underlying finance will prove to be a brutal combination for the fundamental investor in the near future.


Quantamental strategies can be implemented in a macro investment system as well. When looking for historic analogues to current market conditions, what if you could scrub through hundreds of years of economic data, to build a model that will predict future market conditions based on historical examples? Or use big data to detect arbitrage opportunities between credit and debt markets?

The Future of Investing

Combining fundamental investment strategies, with quantitative tools, will be in the necessary toolkit of the investor of the future. As with many industries right now, finance is well-poised to be revolutionized by developments in data analytics technologies, and we should be preparing ourselves by understanding some of the drawbacks to quantitative-only strategies, but learning how to implement some of those tools in traditional fundamental investing methods.