5 Minute Read
23 October 2020
By Conor Naylor
Quantitative investing involves making investment decisions using a systematic approach – following a routine or structure based on long-term historical evidence. It blends features of the active and passive approaches. A data-driven approach gives investors the simplicity of passive indexing while maintaining the potential for active outperformance.
The concept is fundamentally simple. Quantitative investing involves choosing a basket of stocks based on the rules defined in a strategy. Purchase 25 stocks with the lowest Price / Earnings. This is an example strategy; buy the companies which are cheapest relative to the earnings or profit which they produce. This strategy, between 1964 and 2009, returned an average of 16.25% per year [O’Shaughnessy, What works on Wall Street, fourth edition].
To clarify this idea, let’s take a look at an example in another domain. You want to pick a fantasy basketball team. You have statistical data on every player. The decision is solely based on the data you have – height, weight, average-scores-per-game etc. You are not a basketball fan so you cannot add any of your own qualitative judgement. You use your intuition and base your decision on the players height and average scores per game.
Firstly, filter your database of players by height, selecting those over 6’6.
Secondly, rank your players by their points scored per game, and select the top 5. There you have it – a quantitative strategy to choose a fantasy basketball team.
This is essentially the same approach taken by systematic quants. They are not concerned with the investigation of individual stocks. Alternatively, they focus on the statistical characteristics which best explain returns. We have collated these characteristics into 5 top level factors – value, technical, health, dividend and quality. We will discuss these in a later post. Quants can analyze tens of thousands of publicly traded stocks with a stock screener, in seconds. A screener allows us to get a list of the companies which fit the criteria of a given strategy. At Aikido, we take the latest price and fundamental data from over 70,000 publicly traded companies worldwide.
So how do we decide on which strategy to use in our screener? We want to know how a given strategy would have performed historically. A back-tester provides the ability to emulate the performance of a strategy on historical data, so that we can establish some expectations for the future.
Going back to our basketball analogy, imagine you want to experiment with other variables. Perhaps you want to discover how basing your picks on free-throw-percentages would have performed in previous seasons.
The best-tester implements the screener at various dates in history, which tells us what stocks we would have owned had we been following a certain strategy. We can calculate various performance metrics such as return, volatility and various risk indicators, which we will also discuss in later posts. At Aikido, we are building a back-tester across our full dataset of 40 years of financials. The models aim is to outperform a given benchmark such as the S&P 500, over an extended period. The longer time-frame a strategy is implemented, the higher the probability of outperformance.
Quantitative investing allows us to systematize our investing process, analyze vast numbers of stocks simultaneously and base our decisions on proven long-term factors. The quant models do not succumb to the same emotional biases as humans, they act rationally based on the underlying strategy. Finally, quantitative investing through Aikido is a more economical approach to take, as it does not require you to pay management fees on your assets.
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