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The Magic Of Combination



  • This article uses the Netflix Prize as an example of how combining strategies can lead to improved results.
  • It explains how one can combine various strategies using ranking systems.
  • It gives a concrete, backtested example of how combining systems works.
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The Netflix Prize

The Netflix Prize was a competition begun in 2006 to predict user ratings for films. Competitors were given ratings scrubbed of information about the users and were challenged to find, using machine-learning methods, an algorithm based only on the raw data.

Netflix provided each team with about a hundred million ratings that half a million users had given to eighteen thousand movies. Each rating was a set of four numbers: the user number, the movie number, the date of the rating, and the rating itself (one to five stars). This was the training data; there were out-of-sample groups of data that, if correctly predicted, would then determine the winner of the competition.

It took almost three years before two teams passed the threshold Netflix had set for winning the competition. And the secret to their success was their ability to combine a large number of poorly performing algorithms into one that really worked. As Wire magazine wrote at the time, “The secret sauce... was collaboration between diverse ideas... The top two teams beat the challenge by combining teams and their algorithms into more complex algorithms incorporating everybody’s work. The more people joined, the more the resulting team’s score would increase.”

This was especially true near the end of the contest. At that point, the teams incorporated insights that were marginal and produced poor results. They even incorporated the idea that people tended to rate movies slightly differently depending on what day of the week it was. To quote Wire again, “Taken on its own, the fact that a viewer rated a given movie on a Monday is a horrible indicator of what other movies they’ll want to rent... But combined with hundreds of other algorithms from other minds, each weighted with precision, and combined and recombined, that otherwise inconsequential fact takes on huge

My marketplace service,The Stock Evaluator, relies on 88 different factors to provide a weekly comprehensive ranking of over 4,000 stocks. With terrific out-of-sample results, it will guide you to some truly special stocks.

This article was written by

Yuval Taylor profile picture
Weekly evaluation of thousands of stocks based on sound financial metrics.

I am the author of Zora and Langston: A Story of Friendship and Betrayal, as well as other books. In my spare time I invest, primarily in microcaps; investigate investment conundrums; and write about my investigations on Seeking Alpha and on my blog, http://backland.typepad.com/investigations.

I offer a subscription service, The Stock Evaluator, which sends out weekly rankings for close to 10,000 stocks and also tracks my own portfolio; you can reach it here: https://seekingalpha.com/author/yuval-taylor/research.

I'm proud of my investing track record. In 2016, I made 45% on my investments; in 2017, 58%; in 2018, 14%; in 2019, 16%; in 2020, 105%; in 2021, 73%; and in 2022, 22%. 

Analyst’s Disclosure: I am/we are long BBX, ECOM, III, NRC. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.

Seeking Alpha's Disclosure: Past performance is no guarantee of future results. No recommendation or advice is being given as to whether any investment is suitable for a particular investor. Any views or opinions expressed above may not reflect those of Seeking Alpha as a whole. Seeking Alpha is not a licensed securities dealer, broker or US investment adviser or investment bank. Our analysts are third party authors that include both professional investors and individual investors who may not be licensed or certified by any institute or regulatory body.

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