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I asked this question here and was directed to answer it on this stack exchange.


My question is very simple. What is the best [known] performance of a stock prediction algorithm?

I've seen papers with up to 76% accuracy. Has anyone [publicly] done better?

PS. Here's one paper that I'm talking about http://cs229.stanford.edu/proj2012/ShenJiangZhang-StockMarketForecastingusingMachineLearningAlgorithms.pdf

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    $\begingroup$ These contests take place under unrealistic conditions, for example the data is known in advance (i.e. in sample testing). They are a good test of machine learning knowledge and are fun to take part in, but not representative of real world investing/trading. $\endgroup$ – Alex C Nov 17 '15 at 23:06
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All of these papers involve some kind of bias. @Alex pointed out rightly, they assume data is known in advance. Further model is tested on the same data on which it is being run. In reality, there is no such algorithm and strategy exist that can consistently outperform the market. Market is always very close to efficient. If someone able to find algorithm to predict the stock market very closely, even then this algorithm would become redundant the moment it gets disclosed.

Note for cautious : Donot put your money into stock market based on the any published work claim to predict the market accurately(even for 90%).

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It's worth noting that prediction algorithms in the Machine Learning literature, if stated formally, usually come with the assumption that the data points are sampled i.i.d. from some distribution. This distribution is badly violated when the predictions are used to take actions in the real world that affect future data.

For example, one might observe an arbitrage in currency prices, then trade on that opportunity, which then removes the opportunity for arbitrage. So the model will perform well, then the distribution will change, and it will stop performing well.

Note that this doesn't apply to say image classification or speech recognition. Predicting that an image is of a cat won't change whether the image is or is not a cat. This is probably a big part of why Machine Learning is so successful here.

On the other hand, there are lots of domains which exhibit the i.i.d. breaking feedback property. Recommendations are one such example. After Netflix recommends a movie to customers, they become more likely to see that movie, which changes the distribution of the observed data.

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Taking into account how markets works is almost imposible to predict their evolution by using any kind of algorithm... but you can use a machine learning algorithm to get positive statistical expectancy over the game so controling your capital and exposure by using using money management and risk management algorithms is possible to design a profitable trading strategy.

My opinion is that there´s no good algorithms, ther´re good strategies combining prediction, money management, risk exposure, timeframes and differente exchanges.

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