But in market forecasting, if an algorithm is wrong 5% of the time, it could still be a real problem. Models in the papers we reviewed achieved a very high level of accuracy, about 95% - a mark of tremendous success in many areas of life. Just one version of an algorithm should be tested, which would be representative of a real-world investment setup and, therefore, more realistic. For example, if we run three variants of the same strategy, with one losing -40%, the other one losing -20%, and the third one gaining 20%, and then only showcase the 20% gain, clearly this single result misrepresents the performance of the fund. Running multiple variants, and then presenting the most successful one as representative, would be misleading in the finance sector and possibly regarded as illegal. This approach would not work in real-world investment management, where any given strategy can be executed only once, and its result is unambiguous profit or loss - there is no undoing of results. In almost all the cases, the authors presented their highest-performing model as the primary product of their experiment - meaning the best result was cherry-picked, and all the sub-optimal results were ignored. Our immediate observation was that most of the experiments ran multiple versions (in extreme cases, up to hundreds) of their investment model in parallel. We wanted to determine whether these forecasting techniques could be replicated in the real world. These describe different kinds of stock market forecasting experiments using machine-learning algorithms.

We analyzed 27 peer-reviewed studies by academic researchers published between 20. If AI could invest as well as or better than humans, then that could herald a huge reduction in jobs. What is the reason for this discrepancy? Is it entrenched manager culture, or is it something related to the practicalities of real-world investing? And yet, that doesnвЂ™t seem to be happening. These could in theory translate into highly successful mainstream investment strategies for the financial industry. On the other hand, academic research has repeatedly reported highly accurate financial forecasts based on machine-learning algorithms. Also, despite some of them managing billions of dollars, they remain niche and small relative to the size of the larger investment industry. However, they do not release official performance information. Some specialist investment companies, called quant (which stands for вЂњquantitativeвЂќ) hedge funds, declare that they employ AI in their investment decision-making process. In a new study, published in the International Journal of Data Science and Analytics, we have shed some light on whether AI is any better than humans at making money. But as machine learning is based on analyzing huge data sets and finding patterns in them, and financial markets generating enormous amounts of data, it would seem an obvious match. While simple algorithms are commonly used by traders, machine learning or AI algorithms are far less usual in investment decision-making. As such, AI often appears in behind-the-scenes processes such as cybersecurity, anti-money laundering, know-your-client checks or chatbots.ДЄmong so many successful cases, one seems conspicuously absent: AI making money in financial markets. There is also a rapid emergence and growth of the financial technology industry (fintech), where technology startups increasingly challenge established financial institutions in areas such as retail banking, pensions or personal investments. More and more CEOs in the sector declare (explicitly or implicitly) that they run вЂњ technology companies with a banking license. This rapid technological progress has also had a huge impact on the financial services industry. These include chess, arcade games, Go, self-driving cars, protein folding and much more. Artificial intelligence has now closely matched or even surpassed humans in what were previously considered unattainable areas.

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