Any ML algo is only as good as the data we feed it, so it needs to be high-quality data. While sentiment analysis appears to be a productive predictor.Īlternative data is attractive, but for ML to be effective, data sets need to be very large with a long history. Credit card data and footfall data are being used in equities. In others, satellite imagery is being used to assess crop yields in commodity trading. In one well-known example, a fund used flight tracking data to predict a merger. There are many examples of how alternative data is used. Machine Learning used is with alternative data to find new signals or enhance existing ones. The World Economic forum believes we will create 463 Exabyte’s per day by 2025 ! The internet only created one Exabyte a day in 2012 … An Exabyte is a 1 byte followed by 18 zeros!! One poll found 69% of funds are already using alternative data. ML allows a quant to look at far more data in a shorter period.Īlternative data will grow over the next ten years, especially when you consider the quantity of data we create. It has made the accumulation and exploration of data far easier. Machine Learning is most effective at improving parts of the trade life-cycle process, such as data processing & modelling, forecasting & signal research, risk management and execution.ĭata processing & modelling have benefitted from Machine Learning. Some say we’re right at the peak of inflated expectation according to the Gartner hype curve. Machine Learning techniques are statistically driven and have been used by quants for a long time.Īdvances in computer processing power, availability of big data and media attention have created hype. But, while perceived as magic to some, both are rooted in mathematics. Whereas, Machine Learning is a subset of AI, and is the ability of a machine to learn from data with no explicit programming.ĪI and Machine Learning are hot topics in quant trading and can feel new areas. AI is the whole collective conception of a computer being able to think like a human. Here I describe how AI and Machine Learning are used and how it will affect quantitative trading in the next 10 years.įirst, there is a difference between Artificial Intelligence and Machine Learning.
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