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A New York Times article covering the latest Triple Crown horse race winner, American Pharaoh, noted that the horse was identified as having an amazing potential when the animal was only 1 year old. The prediction of success was made by a team of data scientists who estimated the horse’s performance by noting the size of the winner’s heart and other characteristics and comparing them with those of past race winners. On the future potential of the horse, the data scientists advised him, “to sell the house, but keep the horse.” Their prediction paid off – American Pharaoh won. The real victory, however, can beRead More →

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In: Big Data, HFT, Risk
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Markets are bursting at their seams with financial data. Much of the data that researchers are now mining is called Level I, II, and III data; this data comprises information on orders, executions and cancellations across different price levels. Until recently, such data was scarce. Today, it is more accessible, but still little understood. The not-for-profit Big Data Finance 2015 conference taking place at NYU Courant on March 6, 2015, will present selected techniques and results of big data analytics applied to Finance. This article briefly explores a data phenomenon. The relationship between returns and order placement and cancellations is examined and the following findingsRead More →

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In: Big Data, HFT, Regulation, Risk
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When you trade, do you place market orders, limit orders or a combination of both? Do you or should you care? The answer is yes, you should care, particularly in today’s volatile markets, and this article explains why. When investors place a market order, say, an order to buy 100 shares of IBM at market, the investors are trying to tell the exchanges: “buy 100 shares of IBM for my account at the best available price.” However, by the time the order is transmitted to their broker and then by their broker to the exchange, the best available price may have drifted away. The largerRead More →