| | March 20158CIOReviewopinionin myI Feel, Therefore I AmBy John Bates, CMO, Software AG Although the quest for Artificial Intelligence (AI), equipping trading algorithms with human qualities such as self-learning, continues to fascinate, it will be the explosion of the Internet of Things that will soon re-energize trading in capital markets. The Internet of Things (IoT) is rapidly growing through the addition of sensors to machines that allow them to "feel." Once they are equipped with feelings-- particularly sight, sound and touch--machines can behave more intelligently, for example optimizing operations to use less fuel or predicting when they need maintenance. However, an interesting side effect is that the data from the IoT could be a new source of "insider" data for trading firms. For example, if combine harvesters (accessorized with sensors) signal a bumper wheat cropin the U.S. grain belt, traders can take advantage of this information before the crop report is issued. Agriculture is a poster child for IoT innovation; machines like tractors and combine harvesters are increasingly equipped with sensors to communicate crop and harvest data. U.S. equipment manufacturer John Deere's Field Connect monitoring system uses the IoT to give farmers more visibility into their crops, sensing soil and leaf moisture as well as temperature, wind speed and direction, solar radiation, and rainfall. The monitors are installed in customer fields as part of the Field Connect Gateway, which feeds the information to farmers and allows them to make informed decisions. All of this IoT information is valuable to farmers, who learn better moisture-management skills. But it can also be useful to commodities analysts or breakfast cereal manufacturers, who can profit from early knowledge of corn or wheat crop conditions. There are other sources of IoT data too. The increasing prevalence of location-enabled smartphone apps for an enhanced retail experience means that more and more information about the movement of consumers and their buying patterns are becoming available. Similarly the growth of social media provides a voice to the masses--and tapping into that voice provides something of a global sentiment on a number of topics. Imagine if these feeds could also be accessed and combined into trader analytics.Let's say you are a stockbroker; if you could use social media data to determine consumers' moods, you might be able to predict their appetite for certain branded breakfast cereals. If you can tap into supermarket buying patterns then you could see how cereals are performing. You could combine these streams with the agricultural sensor information to provide certain insights. You might see for instance that sentiment is positive on a leading breakfast cereal "Corn Nibbles." Also, a promotional marketing campaign for Corn Nibbles at a large supermarket chain has gone swimmingly.Furthermore, IoT data from combine harvesters tells you that the yield on the corn harvest will be quite low this year. However, right now the price of corn futures is low so it is a good time to buy. Then, you can also buy the stock of the manufacturer of Corn Nibbles Inc. in readiness to sell just after quarterly earnings are announced (which will be good), because you know that the low corn harvest data will soon be announced and will inevitably hit the stock price.Mining Social Media for SentimentSentiment analysis--or opinion mining--already exists, although it is in its infancy. In 2014, during John Bates
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