The IoT has put an array of data before us, but can this information help us understand previously unpredictable trends? As computers get better at connecting all of this information – from common Twitter topics to Google searches – we may be nearing a time when computers will be better equipped to choose stocks than people.
In the past, humans were the brains for everything. They did the research and came up with the ideas for improving marketing stats. They conducted door-to-door surveys to learn information about their businesses. They watched the stock markets carefully to determine the best times to buy and sell.
Humans are obviously still an important part of these operations, but they’re playing a smaller role as big data enters the picture. Now, we have insights on everything from how long a consumer spends on social media to what drives them to make a purchase.
Now, this information is moving away from business topics and becoming a major part of stock market operations. This begs the question: Just how much can big data see? Will it be a better predictor of which stocks to buy and sell than humans?
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Algorithms Have Invaded Global Share Markets
The question of whether to buy or sell now heavily relies on algorithms. This has permeated the stock market and gives calculated suggestions about when the market is up and down. From there, data analysts can make calculated recommendations.
Junsuke Senoguchi, a senior equity strategist at Mitsubishi UFJ Morgan Stanley Securities Co. in Tokyo, created one such algorithm. He developed a robo-advice machine that can suggest certain investments based on a client’s goals and risk tolerances. “I feel I can predict the future,” he says. Senoguchi has used such algorithms multiple times in his trading efforts, finding unprecedented amounts of success.
Senoguchi’s machine is just one of many that can virtually predict the future of the stock market using big data and algorithms. Google Trends is another program that has shown a propensity for helping the beginning investor understand the volatile stock market.
The results of these algorithms are nowhere near perfect, but the data is getting surprisingly more accurate as time goes on. This speaks to the growing amounts of data at our disposal and the unexplored ways there are to use it.
Applications of Stock Predictors
As mentioned previously, algorithms will not render wild success with every use. In fact, there are a number of flaws with the predictions that make even the most experienced of investors skeptical. However, that doesn’t make them useless.
This kind of tool is particularly useful for those just starting out in trading. Investments in penny stocks, for example, could benefit greatly from this software. Penny stocks are infamously volatile, and a decent predictor of what the market looks like for each investment would be ideal for helping beginning traders find more success than failure.
All in all, these big data algorithms can’t predict the future just yet, but they’re surprisingly advanced. It’s a useful help for beginning investors, and it wouldn’t be surprising if the data collection played a larger role in stock predictions and exchanges in the future.
