Big data has its fingerprints on everything these days. It doesn’t matter what industry or business you’re looking at, it’s hard to separate major business decisions from big data. This is certainly true when it comes to finance and trading, where big data’s influence is being felt more and more each day.
Three Ways Big Data is Influencing Finance and Trading
Finance and trading have always relied on robust data and accurate inputs for successful decision making. But as we enter into 2017, it’s become quite clear that big data is revolutionizing finance and trading from the inside out.
Here are some specific topics that need to be discussed in more detail.
1. Technical Analysis
“Technical analysis is the study of prices and price behavior, using charts as the primary tool,” senior market strategist Jeffrey Friedman notes. “Modern-day technical analysis include such principles as the trending nature of prices, prices discounting all known information, moving averages, volume mirroring changes in price, and the identification of support and resistance levels.”
At the heart of any trading strategy is powerful technical analysis that maps out the most likely rate of return and probabilities that specific outcomes will occur. As big data has grown, the accuracy of technical analysis has increased. As a result, traders have found more consistency in their numbers – and therefore, they are able to mitigate risk.
However, we’re just now reaching a point where high-frequency trading (HFT) firms are joining the fold. As InformationWeek’s Greg MacSweeney admits, “The topic of big data in the trading business has often been met with jeers or snickers, since HFT players rely on microsecond latency and utilizing big data usually meant increasing processing time outside of acceptable metrics.”
This is slowly changing, though. HFT firms are realizing that speed isn’t everything. Being able to manipulate data and find palpable advantages is a hugely beneficial differentiating factor.
2. Real Time Analytics
If you’re familiar with algorithmic trading, then you understand how synonymous it’s become with big data. “The automated process enables computer programs to execute financial trades at speeds and frequencies that a human trader cannot,” investor Trevir Nath says. “Within the mathematical models, algorithmic trading provides trades executed at the best possible prices and timely trade placement, and reduces manual errors due to behavioral factors.”
While technical analysis is a major focus of HFT firms, real time analytics has the potential to change the game for individual investors who are looking for the same powerful insights and access that larger organizations have.
The most incredible thing about algorithmic trading is that there are virtually no limitations. Algorithms can be created with both unstructured and structured data. This means they can account for social media activity, stock data, and real time news to make intuitive decisions that take situational factors into account. As these algorithms are tweaked, the industry is seeing an influx of “robo advisors” who are often smarter than their human counterparts.
3. Machine Learning
Big data isn’t just leading to the formation of powerful algorithms. It’s also assisting in the growth of machine learning, which ultimately represents the fullest potential of technology.
With machine learning, algorithms are constantly fed data and actually get smarter over time by learning from past mistakes, logically deducing new conclusions based on past results, and creating new techniques that make sense based on thousands of unique factors.
We’re a long way from having perfect machines that deliver 100 percent accurate insights, but we’re getting closer to a world in which every decision an investor or trader makes is based on millions of data points – and that’s a good thing.
The Growing Role of Big Data
We’ve barely even scraped the surface of the potential of big data and how it can influence finance and trading for the better. In the months and years to come, you can expect to feel the impact in more ways than one.
Technical analysis, real time analytics, and machine learning are just the start.