Driving Trading Efficiency using Data Science

By Craig Foltz on November 27, 2018

 

The world of online trading can be hard to forecast. Long-time investors and traders often embed a kind of murky, undefinable magic within their success stories. There are so many possible factors to consider, many of which arise from a combination of unknown variables, making research-based decisions difficult. However, new strategies arising from the capture of big data and modelling behaviour based on data analytics is here to change that.

This article describes how big data is driving new trading strategies and increasing online trading efficiency.

Making More Informed Decisions

It’s generally accepted that the development of more efficient data capture and more efficient analytical tools has provided businesses with more assets to inform their strategic thinking. The way in which information offers new glimpses into their client database is increasing productivity and allowing business to take full advantage of the entire range of their resources.

"Information is the oil of the 21st century, and analytics is the combustion engine”

  • (Peter Sondergaard, Senior Vice President, Gartner)

The more businesses can usefully utilise their data, the more they will be able to adapt to shifting conditions and make smart decisions about where and how to deploy their workforce. In the world of large-scale online trading, where concrete information can be hard to come by, understanding all aspects of the information that is available becomes even more crucial.

The Creation of New Data

Generally speaking, when people think of data analytics they’re thinking about the ways in which they can interrogate their existing data streams. However, one of the ways in which traders can leverage data moving forward is to think of ways in which to harvest new data. More and more, the collection, definition and organisation of data are going to be the hallmarks that successfully drive trend analysis and allow traders to gain a tactical foothold in the marketplace.

Data creation doesn’t necessarily require new business models. Often, it just involves reimagining information you already classify as redundant and using it in fresh and creative ways. Recent data science innovations (e.g. real-time reporting) can often result in making previously underutilised data more valuable. Traditional model evaluations rely on arbitrary mechanisms and don’t fully impute all variables required to reach a practical solution.

While data science can’t completely remove arbitrariness from the equation, it can open up new veins of strategic thinking. In order to maximise the benefits of data science, organisations will need to adopt strategic, data-driven thinking from the top down.

Increased Liquidity

Online trading platforms are designed to facilitate the simultaneous participation of multiple contributors. The more active participants there are at any given time, the more that volume and competitive tension will be automatically driven by the organic components at play in an online marketplace.

Predictive analytics, a direct byproduct of effective data capture, is encouraging greater online trader participation. One way in which data plays a part in building enhanced customer participation is through the gamification of e-commerce solutions for the next wave of users. Gamification helps get users involved while also running realistic scenarios which measure customer behaviours. The process of unlocking success levels and plateaus within technology has been proven to increase excitement and involvement rates across a wide range of new technologies.

For traders looking for new avenues of trading efficiency, the potential of increased participation and a larger client base are two driving factors that could not be previously be accommodated by traditional transactional models.

Machine learning has come a long way in the past couple of years. Now more than ever, data science can play a huge role in driving up efficiencies for online traders. E-commerce platforms whether they’re B2B or B2C, can make great strides by incorporating data science into their core strategies. 

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Topics: Business, Future, Trends, Food Industry

Author: Craig Foltz

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