What Role Does Big Data Play in Flash Trading?

Flash trading it’s an algorithm-based approach to security is treating that is available on many exchanges. The practice has searched in recent years. In 2009, the Securities and Exchange Commission announced that it was concerned about the rapid growth of flash trading in the United States and abroad.

Big data has played a prominent role in the rise the flash trading. A more sophisticated digital infrastructure has made placing high-volume trades faster and easier than ever. High-frequency trading used to be limited to stock exchanges, but forex and other security speculators have begun using it as well.

Role of High Volume Trading on Financial Markets

How is high-volume trading impacting the future of our financial markets? Here are some trends.

Minimizing volatility

Increasing trade volume has made financial markets more efficient than ever before. Higher efficiency has led to a much lower market volatility.

This is creating fewer opportunities for speculators to take advantage of market inefficiencies.

Reducing trading costs

High-frequency trading has also played a key role in driving down training costs. This has improved overall profitability for many traders.

Driving smaller speculators out of the market

Small speculators need to take advantage of market inefficiencies to make profitable trades. Before high-frequency trading was available, they could forecast price movements with charts. It took a lot of skill, but savvy traders that studied market patterns could pull it off.

Since markets are much more efficient today, it is much more difficult for smaller speculators to take advantage of these trading opportunities.

Big Data Creates New Risks for Flash Traders

While big data has made flash trading more viable in recent years, it has also created a host of new risks. Maureen O’Hara and David Easley discussed these concerns in a piece for Financial Times. They point out that big data algorithms have made it easier to analyze data to determine the impact of different market participants. Algorithms can use big data to predict future decisions made by large institutional investors. Tracking and predicting the movements of institutional investors is important, because they are responsible for at least 80% of all trades.

The problem is that institutions also rely heavily on big data to make decisions. According to one metadata ECN broker, they may make irrational decisions off of erroneous information. O’Hara and Easley cite an incident in 2013 when false information caused a market crash.

 “This new form of HFT can go wrong, such as in the so-called “hash crash” of April 23 2013 – the market drop caused by a bogus tweet about a terrorist attack on Barack Obama, sent from the Associated Press twitter feed. Unlike the crash of May 2010, this was not an incident caused by rapid sales triggering more sales. It was not a speed crash; it was a big data crash. Unless regulators understand the difference, they run the risk that new rules may address an old, expired challenge.”

This shows that while big data has made markets more efficient by opening the doors for flash trading, it has also introduced new risks. Markets will usually operate much more efficiently over the long-term, but there may be more outliers caused by short-term events that precipitate large sell-offs. Market participants must be aware of these risks.

Big Data is Changing the Future of Trading for Better or Worse

Traders must keep up with new technology models to maintain an edge in increasingly complex financial markets. Big data has made markets more efficient and introduced new risks. This brings new challenges for traders in every market.

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