Trading places: Professor Nikola Gradojevic investigates the impact of private information on foreign exchange trading | Gordon S. Lang School of Business and Economics

Trading places: Professor Nikola Gradojevic investigates the impact of private information on foreign exchange trading

Posted on Friday, April 8th, 2016

All traders in the foreign exchange market have access to public information such as media reports and central bank communications to help drive their decisions. While there is an ever-present risk associated with transactions of any size, using public information alone is often not enough to place traders on the podium of profit, which means the use private information is often involved. The specifics surrounding this private information are somewhat of a mystery to the masses, but research by professor Nikola Gradojevic concludes that it is inherently connected to the location where the trading takes place.

In the article, “Informed traders’ arrival in foreign exchange markets: Does geography matter?” published in Empirical Economics, Gradojevic and his co-authors Ramazan Gencay, Richard Olsen and Faruk Selcuk, present their study that investigates the existence of this competitive advantage among traders with access to local private information. To reach their conclusion, the researchers analyzed massive amounts of data from OANDA, the world’s largest electronic retail trading platform, where individual traders are identified by a unique identification number. This allowed the researchers to identify a sample group of traders that made money every month over a year-long period. In addition to profits, the data also gave them a 24-hour record of when and where traders were active. According to Gradojevic, the consistency of their profit indicates the use of private information.

“When a trader makes profits consistently every month over a one-year span we assume they know something,” he said. “We also look for these informed traders by using a theoretical model and finding a variable called the probability of informed trading. We can identify specific days of a week and hours of a day when this variable is high, and not because of market-wide information available across the globe, but because of the private region-specific information.”

Although the study provides evidence of this information, Gradojevic admits that its assumptions might add more mystery to the issue. Naturally, people could become increasingly interested in the type of private information traders possess. According to Gradojevic, it varies but it could be as simple as having superior knowledge of a specific currency and economic environment or a technological innovation.

“In Japan most currency trading of big corporations that trade internationally go through Japanese dealers,” said Gradojevic. “By seeing these orders, the dealers understand where market will go in the future. When local corporations need to convert currency they approach the local bank. The bank will understand the supply and demand of local currency.”

While this information may be rooted locally, the effects on the market can be far-reaching. Gradojevic points out that if banks infer that an organization is using private information to make a major transaction it can start a domino effect that creates a wave of change across the foreign exchange market, that may, in an extreme situation, result in central bank intervention.

“When a large, well-connected company approaches a bank to buy or sell the USD, it can cause the bank to react by adjusting the exchange rate quotes because they think the company is using private information to trade,” said Gradojevic. “This creates a much broader impact on other dealing banks and causes the market to become more volatile before the price adjusts to its stable, efficient level.”

In the future, Gradojevic hopes to extend this research to consider how events identified through the data connect over time.

“Our research setting assumes independence of information events across time periods,” he said. “Hence, it is worth emphasizing that introducing temporal dependency to the model, where past and present information and actions are interconnected, is a key direction of future research.”

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