Emerging Currency Markets to Shift from Series Models to Deep Learning

Industry analysts agree that artificial intelligence and big data have had a profound effect on the global financial industry. The financial analytics market is projected to reach $11.4 billion within the next three years. This statistic has been cited numerous times since it was first published by MarketsandMarkets.

As compelling as this statistic is, it glosses over many of the nuances pertaining to data analytics and artificial intelligence in the financial sector. One discussion that warrants more attention is the growing relevance of deep learning in the currency markets of emerging economies.

Deep Learning Shows Promise for Emerging Economies

A 2017 study published by the Termopil National Economic University in Termopil, Ukraine focused on this emerging topic. The study, titled Deep Learning for Predictions in Emerging Currency Markets talked about the role of artificial intelligence algorithms in currency markets in Africa, Eastern Asia, South America, the Middle East and remote parts of Europe.

Machine learning methods such as shallow neural networks have higher predictive accuracy than time series models when trained on input features carefully crafted by domain knowledge experts. The preponderance of research focuses on developed currency markets. The paucity of research in emerging currency markets, and the crucial role that stable currencies play in such economies, motivates us to investigate the effectiveness of deep networks for exchange rate prediction in emerging markets, the authors write in the introduction of their paper.

To the layperson, this research might sound most promising for forex traders. However, the authors of the study emphasize other benefits with wider implications. Policymakers need to have an accurate understanding of the direction of global currency markets. This is vital if they intend to implement sensible monetary policy. Of course, forex traders also can benefit from this evolving technology.

Most previous research focused on currency markets within developed economies. This tended to create an imbalance in trading opportunities and a deficiency in understanding of fiscal policy for emerging markets. It might be useful for traders looking towards brokers using MT4.

As the authors point out, before the adoption of deep learning technology, currency markets were modeled almost exclusively with time series functions. Time series models provide somewhat reasonable baselines for future market prices. Unfortunately, they give highly inadequate insights for market analysts and fiscal policy experts trying to identify long-term trends in the market.

Artificial intelligence has been utilized as an alternative for forecasting currency prices. The first AI models that were used in place of time series models were artificial neural networks. Four independent studies dating back to 2002 found that artificial neural networks were more accurate for forecasting currency prices then timeseries equations. Unfortunately, the inaccuracy rate was still far too high with regards to long-term trend projections. They were even less accurate for forecasting trends in emerging currency markets, due to the more limited availability of market data.

Deep neural networks have proven to be more robust and reliable. Although they were first introduced almost 50 years ago, new advances in artificial intelligence have made them far more impactful. They have proven to be extremely useful in forecasting trends in currency markets within developed economies. More recent research has indicated that deep neural networks can be extremely reliable predictors for emerging currency markets as well.

Some experts had previously been skeptical about the capacity to identify the long-term direction of currency markets within emerging economies. In 2006, a paper by the European Central Bank talked about the currency crises within emerging economies. The authors raised concerns that it might not be possible to identify these issues before they surfaced. Of course, this paper was published two years before the global recession struck, which made financial experts even less confident in their ability to identify financial prices even within developed economies.

When that paper was first published, artificial intelligence technology applications in the financial industry were still in their infancy. More recent advances have demonstrated that they can be vastly superior to the time series models that we have relied on. This might ease fears about unexpected currency crises transpiring, both in developed and emerging economies.

Deep Learning Could Solve Countless Currency Challenges within Emerging
Economies

Many emerging economies are struggling to modernize monetary policy as they become more impregnated in the global currency markets. New advances in deep learning appear to offer many solutions that they depend on.