3 Ways Predictive Analytics and Big Data Can Help Forex Brokers

The purpose of the Foreign Exchange (Forex), the richest market in liquidities, is to trade currency. Each day of trading, fortunes are made, lost or incrementally increased or decreased, depending on the boldness of the trader and the favors of the gods of the market.

However, modern technology has quickly seeped into the financial industry, this time under two shapes. The first one consists of the methods and practices of predictive analytics.

Trading – the Business of Algorithms

Predictive analytics use already known data to formulate a model that can be used to predict values for different or new data. As such, the end-result is a probability of the target variable based on the input variables. Specifically, trading becomes a business of algorithms, custom indicators, market moods, integrated beliefs and more.

The second shape is that of Big Data. These are collections of data sets that may be analyzed computationally to reveal patterns. When the trading markets moved to electronic platforms in the 1990s, they were one of the first big data generators of human behavior.

Buying and selling were no longer a force of nature, an “invisible hand” subjected to its own unknowable whims, but something that could be studied, understood and most of all, predicted.

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Together, predictive analytics and Big Data potentiate one another and give traders, forex brokers and researchers ways in which to understand the market better.

Provides quick answers

Brokers on the foreign exchange market, and through them the financial institutions, are not investors. Their purpose is to assist international trade by providing currency conversions. However, these numerous conversions can also turn a sizable profit. A broker can obtain and hold a position for a very brief time before closing it for a small profit. This is called scalping trading.

To do so, real-time valuable information and forecasts are essential. While a thorough study of patterns and trends can offer an overview of a larger period, predictive analytics also works to provide quick answers to brokers and financial institutions regarding possible short-term trends.

Removes some of the dangers of trading

Aside from registering a financial event, like the buying of a company by another or the bankruptcy of a private retirement fund, Big Data also records everything that goes on in the market before, during and after that moment. This serves as a powerful database that can be studied, analyzed and integrated into future expected patterns of behavior in the exchange market.

As a result, trading using the predicted outcomes provided by analyzing datasets has become somewhat of a norm for Forex brokers. Once a proverbial wild horse defined by uncertainty, the seemingly reliable control established on the foreign exchange market prompted many to dream of getting rich fast.

Although not without fault and certainly not guaranteeing immense profits, predictive analytics remove some of the risks that are inherent to exchange markets. Dealing in a famously volatile environment, small-size brokers and their penny exchanges are hugely advantaged by any sort of forecast that even remotely touches upon reality.

At the same time, unfaithful practices such as mass dumping can thus be easily identified and punished with the help of big data.

Together, these benefits amount to reducing the risks of operating on the foreign exchange market.

Prevents panic-driven crises

Despite the advanced data-gathering tools and sophisticated methods of analyzing it, the shot-callers of the Forex market remain human and thus subjected to the irrational.

Predictive models fall into two categories – classification and regression models. Dealing with numbers and predictions, the second model is the one used in the predictive analysis. Moreover, regression estimates relationships among variables, establishing patterns within large data sets and the intensity with which one factor determines an outcome.

Other models are based on the sophisticated neural networks model or the Bayesian analysis model, each with its method of registering factors, calculating and predicting. This explanatory power used by predictive models of analysis is in stark contrast to human sentiment.

Striving to become logical and thus more secure and profitable through the use of mathematical models of analysis, markets can also be gripped by fear. Fake news, political struggles, terrorist attacks or even armed conflict can drive prices down and remove the willingness to trade, issuing into a crisis. While such events turn the markets upside down, predictive models keep them at a “business as usual” level, ensuring the stability needed for business to thrive.

Conclusion

Data is the unnoticed blood of modern society. It directs the functioning, resource attribution, and prevalence of issues in society. Access to it and knowledge to wield it can mean the difference between resounding success or crushing bankruptcy for companies and institutions regardless of size.

However, as data has its limitations, the predictive models are anything but infallible. One of the limitations is the lag between data collection, input and the creation of the model. Another is that data, like any other resource or product, decays. Certain factors at play during a period may not matter at all in another period.

While automated trading is at present a reality, it will be some time before it will come to dominate the financial markets. Until then, human uncertainty, eased by these technologic palliatives, is both frightening and reassuring.