Category: Big Data

Traders predict the best time to play the markets. Before the technological advances in the twenty-first century, this was based on historical data, gut instinct and sometimes having the loudest voice. Now, it is possible to predict the perfect time to pitch a trader to deposit more funds or upgrade an account to live and automatically trigger messages to trader at these times. This is done with the harvesting and dissecting trading data through predictive analytics.

Analytics and analytics value

There are many analytics tools available. Many popular tools are descriptive analytics, explaining what has already happened. Descriptive analytics tools are useful, but rely on human interpretation, which can be flawed. They also relate to the past and may not be relevant to the present or future.

In a business environment, predictive analytics are more helpful than looking at past events. Using big data and predictive statistical models allows actions to be planned for the future, based on previous trader behaviour. With predictive analytics, past results can recognise future trends.

Using prescriptive analytics with an automation tool enables Forex brokers to automatically trigger messages to individual traders at the right times based on their individual behaviour and criteria.
Used within a sales and marketing department, the use of data insights can help grow business. The simplest example would be a trader on a demonstration site who makes three consecutively positive trades, when this is triggered by your analytics software, a personalised email template can be sent, inviting them to upgrade to a live account.

How big data can help Forex brokers

The impact of big data on Forex brokers is significant. Thanks to cloud computing, software can analyse every individual trade made by the broker in real time. Patterns can be predicted based on the actions of several thousands of traders previously.

Whilst there are mavericks who behaviour in a unique way, generally people follow patterns of behaviour and once these are understood, they can be used to your advantage. Building a reliable model based on past history is the only way to understand this big data.

Whilst the analytics sound fantastic, you don’t have to start with a clean slate. It is possible with the information you are likely to already have on your trading platform. If you can see the number of trades, when they were made, what time of day, by whom, and much more, data scientists and advanced tools can easily use these codes to analyse your customers, allowing you to come up with better ways to sell your services and close deals. Examples include emailing your traders en masse at peak times. You will even have advance warning when a trader is slowing down, or losing interest, and brief contact or even some one-on-one time could prevent them from becoming a former client.

With the right software, you simply connect your trading platform and the analysis takes place in the background, with insights and prescribed actions sent automatically to your sales and marketing teams. There is no negative impact on your current system by having access to big data.

Will data make marketers obsolete within brokerages

Whilst big data analytics can be useful in giving insight into the way your traders behavior, marketers are still required within a brokerage to provide a human touch, providing support to traders who will be content to know that you have the ability to effectively use the statistical analysis capability at your fingertips.

The amount of data collated continues to rise exponentially due to technological advances. This impacts on the way industries operate and compete. Financial services firms are harvesting and leveraging big data to transform their processes to gain competitive advantage.

Big data is especially promising for financial services since there are no physical products to manufacture, making data one of arguably their most important assets. The increasing volume of market data poses a big challenge for financial institutions, from historical data to actively managing ticker data. Investment banks and asset management firms use vast amounts of data to make sound investment decisions. Insurance and retirement firms need to access past policy and claims information for active risk management.

Big data is especially promising for financial services since there are no physical products to manufacture, making data one of arguably their most important assets. The increasing volume of market data poses a big challenge for financial institutions, from historical data to actively managing ticker data. Investment banks and asset management firms use vast amounts of data to make sound investment decisions. Insurance and retirement firms need to access past policy and claims information for active risk management.

What is Big Data?

Big data is a collection of data from traditional and digital sources inside and outside your business that represents a source for ongoing discovery and analysis. Big data is not just online interactions, but includes offline data.

How to categorize big data

Big data is a mix of unstructured and multi-structured data. Unstructured data comes from information that is not organized or not easily interpreted by traditional databases or data models. This includes data from social media, that helps institutions assess customer needs. Multi-structured data refers to a variety of data formats and types including relational databases and spreadsheets.

What are the V’s of big data?

The term “big data” is relatively new, but the act of gathering and storing large amounts of information for eventual analysis is not. The mainstream definition of big data is known as the V’s of big data:

Volume. Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data, now easier to store due to the technology available.

Velocity. Data arrives at speed and must be dealt with rapidly. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.

Variety. Data comes in structured formats including numeric data in traditional databases and unstructured documents including text documents, email, video, audio, stock ticker data and financial transactions.

Algorithmic trading

Together with big data, algorithmic trading uses vast historical data with complex mathematical models to maximize portfolio returns. Computer programs execute financial trades at speeds and frequencies that a human trader cannot. Within the mathematical models, algorithmic trading provides trades executed at the best possible prices and timely trade placement, and reduces manual errors due to behavioral factors.

Effective use of algorithms incorporating big data, including the leveraging of large volumes of historical data to back-test strategies, produces less risky investments. They also help identify useful data to keep and what data can be discarded. Algorithms can be created with structured and unstructured data combined, incorporating real-time news, social media and stock data, to create better trading decisions. Algorithmic trades are executed solely on financial models and data, requiring minimal interaction with human financial advisors.

The challenges of big data

The collection of various unstructured data has raised concerns over privacy. Personal information can be gathered about an individual’s decision making through social media, emails and health records. Within financial services specifically, the majority of criticism falls onto data analysis. The sheer volume of data requires greater sophistication of statistical techniques in order to obtain accurate results. Algorithms based on economic theory are more accurate for long-term investments opportunities due to trends in historical data. Producing effective results for a short-term investment strategy are more of a challenge for predictive models.