Big Data uses in Financial Trading

7 Big Data Developer Opportunities in Financial Trading

by Rick Martin

In today’s world of financial trading, nothing is bigger than big data, and for good reason. For an industry whose lifeblood is data, the ability to access and analyze huge and ever changing datasets represents nothing less than the new holy grail. Investment institutions are not just big on the idea of using big data to their advantage, they are investing heavily in the technology to make it happen.

What this means for the fintech developer is — you guessed it — opportunity. And what an opportunity it is. Annual revenue for big data and data analytics is projected to reach $187 billion by 2019. Fintechs intent on getting their share of investment dollars must do more than focus on analytics, they must offer big-data solutions that improve every part of the investment process. This includes not only the investment model, but the very business model upon which the firm operates, from investment strategies to marketing to customer engagement.

In this article, we will look at seven of the hottest opportunities for fintechs wanting to penetrate the financial trading market. Development in any one of these areas of technology can translate into market share for innovative developers. Those who can offer solid solutions in multiple areas will enjoy the most varied client base and greater market share.

Algorithmic Trading

The concept of algorithmic trading is rather simple. Computers are programmed to automatically place buy and sell orders based on predetermined conditions. The process of deciding the conditions under which trades are made is another matter altogether. In the fiercely competitive world of financial trading, the rush is on for each trading institution to devise trading algorithms that outperform those of their competitors. Through the integration of trading algorithms with big data, trading automation systems are increasingly making trading decision based on more than stock information. The use of real-time news and social media allows for faster and more accurate trading decisions that are possible with a human broker.

While big data is not a required component of algorithmic trading — trading rules can be based on stock prices, alone, the marriage of big data and trading algorithms makes “algo-trading” synonymous with big-data-based automated trading. From this union of algorithmic trading and big data technologies has emerged several trading strategies and methodologies.

Fintechs wishing to innovate in the explosive algorithmic trading space must not only be strong in algorithm development, they must be, or become, virtual experts on trading strategies. The ability to create powerful algorithms must be coupled with an in-depth understanding of the rules that govern the financial trading process.

High-Frequency Trading

It was inevitable that the speed of supercomputers would be used to facilitate fast — almost instantaneous — automated trading. Hence the emergence of high-frequency trading (HFT). Where a human trader might be able to make a trading decision within a second, at best, HFT platforms, empowered by complex algorithms, can analyze multiple markets at once and make a trading decision in under a millionth of a second. In theory, the faster the trading platform, the more profitable the trader.

Although HFT and algorithmic trading have a symbiotic relationship, with each benefiting from the other, different technologies are involved in each.

Trading houses not only profit from being among the first to make a trade in response to real-time conditions, but exchanges also offer incentives for traders who employ HFT. By reducing the bid-ask spreads that are too small for human traders to affect, HFT improves market liquidity.

HFT is more than high-speed trading, although it is certainly that. HFT platforms also have the power to query and analyze big data sets in order to make intelligent millisecond trading decisions that take into account a myriad of factors no human trader could process.

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HFT opens opportunities for fintechs in the areas of high-speed data transport technology, trading algorithms, big data access, HFT platform development, order-placing technology, and data analytics — which we will discuss in more detail shortly.

Backtesting

Backtesting is a process used to test a trade strategy against historical data. If enough data is available, and the data analyzed from a suitable time frame, the chances of a pending trade meeting the client’s objectives can be increased.

Backtesting can be used to evaluate automated or manual trades. In either case, complex analyses can be performed involving multiple scenarios until a strategy with the desired outcome is identified.

Backtesting provides opportunities for fintechs to innovate in the HFT space, or in the development of stand-alone applications. Those that will excel, here, must either have mature data analytics capabilities in house, or must have an outsource partner with strengths in that area.

Backtesting capability is usually incorporated into a larger platform, but the complexity of backtesting processes opens opportunities for fintechs to who want to focus in this area.

Data Analytics

The ultimate goal of every trader is to be able to predict changes in market forces before they occur. With the amount of data traversing the Internet daily reaching zettabytes, reliable solutions remain elusive. While traders may be eons away from being able to reliably forecast market trends, advances in data analytics are helping them improve the odds.

Data analytics involves taking raw, unstructured data and analyzing it in such as way that useful information can be obtained. In financial trading, the information that is most often sought is that which helps predict market trends, both in terms of the market as a whole, and as it relates to specific stocks.

The technology that drives data analytics is varied, which means more open doors for the innovative fintech. Among the technologies that contribute to data analytics are artificial intelligence (AI), exploratory data analysis (EDA), quantitative data analysis (QDA), confirmatory data analysis (CDA), predictive analytics, and data mining, to name a few.

The data analytics market is poised to top $29 billion by 2019, making it one of the best picks for fintechs entering the financial trading industry.

Business Operations

Investment firms are, firstly, businesses. As such, they are subjected to the same challenges facing every enterprise, from Fortune 100 corporation to the local bakery. The need to improve internal processes, increase operational efficiency, and to make better business decisions is common to them all.

Forward-thinking firms are turning to big data to not only improve their trading processes, but to also affect internal stakeholders, including legal, finance, and sales.

As investment companies become more data-centric internally, the need for powerful tools to optimize business operations will increase. As more firms turn to big data as an asset in organizational management, doors will open for innovative fintechs.

Fintechs wishing to leverage their big data expertise in this area should consider offering solutions that support the three legs of modern business management: analytics, big data, and operations. Together or separate, these three represent avenues of opportunity for the fintech with big data knowhow.

Customer Relationship Management

Customer Relationship Management (CRM) refers to the practices, technologies, and methods companies use to improve the customer experience throughout the sales cycle. Although CRM platforms and software packages exists that do not utilize big data, they fail to tap into the power big data can offer. The trend among financial service companies is to integrate the power of big data with the benefits of platform-based CRMs.

Fintechs looking for opportunities in the lucrative CRM arena can capitalize on using big data to help clients accomplish the following objectives: a more personalized customer experience, improved products and services, and improved self-service. Let’s look at those, now.

Personalized Customer Experience

By engaging the customer based not only on his or her investment information, but also on data derived from social media and other sources, trading houses can significantly increase their own ROI and conversion rates.

Whether customer services is provided by staff or automation, big data can help companies identify customer concerns that may not be apparent from regular interaction with the customer. The more relevant information a company has, the better it is able to serve its customers’ needs from initial contact to after the sale.

Is the customer preparing to send their child to college? Did they just purchase a home? Do they have available credit they may not be aware of? These are examples of customer information big data can reveal to investment houses, and which they can use to better serve their customers’ needs.

Improved Products or Services

Big data can help firms identify hidden customer needs in order to offer new products or services, or to improve existing ones. GE and Verizon are but two examples of mega-companies that use big data to help them identify market needs. By helping financial trading firms benefit from big data, fintechs can help broaden the market opportunities for their clients and for themselves.

Improved Self-Service

Despite frequent customer complaints regarding phone-based self-service, a whopping 91% say they would prefer using self-support, but only if it were tailored to their needs.

Fintechs that can spin big data technology into CRM platforms that meet these and other objectives will dominate the CRM market.

Marketing

It should come as no surprise that big data can supercharge a firm’s marketing efforts by offering a multitude of benefits not available through traditional marketing strategies, alone. Among the advantages big data offers marketing departments is the ability to better understand their target audience, improved price point strategies, and the ability to create real-time personalized marketing campaigns.

Fintech companies that can leverage the power of big data with social media and email marketing will find opportunities to sell powerful marketing products and services to their clients.

In summary, fintechs wanting to develop products or services in the financial trading industry have multitude of technologies from which to chose. Opportunities are there for the picking.

How Ignite Can Help?

Developing big-data products and services for financial trading industry is not a trivial undertaking. The complex and ever-changing landscape requires specialized knowledge and experience in order to succeed — assets which many fintechs do not possess. The key to success in this market is often a technology partner that has the required skills to develop in this lucrative and highly-competitive sector.

Ignite Outsourcing is such a partner. We operate 6 R&D labs across Europe, giving us the capability to meet your greatest challenges head-on. Whether you need assistance with big data analytics, or a CRM platform built from the ground up, we can provide you world-class solutions at outsource rates.

Why not contact us today for a free consultation? Let’s get started.