The first post in this two part series focused on the competitive dynamics in the financial exchange landscape.For established institutions that have huge early mover advantage, the ability to compete with innovative players by using fresh technology approaches is critical to engage customers.This post will focus on a fresh strategy approach from a business and IT perspective.
Traditional players in the financial exchange market have been taken by surprise by the raft of deregulation that has occurred in the business. New entrants, unencumbered by legacy IT and legacy thinking have focused on innovation. Such business models predicated on agile systems, rapid & iterative development and more importantly – a Data First strategy have helped the upstarts capture a good chunk of market share.
Traditional leaders now only conduct around 50-60% of the total traded instrument volumes worldwide.
(Ref – http://www.statista.com/statistics/270127/largest-stock-exchanges-worldwide-by-trading-volume/)
Before we deliver further into business strategy implications, lets first recap the business landscape from the perspective of the incumbent exchange operators –
- Increased competitive dynamics leading to loss of liquidity and trading volumes
- The use of electronic trading now means that systems match buy and sell orders technology without manual intervention thus taking away trading volume from the above incumbents
- Low cost operations as opposed to a personnel intensive business (this is true as of a few years ago) , running their facilities with heavy reliance on technology & automation thus resulting in minimal headcount
- New model of “Maker/Taker Pricing” i.e just paying members without having them to pay the usual exchange fees to trade on the platform as long as their trading adds liquidity rather than takes it – upends the traditional membership route for the traditional exchange ; (ref – Wikipedia)
- Volume based trading incentives that were initially offered by the upstarts generate higher volumes from select customers
In the midst of all this, the technology landscape is undergoing seismic shifts both from a paradigm and from a culture perspective with five major trends being witnessed in the industry –
- Disruptive technology (namely Big Data, Cloud Computing & Mobile Clients) offers a great way to architect agile and flexible computing platforms
- Exploding data volumes both from a size as well as from a variety perspective – given that social media sources like twitter are now being commingled with existing data assets to create curated ‘data pools’ that can aid in realtime decision making
- Moore’s law continues to push processing power capabilities upward and costs downward. Commodity hardware (x86) based Compute,Storage and Networking approaches dominate from a feature perspective while cutting costs down for enterprise IT
- A new problem-solving mindset buoyed by Data Science and Predictive Analytics is changing the manner in which organizations are harnessing data and making decisions
- The common thread tying all the above approaches together – Enterprise Open Source. Providers beginning with open source pioneer Red Hat have gradually moved from upstart to incumbent in large swathes of the financial industry over the last 10 years. Open Source is mission critical and highly proven in stringent environments (like exchanges) that need near 100% uptime
Perhaps more than any other area in financial services – data is the currency in an exchange. Buy Orders for different financial instruments like equities,bonds & options etc are sent in and matched with Sell Orders. Tick data for thousands of different symbols is provided for market participants as a service & is disseminated to tens of thousands of terminals across multiple markets etc. The data management landscape here has long been dominated by relational database technology (RDBMS) which mandate storing information in a structured format on large & expensive servers.
On the other hand, Hadoop & NoSQL systems can ingest any kind of data and replicate it across clusters of hundreds to thousands of commodity servers thus making data access much more cheaper, more agile and able to support a multitude of processing paradigms. What is more, compute applications (e.g. trade lifecycle analytics, market surveillance, regulatory etc) can directly be run on the datasets themselves thus resulting in architectures that are simple yet highly scalable.
So how can firms begin to incorporate the above ideas into their product lifecycle and create a roadmap for innovation? To analyze this, we will use the Value Discipline framework created by Michael Traecey and Fred Wiersema.
This was first proposed by them in the hugely influential article “Customer intimacy and other value disciplines” published in 1993 in the Harvard Business Review (Ref – https://hbr.org/1993/01/customer-intimacy-and-other-value-disciplines) & elucidated further in their groundbreaking book “The Discipline of Market Leaders“.
They postulate that firms which need to succeed in a tremendously competitive marketplace, need to create strategies in three broad areas (or value disciplines) operational excellence, customer intimacy and product leadership.
Ref – http://www.toolshero.com/value-disciplines-treacy-wiersema/
Operational excellence focuses on providing customers with highly reliable products and services. This makes a lot of sense in the exchange markets where previous flash crashes and interruptions in trading garner not just bad press but also carry severe reputational risk. Investing in technologies proven at web scale(read open source) which guarantee high availability and a superior degree of automation is key. Operational excellence as a discipline is also concerned with creating a culture of unafraid experimentation that constantly helps a product or a process or a service improve customer experience.
Product leadership deals with creating leading edge products and services that constantly disarm slower competitors. Techniques that guarantee that the right data is in the hands of the right employee at the right time ensure that contextual services can be offered in real time to customers. This has the effect of optimizing existing workflows while also enabling the iterative creation of new products and business models.
The third prong of value discipline is Customer Intimacy. This is the most important but sadly a discounted virtue, in this area of financial services, as a result of a perception that exchanges are mere facilitators of electronic transactions. This perception is often strengthened due to onerous regulatory and compliance mandates.
How can customer intimacy be increased both from a macro and a micro perspective? For instance –
• Using the valuable trade, position data that they possess, firms can better segment customers and also create models that can learn & predict behavior from historical & current data
• Obtaining a single view of customer (360 degree view) to hyper-target individual institutional customers with better data products & promotions. These products could ultimately be offered as a SaaS (Software As a Service) over APIs
The core argument proposed by Treacy and Wiersema was that firms should pick one of the above areas to excel at while staying competitive in the other two.
Expanding on the themes above, six key business areas where Big Data can help Exchanges create intelligent platforms & applications –
- Risk & Compliance Analytics – Provide tools to their customers that help analyze regulatory compliance, risk & trading analytics. This is combine their historical data, tick data and provide a service to algorithmic traders & bots. This moves exchanges to becoming more of information brokers. Players may already be doing this in silos but Big Data techniques can help augment and enhance existing approaches.
- Trade Surveillance – This is to ensure that abusive trading practices are effectively monitored and detected – thus avoiding reputational risk.
- Post Trade Analysis – Post trade analytics (contrast tick data with the execution data as a way of showing regulators that you did what you were supposed to). This is also important from an audit trail perspective & also from following appropriate regulatory reporting standards
- Trading Analytics – Analytics for High Frequency Trading by combining data from new information sources like Social media with historical information
- 360 degree view of trades a realtime basis – Financial exchanges make revenue from trading commissions. Understanding your customers across all financial instruments and need to understand which customers are doing most business on the exchanges. Customer fees go down as their volumes go up.
- Risk Management – variety of use-cases here ranging from trade risk management, internal & external reporting
The bottom line -in an industry dominated by speeds and feeds of data, Big Data enables a relook at business and IT strategy. Improving Operational excellence, Product leadership & deeper Customer relationships are all key as they driving increased volume, customer loyalty, revenue and ultimately profitability.
Players can only ignore this megatrend at their peril.