Fintech and Enhancing Transparency - The Evolution of ALFA
As a large institutional asset manager, pre-trade transparency has always been an important aspect of our business. Trying to manage a large position, clear a block of risk or determine proper valuation are daily tasks. Given fragmentation and other factors, gaining transparency is an even greater challenge in the Fixed Income markets. Additionally, our particular view of pre-trade transparency may be different from other market participants, and as a result, we frequently need to adopt a customized approach to obtaining it.
Looking back, in 2015 a key topic of discussion in the fixed income community was around the liquidity in the Credit markets. We also started seeing a bifurcation of market liquidity. Different intermediary offerings and venues were popping up claiming they had the solution for market liquidity. In response to this scattered approach, we asked ourselves, “How could a trader efficiently monitor these different pockets of liquidity, and gain an efficient, comprehensive overview.” These factors were the basis of the thought process in creating the Automated Liquidity Filtering and Analytics tool, better known as ALFA.”
Rather than focus on interacting in a siloed manner with specific counterparties and venues to source liquidity directly, we decided to focus, instead, on efficient data gathering, aggregation and analysis to give ourselves a deeper and highly flexible view into market liquidity, and to identify in real time specific sources of liquidity. Again, the desire to have broad insight and flexibility was at the heart of the development of ALFA. With greater efficiency and flexibility, we felt we could achieve better liquidity for our clients, particularly since the goal of the ALFA concept is to focus on the entire market – and to include asset holders as well as intermediaries.
ALFA provides the investor with a holistic, aggregate view of the market by providing all the available relevant information to make that investment decision. This can be done on an individual security basis. ALFA also can be employed in what we believe is a more powerful manner, which is to use it for attribute-based aggregation and liquidity sourcing. As opposed to liquidity sourcing based on a specific security or issuer, attribute-based analytics offer the potential to redefine how the fixed-income investor views liquidity.
We officially launched ALFA at AllianceBernstein in October 2015 and quickly learned was there was real interest from other Asset Managers in the capabilities of ALFA and that this technology could be a market solution to enhance transparency and liquidity. So we partnered with a technology company, ALGOMI , to commercialize the product. This partnership has worked well to date, and there are several firms who are currently onboarding the product. We found that this was not only a solution for the credit desk but across other fixed-income asset classes.
Our goal, aside from ensuring we were identifying and capturing liquidity, when it was available, was to increase pre-trade transparency by managing and organizing data flow. To put this into perspective we on average, get 3mm messages a day. That is a lot of data—if we can leverage it. By managing and organizing data flow we get a better picture of the market, and correspondingly investors gain increased confidence in a bonds valuation. With the increased confidence you increase the likelihood of trading, and with increased trading, there is increased liquidity. Within ALFA we have also created our own ALFA price that is presented alongside bond prices from other vendors, - this provides further perspective and validation, which in turn increases confidence and, we believe, liquidity.
In our opinion, the credit markets are going through an evolution. We have seen a more willingness to change over the past two years than in the previous 20. We believe tools such as ALFA will help foster necessary change. The three things that have changed the most at our firm over the past several years have been our investment process, trader skillsets and new styles of trading. We believe others have had similar experiences and that the changes we have seen are made possible by Fintech tools that help us manage, manipulate and view data.
An example of how our investment process has changed is as follows. Previously an analyst would come up with an investment thesis, discuss the idea with the PM, and check the quant analytics then send the order to trading for execution without having deep insight into the liquidity dynamics. ALFA brings the liquidity discussion to the front of the investment process and helps ensure we bring orders to the desk that are actionable.
The skillsets we are looking for in traders is also changing. We target individuals with diverse backgrounds from derivatives, operations, technology or equities. We are beginning to see the equitization of the fixed income market, and we want to have a dynamic and inquisitive trading team. Tools such as ALFA, which provide a broad and objective window on market liquidity to all users, allow traders to focus more closely on other aspects of the trade execution and completion process.
Even how we trade is changing. For larger scale client flow, we are able to engage in “portfolio trading successfully.” Using the analytics and liquidity perspective ALFA provides we are able to trade a diversified portfolio at, or near, mid-market without taking an individual bond or idiosyncratic risk as we deploy capital. This has shown real promise at our firm for facilitating both efficiency of investment and, more importantly, lower transaction costs for our clients.
We believe significant change is afoot in the fixed-income markets and that key to this change is fintech-driven solutions that are just now enabling us to address the differences and idiosyncrasies of the market. A good example of this change is the ALFA system which enables us to efficiently bring the liquidity discussion to the beginning of our investment process, quickly engage market liquidity with high confidence in valuations, and optimize turn-around time for portfolio trading.