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Clement Perrette Talks Big Data and the Evolution of Fixed Income Asset Management”

Within the global finance realm, the evolution of technology has always played an integral role in helping investors, and finance professionals, create educated plans aimed at garnering the most positive investment return possible. Hedging the probability of potential loss by garnering metrics, performance history, and other tangible educational tools has historically been a means of providing finance professionals with the confidence to make informed choices. Historically, as communication and technology did not yet allow for seamless real-time global communication, fixed income investments, hedge funds, and other investment products were traded on a predominantly local level, with limited interaction between the niche realms of each product, and differing vastly on a global scale.

Due to the Golden Age of Technology, modern technological advancements have created a brand new globalized finance culture. Within this newly connected industry, investment products began to lean on each others’ successes for inspiring change, building trends, and learning from one another. With the ability to complete transactions, and communicate on a real-time basis globally, finance professionals became armed with the communication capacities needed to gain insights, recommend options and solutions, and generally join forces in an effort to garner successful returns that best suit their clients’ needs.

Simultaneously, as the internet boasted the connectivity culture, the idea of utilizing metrics garnered from Big Data became a highly plausible means of gaining a proverbial “leg up” in terms of fool-proofing investment options. With increased availability of quantitative tools to measure potential successes, market predictions, human behavior, and other valuable tools, the usage of quantitative tools has become the new norm. As globalization and technology continue to shape the investment industry, intuitive metrics can be a pivotal asset in the growth strategy of fixed income asset management. Commonly utilized within equity investments, the implementation and utilization of Big Data in the fixed income asset management niche has not yet reached its peak potential, possibly due to the complexity of macroeconomics.

An expert within the finance field, with decades of pertinent experience within, Fixed Income capital markets, Strips, government bonds and derivatives and later fixed income fund management, Clément Perrette has utilized newly burgeoning technology to guide his decision making, and to spearhead fund growth. Responsible for creating, developing, and implementing the French STRIPS- in its fledgling stages, Perrette utilized traditional data and insight from technology notably in the US market. At the time, without the connectivity of the internet, and in the early days of capital markets e globalization,, garnering such insight required more manpower, thoughtful communication, and feedback from various sources.

At Barclays Capital, in his Rates trading management roles Clément Perrette eventually garnered the title of Managing Director, Head of EUR Rates trading technology enhancement, to improve trading systems, he eventually hired a team to build the first generation of automated market making systems with the help of IA experts. These market making automats are now the norm in many banks. Eventually pivoting into the fixed income asset management sector, Perrette took on the role of Senior Fixed Income Fund Manager for RAM Active Investments. RAM a leading Systematic Equity firm. Within this role, Perrette successfully manages a global fixed income fund, and together with the team is working to implement data science tools in the Fixed income management field. The future of the industry lays in the use of mostly macro-economics data to automate investing processes.

In the realm of private equity investments, the role of Big Data has been somewhat more prevalent, and continues to play a pivotal role in the analysis of public companies. For finance professionals, creating a data-driven model can provide analytical insights regarding the company’s overall “wellness”, curating a profile based on financial data, market data, the company’s web presence, and even patent filings. Through the collection of conventional, and not-traditional data that culminates in an insightful look at the company’s health, financial professionals are able to gain knowledge needed to make sound decisions.

In the beginning of the tech boom, financial institutions identified and analyzed rigid data, or only data that could be presented in quantifiable sets. However, with continued technological advancements, modern analysis of Big Data can analyze a myriad of different unstructured parameters. Not only do these technological advancements allow for different types of data to be analyzed within the equity investment sector, they can also analyze data at incredibly high speeds, allowing finance professionals to act on the garnered data swiftly. In a field where dramatic change can occur seemingly at a moment’s notice, access to changing data can keep finance professionals abreast of any potential upcoming curveballs.

Though quantitative measures are the basis of many investment models, understanding how to best implement that data is still a vastly human job. With a wide breadth of metrics available, the ability to utilize reason, logic, and predictive thinking is needed to organize the data in meaningful ways, and spearhead actionable changes based on the analysis. Thus, even though continuing advancements place quantitative tools at the forefront of the equity investment platform, the future will likely see an effective marriage of Big Data, and human intervention in the investment markets.

For the fixed income asset management niche, the implementation of Big Data has been slower than its equity investment counterpart, partially due to macroeconomics. Until recently, data available for analyzation in regard to government bonds has been somewhat sparse, rendering quantitative algorithms unable to perform their duties due to lack of available data. However, with the general public, and passive investors, demanding increased transparency within the realm of fixed income assets, data collection has become more prevalent in the recent few years, as banks and governments seek to maintain relevance. What was once considered proprietary, is now being publicly available, and therefore, able to be analyzed in a useful manner by Big Data.

On a global scale, various measures are being implemented to spearhead the desired transparency within fixed income asset management. In the United States, the Trade Reporting and Compliance Engine (TRACE) was created for this purpose. Similarly, in the United Kingdom, the London Stock Exchange’s approved publication arrangement (APA) has called for increased reporting of data for the fixed income niche. These measures help to create the data that is needed to populate quantitative algorithms, and eventually provide data-driven investment recommendations similar to those in the equity investment sector.

Of course, as with the equity investment niche, the eventual widespread availability of useful Big Data for the fixed income field will not replace human intervention completely, but rather, will work in conjunction with the intellect, intuition, and nuanced experience of traders. Though the use of quantitative metrics will assist traders in making data-backed rational trading decisions, and in analyzing these decisions post-action, the ability to place meaningful action behind data sets will remain a feat for human reasoning. For professionals within the fixed income asset management field, like Clément Perrette, technological and data investments will undoubtedly change the way in which bonds are viewed, analyzed, and traded.

As more data directly related to bond trading practices becomes available for quantitative analysis, increasingly intuitive data sets will undoubtedly emerge. Much like within the equity markets, fixed income markets will benefit from similar utilization of Big Data for increasing trading successes. However, even when the Golden Age of Big Data arrives in the fixed income markets, the human component will always be highly prized, working in conjunction with quantitative methods, rather than being replaced by these methods.

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