Interview: Sylvain Forté, Founder & CEO, SESAMm

What are you most looking forward to at the AI & Data Science in Trading conference?

AI & Data Science in Trading represents a unique opportunity for networking and discovering new approaches. Being part of this conference, which gathers numerous experts & specialists, is a great honour. I am very eager to discover technical use cases, real applications of alternative data in investment. More specifically, I am looking for applications of modern machine learning techniques, including deep reinforcement learning applied to financial use cases. The evolution in datasets used by the industry, such as geolocation for example, and applications of AI & Data Science for fundamental funds are also of high interest to me.

What do you think are the biggest challenges facing data scientists/AI experts/quantitative investors in 2018/2019? Why are they important?

Finding the right data and being able to evaluate and test multiple data sets very quickly, developing real internal pipelines to industrialize this process, and going from around 80 hours of work for each data set to a few minutes – these represent by themselves considerable challenges. Making sure that data scientists/AI experts/quantitative investors take into account and properly address the needs of fundamental investors is also part of these challenges. Succeeding in these will guarantee a significant technological lead to whomever reaches it first.

Looking ahead a year from now, how do you see the structure of your market changing?

We should expect 2 changes. The first change would be the increase of fundamental funds, with many more of them in our market. AI and Data Science will be easier to access and implement and will help them for examples gather new insights, confirm or challenge their signals created from traditional models. The second change concerns alternative data, which should become globally mainstream. Key actors already adopted it but the search for improved returns behind alternative data will be key for its democratization.

What is going to be the biggest area of investment for your organisation/data/machine learning over the next 12 months?

We are currently gaining traction, especially in the US, which creates room for growth and opportunities to improve of our technologies & solutions. We will be investing to have more data, more computing power and more data scientist & quant analysts. This will provide us a wider coverage regarding our types and kinds of sources, faster analyses and shorter development time among other benefits. 

Lastly, we also plan to dedicate more focus on fundamental and quantamental use cases.

What are the most important factors in selecting a new solution partner?

When we are in this process, the most decisive aspect is to make sure that their technical expertise is strong and that the partner is able to adapt to specific needs. We work with machine learning, natural language processing and quantitative analysis technologies and we often require specific skills to respond to our clients’ needs.

Can you share an example of how your system has been used by a new customer? Feel free to include any feedback or practical examples

We created a use case based on our alternative data API (sentiment and emotions) for a large Japanese Asset Manager. We made the data available to the client but also created real investment signals using our internal machine learning and quantitative pipeline. This way our client was able not only to get access to alternative data but also to methodologies for proper implementation. This includes dealing with constraints such as over fitting, high dimensionality etc. and a very transparent sharing of our technologies.

Another example is that of one of our fundamental clients which requested access to precise brands and products related data. We were able to customize the filtering and analysis of data sources to identify long term investment opportunities.

What are the top qualities or skills a quant/PM/data scientist should be able to exhibit?

In my opinion, all of these profiles first need a strong statistics background. The need to deeply understand data and models is greater in the investment world than in any other market. IT competences are of course requested to go from theory to practice in record time. Finally, we greatly appreciate people with and interest for academic research, as a lot of the actual innovation in machine learning comes from universities.

What can be done about the talent war in AI and machine learning and how do you handle this in your organisation?

The market has indeed been challenging but I believe that startups can provide strong incentives to these profiles. This applies to salary, stock-option but also to the work environment, flexibility and ultimately the common goal shared strongly by all employees in a small company. We also provide opportunities to go on international conferences & events related to their field of expertise.

Top tips: How can a quant strategist best engage and support a fundamental business to work together as a successful team?

While quantamental approaches are often mentioned, the real-world applications of this are actually quite rare from our experience. I believe that quants and data scientist should be able to support fundamental teams in the process of evaluating datasets and measuring their relevance initially. Fundamental analysts are then in charge of asking the right question, and proposing a defined format of delivery that suits their needs (visual or not). Their role is also to bring as much fundamental market experience as possible, including in phases that may seem tedious such as annotation. Finally, quants should have a say to final decisions as the statistical validity of alternative datasets must be evaluated for a number of different use cases.

Top tips: How can a Head of Quant or Head of ML best educate their CIO/CEO and Board to maximise budgetary sign off and input?

Speak numbers! Hedge Funds and Asset Manager using alternative data are performing better and raising more AuM. At SESAMm, we actually help our clients build trading use cases so as to help confirm the usefulness of alternative datasets much more quickly. Alternative data is also a formidable marketing tool for investment firms: your clients love AI!

What is your biggest professional achievement to date?

SESAMm has had the opportunity to work with major banks, hedge funds and asset managers worldwide: La Française IS, Société Générale, Groupama AM, Candriam, Nikko Global Wrap, Raiffeisen Bank and many more. We brought real value to our clients and we’re very proud of this. This allowed us to grow quickly and our team now has 30 members and we are already planning to open an office in the US.

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