Disclaimer – The views and opinions expressed in this blog are those of the author and do not necessarily reflect the views of Scalable Capital GmbH or its subsidiaries. Further information can be found at the end of this article.
My apologies in advance - I think it is fair to say that I am more of a "numbers guy" than a man of long words. However, I have now become one of our blog contributors because there are many topics that we Scalable Capital Quants care deeply about and want to share with you. We have started a new category on the Scalable Capital blog called Quant's Perspective where you can read about our daily work, research, experiences, learnings and opinions. So let me put away my abacus for a moment and grab my pen whilst I try to give you an idea of what to expect on this blog, who the authors are and what our motivations are.
My name is Chris and I am Head of Quantitative Investment Strategy at Scalable Capital. Together with my colleagues (Quantitative Strategists, Quantitative Researchers and Financial Engineers), I work on development and implementation of our dynamic risk management technology. Many of us share an academic background in statistics, financial econometrics, financial mathematics, quantitative finance and other related fields. We have always been particularly interested in data and quantitative modeling, with the aim of achieving a holistic view on financial markets and a strong understanding of statistical methods. But besides our ongoing curiosity for research and applications we also enjoy the teaching aspects of academia. So in some ways this blog is also a substitute for our former teaching positions at university - without the old-school chalk boards that we loved so much, but luckily also without the hassle of marking a mountain of exam papers. Sharing things that we learned ourselves and presenting insights from data analyses is a major motivation for this blog, and we sincerely hope that readers will benefit from the topics we cover.
In our day to day job we design, implement, monitor and challenge systematic investment approaches, with particular focus on the dynamic risk management technology that we offer as an investment service for our clients. It feels a bit like university all over again (this could be due to the fact that I shared an office at university with three of my current colleagues), except all the theoretical concepts have now finally come into action. The academic spirit is the same: the passion to develop new concepts, to understand every aspect of financial decision making, obsession with all the little details (we're not afraid to crack the tiniest nut with a sledgehammer if we expect it to generate the smallest improvement in returns). I think the financial industry is changing, and all of us want to be pioneers in a new generation of asset managers, with new tools and different skills. It's not about being a maverick, taking brave but highly speculative bets, nor making investment decisions based on intuition. And it certainly isn’t greed or the champagne lifestyle that motivates us (Wolf of Wall Street is not a motivational movie that we regularly watch). It is about making investing as convenient, smart and efficient as possible, so that people will be able to finance their children's education and generations of pensioners will benefit from an optimised investment for their retirement. To achieve that goal, systematic, quantitative and evidence-based approaches come into action in every part of our value chain. For example:
For all these components, and any other bits and pieces, our goal is to find an optimal solution that is sufficiently automated to be scalable in such a way that we can offer our service at low cost and make it accessible to a wide audience.
First and foremost the blog will be a reflection of all the things that we do in our jobs on a daily basis. All the steps required to get from blueprint to product should be covered eventually. Further to this, we also research various new concepts or statistical methodologies that might not necessarily come into direct action with our own risk management technology. For one thing, research on systematic investing should always come with a range of validity checks to ensure that algorithms do not just reflect edge case solutions. Perturbations to the actual system should not change results on a macro level. If they did, it would certainly be an indicator of undesired fragility of the investment approach. This means that testing slightly different variations of any investment approach is absolutely necessary. But secondly, for legal reasons we are not permitted to publish backtest results of the systematic trading strategy in use. However, most of our convictions are based on very fundamental concepts (like diversification, systematic trading, management of risks, efficient trading) and hence can be illustrated in many ways. So to keep a long story short, don't expect the blog posts to directly reflect the investment strategy that is used as investment service for clients.
To give a (non-exhaustive) example list of rather straightforward topics that are related to our day to day jobs, we intend to write about:
In essence, we are just curious about almost any quantitative application, data analysis, statistical method or machine learning technique, so don't be surprised if once in a while we slip in something quite different to our day to day job. And since many analyses exist somewhere on the web already, we might occasionally just share resources that we stumbled upon and that we deem particularly interesting and worthwhile.
One further point should be mentioned, however. I am firmly of the belief that we can only live up to our own expectations when we follow a path of continuous improvement in our technical skills, and investment into a cutting edge technical stack. Both data-driven decision making and cost efficient customized investment solutions can only thrive with the appropriate technical setup. Our whole quantitative team is heavily involved in software development and technical details. This is an integral part of our philosophy, as we are strong believers that conception and implementation should not be viewed as separate tasks. The closer you are to the data application itself, the better you can learn from the data and eventually iterate on feedback to drive improvements. Only with strong technical skills can you really become powerful and self-dependent. As such, technical content will also play a role in this blog (as you might have guessed from the title, which reflects the very first functionality created in basically any tutorial of every software language: Displaying "Hello world"). Technical skills are something that we look for in applicants who intend to join our journey. So to kind of set the stage, we will also share technical content related to numerical computing languages (e.g. Python, R, Matlab, Julia), visualization tools (e.g. Jupyter, Shiny), automation tools (e.g. airflow), version control (e.g. git, github), data formats (e.g. SQL, HDF5), cloud architecture (e.g. AWS lambda, Jenkins), DevOps (e.g. IDEs, Jenkins, Travis) and the like.
Last but not least, let me emphasize the usual disclaimer: At Scalable Capital we nurture a culture of open discussions and pluralism of opinions. We are fully convinced that only by in-depth consideration of arguments of all perspectives one will end up in a position that allows one to also make the best decisions. The same yardstick we also apply for this blog and hence we encourage our authors to freely express their own opinions. Hence, any opinions expressed in blog posts might not be fully aligned with those of Scalable Capital or its affiliates.
Disclaimer – The views and opinions expressed in this blog are those of the author and do not necessarily reflect the views of Scalable Capital GmbH, its subsidiaries or its employees ("Scalable Capital", "we"). The content is provided to you solely for informational purposes and does not constitute, and should not be construed as, an offer or a solicitation of an offer, advice or recommendation to purchase any securities or other financial instruments. Any representation is for illustrative purposes only and is not representative of any Scalable Capital product or investment strategy. The academic concepts set forth herein are derived from sources believed by the author and Scalable Capital to be reliable and have no connection with the financial services offered by Scalable Capital. Past performance and forward-looking statements are not reliable indicators of future performance. The return may rise or fall as a result of currency fluctuations. Please refer to our risk information.
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