• Mark Monfort

Cool we saw your schedule, but what kind of work did you do?

Another day another post but I guess I'm only really able to be less sporadic since I'm not working yet. I have 3 major categories I said I'd write about and this is the first of the Finance related ones so enjoy.

Finance and I have had a bit of a funny relationship. I had the option to choose commerce as a subject in high school but chose against it since I was already doing 2 and 3 unit math (basically standard and advanced) and thought no, that's enough numbers for my liking (how ironic). In university I did a generic science degree my first go around but accounting in round 2. In my final year I became inspired by the Enron documentary and the investigative opportunities of becoming an auditor so managed to score a graduate job at PriceWaterhouse Coopers in the summer of 2007. Not that there's anything wrong with auditing but after 1.5 years I saw that I wasn't quite the right fit for that type of role. I moved on to an analyst type role after that and then became extremely lucky by being introduced to the world of Business Intelligence and the software that not only empowered companies to improve performance but also enabled me to develop the skills to have a well-travelled career in data analytics. Even though my latest role wasn't purely analytics focused, I would not have been able to do it well without the background and training that I'd had over the years. When I joined the QMG team in 2014 they were clearly well versed in the financial markets since one of the founders (Matthew Unsworth) ran Bank of America Merrill Lynch in Australia and the others had a wealth of banking experience both in Australia and overseas. What they needed was an analytics person and luckily I fit the bill pretty well.

So why finance? Or more specifically, why the investments industry? Part of it could have been due to my fascination with movies like Wall Street and the undeniable potential wealth that goes along with it. The other part of it could have been the allure of all the graphs and charts you get to play with every day (I mean who doesn't feel powerful sitting next to a Bloomberg terminal?). But for me, the main reason was the type of work these guys were doing. I came in having a lack of understanding of the investments industry (didn't even know the difference between the buy and sell side) but when I was shown the product that they were selling I could tell that it had the potential to be game changing. So whilst my technology skills certainly could help us move the dial a long way forward I needed a lot of in-house and external training to understand the finance side aspects of the job and whilst I've been able to learn a lot over these few years its still an industry continues to teach me every day.

Image courtesy of Bloomberg

In terms of the work I did there were a number of facets to the role. In terms of the finance focused one it was all about writing equity research. The main audience of this were institutional clients (i.e. hedge funds and asset management companies). My focus was on economic research across 5 major countries. Each month as the statistics departments of each government would release their datasets we would piece them together into an economic model. Using this supply chain we would then work out what parts of the economy were doing good or bad. We also related that research back to specific stocks and other securities where the main focus was to find potential opportunities that our clients could use to make money. There are a wealth of free websites which can help describe the ins and outs of the investments industry and the role of equity research within that but I'll create a post for that another day.

Right now I'll go through the steps that I would typically go through to producing a research note.

  • Firstly, there'd be the release of the economic dataset where we had a team of analysts working to piece these together into a complete data model

  • Upon receiving this completed model (in raw XLS format) I needed to load that data into our database and run the various quality assurance checks on it (most of these steps being automated)

  • The next step was to load that into a pre-built, interactive online report where, via the use of various menus, drop-downs, charts and tables, we would be able to quickly assess the data at hand

  • I would then review the new data with another colleague and roughly draw out which sectors of an economy were good or bad (based on year-on-year % growth) and which were improving or declining significantly

  • We would also look at stocks related to those sectors and the strength of the relationships between our data and what the historical earnings of those companies. This was done via the use of regression and correlation analysis with the idea being that our data gave us semi-predictive power on a company future earnings. If the historical sales growth for a company like AutoNation in the US had a strong level of correlation with our sector sales growth data for US Automotive Retailers then we could reliably use our data to predict the direction of the companies upcoming earnings (example below)

  • Putting this together required discussion of what would go into the report and most importantly, what would we highlight on the front page. We might highlight specific stocks or sectors/industries because they were topical and in the news or we might see an opportunity to be contrarian to what market experts were saying (e,g. be positive on something where the consensus was mostly negative)

  • After piecing the puzzle together we'd go through a few more reviews and finally send it over to compliance. The compliance process is important in any research division and having it made sure that there were no outlandish statements being made in what we wrote and that the writing style would be consistent across the various researcher reports published by the bank

  • Finally, when the report was published the key was to share this with our equity sales team who would speak about it with their clients and also to speak to clients about things directly as well. Sometimes all that would be required was just to give them an update on industries/sectors or stocks they cared about and other times it required digging into and uncovering more details whenever questions were raised

Typically across any investment bank the equity researchers would be broken down into various industry categories. You'd have a team covering financials (banks, insurance companies) or healthcare or mining and other industries. In my role I was allowed to be a bit more general because my focus was on productising the economic dataset that we created. It meant that one day I could be writing about UK milk producing companies like Dairy Crest or US restaurants like Chipotle or McDonalds. In terms of the universe I covered, it looked like this:

  • 5 countries

  • 8 industries

  • 400+ sectors

  • 1000+ stocks

What I've come to appreciate after this experience is that the amount of financial news coming out every day is immense and learning this whilst also being a participant at a research organisation was like drinking water from a firehose. However, it was a 4 year experience I'll continue to be grateful for and would not change at all. I mentioned this elsewhere, but even though at times I suffered feelings of inferiority (comparing myself with colleagues who had 10+ years of experience), it was always nice to take a step back and look at how far I'd progressed in my knowledge and understanding over the years. I saw a lot of opportunities to exploit market inefficiencies but most importantly, I saw a lot of opportunities where just having better technologies in place would have led to much more impactful research and freed up more time for better decision making. In my next role I'm looking forward to helping firms back in Australia see the positive potential from embracing better technology and having had first hand experience of the type of issues they go through, I think this will definitely make for interesting conversations.

Over and out for now.

#finance #equityresearch #macroeconomics #economics #dataanalysis #investmentbanking

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