• Mark Monfort

Lessons from the Summit

Hi everyone,

I wanted to share some key themes I saw from the latest Investment Data & Technology Summit held on Wednesday August 21. It was a great event and the first one I got to attend so a big thanks to the organisers at FundBusiness (Lawrence Jarvis and team).

The talks were great and consisted of a mix of panels and single speaker presentations. The panelists really complimented each other well and we got a range of different views about the state data in the investments space (including where the hype is/isn't).


Active vs Passive

Passive works well in bull markets. But when bull markets are coming off it will suffer and you need good money managers who can better handle risk. Humans are better than machines at regime switching anywayPassive has its limitations because of its rules base - it is routineRoutine means machines and leads to lowest cost producerPositive way to counteract Passive pressures is to look at a behavioural approach to the markets - e.g humans will crowd as there is safety in numbers and will also be prone to overreactions


Beware the man with the complex algorithm

Applying market knowledge to the data

Machines behave how you program them to behave

Change / culture

Change happens slowly then all at once. One day we're using the same fundamentals, the next (and all of a sudden), firms have a machine learning strategy.Despite all this, it is best to get base level right first and set yourself up for future success.Culture is the overriding layer - this starts from the top and without support from management, the project will not work well.Change management and people management can be a stumbling block


More and more are moving things into the cloud - just not in fullMost who have taken the leap are doing a hybrid cloud approach

Data Analytics

Many are getting started and either hiring people to help set the strategy or have tasked departments to do so

Data Governance / Data Lineage

Need good lineage/source knowledge of your data. Not for when things are going well, but for when things go wrong. E.g. data provider like Bloomberg publishes wrong prices - do you have backups or good understanding of your data to be aware and not make decisions off of bad data

Data Literacy

Most leaders in this space are not data literate so lots of education still requiredMost firms see themselves doing this from an internal perspective first and bringing in consultants/outside experts as needed

Data Virtualisation

No one is doing this yetStill need to solve basic problemsOnce done these can see value in doing data virtualisation

Factor Investing / Factor Analysis

Factor investing is popular but when many are doing it, you end up looking at the same factorsNeed to come up with own factorsSee Taming Factor Zoo article

Predictive models

Don't try to predict share price moves / better off predicting factors / returnsBetter off with creating strategic optionality with your data analytics projects - don't try to create predictions - just build good quality tools with flexibility to give you options for later

Quant activity

Japan seeing most activity in APAC region

Quantamental analysis

Firms are seeing quant work as a strategy enhancement. Running a screen over your data to point you towards areas of interest in the data but you still need stock picking skills after that. A mix of quant plus fundamental is keyNeed to set realistic expectations for quant investing/analysis - cannot expect a finely tuned algorithm run well in all conditionsStill need human factorsIn a way, this is becoming a crowded trade itself as most are following the same kind of patterns and looking at the same data so they end up following each other. Key is to stand out from the crowd and be different

Selling data solutions

Firms still not listening to buyers as well as they canNothing is good unless it provides buyers what they are specifically asking forGood data delivered fast and timely overrides good features


They should come with health warningsCan be used in the wrong way so need to understand what they're doing and have common sense approach

Understanding data

It was easier before when you just had SQL databases and needed to understand data. Now you have advanced databases (noSQL types) and machine learning type black boxes (Tensor Flow)


Important to have this as part of your analysis as you can see much more quickly and spot patterns

Anyway, a lot to digest here and certainly not the only lessons that came out of the talks and conversations that I was able to have. However, it was great to hear what people had to say, see where they are up to and from all of this I can see that the industry has its sights set on conquering the challenges that data presents. It's a lofty goal but if this conference is anything to go by, many firms on all sides of the market are going to be successful.

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