Patrice Rassou, Ashburton Investments’ CIO joins us on this episode of the podcast series. The investment industry is very familiar with the processing and extraction of data in order to make investment decisions. A few decades back this was largely a manual process; now data is available in a digital format with quite a lot of history, allowing for spreadsheets to be built rapidly. There are also alternative sources of data available to triangulate forecasts with what industry experts are saying, thereby improving the quality of these forecasts and also the quality of valuations.

There’s a lot of raw data available, but the real issue is how easy or difficult it is to arrange and process that data in order to be able to extract signals, and how useful these are from a predictive viewpoint. To explore these signals in financial markets, Patrice believes you need to see whether there are relationships that you can observe, and are these persistent and significant enough to make money out of. The volume of data, which is into the billions, really shows that there is a vast field to mine of both structured and unstructured data which requires a lot of computing power to handle the frequency and velocity. The key focus in financial markets now is getting the signals out of this noise. The differentiating factor is making sense of the relationships, and ensuring the data will have a lasting impact that can be explored long-term.

What is alternative data? Instead of using traditional financial information, there has been a whole industry that has sprung up with a swathe of available data. Some examples are satellite imagery that gives data on mobility, traffic outside of shopping malls, ships piling up outside of ports, trucks coming in and out of factories and so forth. There are also social media indicators allowing you to pick up whether your average person is feeling more bullish or more bearish; what their type of buying habits are, what interests them in terms of financial markets chatter, etc. Alternative data used to be quite costly to collect and difficult to integrate, where increasingly there are providers who supply these alternative sources of data, sometimes to the highest bidder to avoid overcrowding.

The world of financial analysis has really evolved from being one driven by humans, to having machines and very sophisticated quantity processes able to handle and analyse huge amounts of data and deliver returns; one Patrice feels is a race between man and machine.

Patrice Rassou, Ashburton Investments&rsquo; CIO joins us on this episode of the podcast series. The investment industry is very familiar with the processing and extraction of data in order to make investment decisions. A few decades back this was largely a manual process; now data is available in a digital format with quite a lot of history, allowing for spreadsheets to be built rapidly. There are also alternative sources of data available to triangulate forecasts with what industry experts are saying, thereby improving the quality of these forecasts and also the quality of valuations.
There&rsquo;s a lot of raw data available, but the real issue is how easy or difficult it is to arrange and process that data in order to be able to extract signals, and how useful these are from a predictive viewpoint. To explore these signals in financial markets, Patrice believes you need to see whether there are relationships that you can observe, and are these persistent and significant enough to make money out of. The volume of data, which is into the billions, really shows that there is a vast field to mine of both structured and unstructured data which requires a lot of computing power to handle the frequency and velocity. The key focus in financial markets now is getting the signals out of this noise. The differentiating factor is making sense of the relationships, and ensuring the data will have a lasting impact that can be explored long-term.
What is alternative data? Instead of using traditional financial information, there has been a whole industry that has sprung up with a swathe of available data. Some examples are satellite imagery that gives data on mobility, traffic outside of shopping malls, ships piling up outside of ports, trucks coming in and out of factories and so forth. There are also social media indicators allowing you to pick up whether your average person is feeling more bullish or more bearish; what their type of buying habits are, what interests them in terms of financial markets chatter, etc. Alternative data used to be quite costly to collect and difficult to integrate, where increasingly there are providers who supply these alternative sources of data, sometimes to the highest bidder to avoid overcrowding.
The world of financial analysis has really evolved from being one driven by humans, to having machines and very sophisticated quantity processes able to handle and analyse huge amounts of data and deliver returns; one Patrice feels is a race between man and machine.

Find out how decision makers are using data analytics to turn insights into action.

RMB’s Head of Data Analytics, Matthew Bernath, talks to business leaders across sectors to explore how data analytics is being used to take the guess work out of decision making.

Find out how decision makers are using data analytics to turn insights into action.
RMB&rsquo;s Head of Data Analytics, Matthew Bernath, talks to business leaders across sectors to explore how data analytics is being used to take the guess work out of decision making.

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