Is the art of making financial predictions similar to forecasting the weather? One hundred years ago weather forecasting was in its infancy and it was difficult to predict the weather from one day to the next. Weather maps were limited by input data and only a few people may have owned barometers and could tell if the pressure was rising or falling. Gradually the world became more connected via the telegraph, and data could be collected from a few remote locations and the first crude weather maps could be created. However, data for temperatures and pressures over the vast expanses of ocean was still missing from early maps, save for a few radio contacts from shipping, and we know that weather patterns at sea strongly influence what happens on land. Today there is a plethora of data from satellites, aircraft, shipping traffic and automated weather beacons. Even a non expert who sees an animated weather map of the last few days can see whether a storm front or hurricane is heading in their general direction, or in the case of California, if a high pressure ridge is blocking all moisture from ever reaching the state again.
So where are the equivalents of weather maps for the economy and the financial markets? Why can’t we make global or even local maps of money flowing around in our economy? Wouldn’t it be instructive to graphically see which market sectors were in the doldrums and which parts of the economy might be overheating?
In the image at the top of this article NASA scientists have mapped out the world’s ocean currents. We can discern deep and stable flows which carry heat energy to northern latitudes. We also see how turbulence causes eddies to spin off from the main flow, and over time we see which flows are constant and which are subject to seasonal fluctuations.
By comparison, it seems economic forecasting today is where weather forecasting was 100 years ago, still using chicken bones and the color of sunsets to to tell us the behavior of stock markets tomorrow.
Weather forecasting improved when reliable data could be recorded and mapped from around the globe. Market forecasting needs global economic data from every sector. It’s interesting to speculate what data needs to be plotted on a global map: the speed of money (e.g. the volume of shares per day) is an important factor, but also the direction its going (e.g. how much goes from the transportation sector to the energy sector). Imagine seeing a map of the flow between the US and Chinese economies!
It’s not rocket science. You don’t have to be NASA to record financial transactions to make a giant map. The data is out there, it just needs to be mined to extract and present the data in the correct graphical way for everyone to see and intuitively understand. Coincidentally, an article in WSJ just announced an initiative dubbed “SPReD”, which stands for Securities Product Reference Data, in which J.P. Morgan, Goldman Sachs, Morgan Stanley are to form a data company. Maybe they have a similar purpose in mind, but I can’t imagine that they’re going to share the insight they gain with everyone.
On the other hand, data mining on this scale is also Google’s area of expertise. The daily price of shares and the volume traded is already tracked on Google Finance. Even the derivatives of these quantities are available in terms of the number of daily puts and gets on these stocks. Let’s throw down a Google gauntlet and ask why can’t the Google analytic’s team crunch the numbers and display the flow of capital in a more digestible, graphical way.
Large scale data analysis is also something we can crowd source. I’m sure many of you have ideas and opinions of what can and cannot be recorded and mapped. The economy is changing with the democratization of the market with firms like Uber and Airbnb. The next big step in this democratization movement is making market data more readily available and displaying it in a more profound way than merely plotting the DOW Index rising and falling each day. Let’s hear from you.
About the author
Patrick Krejcik is a physicist at the SLAC National Accelerator Laboratory on Sand Hill Road, Menlo Park, California, and also author of the novel Sand Hill Road in which the protagonist is a physicist entering the world of venture capital for the first time.