Week 3 round-up

In Week 3, we began to explore how big data might help us understand and even predict behaviour in the stock markets. Here’s a brief summary to help you prepare for Week 4.

Gene Stanley talked to us about how he and his colleagues use big data in combination with approaches from physics to help us understand infrequent but catastrophic stock market crises. You also learnt how data on information flow via the Financial Times, Wikipedia and Google can be linked to trading patterns in financial markets. Finally, we described our own findings which suggest that changes in searches for financial and political information on Google and Wikipedia may have contained early warning signals of stock market moves. Please do heed our warning to be very careful if you’re considering trading yourselves however!

You’ve come up with some excellent suggestions of other data sources that might offer insights into stock market movements – well done. We’ve also seen some great discussions of what might make certain kinds of collective behaviour easier to predict than others. We’re really delighted to see you all getting such a great grasp on this material.

Keep up the fantastic progress with your own analyses of Wikipedia data in R too! We know how useful these skills are in a wide range of areas, and so it’s particularly exciting for us to see you all picking this up.

You’ve been doing a brilliant job of helping each other with any error messages you’ve received while working with R. To make it easy for others to help you find the problem in your code, it’s always a good idea to detail all of the commands you typed in for this exercise before the error occurred, as well as the exact error message R has given you. You might also be surprised at how good Google is at decoding error messages in R and offering useful advice, if you try just copying the error message and pasting it in as a search query – it’s quite possible that many others have seen your error message before.

We very much hope you’ll find that you can build on the practical skills you’ve learned here following the course. Enjoy this week!