I am a masters student looking for some direction on using neural network on market depth data to help predict market liquidity and bid-ask spreads. Can some of the more experienced people give me some research guidance and guide me to some papers that I should be reading? I have tried Google scholar but can't find something meaningful to kick off my research. Thanks
I agree with all Robert says above, but if you already have the data, and you want to quickly create a neural network model and run the analysis, I would suggest the following:
- The Heaton Site has a Wiki, links to papers, links to books, a forum, etc. that will help you get started, but you might try the PluralSight course Introduction to Machine Learning with ENCOG 3 if you want to get up to speed very quickly as this course shows you how to use Heaton's free software to create a neural network model.
In a few hours, you can get setup with code that looks something like this and have your first neural network analysis:
You can try using different approaches. Starting from something not that "heavy" like the NN.
0) Pre study
- you need to prepare your data
(how you will treat a negative spread (i.e. ASK - BID <0),
what will you do if you will have 0 spread and then you will divide some value by it?),
- plan your research ahead - how will you divide your limited data https://en.wikipedia.org/wiki/Cross-validation_(statistics)
1) Time series models
- check if the bid-ask spread tends to be stationary (you can check it using some extracted moving period from your time series), apply statistical tests
- apply ARMA, ARIMA models for periods which bid-ask spread is stationary
- try to figure out what should you do when the time series stop to be stationary
- try to apply mean reverting models
- after doing this you will have some solid understanding of the characteristics of bid-ask spread models and it will be easier to build NN models.
- this book might be useful http://www.amazon.com/Series-Analysis-James-Douglas-Hamilton/dp/0691042896
2) Neural networks
- try to use different input parameters (i.e. you can smooth the data using moving average)
- try to use also "bid ask volume", "time" variables
- try to use different type of neural networks (different layers) and output neurons and discover which one is the best "out of sample" and "in sample"
BTW what is your goal of the research? Where is the science? What do you want to discover or prove? What is your hypothesis? Answering those questions might help to plan the research in advance.