Modeling Agricultural Supply and Demand in Soybean Futures

# Predict Futures Prices based on weather + agricultural data


# Predict Futures Prices based on weather + agricultural data












I’m working in the area of Data Mining and have come up with the following idea for my Masters project.The text may not be the best structured but it’s a working draft to give you a quick idea.

Basic Hypothesis,

- Can a combination of (Weather Data + Agricultural Data + Social Media (twitter, etc) data + other relevant data) be used to aid an investor to buy futures of product / commodity ‘X’

- I plan to focus on testing the hypothesis on ‘Soy Bean Futures’. The core idea is to test the approach, even if I fail, its fine. My method / approach must be correct.

Target,

- potential target audience could be investor, govt agencies or agricultural industries

Method,

- Focus on Soy Bean Futures in USA (Worlds largest Soy producer) to narrow down my problem scope

- more specifically on State of Illinois (leads Soy production in US) to zoom in even further

Technique,

- Understand how the model for pricing of Futures works

- Find Historical trading data on Soy Futures in Illinois [from Quandl?] I still don’t know how I will match Soy Future trading data to where it was produced so that’s an issue, I think

- Weather [temp, humidity, sea pressure, etc] & Agricultural [yields, farm sizes etc] data is easy to get & analyze

- Do some number crunching / data mining to test “IF Weather in Illinois affects Soy yields/ production which is turn affects Soy Futures prices” ; I still need to refine the technique but its a rough idea

Your Input,

- what do you think about the whole idea? totally nuts? not realistic? I need to be Math God to figure this out?

- if you think, that this is even remotely feasible, what are my must do’s, must NOT do’s?

- is my technique fully flawed? what am I missing? under-estimating? How can I improve my technique?

## Answer by pincopallino (score 1, accepted)

https://quant.stackexchange.com/a/14807

Rather than "data mining", I'd try to have a more structured approach to the selection of variables / factors: the key drivers in ag prices are, as always in economics, supply and demand.

Supply is, as you say, determined by planted acreage and yields, which in turn depend on weather and other factors (for example fertilization - can't run heavy on Nitrogen for many years on a row). I'd start from the USDA website.

From what I read in your question, demand and its drivers are missing. Luckily, demand is relatively stable when it comes to food consumption. However, corn ethanol in the past years was a big swing factor resulting in a corn madness.

A more structured approach would help you understand and interpret your results, imo. I think it is a great project, but a big project. You could theoretically invest your entire career on this subject.

## Answer by hroptatyr (score 0)

https://quant.stackexchange.com/a/14789

I think this is a good idea; instead of if I'd ask: to what degree though.

Futures prices reflect a lot of factors, so naturally you will only see some correlation which will change over time and even over different expiries (e.g. due to different liquidity in the back months).

My advice therefore: Make sure you understand how the markets work because you will have to explain a lot of, say, oddities on the way. Other than that I think the approach is sound.