[throwawaymath]

This isn't really correct.

Renaissance has always put massive personnel and technology investment into its data processing and analysis pipeline. But there is no "automatic inference" generation. It's not so much brute forcing alpha as it is streamlining the process of hypothesis testing for research scientists so that strategies can be very rapidly generated and examined.

Automatic inferences would be susceptible to two major risks. First, you'd run into spurious correlations at the dimensionality of data we're talking about. Those spurious signals would have to be pruned, significantly reducing any advantage.

Second, you'd decouple the strategy generation from financial domain expertise. The strategies are not developed in a vacuum - contrary to popular belief, quant trading firms do apply financial acumen.

[codingslave]

I work at the intersection of quant finance and fundamental analysis, it can absolutely be automated. The question of what can be determined from raw data like credit card transactions and mobile phone locations is a whole topic in itself, but thinking that you need manual intervention to trade on those signals is completely misguided and its a waste of time for me to argue with you

[throwawaymath]

I didn't say you need manual intervention. I said you cannot do automatic inference generation. What you're referring to does not provide automatic inference generation, i.e. you cannot brute force hypotheses. That's why you still employ researchers.

More to your specific example, I've also worked with the alternative data you're talking about and it doesn't offer automated inference generation. You implicitly have a hypothesis (or several) in mind when you're working with things like credit card transaction data from Yodlee or Second Measure.

Automation is a continuum. What you're talking about is automating time series analysis. I never said you can't do that.

[codingslave]

"you cannot brute force hypotheses", this isn't really true. Credit card data has notorious gaps and bias, but that doesn't mean that an algorithm cannot determine and make decisions about certain situations within that data. For example, if I receive a daily feed file of walmart transactions and the data is increasing in some kind of confidence measure that walmart will beat earnings, I stand to make a good sum by jumping into the market before competitors. It's common that all competitors are aware of the situation, aware of the possible alpha, and competing on speed/accuracy for it. So the superior ability of my model to take a calculated risk from incomplete data (as well as combine other data sources) is one way for me to make money. I may build a model of the common structure of the transactions, ensuring that any signal coming from the data is a real signal, and not one coming from one of the many data quality issues. In the case that my data quality classifier is pushing out high confidence, the result is saying earnings will beat, and other data sources are saying the same, then my model buys. Completing this kind of analysis by hand is too cumbersome (for my usual length of holding period), the money is in who gets there first. There are many ways, some more conservative.