CODEWITHSUNDEEP
machine-learningstatisticslinear-regressiondata-miningdata-modeling
olivia
olivia

Reputation: 407

Why different stocks can be mergerd together to build a single prediction models?

Given n samples with d features of stock A, we can build a (d+1) dimensional linear model to predict the profit. However, in some books, I found that if we have m different stocks with n samples and d features for each, then they merge these data to get m*n samples with d features to build a single (d+1) dimensional linear model to predict the profit.

My confusion is that, different stocks usually have little connection with each other, and their profit are influenced by different factors and environment, so why they can be merged to build a single model?

Upvotes: 2

Views: 172

Answers (1)

B--rian
B--rian

Reputation: 5880

If you are using R as tool of choice, you might like the time series embedding howto and its appendix -- the mathematics behind that is Taken's theorem:

[Takens's theorem gives] conditions under which a chaotic dynamical system can be reconstructed from a sequence of observations of the state of a dynamical system.

It looks to me as the statement's you quote seem to relate to exactly this theorem: For d features (we are lucky, if we know that number - we usually don't), we need d+1 dimensions.

If more time series should be predicted, we can use the same embedding space if the features are the same. The dimensions d are usually simple variables (like e.g. temperature for different energy commodity stocks) - this example helped me to intuitively grasp the idea.

Further reading

Upvotes: 1

Related Questions