We suggest a method of inspecting the latent space of stock market data using genetic programming. Given black box patterns and (stock, day) tuples a relation matrix is constructed. Applying a low-rank matrix factorization technique to the relation matrix induces a latent vector space. By manipulating the latent vector representations of black box patterns, the geometry of the latent space can be examined. Genetic programming constructs a tree representation corresponding to an arbitrary latent vector representation, allowing us to interpret the result of the inspection.
['Sungjoo Ha', 'Sangyeop Lee', 'Byung-Ro Moon']
Inspecting the Latent Space of Stock Market Data with Genetic Programming