Law discovery from financial data using neural networks
We describe an experimental study for discovering underlying laws of market capitalization using BS (Balance Sheet) items. For this purpose, we apply law discovery methods based on neural networks: RF5 (Rule Finder) discovers a single numeric law from data containing only numeric values, RF6 discovers a set of nominally conditioned polynomials from data containing both nominal and numeric values, and MCV regularizer is used to improve both the generalization performance and the readability. Our preliminary experimental results show that these methods are promising for discovering underlying laws from financial data.