Learning Financial Networks with High-frequency Trade Data.

Financial networks are typically estimated by applying standard time series analyses to price-based economic variables collected at low-frequency (e.g., daily or monthly stock returns or realized volatility). These networks are used for risk monitoring and for studying information flows in financial markets. High-frequency intraday trade data sets may provide additional insights into network linkages by leveraging high-resolution information. However, such data sets pose significant modeling challenges due to their asynchronous nature, complex dynamics, and nonstationarity. To tackle these challenges, we estimate financial networks using random forests, a state-of-the-art machine learning algorithm which offers excellent prediction accuracy without expensive hyperparameter optimization. The edges in our network are determined by using microstructure measures of one firm to forecast the sign of the change in a market measure such as the realized volatility of another firm. We first investigate the evolution of network connectivity in the period leading up to the U.S. financial crisis of 2007-09. We find that the networks have the highest density in 2007, with high degree connectivity associated with Lehman Brothers in 2006. A second analysis into the nature of linkages among firms suggests that larger firms tend to offer better predictive power than smaller firms, a finding qualitatively consistent with prior works in the market microstructure literature.

Metabolic coessentiality mapping identifies C12orf49 as a regulator of SREBP processing and cholesterol metabolism.

Coessentiality mapping has been useful to systematically cluster genes into biological pathways and identify gene functions1-3. Here, using the debiased sparse partial correlation (DSPC) method3, we construct a functional coessentiality map for cellular metabolic processes across human cancer cell lines. This analysis reveals 35 modules associated with known metabolic pathways and further assigns metabolic functions to unknown genes. In particular, we identify C12orf49 as an essential regulator of cholesterol and fatty acid metabolism in mammalian cells. Mechanistically, C12orf49 localizes to the Golgi, binds membrane-bound transcription factor peptidase, site 1 (MBTPS1, site 1 protease) and is necessary for the cleavage of its substrates, including sterol regulatory element binding protein (SREBP) transcription factors. This function depends on the evolutionarily conserved uncharacterized domain (DUF2054) and promotes cell proliferation under cholesterol depletion. Notably, c12orf49 depletion in zebrafish blocks dietary lipid clearance in vivo, mimicking the phenotype of mbtps1 mutants. Finally, in an electronic health record (EHR)-linked DNA biobank, C12orf49 is associated with hyperlipidaemia through phenome analysis. Altogether, our findings reveal a conserved role for C12orf49 in cholesterol and lipid homeostasis and provide a platform to identify unknown components of other metabolic pathways.