Valuation of a fintech company: Lending Club

The objective of this project is to perform a firm valuation analysis of Lending Club, using the Free Cash Flow method and multiples in order to provide insight on the valuation of a company with negative income. Let us know deeply of financial technology (Fintech). Original data was collected via Lending Club‘s website. The FCFF and relative valuation methods were used in the valuation of the target firm. The results reveal that the P/S ratio and EV/Sales ratio are suitable for the valuation of high-tech companies, which are not yet profitable. The most critical factor for Fintech companies is goodwill and the reason why Lending Club suffers a setback is goodwill impairment. Even though a developing industry usually has many problems in terms of supervision, information safety and management, the prospect is still lactiferous. This empirical study helps to understand the benefits and risk factors associated with Fintech and the valuation method for companies with ailing financial health.Este projeto visa realizar uma avaliação da empresa Lending Club, usando o método do Free Cash Flow e dos múltiplos, fornecendo informações sobre a avaliação de uma empresa com resultados negativos. Esta avaliação permite adquirir informação relevante sobre as empresas de tecnologia financeira (Fintech). Os dados originais sobre a empresa foram obtidos através do seu website. Os resultados revelam que os rácios P/S e EV/Vendas são adequados para a avaliação de empresas de tecnologia de ponta, que ainda não são rentáveis. O fator mais crítico para as empresas de Fintech é o goodwill, razão pela qual a Lending Club sofreu um revés na deterioração do seu goodwill. Mesmo que esta indústria em desenvolvimento tenha problemas em termos de supervisão, segurança da informação e de gestão, a perspetiva ainda é favorável. Este estudo empírico ajuda a entender os benefícios e fatores de risco associados às Fintech e aos métodos de avaliação para empresas com problemas de saúde financeira

Inefficiency of K-FAC for Large Batch Size Training

In stochastic optimization, using large batch sizes during training can leverage parallel resources to produce faster wall-clock training times per training epoch. However, for both training loss and testing error, recent results analyzing large batch Stochastic Gradient Descent (SGD) have found sharp diminishing returns, beyond a certain critical batch size. In the hopes of addressing this, it has been suggested that the Kronecker-Factored Approximate Curvature (\mbox{K-FAC}) method allows for greater scalability to large batch sizes, for non-convex machine learning problems such as neural network optimization, as well as greater robustness to variation in model hyperparameters. Here, we perform a detailed empirical analysis of large batch size training %of these two hypotheses, for both \mbox{K-FAC} and SGD, evaluating performance in terms of both wall-clock time and aggregate computational cost. Our main results are twofold: first, we find that both \mbox{K-FAC} and SGD doesn't have ideal scalability behavior beyond a certain batch size, and that \mbox{K-FAC} does not exhibit improved large-batch scalability behavior, as compared to SGD; and second, we find that \mbox{K-FAC}, in addition to requiring more hyperparameters to tune, suffers from similar hyperparameter sensitivity behavior as does SGD. We discuss extensive results using ResNet and AlexNet on \mbox{CIFAR-10} and SVHN, respectively, as well as more general implications of our findings

Charge transport and electron-hole asymmetry in low-mobility graphene/hexagonal boron nitride heterostructures

Graphene/hexagonal boron nitride (G/$h$-BN) heterostructures offer an excellent platform for developing nanoelectronic devices and for exploring correlated states in graphene under modulation by a periodic superlattice potential. Here, we report on transport measurements of nearly $0^{\circ}$-twisted G/$h$-BN heterostructures. The heterostructures investigated are prepared by dry transfer and thermally annealing processes and are in the low mobility regime (approximately $3000~\mathrm{cm}^{2}\mathrm{V}^{-1}\mathrm{s}^{-1}$ at 1.9 K). The replica Dirac spectra and Hofstadter butterfly spectra are observed on the hole transport side, but not on the electron transport side, of the heterostructures. We associate the observed electron-hole asymmetry to the presences of a large difference between the opened gaps in the conduction and valence bands and a strong enhancement in the interband contribution to the conductivity on the electron transport side in the low-mobility G/$h$-BN heterostructures. We also show that the gaps opened at the central Dirac point and the hole-branch secondary Dirac point are large, suggesting the presence of strong graphene-substrate interaction and electron-electron interaction in our G/$h$-BN heterostructures. Our results provide additional helpful insight into the transport mechanism in G/$h$-BN heterostructures.Comment: 7 pages, 4 figure