Essays on banking and financial innovation

This dissertation consists of three chapters. Chapters 2 and 3 examine the ex-ante motivation and the ex-post impact of securitization. Departing from the traditional literature of bank-specific drivers for securitization, I investigate the tax incentive for securitization in a cross country setting. In addition, unlike the prior micro studies of the impacts of securitization, for instance, the adverse selection in the securitization market and so forth, I study the macro impact of securitization on real economy. Another strand of my research focuses on banking regulation, especially macroprudential regulation. I am particularly interested in the fact that banks may ex-ante take risk in anticipation of regulatory forbearance in a systemic banking crisis and its implication for macroprudential regulation. Consequently, chapter 4 analyzes systemic risk-taking at banks in the presence of “too-manyto-fail” bailout guarantee. In sum, shedding light on securitization and systemic risk-taking in the banking sector, this dissertation contributes to the policy debate on bank regulation

Liquidity Risk and the Beta Premium

As opposed to the “low beta low risk” convention, we show that low beta stocks are illiquid and exposed to high liquidity risk. After adjusting for liquidity risk, low beta stocks no longer outperform high beta stocks. Although investors who “bet against beta” earn a significant beta premium under the Fama–French three- or five-factor models, this strategy fails to generate any significant returns when liquidity risk is accounted for. Our work helps understand the beta premium from a new liquidity-risk perspective, and draws useful implications for both fund and corporate managers

Liquidity Risk and the Beta Premium

As opposed to the “low beta low risk” convention, we show that low beta stocks are illiquid and exposed to high liquidity risk. After adjusting for liquidity risk, low beta stocks no longer outperform high beta stocks. Although investors who “bet against beta” earn a significant beta premium under the Fama–French three- or five-factor models, this strategy fails to generate any significant returns when liquidity risk is accounted for. Our work helps understand the beta premium from a new liquidity-risk perspective, and draws useful implications for both fund and corporate managers

Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection

Nonlinear dimensionality reduction lacks interpretability due to the absence of source features in low-dimensional embedding space. We propose an interpretable method featMAP to preserve source features by tangent space embedding. The core of our proposal is to utilize local singular value decomposition (SVD) to approximate the tangent space which is embedded to low-dimensional space by maintaining the alignment. Based on the embedding tangent space, featMAP enables the interpretability by locally demonstrating the source features and feature importance. Furthermore, featMAP embeds the data points by anisotropic projection to preserve the local similarity and original density. We apply featMAP to interpreting digit classification, object detection and MNIST adversarial examples. FeatMAP uses source features to explicitly distinguish the digits and objects and to explain the misclassification of adversarial examples. We also compare featMAP with other state-of-the-art methods on local and global metrics

Human Emotion Recognition Based On Galvanic Skin Response signal Feature Selection and SVM

A novel human emotion recognition method based on automatically selected Galvanic Skin Response (GSR) signal features and SVM is proposed in this paper. GSR signals were acquired by e-Health Sensor Platform V2.0. Then, the data is de-noised by wavelet function and normalized to get rid of the individual difference. 30 features are extracted from the normalized data, however, directly using of these features will lead to a low recognition rate. In order to gain the optimized features, a covariance based feature selection is employed in our method. Finally, a SVM with input of the optimized features is utilized to achieve the human emotion recognition. The experimental results indicate that the proposed method leads to good human emotion recognition, and the recognition accuracy is more than 66.67%