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It is an important task to model realized volatilities for high-frequency data in finance and economics and, as arguably the most popular model, the heterogeneous autoregressive (HAR) model has dominated the applications in this area. However, this model suffers from three drawbacks: (i.) its heterogeneous volatility components are linear combinations of daily realized volatilities with fixed weights, which limit its flexibility for different types of assets, (ii.) it is still unknown what is the high-frequency probabilistic structure for this model, as well as many other HAR-type models in the literature, and (iii.) there is no high-dimensional inference tool for HAR modeling although it is common to encounter many assets in real applications. To overcome these drawbacks, this paper proposes a multilinear low-rank HAR model by using tensor techniques, where a data-driven method is adopted to automatically select the heterogeneous components. In addition, HAR-It\^o models are introduced to interpret the corresponding high-frequency dynamics, as well as those of other HAR-type models. Moreover, non-asymptotic properties of the high-dimensional HAR modeling are established, and a projected gradient descent algorithm with theoretical justifications is suggested to search for estimates. Theoretical and computational properties of the proposed method are verified by simulation studies, and the necessity of using the data-driven method for heterogeneous components is illustrated in real data analysis.

Semi-solid forging of iron-based alloys during solidification has unique characteristics distinct from those of the classical hot forging. With the aim of acquiring precise knowledge concerning the microstructural evolution of bearing steel Cr4Mo4V in this process, a series of semi-solid forging experiments were carried out in which samples were wrapped in a designed pure iron sheath. The effects of forging temperature and forging reduction on the grain morphology and liquid flow behavior were investigated, respectively. By forging solidifying metal (FSM), bulky primary dendrites were broken and spheroidal grains with an average shape factor of 0.87 were obtained at 1360 °C. With the decreasing forging temperature to 1340 °C, the microstructural homogeneity can be improved. On the other hand, it shows that a higher forging reduction (50%) is essential for the spheroidization of grains and elimination of liquid segregation. Those microstructural characteristics are related to different motion mechanisms of solid and liquid phases at different forging temperatures. Additionally, the effect of semi-solid forging on the eutectic carbides was also investigated, and the results demonstrate that the higher diffusion capacity and less liquid segregation jointly lower the large eutectic carbides and consequently cause its uniform distribution during FSM.

In recent years, the field of recommendation systems has attracted increasing attention to developing predictive models that provide explanations of why an item is recommended to a user. The explanations can be either obtained by post-hoc diagnostics after fitting a relatively complex model or embedded into an intrinsically interpretable model. In this paper, we propose the explainable recommendation systems based on a generalized additive model with manifest and latent interactions (GAMMLI). This model architecture is intrinsically interpretable, as it additively consists of the user and item main effects, the manifest user-item interactions based on observed features, and the latent interaction effects from residuals. Unlike conventional collaborative filtering methods, the group effect of users and items are considered in GAMMLI. It is beneficial for enhancing the model interpretability, and can also facilitate the cold-start recommendation problem. A new Python package GAMMLI is developed for efficient model training and visualized interpretation of the results. By numerical experiments based on simulation data and real-world cases, the proposed method is shown to have advantages in both predictive performance and explainable recommendation.

This dissertation studies topics in the areas of information in financial markets. In the first chapter, Should Information be Sold Separately? Evidence from MiFID II, we examine whether selling information separately improves its production. We use a recent regulation in Europe (MiFID II) that unbundles research from transactions to investigate this question. We show that unbundling causes fewer research analysts to cover a firm. This decrease does not come from small- or mid-cap firms but is concentrated in large firms. Contrary to conventional wisdom, the reduction in the coverage quantity is accompanied by an increase in the coverage quality. Further analyses suggest that the enhancement of analyst competition could drive the results: inaccurate analysts drop out (extensive margin) and analysts who stay produce better-quality research (intensive margin). Our findings suggest that selling information separately improves information quality at the cost of reducing information quantity. The second chapter, Going Public or Staying Private: The Cost of Mandated Transparency, focuses on how transparency requirements in public markets affect firms' decisions to go public or stay private. Public markets are transparent institutions, where disclosure is mandatory, and order flows observable. We show that transparency can lead to insufficient information acquisition and inefficient investment. Transparency of order flows in public markets discourages information acquisition. Insufficient information acquisition then exacerbates the cost of imperfect disclosure. When the short-term disclosable signal diverges from the long-run value of a project, entrepreneurs prefer opaque private markets where investors can bargain over the costs of acquiring information. Our model links a firm's preference for public markets to the quality of disclosure metrics. Imperfect communication between investors and entrepreneurs caused by market transparency is a mechanism by which mandatory disclosure may destroy value, leading firms to remain private. In the third chapter, Active and Passive Funds: An Equilibrium Analysis, we provide a benchmark model to analyze investors' equilibrium choices and the welfare consequences of active and passive investing. Active investing is costly, but it brings two benefits: investors can better hedge by freely trading each asset in the portfolio and can acquire information about the possible state of the world. Information acquisition decisions are strategic substitutes. Investors will become active until the net value of being active shrinks to zero. We show that when the cost of acquiring information is low, equilibrium features the coexistence of informed active investors and passive investors. When the cost of acquiring information rises, informed active, uninformed active and passive investors could coexist. Finally, if the cost of being uninformed active is sufficiently low, passive investing is dominated by active investing. The benchmark model allows future research to explore whether the market equilibrium induces the optimal level of information acquisition and active investment.

This paper demonstrates how to apply machine learning algorithms to distinguish good stocks from the bad stocks. To this end, we construct 244 technical and fundamental features to characterize each stock, and label stocks according to their ranking with respect to the return-to-volatility ratio. Algorithms ranging from traditional statistical learning methods to recently popular deep learning method, e.g. Logistic Regression (LR), Random Forest (RF), Deep Neural Network (DNN), and the Stacking, are trained to solve the classification task. Genetic Algorithm (GA) is also used to implement feature selection. The effectiveness of the stock selection strategy is validated in Chinese stock market in both statistical and practical aspects, showing that: 1) Stacking outperforms other models reaching an AUC score of 0.972; 2) Genetic Algorithm picks a subset of 114 features and the prediction performances of all models remain almost unchanged after the selection procedure, which suggests some features are indeed redundant; 3) LR and DNN are radical models; RF is risk-neutral model; Stacking is somewhere between DNN and RF. 4) The portfolios constructed by our models outperform market average in back tests.

We proposed a new Portfolio Management method termed as Robust Log-Optimal Strategy (RLOS), which ameliorates the General Log-Optimal Strategy (GLOS) by approximating the traditional objective function with quadratic Taylor expansion. It avoids GLOS's complex CDF estimation process,hence resists the "Butterfly Effect" caused by estimation error. Besides,RLOS retains GLOS's profitability and the optimization problem involved in RLOS is computationally far more practical compared to GLOS. Further, we combine RLOS with Reinforcement Learning (RL) and propose the so-called Robust Log-Optimal Strategy with Reinforcement Learning (RLOSRL), where the RL agent receives the analyzed results from RLOS and observes the trading environment to make comprehensive investment decisions. The RLOSRL's performance is compared to some traditional strategies on several back tests, where we randomly choose a selection of constituent stocks of the CSI300 index as assets under management and the test results validate its profitability and stability., 14 pages, 13 figures

Article proposed a control method of predictive fuzzy PID for DC/DC converter based on the topology of zero-voltage zero-current switching PWM full-bridge converter which's control chip is 9212dp256. The method is fully compensated digital control system to control delays, greatly saving resources of digital controller. Final results show that the method is a good control, and its control performance is achieved the desired specifications.

For the problem of lead-acid battery charging for a long time, analyzing the causes, discusses the principle of the negative pulse charging based on fuzzy control, made the specific system circuits; shows the simulation and testing results of the system; the results show that the system for improving the lead-acid battery charging time is feasible and effective.

According to meet the requirement of the low power consumption in the handheld RFID reader, it is introduced that a design strategy of power consumption and a scheme of component selection in the design of an handheld RFID reader based on the MSP430F149 MCU, and a method, when combined with the working status of the RFID reader, is proposed to reduce the power consumption through adjusting the clock frequency of MSP430F149 MCU and intelligent managements of system working mode. Test results indicate that the method is useful to reduce the system power consumption.