Firm Value, Cross-Listing Premium and the Sarbanes-Oxley Act

This paper presents empirical evidence on the effects of the Sarbanes-Oxley Act of 2002 on the value of firms and on the cross-listing choice of firms destined to three major markets in North America, Asia and Europe. We use dynamic panel data methods and treatment effects methods to find that Sarbanes-Oxley has had a negative impact on the value of firms worldwide. However, the effect of Sox on the cross-listing decision is positive in the US destination and negative in the Germany destination; and the Hong Kong destination seems to attract cross-listing of firms with lower valuations relative to the US and Germany destination. In terms of the cross-listing decision, the evidence is in favor of crowding in the market where the accounting standards are better, lending support to the signaling and bonding hypotheses of cross-listing choice.Cross-listing, Sarbanes-Oxley, dynamic panel data, treatment effects.

Monitoring-Oriented Programming: A Tool-Supported Methodology for Higher Quality Object-Oriented Software

This paper presents a tool-supported methodological paradigm for object-oriented software development, called monitoring-oriented programming and abbreviated MOP, in which runtime monitoring is a basic software design principle. The general idea underlying MOP is that software developers insert specifications in their code via annotations. Actual monitoring code is automatically synthesized from these annotations before compilation and integrated at appropriate places in the program, according to user-defined configuration attributes. This way, the specification is checked at runtime against the implementation. Moreover, violations and/or validations of specifications can trigger user-defined code at any points in the program, in particular recovery code, outputting or sending messages, or raising exceptions. The MOP paradigm does not promote or enforce any specific formalism to specify requirements: it allows the users to plug-in their favorite or domain-specific specification formalisms via logic plug-in modules. There are two major technical challenges that MOP supporting tools unavoidably face: monitor synthesis and monitor integration. The former is heavily dependent on the specification formalism and comes as part of the corresponding logic plug-in, while the latter is uniform for all specification formalisms and depends only on the target programming language. An experimental prototype tool, called Java-MOP, is also discussed, which currently supports most but not all of the desired MOP features. MOP aims at reducing the gap between formal specification and implementation, by integrating the two and allowing them together to form a system

Learning Complicated Manipulation Skills via Deterministic Policy with Limited Demonstrations

Combined with demonstrations, deep reinforcement learning can efficiently develop policies for manipulators. However, it takes time to collect sufficient high-quality demonstrations in practice. And human demonstrations may be unsuitable for robots. The non-Markovian process and over-reliance on demonstrations are further challenges. For example, we found that RL agents are sensitive to demonstration quality in manipulation tasks and struggle to adapt to demonstrations directly from humans. Thus it is challenging to leverage low-quality and insufficient demonstrations to assist reinforcement learning in training better policies, and sometimes, limited demonstrations even lead to worse performance. We propose a new algorithm named TD3fG (TD3 learning from a generator) to solve these problems. It forms a smooth transition from learning from experts to learning from experience. This innovation can help agents extract prior knowledge while reducing the detrimental effects of the demonstrations. Our algorithm performs well in Adroit manipulator and MuJoCo tasks with limited demonstrations