An Empirical Analysis of the Shanghai and Shenzen Limit Order Books

This paper investigates the market microstructure of the Shanghai and Shenzhen Stock Ex- changes. The two major Chinese stock markets are pure order-driven trading mechanisms without market makers, and we analyze empirically both limit order books. We begin our empirical model- ing using the vector autoregressive model of Hasbrouck and extend the model to incorporate other information in the limit order book. We also study the market impact on A shares, B shares and H shares, and analyze how the market impact of stocks varies cross sectionally with market capital- ization, tick frequencies, and turnover. Furthermore, we distinguish the market impacts of small, average and block trades, and conclude that the market impacts of small trades are signi?cantly lower than those of other trades.limit order book; Chinese stock market; microstructure; VAR model

Dialog Action-Aware Transformer for Dialog Policy Learning

Recent works usually address Dialog policy learning DPL by training a reinforcement learning (RL) agent to determine the best dialog action. However, existing works on deep RL require a large volume of agent-user interactions to achieve acceptable performance. In this paper, we propose to make full use of the plain text knowledge from the pre-trained language model to accelerate the RL agent's learning speed. Specifically, we design a dialog action-aware transformer encoder (DaTrans), which integrates a new fine-tuning procedure named masked last action task to encourage DaTrans to be dialog-aware and distils action-specific features. Then, DaTrans is further optimized in an RL setting with ongoing interactions and evolves through exploration in the dialog action space toward maximizing long-term accumulated rewards. The effectiveness and efficiency of the proposed model are demonstrated with both simulator evaluation and human evaluation.Comment: To be appeared in SIGdial 202

JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy Learning

Dialogue policy learning (DPL) is a crucial component of dialogue modelling. Its primary role is to determine the appropriate abstract response, commonly referred to as the "dialogue action". Traditional DPL methodologies have treated this as a sequential decision problem, using pre-defined action candidates extracted from a corpus. However, these incomplete candidates can significantly limit the diversity of responses and pose challenges when dealing with edge cases, which are scenarios that occur only at extreme operating parameters. To address these limitations, we introduce a novel framework, JoTR. This framework is unique as it leverages a text-to-text Transformer-based model to generate flexible dialogue actions. Unlike traditional methods, JoTR formulates a word-level policy that allows for a more dynamic and adaptable dialogue action generation, without the need for any action templates. This setting enhances the diversity of responses and improves the system's ability to handle edge cases effectively. In addition, JoTR employs reinforcement learning with a reward-shaping mechanism to efficiently finetune the word-level dialogue policy, which allows the model to learn from its interactions, improving its performance over time. We conducted an extensive evaluation of JoTR to assess its effectiveness. Our extensive evaluation shows that JoTR achieves state-of-the-art performance on two benchmark dialogue modelling tasks, as assessed by both user simulators and human evaluators.Comment: Our code, models and other related resources are publicly available at https://github.com/KwanWaiChung/JoT

RET Mutational Spectrum in Hirschsprung Disease: Evaluation of 601 Chinese Patients

Rare (RVs) and common variants of the RET gene contribute to Hirschsprung disease (HSCR; congenital aganglionosis). While RET common variants are strongly associated with the commonest manifestation of the disease (males; short-segment aganglionosis; sporadic), rare coding sequence (CDS) variants are more frequently found in the lesser common and more severe forms of the disease (females; long/total colonic aganglionosis; familial)

The Impact of Derivative Warrants Introductions on the Underlying Stocks：Evidence from Taiwan

[[abstract]]This paper investigates the impact of derivative warrant introductionon the return, risk and trading activity of the underlying assets. Theresult shows that the impact of derivative warrant introductions havemany distinguish features that differ from that of standard options.We found a positive abnormal return at the announcement day, which ismore significant for stocks that are successively issued within twomonths, but the returns decrease after the issuance day. There is apost-issue under-performance of underlying stocks. The results showthat investment bankers have good market timing and that investor haveoptimistic expectation regarding future earnings. Our evidence isconsistent with that financial institutions take advantage of windowsof opportunity by issuing warrants on overvalued equity. We also findthat investment banks tend to issue warrants on stocks which have arecent increase in volatility. The empirical results demonstrate thattrading volume, systematic risk, and liquidity are not affected, whilethe variances of the underlying assets decrease after warrantsintroduced.[[sponsorship]]逢甲大學財務金融系; 靜宜大學財務金融系; 東海大學財務金融系[[conferencetype]]國內[[conferencedate]]20011123~20011123[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]臺中, 臺

Market Risk and Model Risk of Financial Institutions Writing Covered Warrants

[[abstract]]Financial institutions writing derivative warrants are exposed tomodel risk, the risk that models may be incorrectly or inappropriatelyapplied. Fat tail return distributions, forecast error in thevolatility input, and inaccurate hedging calculation are possiblecauses of model risk. Understanding model risk in the valuation andtrading of derivative securities is particularly important foremerging markets, because asset returns are too fat-tailed to benormal, and volatility is hard to forecast accurately by any methodand forecast errors remain very large. This paper provides empiricalsimulation of market risk and model risk for financial institutionswriting derivative warrants in Hong Kong, Taiwan, and Japan markets.Important market imperfections such as transaction costs and issuingcosts are considered in the simulation design. Our empiricalsimulation results show that model risk is higher for derivativewarrant issuers in emerging markets such as Taiwan and Hong Kong . Wealso discuss possible methods for reducing the model risks, namelyreducing the issuing period, volatility markup, and timing ofissuance.[[sponsorship]]中山大學財務管理系; 中山管理學術研究中心亞太財金發展研究室; 台灣證券交易所[[conferencetype]]國內[[conferencedate]]19991211~19991212[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]高雄, 臺