Lying about the price? Ultimatum bargaining with messages and imperfectly observed offers

We introduce the taxicab game, related to the ultimatum game and Gehrig et al.'s (2007) yes/no game. The proposer makes an offer, and simultaneously sends a cheap talk message indicating (possibly falsely) the amount of the offer. The responder observes the message with certainty and the offer with probability p before accepting or rejecting the offer. We investigate versions with p=. 0 and p=. 0.5 along with the ultimatum game as a baseline. Intuition and a model comprising both standard economic agents and others who dislike inequity, lies and lying provide clear predictions that our experimental results support. As the likelihood increases of offers being seen, the offers themselves increase, messages over-state them less, and responders are more likely to accept (even when the offer is unseen). Also, responders are more likely to accept after truthful messages than after lies or when no message is sent.

An analysis of sectoral equity and CDS spreads

In this paper, we find that CDS return shocks are important in explaining the forecast error variance of sectoral equity returns for the USA. The CDS return shocks have different effects on equity returns and return volatility in the pre-crisis and crisis periods. It is the post-Lehman crisis period in which the effects of CDS return shocks are the most dominant. Finally, we construct a spillover index and find that it is time-varying and explains a larger share of total forecast error variance of sectoral equity and CDS returns for some sectors than for others.

The relationship between stock returns and the foreign exchange rate: the ARDL approach

This study employs the ARDL cointegration approach in order to examine the impact
of financial liberalization on the relationships between the exchange rate and share
market performance in China. We discovered that cointegration has existed between the
Shanghai A Share Index and the exchange rate of the renminbi against the US dollar
and Hong Kong dollar since 2005, when the Chinese exchange rate regime became a
flexible, managed, floating system. We found that both the exchange rate and the money
supply influenced stock price, with a positive correlation. We further show that the
money supply increase was largely caused by a huge ‘hot money’ inflow from other
countries in recent years. After local currency appreciation, hot money, followed by
the money supply increase, pushed the market into a high level, based on expectations
regarding the local currency’s further appreciation.

Call auction transparency and market liquidity: Evidence from China

This paper uses the natural experiment offered by the Shanghai Stock Exchange to investigate the impact of opening call auction transparency on market liquidity. We find that the dissemination of indicative trade information during the pre-open call auction session leads to an overall improvement in stock liquidity in the continuous trading session. Bid-ask spreads narrow in the first trading hour because adverse selection risk fell significantly and there is less price volatility in the continuous market. This effect is greater for actively traded securities than illiquid securities. Our findings are robust for different lengths of sample period, different lengths of trading hours after market open, and stocks that had (and had not) reformed the share split structure during our research period.

Stock price comovement: Evidence from India

We examine the extent to which stock prices comove in an emerging economy, India. We first document that stocks listed on the National Stock Exchange (NSE) comove. Further, we find that synchronicity is positively associated with growth and earnings volatility and negatively associated with business group affiliation and leverage.

Controlling shareholder, expropriations and firm’s leverage decision: Evidence from Chinese Non-tradable share reform

This paper examines the effect of excess control rights on the leverage decisions made by Chinese non-SOEs before and after the Non-tradable share reform (NTS reform). We find that firms with excess control rights have more excess leverage and their controlling shareholders use the resources for tunneling rather than investing in positive NPV projects. We also find that excess leverage in firms with excess control rights decreases and the market reaction to announcements of related party transactions are more positive after NTS reform. This confirms that tunneling by the controlling shareholders actually reduced. We argue that in emerging markets where legal protection for creditors and shareholders is weak, controlling shareholders borrow excess debt to tunnel through inter-corporate loans and related party transactions. Furthermore the privatization of these economies can reduce the controlling shareholders' tunneling activities and associated excess leverage which destroys firm value.

Equity market price interactions between China and the other markets within the Chinese states equity markets

This study examines the cointegrating and long-term causal relationships of equity market prices in equity markets of Chinese states namely, Shanghai, Shenzhen, Hong Kong, Taiwan and Singapore. I cover the period between October 5, 1992 and March 20, 2006, taking into account both the Asian financial crisis and the opening-up of China’s equity markets in recent years. First, I analysis the cointegration by utilizing Johansen’s (1988) cointegration tests. I find that a long-term equilibrium relationship measured by cointegration has been established among Shanghai, Shenzhen, Hong Kong and Taiwanese markets and, to a lesser degree, between these markets and the Singapore market since 1998. Secondly, this study examines causality by exploring the bootstrapped Toda-Yamamoto non-causality tests. I find that there is strong evidence of a bi-directional causality between Shanghai and Shenzhen markets after 1998. Furthermore, I also find that there are more causal linkages between the Chinese states equity markets: two mainland Chinese markets, Hong Kong, Taiwan, and Singapore became more dependent on each other. The robustness of the above findings is confirmed by the use of a bootstrap test employed to test the validity of my results.

Quality-based SQL: specifying information quality in relational database queries

A Structured Query Language extension uses an estimator module to evaluate quality profiles that rate the accuracy and completeness of query results. Users receive information that matches their defined quality constraints and better serves their data needs.

The economic significance of CDS price discovery

© 2015 Springer Science+Business Media New York Between 2005 and 2009, we document evident time-varying credit risk price discovery between the equity and credit default swap (CDS) markets for 174 US non-financial investment-grade firms. We test the economic significance of a simple portfolio strategy that utilizes fluctuation in CDS spreads as a trading signal to set stock positions, conditional on the CDS price discovery status of the reference entities. We show that a conditional portfolio strategy which updates the list of CDS-influenced firms over time, yields a substantively larger realized return net of transaction cost over the unconditional strategy. Furthermore, the conditional strategy’s Sharpe ratio outperforms a series of benchmark portfolios over the same trading period, including buy-and-hold, momentum and dividend yield strategies.

What shall i share and with whom? A multi-task learning formulation using multi-faceted task relationships

Multi-task learning is a learning paradigm that improves the performance of "related" tasks through their joint learning. To do this each task answers the question "Which other task should I share with"? This task relatedness can be complex - a task may be related to one set of tasks based on one subset of features and to other tasks based on other subsets. Existing multi-task learning methods do not explicitly model this reality, learning a single-faceted task relationship over all the features. This degrades performance by forcing a task to become similar to other tasks even on their unrelated features. Addressing this gap, we propose a novel multi-task learning model that leams multi-faceted task relationship, allowing tasks to collaborate differentially on different feature subsets. This is achieved by simultaneously learning a low dimensional sub-space for task parameters and inducing task groups over each latent subspace basis using a novel combination of L1 and pairwise L∞ norms. Further, our model can induce grouping across both positively and negatively related tasks, which helps towards exploiting knowledge from all types of related tasks. We validate our model on two synthetic and five real datasets, and show significant performance improvements over several state-of-the-art multi-task learning techniques. Thus our model effectively answers for each task: What shall I share and with whom?