Cross-listing, price discovery and the informativeness of the trading process

This paper analyzes the price discovery process of a set of Spanish stocks cross-listed at the NYSE. Our methodology distinguishes between two sources of information asymmetries. Market-specific information that is revealed through the trading process and public disclosures simultaneously revealed to both markets but subject to informed judgments. We compute the information share of the Spanish and U.S. trading activity during the daily 2-hour overlapping interval. Empirical results show that the NYSE contribution to the price discovery process is not negligible. But the NYSE information is basically trade-unrelated

Competing With the NYSE

We study the stock exchange rivalry between the New York Stock Exchange (NYSE) and the Consolidated Stock Exchange (Consolidated) from 1885 to 1926 using a new database of bid-ask spreads and stock data collected from The New York Times and other primary sources. The magnitude of this important, but largely forgotten rivalry was substantial. From 1885 to 1895, the ratio of Consolidated to NYSE volume averaged 40 percent and reached as high as 60 percent. The market share of the Consolidated averaged 23 percent for approximately 40 years. The Consolidated focused on the relatively liquid securities on the NYSE as measured by bid-ask spreads and trading volume. Our results suggest that NYSE bid-ask spreads fell by more than 10 percent when the Consolidated began to trade NYSE stocks while bid-ask spreads for our quasicontrol group of stocks trading on the Boston Stock Exchange remain unchanged. The effect persisted over the entire history of the stock market rivalry until a series of scandals and investigations of the Consolidated by state regulators led to the demise of the exchange in the 1920s. The analysis suggests three conclusions: (1) the NYSE has faced significant long-run competition (2) the NYSE may be susceptible to a similar level of competition in the future and (3) that the Consolidated may have improved the efficiency of stock prices by contributing to the price discovery process.

Temporal Aggregation of the Returns of a Stock Index Series

The effects of temporal aggregation on asymmetry properties and the kurtosis of returns based on the NYSE composite index are studied. There is less asymmetry in responses to shocks for weekly and monthly frequencies than for the daily frequency. Kurtosis is not smaller for the lower frequencies.symmetric moving average; QGARCH; estimation; kurtosis; Pearson IV; NYSE

Does algorithmic trading improve liquidity?

Algorithmic trading has sharply increased over the past decade. Equity market liquidity has improved as well. Are the two trends related? For a recent five-year panel of New York Stock Exchange (NYSE) stocks, we use a normalized measure of electronic message traffic (order submissions, cancellations, and executions) as a proxy for algorithmic trading, and we trace the associations between liquidity and message traffic. Based on within-stock variation, we find that algorithmic trading and liquidity are positively related. To sort out causality, we use the start of autoquoting on the NYSE as an exogenous instrument for algorithmic trading. Previously, specialists were responsible for manually disseminating the inside quote. As stocks were phased in gradually during early 2003, the manual quote was replaced by a new automated quote whenever there was a change to the NYSE limit order book. This market structure change provides quicker feedback to traders and algorithms and results in more message traffic. For large-cap stocks in particular, quoted and effective spreads narrow under autoquote and adverse selection declines, indicating that algorithmic trading does causally improve liquidity

An Alternative Conditional Asymmetry Specification for Stock Returns

The paper advances the log-generalized gamma distribution as a suitable generator of conditional skewness. Based on the NYSE composite daily returns an asMA-asQGARCH model along with skewness dynamics is estimated. The results indicate a skewness that varies between sizeable negative skewness and almost symmetry. The conditional variance and skewness measures are negatively correlated.Time series, finance, nonlinearity, skewness, gamma, estimation, NYSE

Comparison of the Stock Price Clustering of stocks which are traded in the US and Germany—Is XETRA more efficient than the NYSE?

We analyze intraday trades of German stocks (Daimler Chrysler and SAP) that are traded simultaneously at the German stock market XETRA and the New York Stock Exchange (NYSE). At first glance, the stock price clustering seems to be less pronounced at the NYSE. But after converting Euro-prices into Dollar-prices, we obtain the result that the clustering is stronger at the NYSE indicating that XETRA is more efficient with respect to this measure. This difference in the clustering at the different stock markets should not be observable if the no-arbitrage condition would hold. We also discuss several explanations, like ease of negotiation, convenience and rounding, attraction, odd pricing, and aspiration level for stock price clustering. As a result we see that no model is able to capture all of our empirical observations.behavioral finance; market microstructure; stock price clustering

The Warsaw Stock Exchange Index WIG: Modelling and Forecasting

In this paper we have assessed an influence of the NYSE Stock Exchange indexes (DJIA and NASDAQ) and European Stock indexes (DAX and FTSE) on the Warsaw Stock Exchange index WIG within a framework of a GARCH model. By applying a procedure of checking predictive quality of econometric models as proposed by Fair and Shiller (1990), we have found that the NYSE market has relatively more power than European markets in explaining the WSE index WIG.Warsaw Stock Exchange, stock index, GARCH model, forecasting

Valuation implications of pharmaceutical companies' R&D regulatory approval notifications

This paper examines shareholder wealth effects surrounding applications to, and approvals by, the United States Food and Drug Administration (FDA) for firms listed on the New York (NYSE) and London (LSE) stock exchanges. Applications to the FDA for drug approvals significantly increase shareholder wealth for NYSE firms only. The increase is driven by applications for enhancements to existing drugs, with the market anticipating the application, thus suggesting information leakage. FDA approvals also significantly increase shareholder wealth in both markets. However, there is no evidence of information leakage and the significant post-event abnormal returns support the attention-grabbing hypothesis. Enhanced drug approvals are value-relevant for both markets, which highlights the contribution of real-options to firm value