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Taiwan OTC market is an electronic, order driven, call market. The purpose of this paper is to gain understanding of whether trade size or number of transaction provides more information on explaining price volatility and market liquidity in this market. The paper also aims to investigate how market condition can affect the relationship between information type and trading activities.

The paper uses data from the Taiwan OTC market to run the empirical tests. It divides firms into five size groups based on their market capitalization. Regression equations are run to test: whether number of transactions has a more significant impact on price volatility on the Taiwan OTC market; the impact of market information on number of transactions; the relative impact of firm specific and market information on number of transactions; and the impact of number of transaction of bid‐ask spread.

Findings show that the larger the number of transactions, the higher the price volatility. Smaller firms on the Taiwan OTC market are traded based on firm‐specific information. This relation is further affected by market trends. Especially for the larger firms, when the market is up and the amount of market information increases, number of transactions increases. When the market is down and the amount of market information increases, number of transactions decreases. Finally, it is found spread size is more likely to be influenced by number of transactions, instead of trade size. Overall, based on these empirical results, the information content of number of transactions seems to be higher than that of trade size in the Taiwan OTC market.

Investors now understand that number of transaction actually carry more information than trade size does.

The relation between market information and number of transaction, also that between market information and trade size is influenced by market condition. The paper fills a gap in the literature to show that market condition has an impact on the relation between information type and trader's behavior. A number of transactions are identified that provide more information than trade size does. It is also shown that market conditions can further affect the impact of information on trading activities.

The purpose of this paper is to test if the empirical relationship between the size of trades and market liquidity can be pooled across different block sizes on the London Stock Exchange (LSE).

The authors use pooling and non-pooling econometric tests in a panel framework.

When the authors differentiate between various block sizes, the authors find that for trades in excess of 50,000 shares, there is a positive association between the size of the trade and the bid-ask spread, due to a lack of liquidity in the financial market. The results provide strong evidence that an upstairs market may be required in order to provide liquidity for large block trades on the LSE.

This is the first study to directly test if the LSE requires an upstairs market to provide liquidity for large trade transactions.

In this study, we explore the empirical relationship between trading volume and volatility among KOSPI200 index stock market, futures and options markets. In particular, in explaining the volatility of each market, the trading in other markets, as well as the trading volume of other markets, also served as explanatory variables. In other words, cross-market effects of trading volume by investor types are analyzed. The empirical results show that there exist the cross-market effects of the relationship between trading volume and volatility in deeply integrated financial markets such as KOSPI200 index stock, futures and options markets. That is, the volatility of one market is explained by the trading volume of trader types in other financial markets. And, overall options trading increases the volatility of each market, while the overall futures trading volume of foreign investors reduce the volatility of each market. Trading volume of Individual investors does not reduce the volatilities of KOSPI200 index and futures markets. That is, trading volume of Individual investors in stock, futures, and options markets increase the volatilities of stock and futures. This implies that foreign investors are informed traders, whereas individual investors are liquidity traders.

The London Stock Exchange is currently preparing for the largest changes in its trading practices since the Big Bang. It is already known that the Exchange is proposing to retain its practice of delaying the publication of large trades so as to encourage the execution of such trades, even though this delayed publication also creates an undesirable information imbalance. However, in reality, what matters to market makers is the effect of a large trade on their inventory position, not the size of the trade itself. It is shown that delaying trade publication on the basis of size and inventory delays the publication of far fewer trades than does the current regime, while still offering protection to market participants who provide liquidity by unbalancing their inventory to accommodate large trades.

Given the rising need for measuring and controlling of financial risk as proposed in Basel II and Basel III Capital Adequacy Accords, trading risk assessment under illiquid market conditions plays an increasing role in banking and financial sectors, particularly in emerging financial markets. The purpose of this chapter is to investigate asset liquidity risk and to obtain a Liquidity-Adjusted Value at Risk (L-VaR) estimation for various equity portfolios. The assessment of L-VaR is performed by implementing three different asset liquidity models within a multivariate context along with GARCH-M method (to estimate expected returns and conditional volatility) and by applying meaningful financial and operational constraints. Using more than six years of daily return dataset of emerging Gulf Cooperation Council (GCC) stock markets, we find that under certain trading strategies, such as short selling of stocks, the sensitivity of L-VaR statistics are rather critical to the selected internal liquidity model in addition to the degree of correlation factors among trading assets. As such, the effects of extreme correlations (plus or minus unity) are crucial aspects to consider in selecting the most adequate internal liquidity model for economic capital allocation, especially under crisis condition and/or when correlations tend to switch sings. This chapter bridges the gap in risk management literatures by providing real-world asset allocation tactics that can be used for trading portfolios under adverse markets’ conditions. The approach to computing L-VaR has been arrived at through the application of three distinct liquidity models and the obtained results are used to draw conclusions about the relative liquidity of the diverse equity portfolios.

Financial derivatives markets are a relatively new development globally. In the USA, the first commodity derivatives trading began in Chicago at the Chicago Board of Trade in 1849. However, the first financial derivatives trading did not begin until 1972, when the Chicago Mercantile Exchange began trading futures contracts on seven foreign currencies. These were the world's first official financial futures contracts. In Europe, the oldest financial derivatives market was the London International Financial Futures Exchange, or LIFFE, which began trading financial futures in 1982.

This study investigates whether broker anonymity impairs the ability of the market to detect informed trading in the lead up to takeover announcements. Our research represents the first study in this area to analyse the effects of broker anonymity in the context of significant information asymmetry. Results indicate that informed traders are less detected, and therefore better off when broker identifiers are concealed. This finding has important policy implications for exchange officials deciding whether or not to reveal broker identifiers surrounding trades, especially considering that almost all prior research suggests that broker anonymity is correlated with improved liquidity.

The Chinese stock market is a typical emerging market with special features that are very different from those of mature markets. The objective of this study is to investigate whether and how these features affect the volatility‐volume relation for Chinese stocks.

This paper examines the roles of the number of trades, size of trades, and share volume in explaining the volatility‐volume relation in the Shanghai Stock Exchange with high frequency trade data used.

The results confirm that the volatility‐volume relation is driven mainly by the number of trades on the Chinese stock market. The number of trades explains the volatility‐volume relation better than the size of trades. Furthermore, some results are obtained that differ from those of mature markets, such as the US market. The results show that the second largest sized trades affect the volatility more than other trades on the Chinese market.

The results show that, in the Shanghai Stock Exchange, informed traders camouflage their private information or manipulation behavior through the second largest sized trades. The results may have important implications for work explaining the volatility‐volume relation on the Chinese stock market, further providing a reference by which to regulate emerging markets.

The literature on positive feedback trading has grown considerably in recent years. The purpose of this paper is to provide a review of the theoretical and empirical literature on positive feedback trading and especially the literature related to the Sentana and Wadhwani (1992) model.

This literature review covers theoretical and empirical work in this area and it points out shortcomings and potential extensions of the basic feedback model.

The evidence so far points in the direction of positive feedback trading being present in aggregate stock market indices, index futures, bond markets, foreign exchange markets and individual stocks. There are some important issues that require further investigation. For example, it is likely that feedback trading is a function of longer lags of past return. Likewise, asymmetric behavior during up and down markets appears to be the rule rather than the exception. More importantly, the models should allow for positive as well as negative feedback and be general enough to investigate feedback trading behavior in individual assets and not just the aggregate market.

The discussion points out theoretical and empirical limitations and shortcomings of the extant literature.

This is the first paper to review positive feedback trading, implications, limitations and need for future research.

This study concerns how well stock market regulators prevent trading by using trading halts when they suspect asymmetric information in the market. Security trading halts in the Portuguese stock market are analysed to measure the effectiveness of trading halts imposed by market authorities as well as their timing in interrupting and restarting trading. Stock price returns, abnormal returns and volatility are used to compare the significance of differences for pre‐and post‐halt periods. First the global sample is used to analyse abnormal returns and then it is split into good and bad news halts. A GARCH (1,1) model is also applied and found to be a more sensitive instrument on justifying trading halts. Justification for trading halts tends to rise as event window size increases, suggesting that supervisory authorities tend to spot the dominant changes better. In fact, when very short time‐sampling periods are used weaker justifications for stock halting are found. The opportunity for market authorities to interrupt trading seems to be increasing. In terms of timing they seem, on the whole, to be delayed when imposing trading halts or anticipated when authorising the restart. Nevertheless, when considering good news, although the halt tends to be late the restart seems to be on time. It is concluded that all methodologies should be jointly applied by stock watch departments of supervision authorities for detecting trading under asymmetric information, but special attention is drawn to GARCH methodologies that show superior ability for detecting changes in stock characteristics.