This dissertation is motivated by two major factors. First, there have been no direct studies conducted for the relationship between market integrity and market efficiency and the driving forces behind the cross-sectional variations in market quality. Second, a better understanding the relationships among market integrity, market efficiency and other mechanism design factors for securities exchanges will facilitate securities exchanges achieve a satisfactory level of market quality. This dissertation consists of three chapters. In Chapter 1, a review of literature on market manipulation will be given. A series of common securities market manipulation strategies and corresponding market surveillance alerts will be explained and defined. In Chapter 2, we develop a testable hypothesis that market manipulation as proxied by the incidence of ramping alerts would raise transaction cost for completing larger trades. We find ramping alert incidence positively related to effective spreads in 8 of 10 turnover deciles from most liquid to thinnest-trading securities. The magnitude of the increase in effective spreads when ramping manipulation incidence doubles is economically significant, 30 to 40 basis points in many moderate liquidity deciles. This compares with an average effective spread of 72 basis points for index-listed securities in the most efficient electronic markets worldwide. In Chapter 3, In Chapter 3 of this thesis, we test the correlation between the levels of market integrity as proxied by the incidence of ramping alerts and a combination of proxies for factors from the following four potential drivers deciding the market quality across securities exchanges: ??? Securities Markets Trading Regulations ??? Securities Markets Technologies ??? Securities Market Infrastructure ??? Securities Market Participants The model we developed to test the correlation between the proxies for level of market integrity and seven proxies for the four potential drivers were estimated with Ordinary Least Square (OLS) and Two-stage Least Square (2SLS) error structures assumed, respectively to learn the most about the possible endogeneity of spreads and volatility. By performing Hausman-Wu specification tests, we concluded that simultaneity bias in the thickly-traded deciles is not material for the AI-Volatility and AI-Spread equation pairs. Subsequently, we used the PROBIT model to analyse the probability of adopting RTS across the 240 securities exchange deciles and the likelihood proves to be systematically related to four determinants in our sample. Finally we estimate the structural equations to investigate possible cross-equation correlation of the disturbances with either seemingly unrelated regression (SURL) estimation. Our findings are three-fold. Firstly, in the moderately-traded deciles, we find that the presence of a closing auction (CloseAucDum) reduces the incidence of ramping alerts. Trade-based manipulation proves more difficult when a manipulator???s counterparties can use closing auctions to unwind their intraday exposures. The RTS dummy variable is significantly positively related to alert incidence. In the absence of any panel data on the dynamic effects of adopting RTS, what we are observing in cross section is the perceived vulnerability of certain exchanges to manipulation and their consequent adoption of RTS plus the regulatory regimes required to have a salutary effect on market integrity. Second, in the moderately-traded deciles, we find that the closing auctions and more regulations in pursuit of market integrity lower quoted spreads. RTS and a regulation specifically prohibiting ramping indicate in cross-section the perceived likelihood of more ramping. Thirdly, in terms of the probability of the deployment of a real-time surveillance system, the estimations again differ by liquidity decile grouping. In the moderately-traded deciles, higher alert incidence, the presence of DMA, and higher FDI again increase the likelihood of adopting a real-time surveillance system. Our findings have a couple of policy implications for many securities exchanges in terms of market design and market surveillance. First, the exhibited relationship between alert incidence and effective spreads indicates trade-based manipulation has a significant impact on execution costs. Therefore, the prevention of securities market manipulation not only serves the indirect purpose of improving an exchange???s reputation for market integrity but also contributes directly to achieving a more efficient marketplace. Second, our results indicate that some market design changes can enhance the regulatory efforts to prevent securities market manipulations. For example, to prevent manipulators from marking the closing price, some exchanges could choose to adopt a closing auction or a random closing time, which would make manipulation more costly. Nevertheless, no securities exchange can be designed perfectly. Consequently, exchange and broker-level surveillance backed by effective regulatory enforcement is a necessary and pivotal complement to good design choices.

This thesis is concerned with the econometric modelling of gasoline prices in US. The intention is to characterize the market process in this crucial and significant industry. Overall we have been seeking to identify a mechanism to signal and measure market failure and consequently improve market performance. Firstly we examine the time series properties of gasoline prices using the criteria for perfect arbitrage to test market efficiency from the stationarity of price proportions. This is done by considering market efficiency across in different regions of the US, by applying a range of different stationary tests. In this analysis we collected a comprehensive data set of gasoline prices for all regions of the US mainland for the longest period available. Forni (2004), outlined reasons why the analysis of price proportions may be advantageous; especially when the sample is limited. Stationarity corresponds to a broad market, it is found here that the US gasoline market is on average broad. Except for the Gulf Coast and Lower Atlantic, which may be seen as economically and/or geographically separated, market structure in the rest of the US would not appear to be a problem Next we investigate possible long-run price leadership in the US gasoline market and the inter-relatedness of price behaviour relevant to a competitive market. Following Hunter & Burke (2007) and Kurita (2008) market definition is tested. This is done on an extended regional data set to Kurita and following the analysis in Hunter and Burke on a set of company data for the US.We analysed long-run price leadership through the cointegrated vector auto-regression (VAR) to identify key characteristics of long-run structure in the gasoline market. The analysis of the system of regional prices confirms problems with the Gulf Coast and Lower Atlantic, but also based on the finding that the cointegrating rank is less than N-1 using both types of data ( regional price data and company price data) and the findings on weak exogenity it is suggested that competition across the whole of the US is further limited. We applied further tests to company data on prices and quantity data to investigate further the need to regulate for potential anomalies and to capture more directly consumer harm. The variance screening method applied to recent weekly data indicates that there is too little variation in gasoline prices and this would seem to support the cointegration study. Furthermore we applied a dynamic disequilibrium analysis to attempt to identify long-run demand and supply in the gasoline market. Finding significant variables using the Phillips-Hansen fully modified estimation of the switching regression is necessary to distinguish two long-run equations (S&D). Moreover a comparison is made with a Markov Switching Model (MSM) of prices and this suggests a similar pattern of regime to the quantity information analysed in by our disequilibrium model.

<p><p>The recent SEC ban on short selling has presented an unrivaled opportunity to explore the effects of short selling constraints on crashes, market efficiency, and volatility. In this paper I carry out two groups of empirical tests on the individual banned stocks and a series of portfolios created from them: the first tests the hypothesis that short sale constraints increase the frequency and magnitude of crashes, by testing Hong & Stein’s (2003) model of market crashes. The second tests the hypothesis that short sale constraints reduce market efficiency, by testing Miller’s (1977) model in which stocks that are hard (or impossible) to short tend to exhibit overpricing. In regards to the first group of tests, the results are ambiguous: the frequency and magnitude of crashes increased during the ban period, while the skewness of the returns distribution of the portfolios became more negative, as expected, but these changes hold for the market as a whole, as well. On the other hand, the skewness of the returns distribution of the individual banned stocks became more positive. The second group of tests provides ample support for Miller’s model, as the results coincide with the models predictions: banning short selling leads to positive abnormal returns (overpricing) in the affected stocks. The ban is also related with a decrease in volatility relative to the market, an important result from a policy perspective.</p></p>

The objective of the thesis is to analyze the copper futures market in London Metal Exchange (LME) and to recommend appropriate hedging strategy in copper futures market to the Erdenet Mining Corporation in Mongolia. It uses daily official settlement copper prices of LME in the spot and 3 month futures markets from 2000-2014. Initially, we use cointegration test and ECM to investigate the copper market efficiency. Then OLS, ECM, GARCH, EGARCH and ECM-GARCH models are employed to compute different optimum hedge ratios. Finally, the hedge effectiveness is measured based on minimization of the value of AIC and SBIC. Our result indicate that copper futures market is inefficient. Hedge effectiveness comparison concludes that ECM model gives the best hedging performance. However, ECM-GARCH is accounted to be the best model for hedging strategy since it captures the time-varying conditional heteroscedasticity to ECM model. Powered by TCPDF (www.tcpdf.org)

Thesis (M.M. (Finance & Investment))--University of the Witwatersrand, Faculty of Commerce, Law and Management, Graduate School of Business Administration, 2013. / The evidence put forward by Zhang (2010) indicates that algorithmic trading can potentially generate the momentum effect evident in empirical market research. In addition, upon analysis of the literature, it is apparent that algorithmic traders possess a comparative informational advantage relative to regular traders. Finally, the theoretical model proposed by Wang (1993), indicates that the informational differences between traders fundamentally influences the nature of asset prices, even generating serial return correlations. Thus, applied to the study, the theory holds that algorithmic trading would have a significant effect on security return dynamics, possibly even engendering the momentum effect. This paper tests such implications by proposing a theory to explain the momentum effect based on the hypothesis that algorithmic traders possess Innovative Information about a firm’s future performance. From this perspective, Innovative Information can be defined as the information derived from the ability to accumulate, differentiate, estimate, analyze and utilize colossal quantities of data by means of adept techniques, sophisticated platforms, capabilities and processing power. Accordingly, an algorithmic trader’s access to various complex computational techniques, infrastructure and processing power, together with the constraints to human information processing, allow them to make judgments that are superior to the judgments of other traders. This particular aspect of algorithmic trading remains, to the best of my knowledge, unexplored as an avenue or mechanism, through which algorithmic trading could possibly affect the momentum effect and thus market efficiency. Interestingly, by incorporating this information variable into a simplified representative agent model, we are able to produce return patterns consistent with the momentum effect in its entirety. The general thrust of our results, therefore, is that algorithmic trading can hypothetically generate the return anomaly known as the momentum effect. Our results give credence to the assumption that algorithmic trading is having a detrimental effect on stock market efficiency.

No description available.

The growing empirical literature testing informational efficiency of real estate markets uses data from various contexts and at different levels of aggregation. The results of these studies are mixed. We use a distinctive meta-analysis to examine whether some of these study characteristics and contexts lead to a significantly higher chance for identification of an efficient real estate market. The results generated through meta-regression suggest that use of stock market data and individual level data, rather than aggregate data, significantly improves the probability of a study concluding efficiency. Additionally, the findings neither provide support for the suspicion that the view of market efficiency has significantly changed over the years nor do they indicate a publication bias resulting from such a view. The statistical insignificance of other study characteristics suggests that the outcome concerning efficiency is a context-specific random manifestation for the most part. (authors' abstract)

The annual 10-K report has historically been the most important aspect in assessing the position of a publicly held company. However, as the flow of information has increased with the dawn of new technologies, less and less attention has been paid to these audited financial statements. In order to assess if investors are still reacting to the information contained in the annual report, this paper examines the relationship between accounting ratios and stock price in banks traded on United States stock exchanges. By examining accounting ratios instead of simply looking at Earnings Per Share, new information was revealed regarding what aspects of the annual report investors react to. Ratios that incorporate information that is difficult to predict, such as leverage or allowance accounts were more likely to affect a stock’s performance, while those that contained information that is more readily available from other sources had less of an effect.

Doctor of Philosophy / This dissertation presents the results of three empirical studies on price formation and liquidity in financial futures markets. The research entails three related areas: the effect of taxes on the prices of Australian stock index futures; the efficiency of the information transmission mechanism between the cash and futures markets; and the price and liquidity impact of large trades in interest rate and equity index futures markets. An overview of previous research identifies some important gaps in the existing literature that this dissertation aims to resolve for the benefit of arbitrageurs, investment managers, brokers and regulators.

<p>This study examines the economic impacts of three large-scale terrorist attacks on the Nordic capital markets. Past research has shown evidence of the increasing resilience of the US capital markets towards terrorist attacks. Hereby the Nordic regions capital markets were studied and compared with the US's capital markets, in an intention to find evidence whether or not the same development can be observed in the Nordic countries. The results implied that the Nordic markets did not absorb the shocks as well as the US markets. The analysis was taken into an industry level on the Swedish stock market to get a deeper insight of the impacts of such events. The results indicated the Energy sectors good ability to absorb terrorist attacks in terms of negative abnormal returns and time of recovery. The Financing sector seemed to be the most sensitive sector, since its performance was the weakest in terms of market recovery.</p>