New York Stock Exchange

The efficient market hypothesis has been widely tested and, with few exceptions, found consistent with the data in a wide variety of markets: the New York and American Stock Exchanges, the Australian, English, and German stock markets, various commodity futures markets, the Over-the-Counter markets, the corporate and government bond markets, the option market, and the market for seats on the New York Stock Exchange. Yet, in a manner remarkably similar to that described by Thomas Kuhn in his book, The Structure of Scientific Revolutions, we seem to be entering a stage where widely scattered and as yet incohesive evidence is arising which seems to be inconsistent with the theory. As better data become available (e.g., daily stock price data) and as our econometric sophistication increases, we are beginning to find inconsistencies that our cruder data and techniques missed in the past. It is evidence which we will not be able to ignore.

An important issue in applications of multifactor models of asset returns is the appropriate number of factors. Most extant tests for the number of factors are valid only for strict factor models, in which diversifiable returns are uncorrelated across assets. In this paper we develop a test statistic to determine the number of factors in an approximate factor model of asset returns, which does not require that diversifiable components of returns be uncorrelated across assets. We find evidence for one to six pervasive factors in the cross-section of New York Stock Exchange and American Stock Exchange stock returns.

We compare the topological properties of the minimal spanning tree obtained from a large group of stocks traded at the New York Stock Exchange during a 12-year trading period with the one obtained from surrogated data simulated by using simple market models. We find that the empirical tree has features of a complex network that cannot be reproduced, even as a first approximation, by a random market model and by the widespread one-factor model.

We propose a dynamic econometric microstructure model of trading, and we investigate how the dynamics of trades and trade composition interact with the evolution of market liquidity, market depth, and order flow. We estimate a bivariate generalized autoregressive intensity process for the arrival rates of informed and uninformed trades for 16 actively traded stocks over 15 years of transaction data. Our results show that both informed and uninformed trades are highly persistent, but that the uninformed arrival forecasts respond negatively to past forecasts of the informed intensity. Our estimation generates daily conditional arrival rates of informed and uninformed trades, which we use to construct forecasts of the probability of information-based trade (PIN). These forecasts are used in turn to forecast market liquidity as measured by bid-ask spreads and the price impact of orders. We observe that PINs vary across assets and over time, and most importantly that they are correlated across assets. Our analysis shows that one principal component explains much of the daily variation in PINs and that this systemic liquidity factor may be We thank Mark Ready, Schmuel Baruch, and seminar participants at New York University and the 2002 AFA meetings for helpful comments.

A financial index of the New York stock exchange, the S&P500, is analyzed at 1 min intervals over the 13yr period, January 84-December 96. We quantify the correlations of the absolute values of the index increment. We find that these correlations can be described by two different power laws with a crossover time tx ~ 600min. Detrended fluctuation analysis gives exponents ~ --0.66 and ~2 ~---0.93 for t < t× and t > t×, respectively. Power spectrum analysis gives corresponding exponents fll = 0.31 and f12 = 0.90 for f > f× and f < f×, respectively.

The goal of this article is to examine the impact of 1975 Congressional mandate to integrate the trading of NYSE-listed stocks. The conclusions are: most of the time, the New York Stock Exchange (NYSE) quote matches or determines the best displayed quote, and the NYSE is the most frequent initiator of quote changes. Non-NYSE markets attract a significant portion of their volume when they are posting inferior bids or offers, indicating they obtain order flow for other reasons, such as "payment for order flow." Yet, when a non-NYSE market does post a better bid or offer, it does attract additional order flow. A major goal and ideal ofthe securities markets and the securities industry has been the creation of a strong central market system for securities of national importance, in which all buying and selling in these securities could participate and be represented under a competitive regime. This goal has not as yet been attained.

We examine whether availability of higher quality financial information lessens investor losses during a period seen as a stock market crash. We focus on October 1929, which partly motivated sweeping financial reporting regulations in the 1930s. Using a sample of 540 common stocks traded on the New York Stock Exchange during October 1929, we find that the quality of firms' financial reporting increases with managers' incentives to supply higher quality financial information demanded by investors. Moreover, firms with higher quality financial reporting before October 1929 experienced smaller stock price declines during the market crash.

In this paper, an evolving least squares support vector machine (LSSVM) learning paradigm with a mixed kernel is proposed to explore stock market trends. In the proposed learning paradigm, a genetic algorithm (GA), one of the most popular evolutionary algorithms (EAs), is first used to select input features for LSSVM learning, i.e., evolution of input features. Then, another GA is used for parameters optimization of LSSVM, i.e., evolution of algorithmic parameters. Finally, the evolving LSSVM learning paradigm with best feature subset, optimal parameters, and a mixed kernel is used to predict stock market movement direction in terms of historical data series. For illustration and evaluation purposes, three important stock indices, S&P 500 Index, Dow Jones Industrial Average (DJIA) Index, and New York Stock Exchange (NYSE) Index, are used as testing targets. Experimental results obtained reveal that the proposed evolving LSSVM can produce some forecasting models that are easier to be interpreted by using a small number of predictive features and are more efficient than other parameter optimization methods. Furthermore, the produced forecasting model can significantly outperform other forecasting models listed in this paper in terms of the hit ratio. These findings imply that the proposed evolving LSSVM learning paradigm can be used as a promising approach to stock market tendency exploration. Index Terms-Artificial neural networks (ANNs), evolutionary algorithms (EAs), feature selection, genetic algorithm (GA), least squares support vector machine (LSSVM), mixed kernel, parameter optimization, statistical models, stock market trend mining. I. INTRODUCTION M INING stock market trend or predicting stock price movement direction is regarded as a rather challenging task due to its high volatility, irregularity, and noisy environment in stock markets. Usually, the difficulty in predicting stock price movement direction is attributed to the limitations of many conventional linear forecasting models. For example,

Specialists compete with limit order traders to provide liquidity at the New York Stock Exchange. Since specialists see all system limit orders, they enjoy a unique advantage in this competition. We examine whether the limit order book is informative about future price changes and whether specialists use this information when trading. Our analyses consider three actions specialists can take when a market order arrives: stop the order, immediately fill the order at the quoted price, or immediately fill the order at an improved price. Using SuperDOT limit orders in the TORQ database, we find that the limit order book is informative, especially about short-term price movements. We also find that the specialist uses this information in a way that favors him (and sometimes the floor community) over the limit order traders. The results are more evident for active stocks where the competition between specialists and limit order traders is more intense. We also show that specialists in lower-priced stocks are less likely to initiate such actions because of the binding tick size.

Since 1995 more than 7300 firms have delisted from U.S. stock markets, with almost half of these being involuntary. This paper examines the law and economics of the delisting process. We examine economic rationales for delisting, the legal rules that define it, and the causes of delisting. Using a sample of NYSE firms delisted in 2002, we examine the effects of their delisting and subsequent trading on the Pink Sheets. We find huge costs to delisting, with percentage spreads tripling, volatility doubling, but volume remarkably high. We also show that delisting is applied inconsistently, with some firms trading for months after failing the listing requirements. We argue that the current delisting process is flawed, and we provide some alternatives.

This paper analyzes the components of the bid-ask spread in the limit-order book of the Tokyo Stock Exchange (TSE). While the behavior of spread components in U.S. markets has been extensively studied, little is known about the spread components in a pure limit-order market. We find that both the adverse selection and order handling cost components of the TSE exhibit U-shape patterns independently, in contrast to the findings of Madhavan, Richardson, and Roomans (1997) for U.S. stocks. On the TSE, there does not exist an upstairs market that allows large trades to be prenegotiated or certified as on the New York Stock Exchange (NYSE). This feature of the TSE provides a valuable opportunity to examine the relationship between trade size and spread components. Our results show that the adverse selection cost increases with trade size while order handling cost decreases with it.

To investigate the universality of the structure of interactions in different markets, we analyze the cross-correlation matrix C of stock price fluctuations in the National Stock Exchange (NSE) of India. We find that this emerging market exhibits strong correlations in the movement of stock prices compared to developed markets, such as the New York Stock Exchange (NYSE). This is shown to be due to the dominant influence of a common market mode on the stock prices. By comparison, interactions between related stocks, e.g., those belonging to the same business sector, are much weaker. This lack of distinct sector identity in emerging markets is explicitly shown by reconstructing the network of mutually interacting stocks. Spectral analysis of C for NSE reveals that, the few largest eigenvalues deviate from the bulk of the spectrum predicted by random matrix theory, but they are far fewer in number compared to, e.g., NYSE. We show this to be due to the relative weakness of intra-sector interactions between stocks, compared to the market mode, by modeling stock price dynamics with a two-factor model. Our results suggest that the emergence of an internal structure comprising multiple groups of strongly coupled components is a signature of market development.

Self-regulation is a feature of a number of professions. For example, in the U.S. the government delegates aspects of financial market regulation to self-regulatory organizations (SROs) like the New York Stock Exchange and the National Association of Securities Dealers. We analyse one regulatory task of an SRO, enforcing antifraud rules so agents will not cheat customers. Specifically, we model contracting/enforcement as a two-tier problem. An SRO chooses its enforcement policy: the likelihood that an agent is investigated for fraud and a penalty schedule. Given an enforcement policy, agents compete by offering contracts that maximize customers' expected utility. We assume that the SRO's objective is to maximize the welfare of its members, the agents. We show that the SRO chooses a more lax enforcement policy-meaning less frequent investigations-than what customers would choose. A general conclusion is that control of the enforcement policy governing contracts confers substantial market power to a group of otherwise competitive agents. We also investigate government oversight of the self-regulatory process. The threat of government enforcement leads to more enforcement by the SRO, just enough to preempt any government enforcement.

We perform a comprehensive Monte Carlo comparison between nine procedures available in the literature to detect jumps in financial assets proposed by . We evaluate size and power properties of the procedures under alternative sampling frequencies, levels of volatility, persistence in volatility, degree of contamination with microstructure noise, jump size and intensity. The overall best performance is showed by the Lee and Mykland and intraday procedures, provided the price process is not not very volatile. We propose an improvement to these procedures based on critical values obtained from finite sample approximations of the distribution of the test statistics. We show the validity to use reunion and intersection across procedures and across sampling frequencies for potential users of the tests to minimize spurious jump detection. Finally, we report an empirical analysis using real high frequency data on five stocks listed in the New York Stock Exchange.

Problem statement: Forecasting is a function in management to assist decision making. It is also described as the process of estimation in unknown future situations. In a more general term it is commonly known as prediction which refers to estimation of time series or longitudinal type data. Gold is a precious yellow commodity once used as money. It was made illegal in USA 41 years ago, but is now once again accepted as a potential currency. The demand for this commodity is on the rise. Approach: Objective of this study was to develop a forecasting model for predicting gold prices based on economic factors such as inflation, currency price movements and others. Following the melt-down of US dollars, investors are putting their money into gold because gold plays an important role as a stabilizing influence for investment portfolios. Due to the increase in demand for gold in Malaysian and other parts of the world, it is necessary to develop a model that reflects the structure and pattern of gold market and forecast movement of gold price. The most appropriate approach to the understanding of gold prices is the Multiple Linear Regression (MLR) model. MLR is a study on the relationship between a single dependent variable and one or more independent variables, as this case with gold price as the single dependent variable. The fitted model of MLR will be used to predict the future gold prices. A naive model known as "forecast-1" was considered to be a benchmark model in order to evaluate the performance of the model. Results: Many factors determine the price of gold and based on "a hunch of experts", several economic factors had been identified to have influence on the gold prices. Variables such as Commodity Research Bureau future index (CRB); USD/Euro Foreign Exchange Rate (EUROUSD); Inflation rate (INF); Money Supply (M1); New York Stock Exchange (NYSE); Standard and Poor 500 (SPX); Treasury Bill (T-BILL) and US Dollar index (USDX) were considered to have influence on the prices. Parameter estimations for the MLR were carried out using Statistical Packages for Social Science package (SPSS) with Mean Square Error (MSE) as the fitness function to determine the forecast accuracy. Conclusion: Two models were considered. The first model considered all possible independent variables. The model appeared to be useful for predicting the price of gold with 85.2% of sample variations in monthly gold prices explained by the model. The second model considered the following four independent variables the (CRB lagged one), (EUROUSD lagged one), (INF lagged two) and (M1 lagged two) to be significant. In terms of prediction, the second model achieved high level of predictive accuracy. The amount of variance explained was about 70% and the regression coefficients also provide a means of assessing the relative importance of individual variables in the overall prediction of gold price.

We use NYSE system order data to conduct a controlled experiment examining changes in trader behavior, displayed liquidity supply, and execution quality around the reduction in the minimum price variation to $0.01. Although traders do not substantially reduce their use of traditional limit orders in favor of market orders or non-displayed orders, they do decrease limit order size and cancel limit orders more frequently after decimals than before. These changes in order submission strategy appear to result in less displayed liquidity throughout the limit order book more than 15 cents from the quote midpoint. This reduction in displayed liquidity, however, does not manifest itself in poor execution quality. Even for large system orders, traditional execution quality is not worse with decimals than with fractions. r

We examine the temporal behavior of the spread and depth for common stocks listed on Ž . the Stock Exchange of Hong Kong SEHK , which operates as a purely order-driven mechanism. We find U-shaped intraday and intraweek patterns in the spread and reverse U-shaped patterns in the depth. Our finding is consistent with that of the study of Lee et al.

We study the price dynamics of stocks traded in a financial market by considering the statistical properties both of a single time series and of an ensemble of stocks traded simultaneously. We use the n stocks traded in the New York Stock Exchange to form a statistical ensemble of daily stock returns. For each trading day of our database, we study the ensemble return distribution. We find that a typical ensemble return distribution exists in most of the trading days with the exception of crash and rally days and of the days subsequent to these extreme events. We analyze each ensemble return distribution by extracting its first two central moments. We observe that these moments are fluctuating in time and are stochastic processes themselves. We characterize the statistical properties of ensemble return distribution central moments by investigating their probability density functions and temporal correlation properties. In general, time-averaged and portfolio-averaged price returns have different statistical properties. We infer from these differences information about the relative strength of correlation between stocks and between different trading days. Lastly, we compare our empirical results with those predicted by the single-index model and we conclude that this simple model is unable to explain the statistical properties of the second moment of the ensemble return distribution. 89.90.+n

Theory suggests that reputations, developed in repeated face-to-face interactions, allow nonanonymous, floor-based trading venues to attenuate adverse selection in the trading process. We identify instances when stocks listed on the New York Stock Exchange (NYSE) relocate on the trading floor. Although the specialist follows the stock to its new location, most floor brokers do not. We use this natural experiment to determine whether reputation appears to affect trading costs. We find a discernable increase in the cost of liquidity in the days surrounding a stock's relocation. The increase is more pronounced for stocks with higher adverse selection and greater broker turnover. Using NYSE audit-trail data, we find that the floor brokers relocating with the stock obtain lower trading costs than those brokers who do not move and those that begin trading post-move. Together, these results suggest that reputation plays an important role in the liquidity provision process on the floor of the NYSE. Notes: We thank the New York Stock Exchange for providing data. We thank IN 47405; (812) 855-2696. JEL Classification: G10; G20; G24 1 See Dellarocas (2004).

We introduce a method for combining template matching, from pattern recognition, and the feed-forward neural network, from artiÿcial intelligence, to forecast stock market activity. We evaluate the e ectiveness of the method for forecasting increases in the New York Stock Exchange Composite Index at a 5 trading day horizon. Results indicate that the technique is capable of returning results that are superior to those attained by random choice.

We investigate the properties of correlation based networks originating from economic complex systems, such as the network of stocks traded at the New York Stock Exchange (NYSE). The weaker links (low correlation) of the system are found to contribute to the overall connectivity of the network significantly more than the strong links (high correlation). We find that nodes connected through strong links form well defined communities. These communities are clustered together in more complex ways compared to the widely used classification according to the economic activity. We find that some companies, such as General Electric (GE), Coca Cola (KO), and others, can be involved in different communities. The communities are found to be quite stable over time. Similar results were obtained by investigating markets completely different in size and properties, such as the Athens Stock Exchange (ASE). The present method may be also useful for other networks generated through correlations. PACS. 89.65.-s Social and economic systems -89.75.-k Complex systems -89.90.+n Other topics in areas of applied and interdisciplinary physics (restricted to new topics in section 89)

In this paper we use fractional integration techniques to examine the degree of integration of four US stock market indices, namely the Standard and Poor, Dow Jones, Nasdaq and NYSE, at a daily frequency from January 2005 till December 2009. We analyse the weekly structure of the series and investigate their characteristics depending on the specific day of the week. The results indicate that the four series are highly persistent; a small degree of mean reversion (i.e., orders of integration strictly smaller than 1) is found in some cases for S&P and the Dow Jones indices. The most interesting findings are the differences in the degree of dependence for different days of the week. Specifically, lower orders of integration are systematically observed for Mondays and Fridays, consistently with the "day of the week" effect frequently found in financial data.

We address the general problem of how to quantify the kinematics of time series with stationary first moments but having non stationary multifractal long-range correlated second moments. We show that a Markov process is sufficient to model important aspects of the multifractality observed in financial time series and propose a kinematic model of price fluctuations. We test the proposed model by analyzing index closing prices of the New York Stock Exchange and the DEM/USD tick-by-tick exchange rates obtained from Reuters EFX. We show that the model captures the characteristic features observed in actual financial time series, including volatility clustering, time scaling and fat tails in the probability density functions, power-law behavior of volatility correlations and, most importantly, the observed nonuniversal multifractal singularity spectrum. Motivated by our finding of strong agreement between the model and the data, we argue that at least two independent stochastic Gaussian variables are required to adequately model price fluctuations. r

We select the n stocks traded in the New York Stock Exchange and we form a statistical ensemble of daily stock returns for each of the k trading days of our database from the stock price time series. We study the ensemble return distribution for each trading day and we find that the symmetry properties of the ensemble return distribution drastically change in crash and rally days of the market. We compare these empirical results with numerical simulations based on the single-index model and we conclude that this model is unable to explain the behavior of the market in extreme days.

The benefits of listing a company's stock on a foreign exchange to achieve better global market integration have been quite extensively examined. What has been overlooked in the finance literature is an attempt to explain why the New York Stock Exchange (NYSE) tends to be bypassed in favor of the London market and other exchanges when firms select foreign exchanges for listing. This paper explains the behavior of firms in their selection of foreign stock markets for listing by using a signalling model. Another purpose of this study is to address the current dispute between the NYSE and the Securities and Exchange Commission (SEC) regarding the desire of the NYSE to relax its registration requirements in order to gain more listings by foreign companies.

Abstract: We study the average price impact of a single trade executed in the NYSE. After appropriate averaging and rescaling, the data for the 1000 most highly capitalized stocks collapse onto a single function, giving average price shift as a function of trade size. This function increases as a power that is the order of 1/2 for small volumes, but then increases more slowly for large volumes. We obtain similar results in each year from the period 1995-1998. We also find that small volume liquidity scales as a power of the stock capitalization.

We implement a graphical (or 'charting') heuristic, the 'bull flag', which accepts a particular pattern of historical prices as a signal for a future market price increase, test it with several years of New York Stock Exchange Composite Index history, and find positive results. The results support the validity of technical analysis for stock market price prediction and fail to confirm the efficient markets hypothesis.

This paper develops a framework to nonparametrically test whether discrete-valued irregularly-spaced financial transactions data follow a Markov process. For that purpose, we consider a specific optional sampling in which a continuous-time Markov process is observed only when it crosses some discrete level. This framework is convenient for it accommodates the irregular spacing that characterizes transactions data. Under such an observation rule, the current price duration is independent of a previous price duration given the previous price realization. A simple nonparametric test then follows by examining whether this conditional independence property holds. Monte Carlo simulations suggest that the asymptotic test has huge size distortions, though a bootstrap-based variant entails reasonable size and power properties in finite samples. As for an empirical illustration, we investigate whether bid-ask spreads follow Markov processes using transactions data from the New York Stock Exchange. The motivation lies on the fact that asymmetric information models of market microstructures predict that the Markov property does not hold for the bid-ask spread. We robustly reject the Markov assumption for three out of the five stocks under scrutiny. Finally, it is reassuring that our results are consistent with two alternative measures of asymmetric information.

Many questions about institutional trading can only be answered if one can track high-frequency changes in institutional ownership. In the US, however, institutions are only required to report their ownership quarterly in 13-F filings. We infer daily institutional trading behavior from the "tape", the Transactions and Quotes database of the New York Stock Exchange, using both a naive approach and a sophisticated method that best matches quarterly 13-F data. Increases in our measures of institutional flows negatively predict returns, particularly when institutions are selling. We interpret this as evidence that 13-F institutions compensate more patient investors for the service of providing liquidity. We also find that both very large and very small trades signal institutional activity, while medium size trades signal activity by the rest of the market.

In this paper we investigate the profitability of non-linear trading rules based on nearest neighbour (NN) predictors. Applying this investment strategy to the New York Stock Exchange, our results suggest that, taking into account transaction costs, the NN-based trading rule is superior to both a riskadjusted buy-and-hold strategy and a linear ARIMA-based strategy in terms of returns for all of the years studied (1997)(1998)(1999)(2000)(2001)(2002). Regarding other profitability measures, the NN-based trading rule yields higher Sharpe ratios than the ARIMA-based strategy for all of the years in the sample except for 2001.

In recent years, numerous volatility-based derivative products have been engineered. This has led to interest in constructing conditional predictive densities and confidence intervals for integrated volatility. In this paper, we propose nonparametric kernel estimators of the aforementioned quantities. The kernel functions used in our analysis are based on different realized volatility measures, which are constructed using the ex post variation of asset prices. A set of sufficient conditions under which the estimators are asymptotically equivalent to their unfeasible counterparts, based on an unobservable volatility process, is provided. Additionally, asymptotic normality is established. Based on the interpretation of related asymptotic properties that we derive, we additionally comment on a number of issues, including: the effects of using inconsistent volatility estimators in the presence of jumps and microstructure noise; and the asymptotic trade-offs between using robust realized volatility measures versus non-robust measures with differing data frequencies and different noise magnitudes, for example. The efficacy of the proposed estimators is examined via Monte Carlo experimentation, and an empirical illustration based upon data from the New York Stock Exchange is provided.

The efficient market hypothesis (EMH) is a cornerstone of financial economics. The EMH asserts that security prices fully reflect all available information and that the stock market prices securities at their fair values. Therefore, investors cannot consistently "beat the market" because stocks reside in perpetual equilibrium, making research efforts futile. This flies in the face of the conventional nonacademic wisdom that astute analysts can beat the market using technical or fundamental stock analysis. The purpose of this research is to partially assess whether technical analysts, who predict future stock prices by analyzing past stock prices, can consistently achieve a trading return that outperforms the stock market average return. This is tested using knowlege engineering experimentation with one price history pattern-the "bull flag stock chart"-which signals technical analysts of a future stock market price increase. A recognizer for the stock chart pattern is built using a template-matching technique from pattern recognition. The recognizer and associated trading rules are then tested by simulating trading on over 35 years of daily closing price data for the New York Stock Exchange Composite Index. The experiment is then replicated using the horizontal rotation or mirror image pattern of the "bull flag" (or "bear flag" stock chart) that signals a future stock market decrease. Results are systematic, statistically significant, and fail to confirm the null hypothesis based on a corollary to the EMH: that profit realized from trading determined by this heuristic method is no better than what would be realized from trading decisions based on random choice.

We perform a parallel analysis of the spectral density of (i) the logarithm of price and (ii) the daily number of trades of a set of stocks traded in the New York Stock Exchange. The stocks are selected to be representative of a wide range of stock capitalization. The observed spectral densities show a different power-law behavior. We confirm the

A frequently occurring, yet unexplored, phenomenon of the New York Stock Exchange specialist system is that of reassignments of stocks by specialist firms on the floor of the Exchange. These events change the portfolios at the individual specialist level by reassigning one or more stocks from one individual specialist portfolio to another. We find that reassigned stocks have unusually wide spreads before reassignments and experience a decline in spreads to levels comparable to matched stocks after the reassignment. This improvement in liquidity is associated with a reduced cost of capital for the reassigned firms. We find that portfolio size, and industry and size concentration of the individual specialist portfolios are associated with the decision of specialist firms to reassign stocks.

1 note that brokers might justify non-NYSE routing of market orders if execution quality includes commissions, opportunity costs, price impact, and execution speed in addition to transaction price and presents evidence consistent with their claim. 2 We refer to the original study, SEC (1997), instead of Lightfoot, Martin, Peterson, and Sirri (1997) because it is more detailed with respect to the analysis of limit orders.

Purpose – The paper seeks to analyze the impact of differences between the International Financial Reporting Standards (IFRS) and Generally Accepted Accounting Principles in the United States (US GAAP) in the economic-financial indicators of English companies. Design/methodology/approach – The research is characterized as descriptive-quantitative, using regression and correlation analysis. The study was developed on 37 English companies that negotiate American

The asymmetric moving average model (asMA) is extended to allow for asymmetric quadratic conditional heteroskedasticity (asQGARCH). The asymmetric parametrization of the conditional variance encompasses the quadratic GARCH model of . We introduce a framework for testing asymmetries in the conditional mean and the conditional variance, separately or jointly. Some of the new model's moment properties are also derived. Empirical results are given for the daily returns of the composite index of the New York Stock

We present a new method for articulating scale-dependent topological descriptions of the network structure inherent in many complex systems. The technique is based on "Partition Decoupled Null Models," a new class of null models that incorporate the interaction of clustered partitions into a random model and generalize the Gaussian ensemble. As an application we analyze a correlation matrix derived from four years of close prices of equities in the NYSE and NASDAQ. In this example we expose (1) a natural structure composed of two interacting partitions of the market that both agrees with and generalizes standard notions of scale (eg., sector and industry) and (2) structure in the first partition that is a topological manifestation of a well-known pattern of capital flow called "sector rotation." Our approach gives rise to a natural form of multiresolution analysis of the underlying time series that naturally decomposes the basic data in terms of the effects of the different scales at which it clusters. The equities market is a prototypical complex system and we expect that our approach will be of use in understanding a broad class of complex systems in which correlation structures are resident.

Recent studies have found that analyst ratings have become less biased following the Global Settlement and NASD and NYSE Rules implemented in 2002. Using analyst ratings issued around the expiration of the IPO quiet period, we investigate the possibility of excess returns for one-month, three-month, six-month, nine-month, and one-year holding periods based on rating strength since analyst ratings may be more reliable due to the decline in positive bias. We examine if analyst affiliation, sanctions against investment banks, or heterogeneous ratings influence returns following the end of the quiet period. We find that the existence of analyst coverage can predict future returns.