New York Stock Exchange

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 the unobservable volatility process, is provided. Asymptotic normality is also established. The efficacy of the estimators is examined via Monte Carlo experimentation, and an empirical illustration based upon data from the New York Stock Exchange is provided.

We study the nature of fluctuations in variety of price indices involving companies listed on the New York Stock Exchange. The fluctuations at multiple scales are extracted through the use of wavelets belonging to Daubechies basis. The fact that these basis sets satisfy vanishing moments conditions makes them ideal to extract local polynomial trends, through the low pass or 'average coefficients'. Subtracting the trends from the original time series yields the fluctuations, at different scales, depending on the level of low-pass coefficients used for finding the 'average behavior'. The fluctuations are then studied using wavelet based multifractal detrended fluctuation analysis to analyze their self-similar and non-statistical properties. Due to the multifractality of such time series, they deviate from Gaussian behavior in different frequency regimes. Their departure from random matrix theory predictions in such regimes is also analyzed. These deviations and non-statistical properties of the fluctuations can be instrumental in throwing significant light on the dynamics of financial markets.

In this comment we discuss the problem of reconciling the linear efficiency of price returns with the long-memory of supply and demand. We present new evidence that shows that efficiency is maintained by a liquidity imbalance that co-moves with the imbalance of buyer vs. seller initiated transactions. For example, during a period where there is an excess of buyer initiated transactions, there is also more liquidity for buy orders than sell orders, so that buy orders generate smaller and less frequent price responses than sell orders. At the moment a buy order is placed the transaction sign imbalance tends to dominate, generating a price impact. However, the liquidity imbalance rapidly increases with time, so that after a small number of time steps it cancels all the inefficiency caused by the transaction sign imbalance, bounding the price impact. While the view presented by Bouchaud et al. of a fixed and temporary bare price impact is self-consistent and formally correct, we argue that viewing this in terms of a variable but permanent price impact provides a simpler and more natural view. This is in the spirit of the original conjecture of Lillo and Farmer, but generalized to allow for finite time lags in the build up of the liquidity imbalance after a transaction. We discuss the possible strategic motivations that give rise to the liquidity imbalance and offer an alternative hypothesis. We also present some results that call into question the statistical significance of large swings in expected price impact at long times.

We match a template depicting a "bull flag" 60-day price behavior to closing prices of the New York Stock Exchange Composite Index to detect buying opportunities at a 5 trading day horizon. The results of the experiment indicate that the technique is capable of returning results which are superior to those attained by random choice and constitute evidence that the stock markets are not efficient.

An array of global events, including the global financial crisis, natural disasters, and the recent coronavirus pandemic, have consistently shown the vulnerability of global systems and humans to externally undesirable contagions. In order to further provide alternative approaches to information valuation, this study utilized the economic policy uncertainty (EPU) of 21 leading developed and developing economies (Australia, Brazil, Canada, Chile, China, Colombia, Denmark, France, Germany, Greece, India, Ireland, Italy, Japan, Korea, Netherlands, Russia, Spain, Sweden, the United Kingdom, and the United States of America) over the period January 1997 to May 2021. The information theory reveals the hierarchy of degrees of randomness in the EPU indices; it shows the information flow among the EPU indices through the mutual information metric and the graphical illustration of the information flows using network theory. Importantly, the Entropy measures indicate higher predictability of t...

I introduce parameterised response zero intelligence (PRZI), a new form of zero intelligence (ZI) trader intended for use in simulation studies of the dynamics of continuous double auction markets. Like Gode and Sunder's classic ZIC trader, PRZI generates quote prices from a random distribution over some specified domain of discretely valued allowable quote prices. Unlike ZIC, which uses a uniform distribution to generate prices, the probability distribution in a PRZI trader is parameterised in such a way that its probability mass function (PMF) is determined by a real-valued control variable s in the range [-1.0, +1.0] that determines the strategy for that trader. When s = 0 , a PRZI trader behaves identically to the ZIC strategy, with a uniform PMF; but when s ≈ ±1 the PRZI trader's PMF becomes maximally skewed to one extreme or the other of the price range, thereby making it more or less "urgent" in the prices that it generates, biasing the quote price distribution towards or away from the trader's limit price. To explore the co-evolutionary dynamics of populations of PRZI traders that dynamically adapt their strategies, I show initial results from long-term market experiments in which each trader uses a simple stochastic hill-climber algorithm to repeatedly evaluate alternative s-values and choose the most profitable at any given time. In these experiments the profitability of any particular s-value may be non-stationary because the profitability of one trader's strategy at any one time can depend on the mix of strategies being played by the other traders at that time, which are each themselves continuously adapting. Results from these market experiments demonstrate that the population of traders' strategies can exhibit rich dynamics, with periods of stability lasting over hundreds of thousands of trader interactions interspersed by occasional periods of change. Python source code for PRZI traders, and for the stochastic hill-climber, have been made publicly available on GitHub.

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

O objetivo deste estudo consiste em identificar nas Demonstrações Contábeis e nas Demonstrações Adicionais (Balanço Social e Demonstração do Valor Adicionado) quais são as diferenças e semelhanças das práticas de evidenciação concernentes aos passivos ambientais. Oito companhias que atuam no Brasil, submetidas à Bolsa de Valores de São Paulo (BOVESPA) e que emitiram ADRs na Bolsa de Nova Iorque (NYSE), foram analisadas. O estudo caracteriza-se como exploratóriodescritivo, sendo que a técnica utilizada na pesquisa foi à análise de conteúdo. Observou-se que não houve homogeneidade quanto à formatação e apresentação das demonstrações adicionais. Além disso, as informações ambientais publicadas nos EUA são mais amplas do que as publicadas no Brasil.

We analyze an emerging sustainable trend in asset management: the decarbonization of institutional portfolios. By using broad institutional ownership data, we show that investors exhibit herding behavior in the sense of decarbonization. They are inclined to follow their own or other investors' buys in green stocks and sales in brown stocks over adjacent quarters. Beyond that, we find that Hedge Funds as well as Investment Advisors lead the herd by executing trades in the sense of decarbonization. This is in line with expectations that sophisticated investors, who integrate environmental aspects into their investment decision process, are able to attract imitators. For the aspired achievement of market-wide decarbonization, investors leading the herd should be encouraged to further decarbonize their portfolios in order to trigger follow-up trades.

Regulators impose price limits on daily price movements to protect investors from excessive volatility, but several empirical studies have cast serious doubt on the benefits of such mechanisms. Using a large cross-sectional sample combined with intraday data from the Tokyo Stock Exchange, this study finds evidence that partially supports conventional criticisms that price limits spread out volatility, delay price discovery, and interrupt trading activities. More importantly, the transaction data analysis reveals that price limits help to reduce order imbalance and improve information asymmetry, justifying the existence of price limits on the Tokyo Stock Exchange.

Irrational exuberance has run its course. The first three years of the 21st century brought one of the worst bear markets in history, with equity markets around the world falling some 40 percent in real terms. Yet, despite fortunes lost on Wall Street, many investors remain irrationally optimistic about future long-run equity returns: The pervasive belief is that over the long haul, equities are sure to keep up with inflation. Unlike short-lived bursts of exuberance that may burst like a bubble, the excessive optimism of investors has been long term and systematic. In this article, we use the global database from our recent book, Triumph of the Optimists , to confront the optimism of investors with the reality of history. Equities are risky, so they should offer a higher expected return than cash or government bonds. Historically, they have given a handsome reward for risk. But although continuing to expect a risk premium in the future is rational, many investors overestimate the rewards and underestimate the risks of investing in stocks when the stocks are held over the very long term. U.S. corporations seem especially prone to irrational optimism. reported that for the 380 companies in the S&P 500 Index with defined-benefit plans, projected plan returns averaged around 9 percent in each of the three years 2000, 2001, and 2002. If we assume a classic 60/40 asset allocation to stocks and bonds, average bond yields during this period imply a projected equity return of some 12.5 percent. This percentage, given the implied inflation forecast of 2.0-2.5 percent inferred from conventional and inflation-indexed bond yields, is equivalent to 10 percent in real terms. Note that 10 percent is the average: around one half of all plan sponsors may be forecasting real equity returns higher than 10 percent. Plan sponsors do not necessarily make an explicit assumption that the nominal return on equities will exceed 12.5 percent or that the real return will exceed 10 percent. When formulating their expectations, funds may build in some incremental performance from equity market alphas and may assume superior rewards through alternative assets or through market timing. Of course,

The Standard and Poor's 500 index is a crucial benchmark for investors, financial analysts, and policymakers to assess stock market performance and make informed investment decisions. Accurate S&P 500 closing price prediction is vital for efficient portfolio management and risk mitigation. This study explores the application of neural networks, specifically the Feedforward Neural Network, to predict the S&P 500 closing price. The author used a dataset from 1950 to 2024, including variables such as open, high, low, close, and volume as the initial step. Various training-testing ratios are tested to evaluate the models' performance. The results highlight that an eighty to twenty split yields the best predictive accuracy, with the lowest Mean Absolute Error and Mean Absolute Percentage Error. Additionally, this study compares the Feedforward Neural Network's predictions to polynomial regression models and investigates cluster-wise fitting techniques to enhance accuracy. The findings demonstrate that neural networks can significantly improve the predictive power of S&P 500 closing prices, particularly when combined with advanced regression techniques, providing valuable insights for academic research and practical financial decisions.

The Effective Federal Funds Rate (EFFR) is a crucial interest rate that reflects the cost of banks borrowing funds from each other overnight. This rate is a significant indicator of the financial system's health and stability in the United States. The rate values fluctuate due to various factors, such as changes in monetary policy, supply and demand for reserves, market expectations, events in financial markets, seasonal factors, and changes in regulations. Although it's challenging to pinpoint the exact factors that cause the variation in this rate, the concepts of time series are employed to identify peak points of such changes. The resulting figures are obtained using R software, and the reviews corresponding to each EFFR value are parallel to the business market incidence in the USA. At the end of this paper, all the EFFR values from the year 2016 to 2021 are included, along with their corresponding reviews.

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.

A substantial number of last reported transactions for stocks trading on the New York Stock Exchange occur inside the quoted closing bid-ask spread. The tendency to close inside the spread results in price change magnitudes much smaller than those predicted from binomial models. Moreover, although the change magnitude is biased by the underlying trend of the market, the distribution of next day price change relatives is largely unaffected. The result is a systematic regularity between the location of today's close and tomorrow's close relative to the bid-ask spread.

Competition and Cooperation among Exchanges: A Theory of Cross-listing and Endogenous Listing Standard," Journal of Financial Economics 82 (2006), 445-489. Interested readers should consult this paper for the mathematical analysis and formal proofs supporting the arguments of this article. 1. By "listing standards" we mean not only the initial listing requirements (such as those regarding the profitability record, number of shares (float), and minimum market capitalization), but also the stringency of their disclosure and other regulations, and the rigor with which these regulations are enforced.

CDA, automated mechanism design, ZI-C Agents, hybrid markets, agent-based simulation, evolutionary strategy, BICAS The Continuous Double Auction (CDA) is one of the most popular of all auction markets in the world. Some of the biggest trade markets in the world including the New York Stock Exchange (NYSE) and the Chicago Mercantile Exchange are organised as CDAs. Previous work by Cliff has shown that previously unknown 'hybrid' variants of the traditional CDA can be found to give the most desirable market dynamics. Cliff's results were based on experiments conducted using a computational simulation of the CDA populated by electronic Zero Intelligence Plus (ZIP) traders and his work uses a GA to co-evolve the market mechanism as well as the ZIP agent parameters. The exclusive use of the ZIP trading algorithm in all his work raises questions about the robustness of his results to any change in the trading algorithm used. In this thesis we use a self-adaptive Evolutionary Strategy (ES) to explore the space of possible auction types in a CDA populated by Gode and Sunder's cognitively simple Zero Intelligence (ZI) traders. We show that hybrid CDAs are still preferred over traditional variants and our results provide the first demonstration that hybrid variants of the CDA can provide favourable dynamics for trading strategies other than ZIP.

for their help in preparing the report. The views expressed here are those of Ruben Lee alone and do not necessarily represent the views of any of the sponsors of the report. While every effort has been made to ensure the accuracy of the report, no factual material or statements presented here are guaranteed correct.

for their help in preparing the report. The views expressed here are those of Ruben Lee alone and do not necessarily represent the views of any of the sponsors of the report. While every effort has been made to ensure the accuracy of the report, no factual material or statements presented here are guaranteed correct.

OST ECONOMIC TIME SERIES EXHIBIT seasonal variation or cyclical patterns. Recent literature argues that seasonality should be explicitly accounted for, perhaps by including seasonal dummy variables in the model . This model-based approach to seasonality and cyclical patterns has become more appealing as problems with using seasonally adjusted data continue to surface (e.g.,

TrAgent is a software-agent based model for a stock exchange such as the New York Stock Exchange and the procedure of trading securities on the trading floor. The model comprises the complete process from the initiation of a trading order until its execution. The software agent paradigm is the framework for implementing the proposed model. The properties of intelligent software agents meet the characteristics of the actors on the trading floor and provide capabilities for efficient distributed computing. Because of the space restriction, the main focus of this paper is on stock broker agent, one of the most important agents in TrAgent. Intelligence is provided to the stock broker agent in order to make reasoning and decisions on the profitability of a firm. This intelligence is provided using Fuzzy Expert System. The paper further discusses the design and development issues concerning different components of the model.

We investigate the correlation properties of transaction data from the New York Stock Exchange. The trading activity fi(t) of each stock i displays a crossover from weaker to stronger correlations at time scales 60 -390 minutes. In both regimes, the Hurst exponent H depends logarithmically on the liquidity of the stock, measured by the mean traded value per minute. All multiscaling exponents τ (q) display a similar liquidity dependence, which clearly indicates the lack of a universal form assumed by other studies. The origin of this behavior is both the long memory in the frequency and the size of consecutive transactions.

We reanalyze high resolution data from the New York Stock Exchange and find a monotonic (but not power law) variation of the mean value per trade, the mean number of trades per minute and the mean trading activity with company capitalization. We show that the second moment of the traded value distribution is finite. Consequently, the Hurst exponents for the corresponding time series can be calculated. These are, however, non-universal: The persistence grows with larger capitalization and this results in a logarithmically increasing Hurst exponent. A similar trend is displayed by intertrade time intervals. Finally, we demonstrate that the distribution of the intertrade times is better described by a multiscaling ansatz than by simple gap scaling.

For many externally driven complex systems neither the noisy driving force, nor the internal dynamics are a priori known. Here we focus on systems for which the time dependent activity of a large number of components can be monitored, allowing us to separate each signal into a component attributed to the external driving force and one to the internal dynamics. We propose a formalism to capture the potential multiscaling in the fluctuations and apply it to the high frequency trading records of the New York Stock Exchange. We find that on the time scale of minutes the dynamics is governed by internal processes, while on a daily or longer scale the external factors dominate. This transition from internal to external dynamics induces systematic changes in the scaling exponents, offering direct evidence of non-universality in the system.

Complex systems comprise a large number of interacting elements, whose dynamics is not always a priori known. In these cases -in order to uncover their key features -we have to turn to empirical methods, one of which was recently introduced by Menezes and Barabási. It is based on the observation that for the activity fi(t) of the constituents there is a power law relationship between the standard deviation and the mean value: σi ∝ fi α . For stock market trading activity (traded value), good scaling over 5 orders of magnitude with the exponent α = 0.72 was observed. The origin of this non-trivial scaling can be traced back to a proportionality between the rate of trades N and their mean sizes V . One finds V ∝ N 0.69 for the ∼ 1000 largest companies of New York Stock Exchange. Model independent calculations show that these two types of scaling can be mapped onto each other, with an agreement between the error bars. Finally, there is a continuous increase in α if we look at fluctuations on an increasing time scale up to 20 days.

Fluctuation scaling is observed phenomenon from complex networks through finance to ecology. It means that the variance and the mean of a specific quantity are related as ˙σ2 |n ¸∝ n|A 2α with 1/2 ≥ α ≥ 1 when a parameter A (usually the system size) is varied. A can be the strength of the node, the capitalization of the firm or the area of the habitat. On the other hand, quantities often obey gap scaling meaning that their density function depends on, say, the system size A as P (n|A) = n -1 F (n/A Φ ). This note describes that these two notions cannot coexist except when α = 1. In this way one can empirically exclude the possibility of gap scaling in many complex systems including population dynamics, stock market fluctuations and Internet router traffic, where α < 1.

Complex systems comprise a large number of interacting elements, whose dynamics is not always a priori known. In these cases -in order to uncover their key features -we have to turn to empirical methods, one of which was recently introduced by Menezes and Barabási. It is based on the observation that for the activity fi(t) of the constituents there is a power law relationship between the standard deviation and the mean value: σi ∝ fi α . For stock market trading activity (traded value), good scaling over 5 orders of magnitude with the exponent α = 0.72 was observed. The origin of this non-trivial scaling can be traced back to a proportionality between the rate of trades N and their mean sizes V . One finds V ∝ N 0.69 for the ∼ 1000 largest companies of New York Stock Exchange. Model independent calculations show that these two types of scaling can be mapped onto each other, with an agreement between the error bars. Finally, there is a continuous increase in α if we look at fluctuations on an increasing time scale up to 20 days.

We develop a novel bidding strategy that software agents can use to buy and sell goods in Continuous Double Auctions (CDAs). Our strategy involves the agent forming a bid or ask by assessing the degree of risk involved and making a prediction about the competitive equilibrium that is likely to be reached in the marketplace. We benchmark our strategy against two of the most common strategies for CDAs, namely the Zero-Intelligence and the Zero-Intelligence Plus strategies, and we show that our agents outperform these benchmarks. Specifically, our agents win in 100% of the simulations against the ZI agents and, on average, 75% of the games against the ZIP agents.

We develop a novel bidding strategy that software agents can use to buy and sell goods in Continuous Double Auctions (CDAs). Our strategy involves the agent forming a bid or ask by assessing the degree of risk involved and making a prediction about the competitive equilibrium that is likely to be reached in the marketplace. We benchmark our strategy against two of the most common strategies for CDAs, namely the Zero-Intelligence and the Zero-Intelligence Plus strategies, and we show that our agents outperform these benchmarks. Specifically, our agents win in 100% of the simulations against the ZI agents and, on average, 75% of the games against the ZIP agents.

We provide existence conditions and analytical expressions of the moments of logarithmic autoregressive conditional duration (Log-ACD) models. We focus on the dispersion index and the autocorrelation function and compare them with those of ACD (Engle and Russell 1998) and SCD models. Using duration data for several stocks traded on the New York Stock Exchange, we compare the models in terms of their ability at fitting some stylized facts.

Jumps in prices have been shown to be an indispensable characteristic of asset returns from a modeling perspective, 1 or when volatility models are calibrated with option data. 2 Over the past decade, nonparametric methods afforded by the advent of high-frequency data have made the direct identification of jumps possible and uncovered many more jumps 1 [38, 40]. 2 [10, 22, 36].

specific duties have developed from the "shingle theory," the principle requiring fair dealing by securities dealers, 2 and others have arisen under again, the thirst for facts that most courts have when approaching questions of the existence and scope of duties labelled as fiduciary. See, e.g., Romano, 834 F.2d at 530; Leib, 461 F. Supp. at 953. Among the more thoughtful analyses of the sources and content of fiduciary duties are J. SHEPHERD, THE LAW OF FIDUCIARIES 96-110 (1981) (defining the fiduciary relationship as one where one person acquires powers to act with respect to another's affairs in circumstances where the powers are obtained subject to the condition that they be exercised to benefit another, a so-called "encumbered power"); Frankel, Fiduciary Law, 71 CALIF. L. REV. 795, 802-04 (1983) (finding that the modern phenomena of increasingly greater specialization of knowledge and the development of systemic inequalities of information and access to information creates classes of persons who must "entrust" their well-being to others, and that the necessity of entrustment should create protectable reliance interests); DeMott, Beyond Metaphor: An Analysis of Fiduciary Obligation, 1988 DUKE L.J. 879, 914-15. DeMott's article gives the particularly helpful insight that most satisfactory explanations of the fiduciary relation are descriptive. DeMott emphasizes the importance of analyzing particular relations to arrive at specific requirements of the fiduciary duty in setting of a given relationship. DeMott explains the fiduciary duty as follows: Described instrumentally, the fiduciary obligation is a device that enables the law to respond to a range of situations in which, for a variety of reasons, one person's discretion ought to be controlled because of characteristics of that person's relationship with another. This instrumental description is the only general assertion about fiduciary obligation that can be sustained.

specific duties have developed from the "shingle theory," the principle requiring fair dealing by securities dealers, 2 and others have arisen under again, the thirst for facts that most courts have when approaching questions of the existence and scope of duties labelled as fiduciary. See, e.g., Romano, 834 F.2d at 530; Leib, 461 F. Supp. at 953. Among the more thoughtful analyses of the sources and content of fiduciary duties are J. SHEPHERD, THE LAW OF FIDUCIARIES 96-110 (1981) (defining the fiduciary relationship as one where one person acquires powers to act with respect to another's affairs in circumstances where the powers are obtained subject to the condition that they be exercised to benefit another, a so-called "encumbered power"); Frankel, Fiduciary Law, 71 CALIF. L. REV. 795, 802-04 (1983) (finding that the modern phenomena of increasingly greater specialization of knowledge and the development of systemic inequalities of information and access to information creates classes of persons who must "entrust" their well-being to others, and that the necessity of entrustment should create protectable reliance interests); DeMott, Beyond Metaphor: An Analysis of Fiduciary Obligation, 1988 DUKE L.J. 879, 914-15. DeMott's article gives the particularly helpful insight that most satisfactory explanations of the fiduciary relation are descriptive. DeMott emphasizes the importance of analyzing particular relations to arrive at specific requirements of the fiduciary duty in setting of a given relationship. DeMott explains the fiduciary duty as follows: Described instrumentally, the fiduciary obligation is a device that enables the law to respond to a range of situations in which, for a variety of reasons, one person's discretion ought to be controlled because of characteristics of that person's relationship with another. This instrumental description is the only general assertion about fiduciary obligation that can be sustained.

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

The automated auction has transformed securities markets. Advantages of speed, simplicity, scalability, and low costs drive the rapid adoption of this mechanism to trade equities, bonds, foreign exchange, and derivatives worldwide. But automated auctions depend on public limit orders to provide liquidity, raising natural questions regarding the resiliency of the mechanism under stress. This paper provides an analysis of the stochastic dynamics of liquidity and its relation to volatility shocks using data from a futures market. Aggregate market liquidity exhibits considerable variation, and is inversely related to volatility, as predicted by our model. However, liquidity shocks dissipate quickly, indicating a high degree of market resiliency. This fact has important practical implications, particularly as regards institutional trading, automated market making, logical participation strategies, and market protocols.

We consider the problem of fast time-series data clustering. Building on previous work modeling the correlation-based Hamiltonian of spin variables we present a fast non-expensive agglomerative algorithm. The method is tested on synthetic correlated time-series and noisy synthetic data-sets with built-in cluster structure to demonstrate that the algorithm produces meaningful non-trivial results. We argue that ASPC can reduce compute time costs and resource usage cost for large scale clustering while being serialized and hence has no obvious parallelization requirement. The algorithm can be an effective choice for state-detection for online learning in a fast non-linear data environment because the algorithm requires no prior information about the number of clusters.

I. INTRODUCTION Early empirical studies found that the Japanese stock market was one of the least integrated, had many unique characteristics, and was relatively isolated from other national stock markets, e.g., Ripley (1973). However, the gradual liberalization of the Japanese product and capital markets has substantially changed this picture. The Tokyo Stock Exchange (TSE) is increasingly becoming one of the most important players in the international arena. Along with the New York Stock Exchange (NYSE), they are the largest in terms of capitalization, volume and shares listed, and are perhaps the most influential in the world. The general movement of stock prices on the NYSE and TSE receive considerable attention from the international press, portfolio managers, international mutual funds, and financial institutions, among others. Exploring the nature and degree of interdependence between the two stock exchanges is, therefore, an important task and of interest to both private and institutional investors. Eun and Shim (1989) investigate the international transmission mechanism of stock market movements and conclude that innovations in the U.S. are rapidly transmitted to the rest of the world, whereas innovations in other national markets have a very low impact on the U.S. market. They also find that the Japanese market acts like a follower in the international market. Becker, Firmer& and Gupta (1990) examine the intertemporal relation of the U.S. and the Japanese stock markets and conclude that the U.S. market greatly influences the Japanese market, while the Japanese market has a smaller impact on the U.S. market. Von Furstenberg and Jeon (1989) and Fischer and Palasvirta (1990), on the other hand, find that co-movement in stock prices across markets, particularly the U.

This paper provides a comprehensive study of the impact of changes in lot size or the Minimum Trade Unit (MTU) on the Tokyo Stock Exchange (TSE). The event of the MTU changes in Japan provides an ideal setting to explore several interesting issues related to the investor base, liquidity, noise trading, brokerage commission, and stock value. From a sample of 118 TSE listed firms that reduced their MTU during the 19962000 period, we find a substantial increase in the number of individual investors and improvement in liquidity for small trades. Our results suggest that there is a shift in order flow from large to small trades after the MTU change: trading activity for small-size trades intensifies while activity for large trades dwindles. Our investigation of return volatility also reveals that noise trading increases as more uninformed, small investors enter the market after the MTU change. Further, MTU changes appear to attract more brokerage firms to cover the stock, possibly induce...

Abstract���This study reconsiders the role of jumps for volatility forecasting by showing that jumps have positive and mostly significant impact on future volatility. This result becomes apparent once volatility is correctly separated into its continuous and discontinuous component. To this purpose, we introduce the concept of threshold multipower variation (TMPV), which is based on the joint use of bipower variation and threshold estimation. With respect to alternative methods, our TMPV estimator provides less biased and robust ...

Increasing globalization, complexity of capital markets and the expanding range of exotic financial instruments have made financial risk management difficult to evaluate. As a consequence, a rise in use of more sophisticated risk management systems has not led to better results. Most financial data exhibits time varying volatility and heavy tails therefore an appropriate risk measure should capture these features. Volatility patterns reflect different characteristics in different stock markets. The main aim of this study is to improve on volatility estimation by use of Quantile Autoregression frameworks. To avoid strong assumptions about the form of innovations, an initial proxy of volatility estimator is proposed. The estimator is assumed to capture the intraday volatility based on the conditional Interquantile Autoregressive Range. A class of a-mixing time series models based on quantile regression are used and direct estimation of coefficients as introduced by Koenker and Bassett...

In this paper linear canonical correlation analysis (LCCA) is generalized by applying a structured transform to the joint probability distribution of the considered pair of random vectors, i.e., a trans-formation of the joint probability measure defined on their joint observation space. This framework, called measure transformed canonical correlation analysis (MTCCA), applies LCCA to the data after transformation of the joint probability measure. We show that judicious choice of the transform leads to a modified canonical correlation analysis, which, in contrast to LCCA, is capable of detecting non-linear relationships between the considered pair of random vectors. Unlike kernel canonical correlation analysis, where the transformation is applied to the random vectors, in MTCCA the transformation is applied to their joint probability distribution. This results in performance advantages and reduced implementation complexity. The proposed approach is illustrated for graphical model sel...

exchanges and markets. The theory of SRO regulation is "self-regulation," i.e., that securities professionals should participate in writing rules governing the industry and in judging whether the conduct of industry participants meets those standards. Originally, the SEC set legal "minimums" and the SROs would establish higher standards of behavior and judge their members accordingly. For example, NASD members must meet "just and equitable principles of trade," a standard that is more rigorous than the SEC's requirements, which, at their core, amount to prohibitions against fraud. Self-regulation also means that industry professionals "know where the bodies are buried," and can determine quickly whether an activity is unfair to investors or is appropriate and consistent with industry norms. Reinforcing these regulatory strictures is the SEC, which can take regulatory or disciplinary action if the SROs fail to do so. In the famous words of William O. Douglas, "the Government would keep the shotgun, so to speak, behind the door, well oiled, cleaned and ready for use but with the hope it would never be used." 10 Over time, the SEC has demanded that the SROs beef up enforcement efforts, expand their regulatory programs, and have key committees or boards controlled by public members. 11 Although Congress has updated the self-regulatory system from time to time, its essential components remain unchanged. REEXAMINING THE ORGANIZATION OF SROS

The paper deals with the problem of defining money in a system with derivatives. We conclude that derivatives have to be included in the definition of money, and support our conclusions with an econometric test on the New York Stock Exchange (NYSE) and Chicago Board of Trade indexes. We focus on the direct relationship between derivatives' supply and the interest rate, the analytical basis of speculative money demand introduced by Keynes and the foundation of the Fratianni-Savona model to single out the international monetary base. Consequently, monetary aggregates measured by international institutions, such as the Bank for International Settlements, underestimate the actual offshore market size. Derivatives are the primary instruments used by speculators. There is money, mainly in reserve currencies, that is not controlled and that may cause systemic instability (e.g., the recent Asian crisis).

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.

This paper develops a family of autoregressive conditional duration (ACD) models that encompasses most specifications in the literature. The nesting relies on a Box-Cox transformation with shape parameter λ to the conditional duration process and a possibly asymmetric shocks impact curve. We establish conditions for the existence of higher-order moments, strict stationarity, geometric ergodicity and β-mixing property with exponential decay. We next derive moment recursion relations and the autocovariance function of the power λ of the duration process. Finally, we assess the practical usefulness of our family of ACD models using NYSE price duration data on the IBM stock. The results warrant the extra flexibility provided either by the Box-Cox transformation or by the asymmetric response to shocks.

We present an algorithm producing a dynamic non-self-financing hedging strategy in an incomplete market corresponding to investor-relevant risk criterion. The optimization is a two stage process that first determines admissible model parameters that correspond to the market price of the option being hedged. The second stage applies various merit functions to bootstrapped samples of model residuals to choose an optimal set of model parameters from the admissible set. Results are presented for options traded on the New York Stock Exchange.