NYSE Composite Index Research Articles

Asymmetric volatility spillover from retail trading of noise traders: evidence from the gamestop short squeeze

Purpose-Recently, it has been observed that social trading and social media-based stocks are very popular especially for retail investors in the financial world. However, coordinated transactions of retail investors through social platforms carry the risk of classical noise trading, and the noise-based transactions of these investors, described as irrational investors, constitute a new source of systematic risk in the market. Considering the mentioned issues, this study aims to reveal the volatility and asymmetric volatility effects of social media-based noise trading of retail investors through the recent GameStop short squeeze. Methodology-The volatility and asymmetrical volatility spillovers from the social media-based retail transactions of the noise traders to the stock market were investigated using the EGARCH method, following Verma and Verma (2007), on the GameStop short position squeeze case. Findings-In the study, positive and statistically significant volatility spillover coefficient representing the spillover effect of the volatility in the GME index towards the NYSE Composite index and a negative and significant asymmetry coefficient revealed that the volatility spread from the GME index to the NYSE Composite index was asymmetrical. In this context, negative shocks in the GME index have a greater impact on the volatility of the NYSE Composite index than positive shocks. In addition, the findings of the study showed that there was no asymmetric volatility spillover from the NYSE Composite index to the GME index, and that past volatility in the GME index had a greater impact on present conditional volatility than the NYSE Composite index. Conclusion-This study determined that there was an asymmetrical volatility spillover from the GME index to the NYSE Composite index, and revealed that the social media-based retail transactions of noise traders may have not only volatility but also asymmetric volatility spillover effects in the markets. Keywords: GameStop, meme stocks, noise traders, retail trading, social media, asymmetric volatility JEL Codes: G10, G11, G14, G41

Crash Diagnosis and Price Rebound Prediction in NYSE Composite Index Based on Visibility Graph and Time-Evolving Stock Correlation Network.

This study proposes a framework to diagnose stock market crashes and predict the subsequent price rebounds. Based on the observation of anomalous changes in stock correlation networks during market crashes, we extend the log-periodic power-law model with a metric that is proposed to measure network anomalies. To calculate this metric, we design a prediction-guided anomaly detection algorithm based on the extreme value theory. Finally, we proposed a hybrid indicator to predict price rebounds of the stock index by combining the network anomaly metric and the visibility graph-based log-periodic power-law model. Experiments are conducted based on the New York Stock Exchange Composite Index from 4 January 1991 to 7 May 2021. It is shown that our proposed method outperforms the benchmark log-periodic power-law model on detecting the 12 major crashes and predicting the subsequent price rebounds by reducing the false alarm rate. This study sheds light on combining stock network analysis and financial time series modeling and highlights that anomalous changes of a stock network can be important criteria for detecting crashes and predicting recoveries of the stock market.

ARE STOCK MARKETS AND CRYPTOCURRENCIES CONNECTED?

This study explores the connectedness between cryptocurrencies (Bitcoin, Ethereum, Ripple, Bitcoin cash and Ethereum Operating System) and major stock markets (NYSE composite index, NASDAQ composite index, Shanghai Stock Exchange, Nikkei 225 and Euronext NV). Using the asymmetric dynamic conditional correlation (ADCC) and wavelet coherence approaches, we document a significant time-varying conditional correlation between the majority of the cryptocurrencies and stock market indices and that the negative shocks play a more prominent role than the positive shocks of the same magnitude. Overall, our findings explore potential avenues for diversification for investors across cryptocurrencies and major stock markets.

Share Price Analysis for Selected Companies based on Human Resource Practices

This paper investigates the links between human resources, finance and investments. These departments should work towards the same goal of producing profitability for the organisation and a high level performance. Without this, the organisation does not benefit from the skills in both teams. To highlight the link, finance and investment related measures linked to the price of 12 top ranking and 12 bottom ranking companies from the 2017 Workforce 100 list are evaluated to determine if differences exist in their share price performance. The selected companies are evaluated against selected benchmarks, NASDAQ Composite index as well as the NYSE Composite Index, to analyse their financial performance on a very high level. The analysis includes visual display of the logged share prices of the selected companies included in the study followed by year percentage change comparison and P/E ratio comparison to determine any fundamental differences. The top 12 ranking listed companies showed a higher year percentage change in 2017 based on share prices as well as a higher P/E ratio.

Do Corporate Name Changes Affect Share Price? An Event Studies Analysis of Corporate Rebranding on the NYSE

I conduct an event studies analysis of the effect of corporate name changes on short-term stock price movements for companies listed on the New York Stock Exchange. Using the NYSE Composite Index as a baseline for comparison, I find that changing corporate name has a small positive effect on share price over the subsequent thirty days.

Efficiency of Capital Markets: A Study with Special Reference To G7 Nations Stock Markets

This study aims to explore the linkages among G7 nations’ stock market indices, namely, CAC 40, FTSE 100, FTSE MIB, GDAXI, NIKKEI 225, NYSE COMPOSITE and S&P TSX COMPOSITE during the study period from April 2004 to March 2014 by using Co integration Test, Vector Error Correction Model (VECM) and Granger Causality Test. The results of Johansen Co integration Test found that daily returns of G7 nations stock market indices were Co Integrated, The co efficient value of Vector Error Correction Model implied that the FTSE MIB, GADAXI and S&P Tesx Composite did not witness short term relationship with CAC 40 INDEX, FTSE 100, NIKKEI 225 and NYSE COMPOSITE INDEX, rest of the indices recorded short run relationship. The results of Granger Causality Test exhibited bidirectional relationship between following indices - S&P TSX COMPOSITE and CAC 40 INDEX, S&P TSX COMPOSITE and FTSE MIB, S&P TSX COMPOSITE and NYSE COMPOSITE INDEX, CAC 40 and FTSE MIB. Unidirectional causal relationship was found between FTSE 100 and GDAXI, FTSE MIB and NIKKEI 225, NIKKEI 225 and S&P TSX COMPOSITE. Finally the study concluded that G7 nations’ stock market indices recorded both Short and Long run linkages during the study period, hence international investors G7 nations' investors could go for both long run and short run diversification for reducing their investment risk in future, if investors diversify their investment in to listed companies of G7 nations' stock market indices.

When noise trading fades, volatility rises

We hypothesize and test an inverse relation between liquidity and price volatility derived from microstructure theory. Two important facets of liquidity trading are examined: volume and noisiness. As represented by the expected turnover rate (volume) and realized average commission cost per share (noisiness) of NYSE equity trading, both facets are found negatively associated with the ex post and ex ante return volatilities of the NYSE stock portfolios and the NYSE composite index futures. Furthermore, the inverse association between noisiness and volatility is amplified in times of market crisis. The negative noisiness–volatility relation is also supported by our analysis on the effects of trade size on price volatility. The overall results demonstrate that volatility increases as noise trading declines.

Adaptive Evolutionary Neural Networks for Forecasting and Trading without a Data‐Snooping Bias

AbstractIn this paper, we present two neural‐network‐based techniques: an adaptive evolutionary multilayer perceptron (aDEMLP) and an adaptive evolutionary wavelet neural network (aDEWNN). The two models are applied to the task of forecasting and trading the SPDR Dow Jones Industrial Average (DIA), the iShares NYSE Composite Index Fund (NYC) and the SPDR S&P 500 (SPY) exchange‐traded funds (ETFs). We benchmark their performance against two traditional MLP and WNN architectures, a smooth transition autoregressive model (STAR), a moving average convergence/divergence model (MACD) and a random walk model. We show that the proposed architectures present superior forecasting and trading performance compared to the benchmarks and are free from the limitations of the traditional neural networks such as the data‐snooping bias and the time‐consuming and biased processes involved in optimizing their parameters. Copyright © 2015 John Wiley & Sons, Ltd.

Multivariate Option Pricing with Pair-Copulas

We propose a copula-based approach to solve the option pricing problem in the risk-neutral setting and with respect to a structured derivative written on several underlying assets. Our analysis generalizes similar results already present in the literature but limited to the trivariate case. The main difficulty of such a generalization consists in selecting the appropriate vine structure which turns to be of D-vine type, contrary to what happens in the trivariate setting where the canonical vine is sufficient. We first define the general procedure for multivariate options and then we will give a concrete example for the case of an option written on four indexes of stocks, namely, the S&P 500 Index, the Nasdaq 100 Index, the Nasdaq Composite Index, and the Nyse Composite Index. Moreover, we calibrate the proposed model, also providing a comparison analysis between real prices and simulated data to show the goodness of obtained estimates. We underline that our pair-copula decomposition method produces excellent numerical results, without restrictive assumptions on the assets dynamics or on their dependence structure, so that our copula-based approach can be used to model heterogeneous dependence structure existing between market assets of interest in a rigorous and effective way.

When Noise Trading Fades, Volatility Rises

We hypothesize and test an inverse relation between liquidity and price volatility derived from microstructure theory. Two important facets of liquidity trading are examined: thickness and noisiness. As represented by the expected volume (thickness) and realized average commission cost per share (noisiness) of NYSE equity trading, both facets are found negatively associated with the ex post and ex ante price volatilities of the NYSE stock portfolios and the NYSE composite index futures. Furthermore, the inverse association between volatility and noisiness is amplified in times of market crisis. The overall results demonstrate that volatility increases as noise trading declines. All findings retain statistical significance and materiality after controlling for a number of specifications.

Dow Jones U.S. Select Dividend Index: Returns Analysis and Maximizing theCoefficient of Determination

The study examined the characteristics of the equity return series of Dow Jones U.S. Select Dividend Index, S&amp;P 500 index, and NYSE composite index using the first four moments—mean (first moment), variance or standard deviation (second), skewness (third), and kurtosis (fourth)—from December 2001 to September 2011. Returns given by Dow Jones U.S. Select Dividend Index dwindled less than the market indexes in the last ten years, especially during 2008 when the U.S. was hit by a recession. The relationship between different market indexes, U.S. GDP, and LIBOR with Dow Jones U.S. Select Dividend Index was analyzed by calculating the coefficient of correlation. The regression analysis was performed to maximize the coefficient of determination. Results implied that both market indexes, U.S. GDP and LIBOR, when used together, better predicted the movements in the Dow Jones U.S. Select Dividend Index. It was also found that the Dow Jones U.S. Select Dividend Index is least correlated with U.S. GDP but highly correlated with LIBOR. <b>TOPICS:</b>Volatility measures, equity portfolio management

Forecasting volatility with support vector machine‐based GARCH model

AbstractRecently, support vector machine (SVM), a novel artificial neural network (ANN), has been successfully used for financial forecasting. This paper deals with the application of SVM in volatility forecasting under the GARCH framework, the performance of which is compared with simple moving average, standard GARCH, nonlinear EGARCH and traditional ANN‐GARCH models by using two evaluation measures and robust Diebold–Mariano tests. The real data used in this study are daily GBP exchange rates and NYSE composite index. Empirical results from both simulation and real data reveal that, under a recursive forecasting scheme, SVM‐GARCH models significantly outperform the competing models in most situations of one‐period‐ahead volatility forecasting, which confirms the theoretical advantage of SVM. The standard GARCH model also performs well in the case of normality and large sample size, while EGARCH model is good at forecasting volatility under the high skewed distribution. The sensitivity analysis to choose SVM parameters and cross‐validation to determine the stopping point of the recurrent SVM procedure are also examined in this study. Copyright © 2009 John Wiley &amp; Sons, Ltd.

Analysis and Comparison of ARCH Effects for Shanghai Composite Index and NYSE Composite Index

In this article, we used ARCH class model to analyze and compare the income fluctuations of Shanghai Stock Market and NYSE, and obtained the characters of fluctuation for two markets and the models which fitted to describe them. At the same time, we also analyzed the fluctuation overflow effect of two markets, validated the character that Chinese stock market was not relatively mature and possessed low international market integration degree, and put forward relative policy advances.

Improving moving average trading rules with boosting and statistical learning methods

AbstractWe present a system for combining the different types of predictions given by a wide category of mechanical trading rules through statistical learning methods (boosting, and several model averaging methods like Bayesian or simple averaging methods). Statistical learning methods supply better out‐of‐sample results than most of the single moving average rules in the NYSE Composite Index from January 1993 to December 2002. Moreover, using a filter to reduce trading frequency, the filtered boosting model produces a technical strategy which, although it is not able to overcome the returns of the buy‐and‐hold (B&amp;H) strategy during rising periods, it does overcome the B&amp;H during falling periods and is able to absorb a considerable part of falls in the market. Copyright © 2008 John Wiley &amp; Sons, Ltd.

Mean reversion in the US stock market

This paper revisits the evidence for the weaker form of the efficient market hypothesis, building on recent work by Serletis and Shintani [Serletis A, Shintani M. No evidence of chaos but some evidence of dependence in the US stock market. Chaos, Solitons & Fractals 2003;17:449–54], Elder and Serletis [Elder J, Serletis A. On fractional integrating dynamics in the US stock market. Chaos, Solitons & Fractals 2007;34;777–81], Koustas et al. [Koustas Z, Lamarche J.-F, Serletis A. Threshold random walks in the US stock market. Chaos, Solitons & Fractals, forthcoming], Hinich and Serletis [Hinich M, Serletis A. Randomly modulated periodicity in the US stock market. Chaos, Solitons & Fractals, forthcoming], and Serletis et al. [Serletis A, Uritskaya OY, Uritsky VM. Detrended Fluctuation analysis of the US stock market. Int J Bifurc Chaos, forthcoming]. In doing so, we use daily data, over the period from 5 February 1971 to 1 December 2006 (a total of 9045 observations) on four US stock market indexes – the Dow Jones Industrial Average, the Standard and Poor’s 500 Index, the NASDAQ Composite Index, and the NYSE Composite Index – and a new statistical physics approach – namely the ‘detrending moving average (DMA)’ technique, recently introduced by Alessio et al. [Alessio E, Carbone A, Castelli G, Frappietro V. Second-order moving average and scaling of stochastic time series. Euro Phys J B 2002;27;197–200.] and further developed by Carbone et al. [Carbone A, Castelli G, Stanley HE. Time dependent hurst exponent in financial time series. Physica A 2004;344;267–71, Carbone A, Castelli G, Stanley HE. Analysis of clusters formed by the moving average of a long-range correlated time series. Phys Rev E 2004;69;026105.]. The robustness of the results to the use of alternative testing methodologies is also investigated, by using Lo’s [Lo AW. Long-term memory in stock market prices. Econometrica 1991;59:1279–313.] modified rescaled range analysis. We conclude that US stock market returns display anti-persistence (mean reversion).

Testing for contagion under asymmetric dynamics: Evidence from the stock markets between US and Taiwan

This article is an attempt to test, through the use of forward forecasting test on dynamic conditional correlation (DCC), for contagion between Taiwan and US stocks under asymmetry. The process includes three steps. The first step uses the iterated cumulative sums of squares (ICSS) algorithm to detect the structural breaks of market return. The second step creates dummy variables for breaks, estimates EGARCH model of conditional generalized error distribution, and computes dynamic conditional correlation coefficients of DCC multivariate GARCH model. The third step employs one-step and N-step forecast test to check for contagion effect. The evidences prove the asymmetric leverage effect of Taiwan weighted stock index and New York—NYSE Composite Index. Interestingly, we discovered that there are two kinds of contagion, “positive” and “negative”, between markets.

PRICE FORMATION BASED ON PARTICLE-CLUSTER AGGREGATION

In the present work, we propose a microscopic model of financial markets based on particle-cluster aggregation on a two-dimensional small-world information network in order to simulate the dynamics of the stock markets. "Stylized facts" of the financial market time series, such as fat-tail distribution of returns, volatility clustering and multifractality, are observed in the model. The results of the model agree with empirical data taken from historical records of the daily closures of the NYSE composite index.

“Doing Well While Doing Good” Revisited: A Study of Socially Responsible Firms' Short‐Term versus Long‐term Performance

This article reexamines the “doing well while doing good” debate within the financial management literature, using comparisons among socially responsible mutual funds (SRMF(, the NYSE Composite Index, and a portfolio made up of firms most valued by SRMF managers )MostSRF(. The performance of MostSRF did no better or no worse than the over all market or SRMF in three to five year comparisons. However, results from the ten‐year performance comparison refute earlier studies and indicate that the market prices social responsibility characteristics in the long run. Given MostSRF out performed the other two indices in this time line, a new paradigm for understanding the impact of SRI is revealed.

Liquidity and The Law of One Price: The Case of the Cash/Futures Basis

Deviations from no-arbitrage relations should be related to frictions associated with transacting; in particular to market illiquidity, because frictions impede arbitrage. Thus, financial market liquidity may play a key role in moving prices to fair values. At the same time, a wide futures/cash basis may trigger arbitrage trades and thereby affect liquidity. We test these ideas by studying the joint dynamic structure of aggregate NYSE market liquidity and the NYSE Composite index futures basis for a relatively long time-period, over 3000 trading days. We find that liquidity and the basis forecast each other in addition to being contemporaneously correlated. There is evidence of two-way Granger causality between the short-term absolute basis and effective spreads, and quoted and effective spreads Granger-cause longer-term absolute bases. These results are preserved after including a proxy for arbitrage financing costs, the Federal Funds rate, which bears an independent positive and significant relation with the short-term absolute basis. Impulse response functions indicate that shocks to the absolute basis predict future stock market liquidity. Overall, the evidence suggests that stock market liquidity enhances the efficiency of the futures/cash pricing system.

NYSE Sector Returns and Political Cycles

We address three issues regarding the relationship between political party affiliation and returns in the equities markets, as measured by the NYSE Composite Index and its sub-indexes. First, we find a tendency for returns to be greater during Democratic presidential administrations; however, this result is statistically insignificant. Second, we conclude that returns during the last two years of presidential administrations are greater than during the first two years. Third, we examine the relationship between the majority party in each house of Congress and equity returns. We raise the possibility that party affiliation of Congress is a factor in explaining returns.