This paper provides evidence of informed trading by individual investors around earnings announcements using a unique data set of NYSE stocks. We show that intense aggregate individual investor ...buying (selling) predicts large positive (negative) abnormal returns on and after earnings announcement dates. We decompose abnormal returns following the event into information and liquidity provision components, and show that about half of the returns can be attributed to private information. We also find that individuals trade in both return-contrarian and news-contrarian manners after earnings announcements. The latter behavior has the potential to slow the adjustment of prices to earnings news.
Which Shorts Are Informed? BOEHMER, EKKEHART; JONES, CHARLES M.; ZHANG, XIAOYAN
The Journal of finance (New York),
April 2008, Letnik:
63, Številka:
2
Journal Article
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We construct a long daily panel of short sales using proprietary NYSE order data. From 2000 to 2004, shorting accounts for more than 12.9% of NYSE volume, suggesting that shorting constraints are not ...widespread. As a group, these short sellers are well informed. Heavily shorted stocks underperform lightly shorted stocks by a risk-adjusted average of 1.16% over the following 20 trading days (15.6% annualized). Institutional nonprogram short sales are the most informative; stocks heavily shorted by institutions underperform by 1.43% the next month (19.6% annualized). The results indicate that, on average, short sellers are important contributors to efficient stock prices.
We show that market-maker balance sheet and income statement variables explain time variation in liquidity, suggesting liquidity-supplier financing constraints matter. Using 11 years of NYSE ...specialist inventory positions and trading revenues, we find that aggregate market-level and specialist firm-level spreads widen when specialists have large positions or lose money. The effects are nonlinear and most prominent when inventories are big or trading results have been particularly poor. These sensitivities are smaller after specialist firm mergers, consistent with deep pockets easing financing constraints. Finally, compared to low volatility stocks, the liquidity of high volatility stocks is more sensitive to inventories and losses.
The neural network, one of the intelligent data mining technique that has been used by researchers in various areas for the past 10 years. Prediction and analysis of stock market data have got an ...important role in today’s economy. The various algorithms used for forecasting can be categorized into linear (AR, MA, ARIMA, ARMA) and non-linear models (ARCH, GARCH, Neural Network). In this paper, we are using four types of deep learning architectures i.e Multilayer Perceptron (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for predicting the stock price of a company based on the historical prices available. Here we are using day-wise closing price of two different stock markets, National Stock Exchange (NSE) of India and New York Stock Exchange (NYSE). The network was trained with the stock price of a single company from NSE and predicted for five different companies from both NSE and NYSE. It has been observed that CNN is outperforming the other models. The network was able to predict for NYSE even though it was trained with NSE data. This was possible because both the stock markets share some common inner dynamics. The results obtained were com- pared with ARIMA model and it has been observed that the neural networks are outperforming the existing linear model (ARIMA).
Many questions about institutional trading can only be answered if one tracks high-frequency changes in institutional ownership. In the United States, 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 a sophisticated method that best predicts quarterly 13-F data from trades of different sizes. We find that daily institutional trades are highly persistent and respond positively to recent daily returns but negatively to longer-term past daily returns. Institutional trades, particularly sells, appear to generate short-term losses—possibly reflecting institutional demand for liquidity—but longer-term profits. One source of these profits is that institutions anticipate both earnings surprises and post-earnings announcement drift. These results are different from those obtained using a standard size cutoff rule for institutional trades.
Objective: The purpose of the study is to identify the influence of merger and acquisition on the technology companies’ profitability. Method: Therefore, the five medium-sized technology companies, ...listed on New York Stock Exchange, and profitability ratios, including return on assets, return on equity, and earnings per share, have been selected. The Independent sample T-test is used for this study. The financial data that has been gathered from the U.S. Securities and Exchange Commission, companies’ annual reports, and NASDAQ website over the period from 2003 to 2020. Results: The results indicate that the earnings per share ratio increased while both returns on assets and return on equity ratios deteriorated. However, the earnings per share ratio significantly improved and the return on equity significantly declined while there was no significant impact on return on assets. The study concludes that the overall profitability decreased whereas the earnings per share faced a significant rise and return on equity ratios was found to be decreased significantly. Originality / Relevance: This study is one of the few studies which have evaluated the effect of the merger and acquisition on the profitability of medium-sized technology companies listed on New York Stock Exchange, over the period from 2003 to 2020. Theoretical/methodological contributions: The paper contributes to the existing literature on merger and acquisition by providing empirical evidence of the impact of merger and acquisition on profitability. Social/management contributions: This study will help technology investors and investment management companies to make investment decisions, and can also be helpful for the acquirer companies to handle the consequences of the merger on profitability.
We propose a Conditional Autoregressive Wishart (CAW) model for the analysis of realized covariance matrices of asset returns. Our model assumes an autoregressive moving average structure for the ...scale matrix of the Wishart distribution. It accounts for positive definiteness of covariance matrices without imposing parametric restrictions, and can be estimated by Maximum Likelihood. We also propose extensions of the CAW model obtained by including a Mixed Data Sampling (MIDAS) component and Heterogeneous Autoregressive (HAR) dynamics for long-run fluctuations. The CAW models are applied to realized variances and covariances for five New York Stock Exchange stocks.
The financial crisis of 2008 made Americans keenly aware of the impact Wall Street has on the economic well-being of the nation and its citizenry. Ott shows how the government, corporations, and ...financial institutions transformed stock investment from an elite to a mass practice at the beginning of the twentieth century.
Evoking a heady mix of the Cranbrook campus viewed from his window and the liquid viscerality of Hieronymus Bosch's Garden of Earthly Delights, which he was architecturally deconstructing at the ...time, architect and teacher Hani Rashid recounts his days as a Cranbrook student and the profound influence across the decades of those experiences on the work of his New York practice Asymptote Architecture.
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