scholarly journals STATISTICAL PROPERTIES OF STATISTICAL ENSEMBLES OF STOCK RETURNS

2000 ◽  
Vol 03 (03) ◽  
pp. 405-408 ◽  
Author(s):  
FABRIZIO LILLO ◽  
ROSARIO N. MANTEGNA
Keyword(s):  
New York ◽  
Stock Returns ◽  
Stock Price ◽  
Stock Exchange ◽  
Temporal Correlation ◽  
Statistical Properties ◽  
Central Moments ◽  
York Stock Exchange ◽  
Daily Stock Returns ◽  
Correlation Properties

We select n stocks traded in the New York Stock Exchange and 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 analyze each ensemble of stock returns by extracting its first four central moments. We observe that these moments are fluctuating in time and are stochastic processes themselves. We characterize the statistical properties of central moments by investigating their probability density function and temporal correlation properties.

scholarly journals Margin requirements and Stock Market Volatility in Bangladesh

2012 ◽  
Vol 3 (2) ◽  
pp. 29
Author(s):  
A. F. M. Mainul Ahsan ◽  
Mohammad Osman Gani ◽  
Md. Bokhtiar Hasan
Keyword(s):  
Stock Returns ◽  
Stock Price ◽  
Stock Exchange ◽  
Price Volatility ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Margin Requirements ◽  
Margin Requirement ◽  
Daily Stock Returns ◽  
The Impact

Officially margin requirements in bourses in Bangladesh were initiated on April 28, 1999, to limit the amount of credit available for the purpose of buying stocks. The goal of this paper is to measure the impact of changing margin requirement on stock returns' volatility in Dhaka Stock Exchange (DSE). The impact of margin requirement on stock price volatility has been extensively studied with mixed and ambiguous results. Using daily stock returns, we found mixed evidence that SEC's margin requirements have significant impact on market volatility in DSE.

scholarly journals The Market Reaction Around Ex-Dates of Stock Splits Before and After Decimalization

2005 ◽  
Vol 08 (02) ◽  
pp. 201-216 ◽  
Author(s):  
Robin K. Chou ◽  
Wan-Chen Lee ◽  
Sheng-Syan Chen
Keyword(s):  
New York ◽  
Stock Price ◽  
Trading Volume ◽  
Stock Exchange ◽  
New York Stock Exchange ◽  
Short Term ◽  
Tick Size ◽  
York Stock Exchange ◽  
Before And After ◽  
Abnormal Trading Volume

This paper examines the stock price behavior around the ex-split dates both before and after the decimalization on the New York Stock Exchange (NYSE). We find that the abnormal ex-split day returns decrease and the abnormal trading volume increases in the 1/16th and decimal pricing eras, relative to the 1/8th pricing era. These findings are consistent with the microstructure-based explanations for the ex-day price movements. Our study also supports the hypothesis that short-term traders perform arbitrage activities during the ex-split dates when transaction costs become lower after the tick size is reduced.

scholarly journals Time for Business: Business Ethics, Sustainability, and Giorgio Agamben’s ‘Messianic Time’

2016 ◽  
Vol 2 (3) ◽  
pp. 39-51
Author(s):  
Jeremy Kidwell
Keyword(s):  
New York ◽  
Stock Price ◽  
Stock Exchange ◽  
Recent Attempt ◽  
New York Stock Exchange ◽  
York Stock Exchange ◽  
Modern Business ◽  
Future Consequences ◽  
Economic Future ◽  
Hourly Basis

Contemporary business continues to intensify its radical relation to time. The New York Stock Exchange recently announced that in pursuing (as traders call it) the ‘race to zero’ they will begin using laser technology originally developed for military communications to send information about trades nearly at the speed of light. This is just one example of short-term temporal rhythms embedded in the practices of contemporary firms which watch their stock price on an hourly basis, report their earnings quarterly, and dissolve future consequences and costs through discounting procedures. There is reason to believe that these radical conceptions of time and its passing impair the ability of businesses to function in a morally coherent manner. In the spirit of other recent critiques of modern temporality such as David Couzen Hoys The Time of Our Lives, in this paper, I present a critique of the temporality of modern business. In response, I assess the recent attempt to provide an alternative account of temporality using theological concepts by Giorgio Agamben. I argue that Agamben’s more integrative account of messianic time provides a richer ambitemporal account which might provide a viable temporality for a new sustainable economic future.

scholarly journals The impact of earnings management on market liquidity

2020 ◽  
Vol 17 (2) ◽  
pp. 389-396
Author(s):  
Do Thi Van Trang ◽  
Dinh Hong Linh
Keyword(s):  
Earnings Management ◽  
Stock Returns ◽  
Stock Price ◽  
Stock Exchange ◽  
Fixed Effect Model ◽  
Market Liquidity ◽  
Listed Firms ◽  
Daily Stock Returns ◽  
The Impact ◽  
Dollar Volume

This article investigates the impact of earnings management on market liquidity measured by the depth of the market. Managers have desired to provide amazing performance of companies, manage their earnings through non-discretionary accruals. Consequently, investors have trouble evaluating the stock value and misunderstanding of the market liquidity because of manipulated information.To this aim, the fixed-effect model (FEM) is implemented to analyze the financial information of 170 listed firms on the Vietnam Stock Exchange over the period 2013–2016. The empirical results emphasized that market liquidity is influenced by earnings management that means the higher level of earnings management, the better equity liquidity. The findings provide additional insight into the determinants of stock liquidity such as earnings management, firm size, daily trading dollar volume of stock, average daily trading dollar volume of the firm, daily returns of stock, daily stock returns, average closing stock price of the firm.

scholarly journals Macroeconomic sensitivity and firm level volatility: the case of New York Stock Exchange

2021 ◽  
Vol 19 (02) ◽  
pp. 198-211
Author(s):  
Muhammad Saqib Bashir Butt ◽  
Hasniza Mohd. Taib
Keyword(s):  
New York ◽  
Exchange Rate ◽  
Stock Returns ◽  
Stock Exchange ◽  
Oil Prices ◽  
Yield Spread ◽  
New York Stock Exchange ◽  
Firm Level ◽  
Macroeconomic Factors ◽  
York Stock Exchange

Purpose – This paper investigates whether the macroeconomic factors affect the firm stock returns volatility differently depending on their location in different sectors. For this purpose, daily financial time-series data for 683 firms located in nine US sectors for the period of 2000 to 2017 are employed. Research methodology – The GARCH (1,1) model was applied to each firm located in nine US sectors. The four macroeconomic factors, namely, exchange rate, treasury yield spread, oil prices, and market return, are included in both mean and variance equations of GARCH (1,1) model to estimate the effect. Research limitations – This research study is limited to the New York Stock Exchange; therefore, it can be extended to the other economies as well. Further, this study uses one firm feature that is the sectoral location of the firm; it is recommended that some other firm features should be studied to explore the volatility behaviour of firms. In the methodological part, this study does not include the lag effect, since it is recognised in the literature that the investors underreact to public information, so future research can be extended to test the underreaction hypothesis. Practical implications – This study has implications for the investors and policymakers. Since it has emerged from the findings that some sectors are more sensitive than others to macroeconomic changes, so this knowledge will help the investors to diversify their portfolio and policymakers to maintain macroeconomic discipline. Originality/Value – The main contribution of this study is that it undertakes the assumption of heterogeneous nature of firms and conducts a detailed firm level analysis by sector covering a more extended period of time to investigate the impact of four macroeconomic factors, namely, exchange rate, treasury yield spread, oil prices, and market return on firm stock returns, volatility using daily data. Further, this study contributes by including all the macroeconomic factors together as an exogenous variable in mean and conditional variance equations of the GARCH (1,1) model to investigate the effect simultaneously.

Analysis of Big Data Using Two Mapper Files in Hadoop

2021 ◽  
Vol 13 (1) ◽  
pp. 69-77
Author(s):  
Jyotsna Malhotra ◽  
Jasleen Kaur Sethi ◽  
Mamta Mittal
Keyword(s):  
New York ◽  
Big Data ◽  
Stock Price ◽  
Stock Exchange ◽  
Uncertain Data ◽  
Real Life ◽  
Big Data Analytics ◽  
Private Companies ◽  
Government Organizations ◽  
York Stock Exchange

Nowadays, a large amount of valuable uncertain data is easily available in many real-life applications. Many industries and government organizations can exploit this data to extract valuable information. This information can help the managers to enhance their strategies and optimize their plans in making decisions. In fact, various private companies and governments have launched programs with investments and funds in order to maximize profits and optimize resources. This vast amount of data is called big data. The analysis of big data is important for future growth. This paper depicts big data analytics through experimental results. In this paper, data for New York stock exchange has been analyzed using two mapper files in Hadoop. For each year, the calculation of maximum and minimum price of every stock exchange and the average stock price is done.

International Stock Price Co-movement

2013 ◽  
Vol 12 (3) ◽  
pp. 157-191 ◽  
Author(s):  
Kenjiro Hirayama ◽  
Yoshiro Tsutsui
Keyword(s):  
New York ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
New York Stock Exchange ◽  
Common Shocks ◽  
Standard Time ◽  
Minute Period ◽  
York Stock Exchange ◽  
International Investors

Two possible causes of international stock price co-movement are examined: the existence of global common shocks and portfolio adjustments by international investors. Empirical analyses indicate that the former explains a significant part of the co-movement and the latter is unlikely to play an important role. We extend the analysis to intra-day high-frequency data. For example, when the Tokyo Stock Exchange begins its daily trading at 9:00 A.M. Japan Standard Time (JST), stock prices in Tokyo exhibit responses to preceding changes in New York. An analysis with minute-byminute data indicates that Tokyo's response to New York dissipates within about six minutes after opening. On the other hand, when the New York Stock Exchange (NYSE) opens at 9:30 A.M. Eastern Standard Time (EST), its response to Tokyo dissipates within 14 minutes. Thus, the movement of stock prices is transmitted rapidly across countries. Finally real-time simultaneous interactions between Shanghai (Shenzhen) and Tokyo are analyzed for a 30-minute period in the morning and a 60-minute period in the afternoon. Investors in Tokyo are watching stock prices in Shanghai, but not vice versa. Tight regulations on Chinese investors to prevent them from holding foreign stocks may be the reason why they do not pay any attention to stock price movements in Tokyo.

scholarly journals A Time-Series Approach to Non-Self-Financing Hedging in a Discrete-Time Incomplete Market

2008 ◽  
Vol 2008 ◽  
pp. 1-20 ◽  
Author(s):  
N. Josephy ◽  
L. Kimball ◽  
V. Steblovskaya
Keyword(s):  
New York ◽  
Time Series ◽  
Stock Price ◽  
Stock Exchange ◽  
Market Price ◽  
Incomplete Market ◽  
Model Parameters ◽  
York Stock Exchange ◽  
Risk Criterion ◽  
Time Series Approach

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 market calibrated model parameters that correspond to the market price of the option being hedged. In the second stage, an optimal set of model parameters is chosen from the market calibrated set. This choice is based on stock price simulations using a time-series model for stock price jump evolution. Results are presented for options traded on the New York Stock Exchange.

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