scholarly journals The Mildest Recession: Output, Profits, and Stock Prices as the U.S. Emerges from the 2001 Recession

10.3386/w8938 ◽  
2002 ◽  
Author(s):  
William Nordhaus
Keyword(s):  
Stock Prices ◽  
The U.S

scholarly journals The Effects from The U.S. and Japan to Emerging Stock Markets in Asia and Vietnam

2021 ◽  
Author(s):  
SDAG Lab
Keyword(s):  
Emerging Markets ◽  
Stock Prices ◽  
Stock Markets ◽  
Spillover Effect ◽  
Spillover Effects ◽  
The Philippines ◽  
Volatility Spillovers ◽  
Return Effect ◽  
Asian Emerging Markets ◽  
The U.S

The subprime mortgage crisis in the U.S. in mid-2008 suggests that stock prices volatility do spillover from one market to another after international stock markets downturn. The purpose of this paper is to examine the magnitude of return and volatility spillovers from developed markets (the U.S. and Japan) to eight emerging equity markets (India, China, Indonesia, Korea, Malaysia, the Philippines, Taiwan, Thailand) and Vietnam. Employing a mean and volatility spillover model that deals with the U.S. and Japan shocks and day effects as exogenous variables in ARMA(1,1), GARCH(1,1) for Asian emerging markets, the study finds some interesting findings. Firstly, the day effect is present on six out of nine studied markets, except for the Indian, Taiwanese and Philippine. Secondly, the results of return spillover confirm significant spillover effects across the markets with different magnitudes. Specifically, the U.S. exerts a stronger influence on the Malaysian, Philippine and Vietnamese market compared with Japan. In contrast, Japan has a higher spillover effect on the Chinese, Indian, Korea, and Thailand than the U.S. For the Indonesian market, the the return effect is equal. Finally, there is no evidence of a volatility effect of the U.S. and Japanese markets on the Asian emerging markets in this study.

scholarly journals The effects from the United States and Japan to emerging stock markets in Asia and Vietnam

2020 ◽  
Author(s):  
AISDL
Keyword(s):  
United States ◽  
Emerging Markets ◽  
Stock Prices ◽  
Stock Markets ◽  
Spillover Effect ◽  
Spillover Effects ◽  
The United States ◽  
The Philippines ◽  
Asian Emerging Markets ◽  
The U.S

The subprime mortgage crisis in the United States (U.S.) in mid-2008 suggests that stock prices volatility do spillover from one market to another after international stock markets downturn. The purpose of this paper is to examine the magnitude of return and volatility spillovers from developed markets (the U.S. and Japan) to eight emerging equity markets (India, China, Indonesia, Korea, Malaysia, the Philippines, Taiwan, Thailand) and Vietnam. Employing a mean and volatility spillover model that deals with the U.S. and Japan shocks and day effects as exogenous variables in ARMA(1,1), GARCH(1,1) for Asian emerging markets, the study finds some interesting findings. Firstly, the day effect is present on six out of nine studied markets, except for the Indian, Taiwanese and Philippine. Secondly, the results of return spillover confirm significant spillover effects across the markets with different magnitudes. Specifically, the U.S. exerts a stronger influence on the Malaysian, Philippine and Vietnamese market compared with Japan. In contrast, Japan has a higher spillover effect on the Chinese, Indian, Korea, and Thailand than the U.S. For the Indonesian market, the return effect is equal. Finally, there is no evidence of a volatility effect of the U.S. and Japanese markets on the Asian emerging markets in this study.

The Impact of COVID-19 on Stock Market in China

2021 ◽  
Vol 16 (4) ◽  
pp. 714-743
Author(s):  
Nan Li ◽  
◽  
Yuhong Zhu ◽  
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Structural Break ◽  
Equity Premium ◽  
Liquidity Premium ◽  
Stock Liquidity ◽  
Spring Festival ◽  
Before And After ◽  
The Impact ◽  
The U.S

This paper studies the impact of the COVID-19 on the stock ambiguity, risks, liquidity, and stock prices in China stock market, before and after the outbreak of COVID-19 during the Chinese Spring Festival holidays in 2020. We measure stock ambiguity using the intraday trading data. The outbreak of COVID-19 has a significant impact on the average stock ambiguity, risk, and illiquidity in China and induces structural break in the market average ambiguity. However, the equity premium and liquidity premium change little during the same period. The market average stock ambiguity and risks decrease, and stock liquidity improves to pre-pandemic levels as the pandemic is under control in China. The market average stock ambiguity and risks in China increase again when the confirmed new cases in the U.S. surge in the second half of 2020. We also find a “flight-to-liquidity” phenomenon, and the equally-weighted (value-weighted) 20-trading-day liquidity premium declined significantly to about –4.42% (–6.48%) during the fourth quarter of 2020.

scholarly journals Context Based Predictive Information

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 645
Author(s):  
Yuval Shalev ◽  
Irad Ben-Gal
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Lossy Compression ◽  
Compression Algorithm ◽  
Predictive Information ◽  
Real Dataset ◽  
Information Bottleneck ◽  
The U.S

We propose a new algorithm called the context-based predictive information (CBPI) for estimating the predictive information (PI) between time series, by utilizing a lossy compression algorithm. The advantage of this approach over existing methods resides in the case of sparse predictive information (SPI) conditions, where the ratio between the number of informative sequences to uninformative sequences is small. It is shown that the CBPI achieves a better PI estimation than benchmark methods by ignoring uninformative sequences while improving explainability by identifying the informative sequences. We also provide an implementation of the CBPI algorithm on a real dataset of large banks’ stock prices in the U.S. In the last part of this paper, we show how the CBPI algorithm is related to the well-known information bottleneck in its deterministic version.

scholarly journals The effects from the United States and Japan to emerging stock markets in Asia and Vietnam

2020 ◽  
Vol 3 (4) ◽  
pp. 438-448
Author(s):  
Nguyen Thi Ngan ◽  
Nguyen Thi Diem Hien ◽  
Hoang Trung Nghia
Keyword(s):  
United States ◽  
Emerging Markets ◽  
Stock Prices ◽  
Stock Markets ◽  
Spillover Effect ◽  
Spillover Effects ◽  
The United States ◽  
The Philippines ◽  
Asian Emerging Markets ◽  
The U.S

The subprime mortgage crisis in the United States (U.S.) in mid-2008 suggests that stock prices volatility do spillover from one market to another after international stock markets downturn. The purpose of this paper is to examine the magnitude of return and volatility spillovers from developed markets (the U.S. and Japan) to eight emerging equity markets (India, China, Indonesia, Korea, Malaysia, the Philippines, Taiwan, Thailand) and Vietnam. Employing a mean and volatility spillover model that deals with the U.S. and Japan shocks and day effects as exogenous variables in ARMA(1,1), GARCH(1,1) for Asian emerging markets, the study finds some interesting findings. Firstly, the day effect is present on six out of nine studied markets, except for the Indian, Taiwanese and Philippine. Secondly, the results of return spillover confirm significant spillover effects across the markets with different magnitudes. Specifically, the U.S. exerts a stronger influence on the Malaysian, Philippine and Vietnamese market compared with Japan. In contrast, Japan has a higher spillover effect on the Chinese, Indian, Korea, and Thailand than the U.S. For the Indonesian market, the return effect is equal. Finally, there is no evidence of a volatility effect of the U.S. and Japanese markets on the Asian emerging markets in this study.

scholarly journals Stock Price Behavior In An Underdeveloped Capital Market: Nigeria In Contrast To The U.S.

2011 ◽  
Vol 6 (4) ◽  
pp. 71
Author(s):  
Zakri Bello
Keyword(s):  
Random Walk ◽  
Statistical Analysis ◽  
Capital Market ◽  
Stock Prices ◽  
Stock Price ◽  
Temporal Changes ◽  
Random Walk Model ◽  
Price Behavior ◽  
Price Data ◽  
The U.S

Most of the studies of stock price behavior agree that temporal changes in prices follow the random walk model. With few exceptions these studies were based on American stock price data. The purpose of the present research is to study the behavior of Nigerian stock prices to find out if the observed behavior of American stock prices can be generalized to a small and thinly traded capital market. The findings reveal that Nigerian stock prices do not conform to the random walk model when traditional statistical analysis applied.

scholarly journals The effects from the United States and Japan to emerging stock markets in Asia and Vietnam

2020 ◽  
Author(s):  
Nguyen Thi Ngan
Keyword(s):  
United States ◽  
Emerging Markets ◽  
Stock Prices ◽  
Stock Markets ◽  
Spillover Effect ◽  
Spillover Effects ◽  
The United States ◽  
The Philippines ◽  
Asian Emerging Markets ◽  
The U.S

The subprime mortgage crisis in the United States (U.S.) in mid-2008 suggests that stock prices volatility do spillover from one market to another after international stock markets downturn. The purpose of this paper is to examine the magnitude of return and volatility spillovers from developed markets (the U.S. and Japan) to eight emerging equity markets (India, China, Indonesia, Korea, Malaysia, the Philippines, Taiwan, Thailand) and Vietnam. Employing a mean and volatility spillover model that deals with the U.S. and Japan shocks and day effects as exogenous variables in ARMA(1,1), GARCH(1,1) for Asian emerging markets, the study finds some interesting findings. Firstly, the day effect is present on six out of nine studied markets, except for the Indian, Taiwanese and Philippine. Secondly, the results of return spillover confirm significant spillover effects across the markets with different magnitudes. Specifically, the U.S. exerts a stronger influence on the Malaysian, Philippine and Vietnamese market compared with Japan. In contrast, Japan has a higher spillover effect on the Chinese, Indian, Korea, and Thailand than the U.S. For the Indonesian market, the return effect is equal. Finally, there is no evidence of a volatility effect of the U.S. and Japanese markets on the Asian emerging markets in this study.

scholarly journals Why the CHIPS Are Down: Stock Buybacks and Subsidies in the U.S. Semiconductor Industry

2021 ◽  
Author(s):  
William Lazonick ◽  
◽  
Matt Hopkins ◽  
Keyword(s):  
United States ◽  
Stock Prices ◽  
Semiconductor Industry ◽  
The United States ◽  
Securities And Exchange Commission ◽  
Open Market ◽  
Chip Technology ◽  
The Government ◽  
Stock Buybacks ◽  
The U.S

The Semiconductor Industry Association (SIA) is promoting the Creating Helpful Incentives to Produce Semiconductors (CHIPS) for America Act, introduced in Congress in June 2020. An SIA press release describes the bill as “bipartisan legislation that would invest tens of billions of dollars in semiconductor manufacturing incentives and research initiatives over the next 5-10 years to strengthen and sustain American leadership in chip technology, which is essential to our country’s economy and national security.” On June 8, 2021, the Senate approved $52 billion for the CHIPS for America Act, dedicated to supporting the U.S. semiconductor industry over the next decade. As of this writing, the Act awaits approval in the House of Representatives. This paper highlights a curious paradox: Most of the SIA corporate members now lobbying for the CHIPS for America Act have squandered past support that the U.S. semiconductor industry has received from the U.S. government for decades by using their corporate cash to do buybacks to boost their own companies’ stock prices. Among the SIA corporate signatories of the letter to President Biden, the five largest stock repurchasers—Intel, IBM, Qualcomm, Texas Instruments, and Broadcom—did a combined $249 billion in buybacks over the decade 2011-2020, equal to 71 percent of their profits and almost five times the subsidies over the next decade for which the SIA is lobbying. In addition, among the members of the Semiconductors in America Coalition (SIAC), formed specifically in May 2021 to lobby Congress for the passage of the CHIPS for America Act, are Apple, Microsoft, Cisco, and Google. These firms spent a combined $633 billion on buybacks during 2011-2020. That is about 12 times the government subsidies provided under the CHIPS for America Act to support semiconductor fabrication in the United States in the upcoming decade. If the Congress wants to achieve the legislation’s stated purpose of promoting major new investments in semiconductors, it needs to deal with this paradox. It could, for example, require the SIA and SIAC to extract pledges from its member corporations that they will cease doing stock buybacks as open-market repurchases over the next ten years. Such regulation could be a first step in rescinding Securities and Exchange Commission Rule 10b-18, which has since 1982 been a major cause of extreme income inequality and loss of global industrial competitiveness in the United States.

scholarly journals Evaluating the social impact of COVID-19 with a big data approach

2021 ◽  
Vol 257 ◽  
pp. 03065
Author(s):  
Jiacheng Chen
Keyword(s):  
Performance Analysis ◽  
Correlation Analysis ◽  
Stock Prices ◽  
Stock Price ◽  
Social Impact ◽  
Service Industry ◽  
Listed Companies ◽  
The Social ◽  
The Impact ◽  
The U.S

According to the CNN news, until the first day of year 2021, the total number of COVID-19 infections in the U.S. has exceeded 20 million and resulted in 350, 000 deaths. A review of the literature shows that COVID-19 has created a huge crisis in various industries such as offline department stores, tourism, airlines, and restaurants, but also contributes to the online service industry, medical and biopharmaceuticals. The quantitative assessment of the social impact of COVID-19 is based on various types of data. In this paper, stock prices of listed companies are used as indicators to explore the impact of the epidemic on stock prices, which further reflects the impact on different industries. Since the infection information and stock price data of listed companies are easily accessible, this article combines these data and conduct two analyses: correlation analysis and performance analysis, taking 468 listed companies in the U.S. stock market. In the correlation analysis, it is confirmed that the impact of COVID-19 on different industries or companies is different. In the performance analysis, this article predicts the performance of company stock prices before and after the outbreak by using different companies’ basic information and find that the XGBoost model works best in the 2-classes case and the random forest model works best in the 5-classes case.

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