US Stock Prices Research Articles

International monetary spillovers to frontier financial markets: Evidence from Bangladesh

AbstractThis paper investigates international monetary spillovers to stock prices in Bangladesh, a frontier market that has been excluded from prior studies in the literature. Using daily stock price data for over 300 publicly traded firms in a high‐frequency event study framework, we find that contractionary monetary shocks originating from the US, euro area, and China lower stock prices, with Chinese monetary shocks having the largest impact. Contractionary shocks originating from India, on the other hand, lead to a statistically significant increase in stock returns. The positive response is driven by a small number of policy decisions. When these outlier decisions are removed from the sample, contractionary Indian monetary shocks lead to a decline in stock prices in line with spillovers from the other countries.

Stock Prices and Exchange Rates under Monetary Policy Changes: The Cases of Japan and the US

This study examines exchange rates and stock prices in the context of US and Japanese monetary policy shifts. Also, the effects of announcements made by the monetary authorities are examined. The empirical results show that US stock prices have positive impacts on Japanese stock prices. As predicted, there is a negative correlation between Japanese interest rates and stock prices. Depreciation of the Japanese yen leads to rising stock prices. Although the Bank of Japan's announcement of “positive” news that the end of quantitative easing is expected to cause stock prices to decline, they instead increased. In the case of the US, however, appreciation of the US dollar promotes rising stock prices. This result is the opposite of the Japanese case. It may be concluded that there exists good communication between the monetary authorities and the markets, and that the news causes no unexpected effects or confusion on the financial markets.

Machine learning portfolios for US stock prices: Directional forecasting before and during the COVID-19 pandemic

Machine learning portfolios for US stock prices: Directional forecasting before and during the COVID-19 pandemic

An impact assessment of the COVID-19 pandemic on Japanese and US hotel stocks

This study proposes two new regime-switching volatility models to empirically analyze the impact of the COVID-19 pandemic on hotel stock prices in Japan compared with the US, taking into account the role of stock markets. The first model is a direct impact model of COVID-19 on hotel stock prices; the analysis finds that infection speed negatively affects Japanese hotel stock prices and shows that the regime continues to switch to high volatility in prices due to COVID-19 until September 2021, unlike US stock prices. The second model is a hybrid model with COVID-19 and stock market impacts on the hotel stock prices, which can remove the market impacts on regime-switching volatility; this analysis demonstrates that COVID-19 negatively affects hotel stock prices regardless of whether they are in Japan or the US. We also observe a transition to a high-volatility regime in hotel stock prices due to COVID-19 until around summer 2021 in both Japan and the US. These results suggest that COVID-19 is likely to affect hotel stock prices in general, except for the influence of the stock market. Considering the market influence, COVID-19 directly and/or indirectly affects Japanese hotel stocks through the Japanese stock market, and US hotel stocks have limited impacts from COVID-19 owing to the offset between the influence on hotel stocks and no effect on the stock market. Based on the results, investors and portfolio managers should be aware that the impact of COVID-19 on hotel stock returns depends on the balance between the direct and indirect effects, and varies from country to country and region to region.

The role of stock markets in the US, Europe, and China on oil prices before and after the COVID-19 announcement

This research investigates the asymmetric effects of the three major stock prices of the US, Europe, and China on WTI and Brent oil futures prices before and after the COVID-19 announcement by covering weekly data from January 2015 to April 2021. The results of the nonlinear autoregressive distributed lag (NARDL) model show that the US stock price has a significant positive effect in both models prior to the COVID-19 announcement but loses its effect on the WTI oil futures price after the COVID-19 announcement. Its impact on the Brent oil futures price remains after the COVID-19 announcement. The Europe stock price has a significant positive effect in all states. China stock price is not significant in the pre-COVID-19 period, but it has a significant effect after the COVID-19 announcement in both models. However, the only positive asymmetric changes in China stock price show a significant effect on the Brent oil futures price. Before COVID-19, the US stock price is the strongest, while the Europe stock price is the strongest after the COVID-19 announcement.

Frequency spillovers between green bonds, global factors and stock market before and during COVID-19 crisis

This paper examines frequency dynamic spillovers in return and volatility and the hedging ability of Green Bonds, gold, silver, oil, the US dollar index, and volatility index against downside US stock prices before and during the COVID-19 pandemic outbreak and for the short and long run. To do so, we use the Diebold and Yilmaz (2014), the TVP-VAR model, and the frequency spillover index by Baruník and Křehlík (2018). We show that the short-term volatility spillovers dominate their long-term counterparts. Green Bond is net transmitters of spillovers in the system at the short term and net receivers at the long term. S&P500 and silver (USDX and oil) are net transmitters (receivers) of short- and long-term spillovers. Gold and VIX are net receivers of short-term spillovers and net transmitters of long-term spillovers. COVID-19 crisis has more effects on the short-term spillover, which reaches its highest level early 2020. COVID-19 and time horizons lead the direction and the magnitude of spillovers. The Quantile-on-Quantile regression analysis shows significant nonlinear relationships between markets under study. More interestingly, we show that green bonds and gold are safe haven assets for US equity investors during COVID-19. On the other hand, a mixed portfolio offers higher diversification benefits. Finally, hedging effectiveness is dependent on COVID-19 and time horizon.

The Impact of Macroeconomic Factors on the US Stock Exchange

The aim of the present study was to analyze the impact of crude oil prices, the US treasury 10-year bond yield rate, and gold prices on S&P 500 stock prices using daily data of the period from 04/01/2010 to 19/11/2022. For that purpose, this study employed the ARDL method. According to the results of the ARDL method, there was no statistically significant relationship between S&P 500 stock prices and gold prices, oil prices, and US treasury 10-year bond yield rates in the long term. However, while the impact of US treasury 10-year bond yield rates and gold prices was positive in the long term, the impact of oil prices on US stock prices was negative. In the short term, the impact of oil and gold prices on stock prices was statistically significant. Consequently, study of the short-term coefficients revealed a positive relationship between US stock prices and gold and oil prices. As a result, gold and oil prices had a positive impact on US stock prices in the short term.

What drives US stock markets during the COVID-19 pandemic? A global sensitivity analysis

This paper identifies robust determinants of US stock price movements in the economic shadow of the COVID-19 crisis and in the presence of model uncertainty, using several influential factors highlighted in relevant research. Our investigation performs an extreme bounds analysis (EBA), a global sensitivity framework capable of handling the problem of model uncertainty. We document that excess market returns, term spread, implied volatility, oil, Twitter-based economic uncertainty, and European and Chinese stock returns are the only variables that are robust to all possible variations in the condition set of information. The results also reveal the irrelevance of newly reported COVID-19 cases and deaths as novel drivers that contribute to the formation stock prices, thus lending support to the “psychophysical numbing” phenomenon.

The COVID-19 pandemic and the degree of persistence of US stock prices and bond yields

This paper analyses the possible effects of the Covid-19 pandemic on the degree of persistence of US monthly stock prices and bond yields using fractional integration techniques. The model is estimated first over the period January 1966-December 2020 and then a recursive approach is taken to examine whether or not persistence has changed during the following pandemic period (up to February 2021). We find that the unit root hypothesis cannot be rejected for stock prices while for bond yields the results differ depending on the maturity date and the specification of the error term. In general, bond yields appear to be more persistent, although there is evidence of mean reversion in case of 1-year yields under the assumption of autocorrelated errors. The recursive analysis shows no impact of the Covid-19 pandemic on the persistence of stock prices, whilst there is an increase in the case of both 10- and 1- year bond yields but not of their spread.

On financial market correlation structures and diversification benefits across and within equity sectors

We study how to assess the potential benefit of diversifying an equity portfolio by investing within and across equity sectors. We analyse 20 years of US stock price data, which includes the global financial crisis (GFC) and the COVID-19 market crash, as well as periods of financial stability, to determine the ‘all weather’ nature of equity portfolios. We establish that one may use the leading eigenvalue of the cross-correlation matrix of log returns as well as graph-theoretic diagnostics such as modularity to quantify the collective behaviour of the market or a subset of it. We confirm that financial crises are characterised by a high degree of collective behaviour of equities, whereas periods of financial stability exhibit less collective behaviour. We argue that during times of increased collective behaviour, risk reduction via sector-based portfolio diversification is ineffective. Using the degree of collectivity as a proxy for the benefit of diversification, we perform an extensive sampling of equity portfolios to confirm the old financial adage that 30–40 stocks provide sufficient diversification. Using hierarchical clustering, we discover a ‘best value’ equity portfolio for diversification consisting of 36 equities sampled uniformly from 9 sectors. We further show that it is typically more beneficial to diversify across sectors rather than within. Our findings have implications for cost-conscious retail investors seeking broad diversification across equity markets.

Expansion of Stock Portfolio Risk Analysis Using Hybrid Monte Carlo-Expected Tail Loss

Monte Carlo-Expected Tail Loss (MC-ETL) is the new expansion method that combines simulation and calculation to measure investment risk. This study models US stock prices using ARIMA-GARCH and forms an optimized portfolio based on Multi-Objective that aims to analyze the portfolio investment return. The next portfolio return will be simulated using the Monte Carlo (MC) method, measured based on the Expected Tail Loss (ETL) calculation. The optimized portfolio comprises 5 US stocks from 10 years of data, with the biggest capitalization market on February 25, 2021. MSFT has the most considerable weight in the optimized portfolio, followed by GOOG, AAPL, and AMZN, whereas TSLA shares have negligible weight. Based on the simulation result, the optimized portfolio has the smallest ETL value compared to its constituent stocks, which is ±0.029 or about 2.9%. This value means that the optimized portfolio is concluded as an investment choice for investors with a low level of risk.

Clustering-enhanced stock price prediction using deep learning

In recent years, artificial intelligence technologies have been successfully applied in time series prediction and analytic tasks. At the same time, a lot of attention has been paid to financial time series prediction, which targets the development of novel deep learning models or optimize the forecasting results. To optimize the accuracy of stock price prediction, in this paper, we propose a clustering-enhanced deep learning framework to predict stock prices with three matured deep learning forecasting models, such as Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU). The proposed framework considers the clustering as the forecasting pre-processing, which can improve the quality of the training models. To achieve the effective clustering, we propose a new similarity measure, called Logistic Weighted Dynamic Time Warping (LWDTW), by extending a Weighted Dynamic Time Warping (WDTW) method to capture the relative importance of return observations when calculating distance matrices. Especially, based on the empirical distributions of stock returns, the cost weight function of WDTW is modified with logistic probability density distribution function. In addition, we further implement the clustering-based forecasting framework with the above three deep learning models. Finally, extensive experiments on daily US stock price data sets show that our framework has achieved excellent forecasting performance with overall best results for the combination of Logistic WDTW clustering and LSTM model using 5 different evaluation metrics.

Long-Run Linkages between US Stock Prices and Cryptocurrencies: A Fractional Cointegration Analysis

Long-Run Linkages between US Stock Prices and Cryptocurrencies: A Fractional Cointegration Analysis

Volatility spillovers during market supply shocks: The case of negative oil prices

This paper applies a TVP-VAR model to explore dynamic connectedness between West Texas Intermediate crude oil and other US energy prices, stock prices and exchange rate markets during the April 2020 supply shock leading to negative WTI crude oil prices. This period, while coinciding with the escalation of the COVID-19 pandemic, is also associated with non-uniform government pandemic responses, widespread expectations of global economic slowdown, and the combined effects of international political influence, all of which generated immense financial stress. A number of distinct results are identified. Firstly, while WTI is broadly identified as a volatility receiver from all of the analysed markets, during the negative pricing events WTI rapidly becomes a volatility transmitter. The inherent signal within such an unexpected market movement sent very sharp contagion effects throughout traditional financial markets. Spillovers to stock, currency and futures markets are also substantial. This negative valuation event, although reaffirming the status of the efficient markets hypothesis, has potentially endangered the role of WTI as a safe-haven during future periods of financial stress and crises.

Do oil prices and exchange rates affect the US stock market? New evidence from the asymmetric cointegration approach

In this paper, we study the dynamics between US stock prices, exchange rates and oil prices. The data used are quarterly, covers the period from 1986 to 2016 and includes the Standard & Poor's 500 spot prices, the West Texas Intermediate spot prices and the effective exchange rate of US Dollar. We examine the presence of different sources of nonlinearities. The empirical analysis is based on the asymmetric ARDL cointegration methodology proposed by Shin et al (2011). The evidence implies that ignoring possible non-linearities lead to misleading results. The analysis reveals new evidence such as the existence of several structural brakes and asymmetries in both long-run and short-run relationships among the examined variables and that could be of major importance for researchers and other market participants.

Economic Integration and Stock Market Development: Evidence from Nigeria

ABSTRACT This study assesses the relationship between economic integration and stock market performance in Nigeria alongside her top trading partners. The autoregressive distributed lag in the context of the new trade theory was utilized. The study finds a negative relationship between the US stock price and trade integration with Nigeria. India’s stock price affects and is affected by the degree of integration with Nigeria. It is therefore recommended that where trade integration significantly and positively drives the stock market of Nigeria, authorities should encourage trade policy while being conscious of trade policy arrangements with countries where such a policy is stock market inhibiting.

Forecasting US stock price using hybrid of wavelet transforms and adaptive neuro fuzzy inference system

Forecasting US stock price using hybrid of wavelet transforms and adaptive neuro fuzzy inference system

Broker-dealer leverage volatility and the US stock prices

PurposeBroker-dealer leverage volatility increases during booms and crisis periods, but its impact on stock prices is relatively unexplored. This paper aims to investigate whether broker-dealer leverage volatility is a key driver for stock prices.Design/methodology/approachThis paper collects the US quarterly data of broker-dealer book leverage and three leading stock market indicators (S&P 500, DJIA and Nasdaq) for the period of 1967–2018. The research uses a multivariate GARCH-in-mean VAR to examine the impact of leverage volatility on each of the stock market indicators. A split-sample analysis (pre-1990 and post-1990) has also been performed to show the robustness of the result.FindingsThe research finds that broker-dealer leverage volatility does not have any significant impact on stock prices.Originality/valueBroker-dealers are important financial intermediaries, and there is a huge literature exploring the relationship between their leverage and asset prices. But, the relationship between broker-dealer leverage volatility and asset prices is not explored yet. This study fills the gap and provides the first evidence that broker-dealer leverage volatility does not play any major role in the theory of stock pricing. The research proposes that the stock holding decisions of the investors should depend only on the first moment of leverage and not on the second moment of leverage. The study concludes that high broker-dealer leverage volatility is not a sinister signal for the US stock market.

Spillover Effects of US Unconventional Monetary Policy: (Evidence from Selected Asian Countries)

In this study, we evaluate the impact of US monetary policy between 2008 and 2018 (after implementation of quantitative easing policy) on assets, bonds, exchange rates of selected East Asian countries (Japan, South Korea, and Thailand). Our finding emphasized the significant role of US monetary policy on the East Asia financial markets especially in the case of South Korea. Results show that the US Treasury bill spread had the long run and US corporate spread had the short run effects on the asset markets of these countries. More specifically, sovereign yields respond significantly to US term spreads and stock prices respond largely to US corporate spread. The responses of exchange rate and house prices to US monetary policy are significant but attenuate.

Kestasioneran Kurs Dolar Amerika dan Harga Saham Pada Masa Pandemi Covid 19 Tahun 2020

This study aims to determine the stationarity of the US dollar exchange rate and stock prices during the Covid 19 pandemic. One of the interesting stocks to study is the stock price of PT Telecommunications Indonesia Tbk. The augmented Dickey-Fuller test was used to determine the stationarity of the two variables. The research data is sourced from secondary data through the official website of Bank Indonesia to view the US Dollar Exchange Rate and through the website investing to see the stock price of PT. Telekomunikasi Indonesia Tbk during the Covid 19 pandemic period in Indonesia occurred from March to December 2020. The results showed that the check of the stationary stock price of PT. Telekomunikasi Indonesia Tbk and the US Dollar exchange rate at level I (0) was not stationary. While the two variables are stationary in the first differencing conditions I (1).