USA Stock Market Research Articles

Basic Analysis of Three Stock in the Real Estate Industry

This academic article aims to conduct a comprehensive investment analysis of three large stocks in the USA market: Realty Income Corporation (Stock O), AvalonBay Communities Inc. (Stock AVB), and Welltower Inc. (Stock WELL). First, the PEST analysis, which includes political, economic, social, and technological aspects, is involved in the article to present the whole macroeconomic background. Moreover, the PEST results show that the total environment is positive to invest in the real estate industry. Then the stock analysis covers crucial factors such as market risk, debt ratios, current ratios, price-to-earnings (P/E) ratios, price/earnings-to-growth (PEG) ratios, return on assets (ROA), discounted cash flow (DCF), momentum, insider ownership, and investment preferences and give some suggestions to different investors, such as PEG investors, index investors, inside buyers and so on The findings presented herein provide valuable insights for investors seeking informed decisions in the dynamic landscape of the USA stock market.

Modelization and Calibration of the Power-Law Distribution in Stock Market by Maximization of Varma Entropy

Proper description of the return distribution is crucial for investment practitioners. The underestimation of the tail risk may lead to severe consequences, even for assets with moderate fluctuations. However, many empirical studies found that the distribution tails of many financial assets drop off more slowly than the Gaussian distributions. Therefore, we intend to model and calibrate the heavy tails observed in financial fluctuations in this study. By maximizing the Varma entropy with value-at-risk and expected shortfall constraints, we obtain the probability distribution of stock return and observe that the tail of stock return distribution is a power law. Since the variance of the real stock portfolio may be a random variable, using the mean-VaR-ES constraints to maximize the Varma entropy effectively avoids the problem of assuming that the variance is a constant value under the traditional mean-variance constraint. Therefore, the deduced theoretical model would be more consistent with the real market. Using high-frequency data from China’s stock markets, we calibrate our theoretical model and give the concrete form of probability density distribution p(x) for different time intervals. The calibration results show that the tail of the stock return distribution is a power law with most of the power-law orders between −2 and −7. We prove the robustness of our results by calibrating the Varma entropy for S&P 500 of the USA stock market and different stock market indices in China’s A-share market. Our research’s findings not only offer a theoretical perspective for researchers but also give investing professionals a theoretical foundation on which to base their decisions.

Research on Co-movement Between China and USA Stock Market under the Impact of COVID-19

The worldwide economic market has fluctuated wildly due to COVID-19, therefore revealing the co-movement of international stock markets should reduce its destructive force and shock. Based on the "Economic Basic Hypothesis" and "Market Contagion Hypothesis," this work uses VECM and GARCH models to examine international market volatility using the Shanghai Composite Index (SSEC), Shenzhen Composite Index (SZSE), and S&P 500 index. The total trading data of three indexes are divided into 2 periods, with September 01, 2019 as the cut-off point. The period 1 is from January 5, 2015 to August 30, 2019 and the period 2 is from September 3, 2019 to December 30, 2022. The study shows that: 1) During the COVID-19, the short-term return of both stock markets has declined and volatility has increased; 2) there is no reliable co-integration relationship between S&P 500, SSEC and SZSE which also declaims no long-run equilibrium between two markets; 3) before and during the COVID-19, the close to close return rate of America stock market guided the return rate of China stock market; 4) both S&P 500, SSEC shows an asymmetric effect and the leverage effect is distinct; 5) the co-movement was stronger when the rate of return was relatively high; 6) volatility spillover effect of China to USA stock market was significant.

Clustering and anomalies of USA stock market volatility index data

Actuality. Investing money is an important way to improve the financial condition of both an individual and society as a whole. The problem of understanding financial data and making decisions regarding the investment of money in a certain project at this moment in time is relevant. The object of the study is the process of establishing the dependence of the value of the asset on time. The subject of research is mathematical models of data processing, data clustering and anomaly detection. The purpose of this work is to develop a method for effective investment of money using data processing and analysis methods for CVOE volatility index values in the USA, determination of clusters based on actions with assets, as well as checking the presence of anomalous data. Research results. Official data of volatility index values were selected and prepared for further analysis by removing incomplete sets and further normalization. Clustering of time series was carried out and the array was divided into five homogeneous groups. Clusters determine the ranges of the volatility index, which reflect the different sentiments of investors in the market and encourage appropriate actions with assets: to sell, to wait of index increasing, to buy, to remove money from developing projects and invest in stable ones, to wait of index decreasing. Segmentation of data, application of window function, centroids for segments were determined and signal reconstruction was carried out. Data anomaly points were identified. A comparative analysis was carried out based on the results of constructed initial data, reconstructed data and reconstruction error.

The Spillover Effect of Global Uncertainty on BRICS Stock Markets

Using monthly data spanning from 1993 to 2021 and employingthe DCC-GARCH model, this study examines the role ofEconomic Policy Uncertainty (EPU) as a potential exogenousfactor impacting the correlation of Brazil, Russia, India, andChina (BRIC) economies’ stock markets, which is new to theliterature. Further, this dynamic correlation series is used as adependent variable while EPU of BRIC and USA is used as anindependent variable by utilizing the autoregressive distributedlag (ARDL) model. The study finds a positive and significantshort-run as well as the long-run impact of Russia’s and theUS’s EPU on their stock markets. In other words, as the EPUof the USA increases, the correlation of BRIC with the USAStock Market and the World Stock Market increases, suggestingminimum diversification opportunities for the investors. Thestudy also recommends that investors diversify their portfoliosby considering cross borders assets avenues to gain maximumreturns and reduce portfolio risk.JEL Classification: E44, C32, E52, E60, E62, C58How to Cite:Nawaz, A. D., Bhutto, N. A., & Khan, S. (2023). The Spillover Effect of Global Uncertainty on BRICS Stock Markets. Etikonomi, 22(1), 45–64. https://doi.org/10.15408/etk.v22i1.24617.

Cointegration and Causality Relationship of the USA Stock Market with Selected World Markets

This article, based on data from 1995 to 2022, considers the commercial and economic processes between the USA, Japan and China and stock indexes indicators of these countries. In the course of research, the authors have created multiply regression model, adequacy of the model was determined using the Fisher’s F-test, Student’s t-test, and the stability of the parameters of the regression model was checked using the CUSUM test. Stationarity of the time series was checked by the Dickey-Fuller test. As a result, econometrically sound recommendations are developed, which allows to conduct dynamic analyses to effectively regulate economic processes, integration and international trade, foreign investment operations between the three countries. The Granger causality of the relationship was investigated. Johansen tests were implemented to find the cointegration space, after which a vector error correction model was constructed that describes the long-term equilibrium relationship between the studied indicators and the path to return to the equilibrium trajectory in case of deviation from it. During the modelling, all necessary statistical procedures were used to identify and evaluate the parameters of the model and check its adequacy, accuracy of short-term and long-term forecast values using Eviews 8 tools. The results show that not only for the countries studied here, but also for each country, conduction of monitoring by the governments by using the methodology of the vector model of error correction is very important in order to ensure effective regulation of foreign trade and to participate in regional and global integration processes.

Mean--variance portfolio selection problem: Asset reduction via nondominated sorting

Due to recent globalization of financial markets, investors have access to large numbers of assets. It may be beneficial for them to focus on a limited number of assets filtered out by some meaningful procedure. This, however, can result in selecting portfolios which, in terms of reward and risk, are not efficient in the original set of assets. A challenge is to have ways for determining subsets of assets in which the effect of lost efficiency would be minimal. To meet this challenge, we propose a method for asset reduction, based on the notion of layers of maxima and the concept of nondominated sorting. We conduct experiments on large problems derived from the USA stock market data. Our approach resulted in a much smaller loss of efficiency compared to two representative asset reduction methods known from the literature. We test the approach viability via computational experiments on the mean–variance problem of portfolio selection, with and without the cardinality constraints, and real-life data consisting of up to 1000 assets.

SPILLOVER AND QUANTITATIVE LINK BETWEEN CRYPTOCURRENCY SHOCKS AND STOCK RETURNS: NEW EVIDENCE FROM G7 COUNTRIES

The objective of this article is to analyze the co-movements in the G7 stock markets, such as DJ index, S&P500 (representing the USA stock market), FTSE 100 (United Kingdom), S&P/TSX (Canada), DAX 30 (Germany), CAC 40 (France), Nikkei 225 (Japan), Italy Ds market (Italy) and the cryptocurrencies Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH) and Crypto 10, during the period of February of 2018 to November of 2021. The results show that the cryptocurrencies BTC, ETH, and LTC increase the co-movements between their pairs, while the Crypto 10 index reduces the number of shocks when compared with the sub-period before COVID-19. Regarding the stock markets, DJ index kept the same level of shocks, whereas the Nikkei 225 decreased. For Germany (DAX), EUA (S&P500), Canada (S&P/TSX), United Kingdom (FTSE 100), France (CAC40), and Italy (Italy Ds Market) markets the results show an increase in movements during the global pandemic period. It is then possible to conclude the existence of evidence regarding synchronization and high co-movements, the results put at risk the implementation of efficient portfolio diversification strategies. These conclusions also open space for the market regulators to take steps to ensure better information on the dynamics of the international financial markets.

Exchange Rate and Stock Markets During Trade Conflicts in the USA, China, and India

During trade tussles, most of the stock exchanges are impacted, both directly and indirectly, thereby influencing the exchange rates of the countries. This article gives a comprehensive insight into the effects of exchange rate on the USA, China, and Indian stock market, that is, the Dow Jones industrial average index (DJI), Shanghai Stock Exchange (SSE) composite index, and Nifty50 index during trade conflicts. Trade conflicts here talk about the recent trade war between the USA and China, which was invariably fallen in line with the outbreak of COVID-19. Sample of this study comprises the daily closing price from different indices and exchange rate values US dollar, yuan, and rupee. This article uses ordinary least square (OLS) model and generalized autoregressive conditional heteroskedasticity (GARCH) model to analyze the volatility and influence of exchange rate over the stock market. Results from OLS model indicated that the fluctuations in exchange rate have minimum impact on the daily closing price of stock indices, that is, DJI, SSE, and Nifty50 in the USA, China, and India, respectively, however, exchange rate impacts volume of shares traded in all three stock exchanges. The GARCH model implies that the conditional variance is less volatile for Nifty volume, but highly volatile for SSE and DJI stock volumes traded. The findings of the study provide insights to the domestic and foreign investors regarding their investment in the stock market during trade conflicts and mostly during COVID-19 situation.

Return and volatility spillover between India, UK, USA and European stock markets: The Brexit impact

The 2016 Brexit referendum created potential turmoil in financial markets. The purpose of this study is to examine the impact of the Brexit referendum on the return and volatility spillover between the EU, the UK, and the USA stock markets and the Indian stock market during the pre- and post-Brexit referendum period. The VAR and bivariate GARCH BEKK models were employed. The study results suggest that before the Brexit referendum, Indian stock market returns made no significant return spillover on the other markets. On the contrary, following the referendum, Indian stock returns significantly spilled over to France, Germany, the UK, and the USA stock market returns. The study results also identified a substantial increase in the bidirectional volatility spillover between India-France, India-UK, and India-USA during the post-Brexit referendum period. Therefore, the investors’ opportunity to invest simultaneously in India, UK, EU, and US stock markets for portfolio diversification is limited. India was affected mainly by its own past shocks before the Brexit referendum. However, after the Brexit referendum, Indian markets are getting more and more integrated with other markets. In order to reap the diversification benefits, a prudent investment strategy will need to be developed in the future, especially during times of economic and political uncertainty and market crisis.

Volatility Spillover Across Sovereign Bond Markets Between African, Emerging and USA Economies

Abstract This study attempted to examine the volatility spillover between the sovereign bond returns of South Africa and Ghana and the emerging market bond return, USA stock market return and the world long term interest rate using weekly data in the period of 2014–2022. The research used dynamic and constant conditional correlation generalized auto-regressive conditional Heteroskedasticsticity models. The result showed that the volatility of long-term world bond interest rate and USA stock market return affected the Ghana sovereign bond return positively and negatively, respectively. Similarly, the volatility of emerging market bond return and long-term world interest rate affected the South African sovereign bond return positively and negatively, respectively. Thus, policy intervention is needed to contain the negative impact of stock market and long-term world interest rates.

Research on stock trend prediction method based on optimized random forest

AbstractAs a complex hot problem in the financial field, stock trend forecasting uses a large amount of data and many related indicators; hence it is difficult to obtain sustainable and effective results only by relying on empirical analysis. Researchers in the field of machine learning have proved that random forest can form better judgements on this kind of problem, and it has an auxiliary role in the prediction of stock trend. This study uses historical trading data of four listed companies in the USA stock market, and the purpose of this study is to improve the performance of random forest model in medium‐ and long‐term stock trend prediction. This study applies the exponential smoothing method to process the initial data, calculates the relevant technical indicators as the characteristics to be selected, and proposes the D‐RF‐RS method to optimize random forest. As the random forest is an ensemble learning model and is closely related to decision tree, D‐RF‐RS method uses a decision tree to screen the importance of features, and obtains the effective strong feature set of the model as input. Then, the parameter combination of the model is optimized through random parameter search. The experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization, which is 0.18 higher than the average accuracy of light gradient boosting machine model. Combined with the performance of the ROC curve and Precision–Recall curve, the stability of the model is also guaranteed, which further demonstrates the advantages of random forest in medium‐ and long‐term trend prediction of the stock market.

Size effect and growth options over firm lifecycle

The size effect can be attributed to the relative importance of growth options versus assets-in-place in total assets. By incorporating the changing trend of growth options over firm lifecycle into the dynamic investment model developed by Berk et al. (1999), this paper demonstrates that the gradual exhausting or exercising of growth options would cause a downward pattern of size effect over firm lifecycle, while new growth options created by R&D or innovative investments can intensify size effect. Using a sample of listed firms in USA stock market during the period of 1963–2018, both cross-sectional evidence on individual stocks and time-series evidence on portfolios analysis support theoretical predictions well, and empirical findings are robust for different methodologies, different measures of the exercising/creating of growth options, and for China's stock market.

The impact of investor sentiment for the U.S. stock market based on Fama-French 3-factor model

Particularly, it is difficult to accurately measure investor sentiment due to the inherent complexity and dynamic change. This paper tests the impact of investors’ behavior in the U.S. equity market. By using monthly data from February 2014 to December 2018, the impacts of investor sentiment are examined. Besides, Fama-French risk factors are investigated in a new multiple factor asset pricing model. Specifically, the investor sentiment is measured by six-variable composite index. Empirical results indicate that the investor sentiment is a composition of systemic risk. In this case, the Fama-French three factor model with investor sentiment factor can fully explains the return of stocks in the USA stock market. By comparing the trend of investor sentiment and market index, investor sentiment will affect asset pricing and market volatility, i.e., verifies the effectiveness of investor sentiment index in the U.S, stock market.

Estimating market index valuation from macroeconomic trends

We discuss USA stock market data from 1789 until 2020, focusing our attention on the S&P 500 index (1957–2020). We find that the data can be split into two periods, (1789–1948) and (1948–2020), displaying roughly 2% and 7% growth rates, respectively. The index variations from each trend appear similar, suggesting some degree of stationarity in market fluctuations. We then correlate market behavior to macroeconomic data, such as world (and USA) population growth and gross domestic product (GDP), on different time horizons. The analysis signals that the S&P 500 might be overvalued, possibly undergoing a series of bubbles, since the 1990s. To understand this behavior, we introduce a model for bubbles, showing that they can be caused by a lack of correlations between stock prices and a virtual market index, the latter calculated self-consistently from the stock prices. We argue that variations, $ \Delta\gamma $, in the parameter (or decoupling factor $ \gamma $), are anticorrelated to variations of the Federal Funds Rate (FFR), which may trigger a bubble phenomenon ($ \gamma\to1 $) when persistent rate cuts become too pronounced. The FFR are confronted with the consumer price index (CPI) in the period (1955–2020) as an attempt to complete the picture. Our analyses suggest that the strong departure of the S&P 500 from historical fundamental trends within (1990–2020) may reflect the development of financial anomalies, in part related to monetary policies, which should be carefully addressed in the near future.

Article Herding Behaviour: Empirical Analysis of Pakistan, China, USA Stock Market

The present study investigates market-wide herding of the stock market industry indices of Pakistan, China, and the USA, and cross-border herding of Pakistan stock market with the Chinese stock market and USA stock market. With Cross-Sectional-Absolute-Deviation, this study checks whether geographical distance matters in influencing the stock markets or not and if the USA is it's major influential and cannot be ignored. Market-wide herding in Pakistan is found only during 2004 and 2008, and across border herding for Pakistan is only found from the USA, which supports the asset pricing model and market efficiency hypotheses. Pakistan market does not herd around China- this negates that geographical distance matters and influences in determining investor behavior in stock markets. It is also revealed that the Pakistan stock market does not observe as much herding behavior in stock investment as other markets (such as the USA and China), so it can be said that the Pakistan Stock market is efficiently operating in the context of herding.
 JEL Classification: G02, G11, G14, G1

Investor Behavior: Does Tax Avoidance and Liquidity Preference Culture Drive Equity Prices in Pakistan

The present study investigates market-wide herding of stock market, industry indices of Pakistan, China and USA, A-cross border herding of Pakistan stock market with Chinese stock market and USA stock market. With Cross-Sectional-Absolute-Deviation, to check whether geographical distance matters to influence the stock markets or not and USA is its major influential, cannot be ignored. Market-wide herding in Pakistan is found only during 2004 and 2008 and A-cross border herding for Pakistan is only found from the USA which support asset pricing model and market efficiency. Pakistan market do not herd around China, this negates geographical distance matters, and influence in determining investor behaviour in stock markets. It is revealed, Pakistan stock market does not observe as much herding behaviour in stock investment as other markets (USA and China), so it can be said that Pakistan stock exchange index which is representative of Pakistan Stock market is efficiently operating in contest of Herding.

International stock markets Integration and dynamics of volatility spillover between the USA and South Asian markets: evidence from Global financial crisis

PurposeThe purpose of this paper is to investigate the cointegration and volatility spillover dynamics between the USA and South Asian stock markets, namely, India, Pakistan and Sri Lanka. The main objective of this study is to provide the knowledge about integration of financial market and volatility spillovers before, during and after global financial crisis to investors, fund managers and policy-makers.Design/methodology/approachThe Johansen and Juselius cointegration test, Granger Causality test and bivaraite EGARCH model have been applied in this study to examine integration and volatility spillovers between selected stock markets.FindingsThe findings show that long-term integration between the USA market and South Asian emerging stock markets. It is found that USA stock market has causal relationship with emerging stock markets in short-term. The findings of EGARCH model reveal that asymmetric volatility spillover effects significant in all selected stock markets in pre, during and post-crisis periods. Furthermore, significant volatility spillover is found from stock markets of USA to all selected South Asian markets during and post-crisis periods. However, volatility spillovers from USA to India and Sri-Lanka markets are significant, while insignificant in case of Pakistani market in pre-crisis period. Overall, we find that returns and volatility spillover effects are higher in financial crisis period as compared to non-financial crisis period.Practical implicationsThe findings of this paper have important implications for investors, portfolio managers and policy-makers. They can take potential benefits from international portfolio diversification by considering all these facts. The understanding and knowledge of across volatility transmission help them to maximize the gains from diversification and minimize the risk. Policy-makers can develop such strategies which protect the markets of these economies from future financial crisis.Originality/valueAlthough in finance literature numerous studies have been conducted on integration between different stock markets, most of the studies investigated the integration and volatility spillovers between developed stock markets. However, many studies also analyzed the integration among emerging stock markets in literature review but it is hard to find studies in the context of South Asian stock markets on the effect of global financial crisis on stock markets. The main contribution of this study is to investigate the stock markets integration and volatility transmission between the USA and South Asia by considering the effect of recent 2007 US subprime financial crisis.

Bitcoin, gold, and commodities as safe havens for stocks: New insight through wavelet analysis

In this study, we compare the safe-haven properties of Bitcoin, gold, and the commodity index against world, developed, emerging, USA, and Chinese stock market indices for the period 20 July 2010–22 February 2018. We apply the wavelet coherency approach and show that the overall dependence between Bitcoin/gold/commodities and the stock markets is not very strong at various time scales, with Bitcoin being the least dependent. We study the diversification potential at the tail of the return distribution through wavelet value-at-risk (VaR) and reveal that the degree of co-movement between gold and stock returns affects the portfolio’s VaR level. Specifically, the benefits of diversification vary in the time-frequency space, with Bitcoin exhibiting a superiority over both gold and commodities. Our findings are useful for investors and financial advisors searching for the best asset among Bitcoin, gold, and commodities to hedge extreme negative movements in stock market indices, while accounting for the heterogeneity in the horizons of investors.

USA stock market falls in 2020

USA stock market falls in 2020