American Stock Market Research Articles

Geometric brownian motion: an alternative to high-frequency trading for small investors

High-frequency trading (HFT) involves short-term, high-volume market operations to capture profits. To a large extent, these operations take advantage of early access to information using fast and sophisticated technological tools running on supercomputers. However, high-frequency trading is inaccessible to small investors because of its high cost. For this reason, price prediction models can substitute high-frequency trading in order to anticipate stock market movements. This study is the first to analyze the possibility of applying Geometric Brownian Motion (GBM) to forecast prices in intraday trading of stocks negotiated on two different stock markets: (i) the Brazilian stock market (B3), considered as a low liquidity market and (ii) the American stock market (NYSE), a high liquidity market. This work proposed an accessible framework that can be used for small investors. The portfolios formed by Geometric Brownian Motion were tested using a traditional risk measure (mean-variance). The hypothesis tests showed evidences of promising results for financial management.

A Mental Account-Based Portfolio Selection Model With an Application

To obtain better optimal portfolios, academics develop the behavioral portfolio theory (BPT) with two mental accounts by minimizing the risk as well as maximizing the return. However, there are some limitations to the existing BPT. To circumvent the limitations, this paper proposes a new portfolio selection (PPMPSM) model with two mental accounts in which the lower-level problem of the model is used to avoid from getting big loss while the upper-level problem corresponds to the mental account is used to get good profit. We formulate our proposed PPMPSM model by replacing the probability terms with the expectation of indicator function and then design a sequential convex approximation algorithm to obtain the optimal solution of our proposed model. We prove that the optimal portfolio obtained by our proposed algorithm converges. We demonstrate the superiority and applicability of our proposed PPMPSM models, including both Maslow (0.10) and Maslow (0.11) models, over the traditional portfolio selection models, including the model generated by using the naive equal-weighted (EW) strategy, the model satisfying investors' safety needs (Safety model), and the model satisfying the self-actualization needs (SA model) by using trading data from the American stock market. We find that our proposed PPMPSM model can obtain the highest expected return and generate the highest final cumulative wealth such that the cumulative wealth for both of our proposed PPMPSM models, Maslow(0.10) and Maslow(0.11), uniformly outperform those for the Safety and EW strategies in any week and perform the best among all the strategies in the last 2 weeks, including the SA strategy.

Bioceres S.A: Ag Biotechnology Expansion

In this case we present an investment decision-making situation in which a stockholder of an Argentine Agribusiness Biotechnology company has to analyze the company’s strategy and decide if he will further invest in the firm. The company name is Bioceres; it is a fully integrated crop productivity provider in the areas of seed traits, biologicals, molecular farming and metabolic engineering. It has annual revenues of around 150 million dollars, and net losses of 10 million dollars. In the near future Bioceres will launch, at a global scale, its leading technology called HB4®, which provides drought and salinity tolerance to crops such as soybeans, alfalfa and wheat. By early 2019 Bioceres is intending to go public in the American stock market.

Аналітична оцінка клієнтського капіталу світових компаній-лідерів

The example of reporting on the activities of 100 leading companies in the US stock market investigated the availability and disclosure of intangible assets that characterize client capital. Based on the systematization of the reporting data of these companies, the types of intangible assets that can be an information base for the analysis of client capital are identified. (client lists, customer bases, order portfolio, etc.) and marketing-related ones (trademarks, trade names, brands). They raise client capital, both in the total assets of the leading companies of the American stock market during 2014-2018, and intangible assets in particular. Based on the results of 2018. found that raw materials, telecommunications, industrial, technology and consumer goods companies (out of a list of 100 leading companies) exhibit a high proportion of those that disclose intangible assets that characterize client capital in their own reporting. The dynamics of change in the absolute average value of intangible assets characterizing client capital is determined on the example of the surveyed companies for 2014-2018.

Bitcoin and gold price returns: A quantile regression and NARDL analysis

This research analyses the sensitivity of Bitcoin returns to changes in gold price returns and some other international risk factors such as US stock market returns, interest rates, crude oil prices, the volatility index of the American stock market (VIX) and the Saint Louis financial stress index (STLFSI). This study applies the quantile regression approach for the 2010–2018 period. For robustness, this paper splits the whole sample period into two different subsamples: a more volatile and a less volatile sub-period. Moreover, to capture both long- and short-run asymmetries between Bitcoin and gold price returns, an asymmetric nonlinear cointegration approach (NARDL) is applied. The results evidence that the most relevant risk factor is the VIX index, followed by changes in the STLFSI stress index, and both show negative and statistically significant effects on Bitcoin returns in most periods and quantiles. The US stock market returns have statistically significant effects (with positive sign) on Bitcoin returns in all periods and specifically in high quantiles. Bitcoin returns show negative statistically significant sensitivity to changes in nominal interest rates in the highest quantile and the full period. Moreover, Bitcoin returns show negative and statistically significant sensitivity to oil returns at low quantiles, by serving as a safe-haven asset during economic turmoil. Therefore, in general, the sensitivity of Bitcoin returns to movements in international risk factors tends to be more pronounced in extreme market conditions (bullish and bearish scenarios), showing the highest explanatory power in the lowest quantile. Finally, we have applied the non-linear ARDL approach to analyse the long- and short-run relations between Bitcoin and gold price returns and have found a positive and statistically significant connectedness between them.

META-ANALYSIS ON AMERICAN PRESIDENTIAL ELECTION - 2016 AND AMERICAN STOCK MARKET

This paper aims to analyze the efficient stock market hypothesis as responsive to American Presidential Election, 2016. The meta-analysis has been done combining content analysis and event study methodology. The all major newspapers, news channels, public polls, literature and five important indices as Dow Jones Industrial Average (DJIA), NASDAQ Stock Market Composit Indexe (NASDAQ-COMP), Standard & Poor's 500 Index (SPX-500), New York Stock Exchange Composite Index (NYSE-COMP) and Other U.S Indexes-Russell 2000 (RUT-2000) are critically examined and empirically analyzed. The findings from content analysis reflect that stunned winning of Mr Trump from Republican Party worked as shock for American stock market. From event study, findings confirmed that all the major indices reflected a decline on winning of Trump and losing of Ms. Clinton from Democratic. The results are supported empirically and practically through the political event like BREXIT that resulted in shock to Global stock index and loss of $2 Trillion.

Short Term Stress of COVID-19 on World Major Stock Indices

The main objective of this study is to check short term stress of COVID-19 on the American, European, Asian, and Pacific stock market indices, furthermore, the correlation between all the stock markets during the pandemic. Secondary data of 41 stock exchange from 32 countries have been collected from investing.com website from 1st July 2019 to 14th May 2020 for the stock market and the COVID-19 data has been collected according to the first cases reported in the country, stocks market are classified either developed or emerging economy, further divided according to the subcontinent i.e. America, Europe, and Pacific/Asia, the main focus in the data is the report of first COVID-19 cases. The study reveals that there is volatility in the all the 41 stock market (American, Europe, Asia, and Pacific) after reporting of the first case and volatility increase with the increase of COVID-19 cases, moreover, there is a significant negative relationship between the number of COVID-19 cases and 41 major stock indices of American, Europe, Asia and Pacific, European subcontinent market found more effected from the COVID-19 than another subcontinent, there is Clustering effect of COVID-19 on all the stock market except American's stock market due to smart capital investing.

The analysis of COVID-19 impact on the internet and telecommunications service sector through modelling the dependence of shares of Russian companies on the American stock market

Relevance. The coronavirus pandemic has both negative and less obvious positive effects on the world economy. In order to better understand these processes, it is necessary to examine the sectors that have shown growth against the general decline in production. Such sectors include the Internet and telecommunication services. Research objective. The purpose of this study is to model the impact of the pandemic and foreign companies on the value of shares of Russian tech companies. Data and methods. The study involves daily share price data of such American corporations as Google, PayPal, Netflix, Adobe, and the Russian company Yandex. Moreover, we used the dummy variable Covid-19. The econometric analysis was conducted by using vector autoregression (VAR). The direction of cause-and-effect relationships was investigated with the help of the Granger test, and the effect of single shocks, through impulse response functions (IRF). Results. A stable VAR model was built. The IRF graphs were used to describe the impact of the pandemic and the value of US. companies on Russian companies. Conclusions. The study shows that the 2020 pandemic has proven to be a positive shock for companies in the ICT sector, contributing to increased demand for their services and market capitalization. The pandemic has affected both Russian and foreign companies. The study has also found the influence of the American stock market on share prices in Russia. Russian companies reacted to changes in the American stock market with a lag of up to 10 days.

Volatility Spillovers between Major International Financial Markets During the COVID-19 Pandemic

This paper studies the dynamic change of volatility spillovers between several major international financial markets during the global COVID-19 pandemic using Diebold and Yilmaz’s connectedness index. We found that the total volatility spillover in this March reached its highest level of recent ten years, while the pandemic developed to its worst stage in this April. Results of total directional spillover show that American and British stock markets are main spillover transmitters during the pandemic, while Chinese and Japanese stock markets, as well as GBP/USD exchange rate are spillover recipients. The pairwise directional spillover between American and British stock markets is larger than other pairs. GBP/USD exchange rate and WTI crude oil futures market mainly receive spillovers from American stock market. Results show that the COVID-19 pandemic has caused huge shocks to international financial markets, especially of those countries with severe pandemics, and the pandemic led to increased spillovers between financial markets.

中美股市联动性成因及疫情期间联动性特点浅研究

中美股市联动性成因及疫情期间联动性特点浅研究

VCRIX - A Volatility Index for Crypto-Currencies

Public interest, explosive returns, and diversification opportunities gave stimulus to the adoption of traditional financial tools to crypto-currencies. While the CRIX offered the first scientifically-backed proxy to the crypto-market (analogous to S&P 500), measuring the forward-oriented risk in the crypto-currency market posed a challenge of a different kind. Following the intuition of the fear for the American stock market, the VCRIX volatility index was created to capture the investor expectations about the crypto-currency ecosystem. VCRIX is built based on CRIX and offers a forecast based on the Heterogeneous Auto-Regressive (HAR) model. The HAR model was selected as the most suitable out of a horse race of volatility models, with two proxies for implied volatility, namely the 30 days mean annualized volatility and realized volatility. The model was further examined by the simulation of (resulting in a correlation of 78% between the actual and a VIX version estimated with the VCRIX technology). Trading strategies confirmed the predictive power of VCRIX and supported the selection of the 30 days means annualized volatility proxy. The best performing trading strategy with the use of VCRIX outperformed the benchmark strategy for 99.8% of the tested period and 164% additional returns. VCRIX provides forecasting functionality and serves as a proxy for the investors' expectations in the absence of a developed crypto derivatives market. These features provide enhanced decision making capacities for market monitoring, trading strategies, and potentially option pricing.

Global Economic Factors and the Latin American Stock Markets

This study aimed to analyze the structural dependence between the stock markets of the Latin American countries – Brazil, Chile, Colombia, Mexico, and Peru and the global stock market return and volatility, the commodity prices, and the US political-economic uncertainty from 1998 to 2017. We applied the quantile regression approach under different market circumstances and the results show that global stock market return and commodity prices are the most significant influencers in Latin America. These results are of interest to stakeholders, such as international investors and portfolio managers in the Latin American stock market, due to the market’s growing dynamism after integration into global financial markets and its role in investment diversification.

Forecasting of Stock Price Using Autoregressive Integrated Moving Average Model

Finance sector is highly volatile where the stock prices fluctuate rapidly and it is usually challenging to forecast. The unstable conditions and rapid changes can drastically modify the monetary value of an organization or an individual. Hence, the prediction of stock prices continues to remain as one of the sizzling and vital topics in the applications of data mining in the finance sector. This forecasting is significant as it has the potential to reduce the losses that happen mainly due to erroneous intuitions and blind investment. Moreover, the prediction of stock prices endure to increase in complexity with accumulation of more and more historical data. This paper focuses on American Stock Market (New York Stock Exchange and NASDAQ Stock Exchange). Taking into account the complexity of the prediction, this research proposes Autoregressive Integrated Moving Average (ARIMA) model for estimating the value of future stock prices. ARIMA demonstrated better results for prediction as it can handle the time series data very well which is suitable for forecasting the future stock index.

Recruiting Investors Sentiment in Forecasting Volatility (An Examination on American Stock Market)

The study is to examine the volatility fluctuations based on investor sentiment as various studies has been carried out in the past concentrating mostly on the current reaction of investors sentiment depending on historical volatility estimations. Closing data of NYSE index is considered as independent variable in analyzing both the historical volatility and sentiment index as they are valuated from them. Observation of 807 trading days from the period of 2015-2018 from American Stock Exchange is considered for the study. The study also helps to determine the use of dependent (ARMS Index and Historical Volatility) and independent variable (NYSE Closing price data) among themselves and the reliability of the independent variable. The viability of the dependent variable in deriving the values of the independent variable is analyzed and it as found out that both the dependent variables can act as independent variable in examining the other dependent variable.

Multifractality, efficiency and cross-correlations analysis of the American ETF market: Evidence from SPY, DIA and QQQ

Exchange-traded funds (ETFs) are the most popular products in the financial industry today. Although an extensive body of literature focuses on the multifractal analysis of some stock markets, the multifractal behavior of the ETF market have not been very widely studied. This paper aims to compare and examine the weak form efficiency and interdependence about the American ETF market comprehensively. The main contribution of this paper is as follows. First, as far as the empirical methodological framework is concerned, we apply the MF-DFA and MF-X-DFA to test and rank the efficiency of three major ETFs in the American stock market, such as SPY, DIA and QQQ. Moreover, MF-DMA and MF-X-DMA are considered to make the results more robust. Our results provided clear evidence that the American ETF market was multifractal in nature. It is noticed that QQQ exhibits the strongest level of efficiency. Second, the efficiency of the American ETF market was diminished after the global financial crisis, which is consistent with the results of Tiwari et al [1]. Furthermore, time-varying efficiency levels are determined through a rolling window approach. Third, using MF-X-DFA and MF-X-DMA, we show that there also exists multifractal characters in cross-correlations and it is anti-persistent. To the best of our knowledge, no attention has been given to investigation on cross-correlations of the American ETF market. Finally, in order to study the impacts of the global financial crisis on the multifractal behavior, we investigate and compare the multifractal properties before and after the global financial crisis. We find the multifractal characteristics of cross-correlations have been stronger after the global financial crisis.

Key Concerns on Petroleum Proved Reserves Evaluation in Different Development Stage for International Cooperated Assets under SEC Rules

The petroleum proved reserves estimation of companies listed in American stock market must follow the SEC rules. However, there are no specified guidelines on the reserves evaluation besides the issued general regulations. This paper presents the procedures and key concerns for sub-classification of proved reserves in different development stages which are summarized from many practical cases. The important concerns include the well test data, initial well productivity, recoverable volumes assessment, five-year development workload and investment, reservoir connectivity, evaluation unit classification, historical performance identification, infill wells recognition, operating cost split, abandonment cost, etc. This study can provide a meaningful reference for SEC reserves evaluation and assets transaction in petroleum industry.

Incidencia de las fluctuaciones del índice VIX en la volatilidad de los mercados bursátiles latinoamericanos

En este trabajo se estudia la incidencia de las fluctuaciones del índice VIX sobre la volatilidad de los rendimientos de los principales mercados latinoamericanos (Brasil, Chile, Colombia, México y Perú) durante el periodo que va de enero del 2003 hasta principios de abril del 2017. El análisis econométrico utiliza un modelo Multivariado GARCH Asimétrico con correlaciones dinámicas A-MGARCH (DCC), y sus resultados muestran una relación estadísticamente significativa tanto en el corto como en el largo plazo en solamente dos casos: Chile y Perú. También se estudia el contagio de la volatilidad entre pares de países y la presencia de asimetría en el comportamiento de la volatilidad de los mercados estudiados. Como conclusión, se proponen algunas recomendaciones sobre la cobertura de los riesgos de mercado para aquellos inversionistas cuyos portafolioscontienen acciones de empresas latinoamericanas

Multifractal Analysis of the Interaction between Chinese and American Stock Markets

In this paper, we select yield series of the SSE index and the S & P 500 index as the research object. Firstly, we take the financial crisis as the dividing point, and decompose the whole sample period into three periods: before the financial crisis, during the financial crisis, and after the financial crisis. Secondly, the degree of interaction between Chinese and American stock markets was tested and calculated in stages, and the cross-correlation relationship became more significant after the financial crisis. Then the MF-DCCA method is used to analyze the multifractal interaction of the whole period. It is found that the interaction relationship is multifractal in the short-term and long-term, and shows stronger in the short-term. In addition, the interaction relationship is persistent for small fluctuations in the short-term, and it is anti-sustainability in the case of large fluctuations; it is persistent in all fluctuations in the long run. Finally, the multifractal analysis was carried out for the three periods. It was found that during the financial crisis, the interaction had stronger multifractality and volatility, and the risk was higher.

RELATION BETWEEN THE MACROECONOMIC VARIABLES AND THE STOCK RETURN IN COMPANIES OF THE FINANCE AND INSURANCE SECTOR FROM LATIN AMERICAN STOCK MARKET

This paper aims to verify the relation between the macroeconomic indicators with the Stock Return (SR) in public companies of the finance and insurance sector from Latin America. Data were analyzed from 2010 to 2017 through dynamic panel analysis via Generalized Method of Moments (GMM) by two approaches: Arellano-Bond e System. Results pointed that the Stock Return showed a positive relation with Exchange Rate, but a negative relation with Gross Domestic Product. It is concludedthat macroeconomic variables interfere with the shareholder return of companies in the Finance and Insurance sector.

On the Volatility Spillover between Agricultural Commodities and Latin American Stock Markets

Addressing the volatility spillovers of agricultural commodities is important for at least two reasons. First, for the last several years, the volatility of agricultural commodity prices seems to have increased. Second, according to the Food and Agriculture Organization, there is a strong need for understanding the potential (negative) impacts on food security caused by food commodity volatilities. This paper aims at investigating the presence, the size, and the persistence of volatility spillovers among five agricultural commodities (corn, sugar, wheat, soybean, and bioethanol) and five Latin American (Argentina, Brazil, Chile, Colombia, Peru) stock market indexes. Overall, when a negative shock hits the commodity market, Latin American stock market volatility tends to increase. This happens, for instance, for the relationships from corn to Chile and Colombia and from wheat to Peru and Chile.