US Stock Research Articles

The historical lepto-variance of the US stock returns

<abstract> <p>Regression trees (RT) involve sorting samples based on a particular feature and identifying the splitting point that yields the highest drop in variance from a parent node to its children. The optimal factor for reducing mean squared error (MSE) is the target variable itself. Consequently, employing the target variable as the basis for splitting sets an upper limit on the reduction of MSE and, equivalently, a lower limit on the residual MSE. Building upon this observation, we define lepto-regression as the process of constructing an RT of a target feature on itself. Lepto-variance pertains to the portion of variance that cannot be mitigated by any regression tree, providing a measure of inherent variance at a specific tree depth. This concept is valuable as it offers insights into the intrinsic structure of the dataset by establishing an upper boundary on the "resolving power" of RTs for a sample. The maximal variance that can be accounted for by RTs with depths up to <italic>k</italic> is termed the sample k-bit macro-variance. At each depth, the overall variance within a dataset is thus broken into lepto- and macro-variance. We perform 1- and 2-bit lepto-variance analysis for the entire US stock universe for a large historical period since 1926. We find that the optimal 1-bit split is a 30–70 balance. The two children subsets are centered roughly at −1% and 0.5%. The 1-bit macro-variance is almost 42% of the total US stock variability. The other 58% is structure beyond the resolving power of a 1-bit RT. The 2-bit lepto-variance equals 26.3% of the total, with 42% and 47% of the 1-bit lepto-variance of the left and right subtree, respectively.</p> </abstract>

The influences of the US stock market on virtual currency price under us monetary policy threshold

This study uses a panel smooth transition regression model to investigate the nonlinear relationship between virtual currency and the stock market under the US monetary policy threshold from 7 August 2015 to 27 October 2020. A statistical test showed a threshold effect and confirmed that the relationship between the US stock market and virtual currency is nonlinear. Furthermore, virtual currency fluctuation has asymmetric responses to the US stock market?s fluctuation based on the threshold value. When the federal fund rate exceeds the threshold value, the changes in the S&P500 with a lag of one positively affect the fluctuation of virtual currency.

ARIMA with Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction in the US stock market

Absteact: Stock price forecasting is considered one of the most difficult tasks in financial forecasting. Combining ARIMA with neural networks helps to enhance the model’s predictive capabilities when dealing with complex, nonlinear time series data. Attention-based CNN-LSTM and XGBoost hybrid model achieves the accuracy of stock prediction results. However, the predictive effect of this hybrid model has only been confirmed by Chinese stock market data. Therefore, this paper proposes to use ARIMA with Attention-based CNN-LSTM and XGBoost hybrid model to predict the stock price of five different types of companies in the US stock market. Experimental results show that this hybrid model performs well in the training phase and has small prediction errors. However, during the testing phase, the model performs better in predicting stocks with larger price fluctuations, while the prediction error for stocks with more stable prices increases significantly. This shows that this hybrid model has good predictive ability when dealing with stocks with large market fluctuations, but it still needs further optimization for stable stocks. This study further demonstrates the potential of this hybrid model in stock price prediction, especially during market fluctuations, as it can be used as an effective tool to avoid risks and obtain returns.

The historical transition of return transmission, volatility spillovers, and dynamic conditional correlations: A fresh perspective and new evidence from the US, UK, and Japanese stock markets

<abstract> <p>This paper quantitatively investigated the historical transition of return transmission, volatility spillovers, and correlations between the US, UK, and Japanese stock markets. Applying a vector autoregressive (VAR)-dynamic conditional correlation (DCC)-multivariate exponential generalized autoregressive conditional heteroscedasticity (MEGARCH) model, we derived new evidence for four historical periods between 1984 and 2024. First, we found that the return transmission from the US to the other markets has historically become stronger, whereas recently, the return transmission from the UK to the US has disappeared. Second, we clarified that volatility spillovers from the US to the other markets have historically become stronger, whereas recently, volatility spillovers from the UK to the US have also disappeared. Third, our analyses of the historical constant correlations and DCCs revealed that stock market connectedness has gradually tightened between the US and Japan and between the UK and Japan, whereas recently, the connectedness between the US and UK has weakened. Fourth, our VAR-DCC analyses also revealed that volatility spillovers between the US, UK, and Japanese stock markets have been asymmetric. Fifth, we further showed that the skew-<italic>t</italic> errors incorporated into our VAR-DCC model are effective in estimating the dynamic stock return linkages between the US, the UK, and Japan. Finally, based on our findings, we derived many significant and beneficial interpretations and implications for historically and deeply considering return transmission, volatility spillovers, and DCCs between international stock markets.</p> </abstract>

ANALYSIS OF THE PRACTICE OF ATTRACTING FOREIGN INVESTMENTS THROUGH THE FUND MARKET

The article examines the meaning of the stock market and the definitions of local economists. Also, the practice of attracting foreign investments through the stock market has been analyzed. The dynamics of investments in securities and the composition of investors investing in sovereign Eurobonds of Uzbekistan were studied. The volume of investments in the US stock market was analyzed. Proposals and recommendations for attracting foreign investments through the stock market have been developed.

Why Do US Stocks Outperform EM and EAFE Regions?

Why Do US Stocks Outperform EM and EAFE Regions?

НЕДООЦЕНКА IPO: АНАЛИЗ ПАРАМЕТРОВ, ВЛИЯЮЩИХ НА ДОХОДНОСТЬ В ПЕРВЫЙ ДЕНЬ ТОРГОВ

The short-term performance of the companies that are coming public on the US stock market is examined in this paper. 505 initial public offerings from the start of 2018 to the end of 2020 formed up the sample underthe analysis. It was discovered that the sample as a whole had an average underpricing of 25,84%, and that 125 initial public offerings (IPOs) had negative first-day returns with an average overpricing of 10,08%. The OLS regression model was applied to examine the relationship between the initial returns and the 1 independent variables. The analysis revealed that the initial performance of companies going public is strongly statistically related to the age of the company, investor sentiment prior to the IPO, the risk variable, and the volatility at the IPO date, but not the volume of the IPO, the proxy for the underwriter’s rating, or the hot-issue periods.

Intangible Assets and US Stock Returns: An analysis using the Index Method, Panel Regression, and Machine Learning

This study examines the impact of intangible assets on stock returns in the U.S. using the Drucker Institute indices, which assess companies based on customer satisfaction, employee engagement and development, innovation, social responsibility, and financial stability. The relevance of this study lies in the growing importance of considering non-financial indicators in investment decision-making. The objective is to determine how these indices affect stock returns across different sectors. The hypotheses posit that each index has a positive impact. The study employs both panel regression with fixed effects and machine learning methods using XGBoost with Shapley values to analyze data from U.S. companies for the period from June 30, 2016, to June 30, 2023. The results indicate that social responsibility has a broadly positive impact on stock returns across various sectors. Innovation significantly affects returns only in the technology sector. Customer satisfaction and financial stability exhibit varying effects depending on the sector, while employee engagement and development show only negative impacts in the energy sector. The significance of this research lies in its contribution to understanding the role of intangible assets in shaping stock performance. We show that investors can achieve both ethical satisfaction and higher financial returns by prioritizing investments in companies with strong social responsibility records. Additionally, we draw the attention of investors and researchers to the importance of considering sectoral affiliation when analyzing companies. The use of advanced analytical tools, such as XGBoost with Shapley values, underscores the potential of machine learning in uncovering complex relationships in financial data. This approach proves to be highly promising for future research.

Assessment of the impact of big data analysis on decision-making in stock trading processes

This paper aims to construct a trader model tailored to offer individualized investment strategies for users engaged in the US stock market. The approach taken involves two key aspects: forecasting stock prices and discerning users' personality traits to formulate optimal trading recommendations. The framework is realized through the integration of neural networks and the Moving Averages algorithm. This combined approach allows forecasting upcoming changes in stock prices and generating appropriate trading signals. Complementing this, a user risk assessment is administered, and its outcomes are leveraged to furnish personalized trading advice, offering invaluable support for informed decision-making amid intricate real-time scenarios. By synergizing predictive modeling and user-specific insights, this comprehensive system contributes to enhancing trading precision and user empowerment. This work can be useful in decision making for a wide range of stock market participants, as well as for institutions specialized for this area of operation.

Analysis of the clearing mechanism of capital markets of USA, the regulations in the derivative segment, major hiccups in the last 10 years and a comparison of sectoral performance and expected performance in the near future

A crucial component of any financial market is the clearing system. It is the method used to match and settle deals, assuring the protection of all parties engaged in a transaction. The Securities and Exchange Commission (SEC) in the United States regulates the clearing system for the stock market, while the Commodity Futures Trading Commission (CFTC) regulates the clearing system for the commodity market. In response to changes in the market and hazards, the laws governing the derivatives sector have changed throughout time. Derivative markets have been subject to much more regulation since the 2008 financial crisis. This occurred as a result of the perception that derivatives had a significant role in the crisis. In the past ten years, there has been some fluctuation in the stock market's and commodities market's sectoral performance in the United States. The S&P 500 index has increased in value by more than double, signaling generally positive stock market performance. The commodity market, on the other hand, has been more erratic, with some commodities, like gold and oil, seeing huge price changes. In the short future, it is difficult to predict how the stock market and commodities markets will perform. While some analysts predict that the stock market will continue to do well, others think a correction is almost due. The commodities market is predicted to be unstable as well, with prices influenced by a variety of variables such as supply and demand, economic expansion, and geopolitical developments. This research paper will examine the clearing system for the US stock and commodities markets, the rules governing the derivatives market, significant setbacks during the past ten years, and a comparison of recent sector performance and anticipated performance. The ramifications of these discoveries for the future of the financial markets will also be covered in the discussion.

The Impact of the Federal Reserve Raises the Interest Rates on the US Stock Market Index: The Dynamic Perspective

In order to battle growing inflation, the Federal Reserve raised interest rates in March 2022 for the first time since 2018. This action signals the conclusion of the Federal Reserve's accommodative monetary policy adopted during the pandemic, coinciding with significant inflationary trends in the United States. One of the main focuses that this may bring is the effect on stock price. This article will use past data on NASDAQ stock prices to forecast future trends and will discuss the analysis. At the end, the research finds that Feds policy has let NASDAQ index decrease than expected trends in a long-term perspective. This suggests that the Fed's policy may have a more pronounced impact on long-term trends rather than short-term fluctuations. It also implies that investors should adopt a strategic, long-term approach to their investments, rather than reacting to short-term market noise. Investors may also consider implementing risk management strategies, including stop-loss orders and options contracts, to protect against sudden market downturns that may result from interest rate hikes.

The Impact of Natural Disaster on US Stock Market Index: Using DID, ARMAX-GARCH, and Random Forest

The stock market might be impacted by natural disasters, which can cause significant market disruptions and financial uncertainty, as already shown in several existing studies. This study investigates the role of natural disasters in stock market movements in the US specifically, hypothesizing that incorporating natural disasters leads to more accurate stock market predictions. Various models, including Difference-in-Differences (DID), ARMA-GARCH/ARMAX-GARCH, and Random Forest, are employed to analyze the S&P 500 index's daily returns and the impact of natural disasters. The study reveals that the ARMA-GARCH model effectively captures market volatility but does not account for all influencing factors such as interest rate, inflation rate, and natural disasters. The DID method isolates natural disaster effects, showing significant but inconsistent impacts. The Random Forest model, incorporating a disaster severity index, yields more accurate predictions than the one without, reinforcing the hypothesis and supporting previous studies. Although the study highlights the importance of considering natural disasters in stock market predictions, it also underscores the market's complexity, indicating that more features and factors should be considered in future research.

ESG news and long-run stock returns

This paper investigates the persistence of ESG news' effects on US stock returns. Utilizing a rolling-portfolio approach to calculate overlapping returns of news-based portfolios, extending up to twelve months post-news release, we find that market reactions to ESG news are fleeting and reversing within a month in the most recent sample period. This suggests ESG news has no enduring influence on stock returns. Notably, the reactions are prominent for social and governance themes but not for environmental-related news. This study is one of the first attempts to understand how ESG news can affect a company's value over the long term.

Dynamic spillovers in higher moments and jumps across ETFs and economic and financial uncertainty factors in the context of successive shocks

Hedging is a particularly important tool in the Exchange Traded Fund (ETF) markets where market makers seek the best ways to mitigate the uncertainty of their exposures. This study relies on high frequency data to assess the spillover effects among ten US sector ETFs and various economic and financial uncertainty indexes based on realized volatility, realized higher moments as well as jumps under a relatively new spillover framework. Next, a time-varying parameter vector autoregression (TVP-VAR) model is used to examine the dynamic connectedness among ETFs and uncertainty factors volatilities while avoiding the sensitivity of spillover results to the choice of the rolling window. Our results showcase higher total connectedness between the different uncertainty indexes and ETFs, though with varying sensitivities. Notably, skewness and kurtosis can spread from one market to another, especially during times of market turbulence, reflecting the significant spillovers in higher-order moments. Interestingly, the market’s 30-day forward looking expectations of US stock market volatility (VIX) has stronger effect on the US sector equity ETFs than the expected 30-day volatility of returns on oil and gold. This analysis emphasizes the implications and contributions of assessing the spillover in higher-order moments covering volatility, skewness, and kurtosis to portfolio hedging and financial risk management. Overall, the results are of considerable practical interest for economic and market agents who are keen to understand market integration and systemic risk propagation to infer asymmetric or fat tail risk related to extreme or downside/upside risks.

Tools for studying the digital development rates of economic systems at country and region level

National economy digitalization is a powerful economic growth engine and one of the key areas for developing and maintaining the competitiveness of the state. Digital economy potential lies in accelerating the pace of economic development of enterprises, industries, regions and countries, and in improving in living standards. Under the conditions of digitalization, a tool for assessing the pace of economic system development at the country, region, industry and enterprise level becomes a source of competitive advantages. Therefore, the relevance of the study is evident. The purpose of the study is to develop tools for studying digital development rates at country and region level. The database of the study was represented by the proceeding of well-known foreign and Russian scientists and statistical data on the main indices of the US and Chinese stock markets. The methodological base was represented by the ISPI (Information System Portfolio Investor) digital model. The effectiveness of the obtained results was verified using the PRM (Profitability-Risk model) tool. The result of the study was an analysis of digital development rates at the country level by the case of the United States and China. The analysis was based on the calculation of the main parameters of the portfolio theory: expected return, risk level, return-to-risk ratio. Scatterplots were constructed for the main US and Chinese stock market indices based on the fitted data. They show the mutual distribution of groups of main indices and allow to determine the most promising areas for investment, including digital development. The findings confirm the possibility of using the proposed tools to determine the most attractive sectors for investment in the world stock markets and countries, and/or countries and regions that need investment for their development, including digital one. The introduction of the proposed tools into practice at the enterprise, industry, region and country level will contribute to the further development of the digitalization in all aspects of society at the enterprise, industry, region and country level.

Market return spillover from the US to the Asia-Pacific Countries: The Role of Geopolitical Risk and the Information & Communication Technologies.

This study examines the market return spillovers from the US market to 10 Asia-Pacific stock markets, accounting for approximately 91 per cent of the region's GDP from 1991 to 2022. Our findings indicate an increased return spillover from the US stock market to the Asia-Pacific stock market over time, particularly after major global events such as the 1997 Asian and the 2008 global financial crises, the 2015 China stock market crash, and the COVID-19 pandemic. The 2008 global financial crisis had the most substantial impact on these events. In addition, the findings also indicate that US economic policy uncertainty and US geopolitical risk significantly affect spillovers from the US to the Asia-Pacific markets. In contrast, the geopolitical risk of Asia-Pacific countries reduces these spillovers. The study also highlights the significant impact of information and communication technologies (ICT) on these spillovers. Given the increasing integration of global financial markets, the findings of this research are expected to provide valuable policy implications for investors and policymakers.

Weighted Shapley values of efficient portfolios

Shapley value theory, which originally emerged from cooperative game theory, was established for the purpose of measuring the exact contribution of agents playing the game. Subsequently, the Shapley value was used in finance to decompose the risk of optimal portfolios, attributing to the various assets their exact contribution to total risk and return. In the present paper, the Shapley value results of Shalit [Annals of Finance 17(1) (2021), 1–25] are extended by using weighted Shapley values to decompose the risk of optimal portfolios. The weighted concept, as axiomatized by Kalai and Samet [Journal of Game Theory 16(3) (1987), 205–222], provides a solution to cooperative games when the symmetry of players cannot be justified. The weighted Shapley value theory is applied to model efficient mean-variance portfolios and price their constituents. The computation is carried out for the 13 most traded US stocks in 2020 and the results are compared with the standard Shapley values.

The 2016 US presidential election, opinion polls and the stock market

Purpose The purpose of this paper is to study the impact of the 2016 presidential election polls on the stock market. Design/methodology/approach The empirical model includes daily stock returns as the dependent variable and past asset prices, 10-year treasury rates, opinion polls and VIX (market uncertainty) as explanatory variables with a one-year lag. The model was estimated using two sets of daily polling data: from July 1, 2015, to November 8, 2016, and from June 1, 2016, to November 8, 2016. Additional descriptive statistics, such as means and standard deviations, were also calculated. Findings The estimated results did not reveal any statistically significant effects of opinion polls in favor of one candidate over another on stock returns. Simple statistical tests, however, show that the market performed better when Trump held a polling advantage over Clinton. Originality/value To the best of the authors’ knowledge, this is the only study that has examined the effects of the 2016 presidential election polls on the US stock market. This study adds value to the understanding of the relationship between election polls and the stock market in the USA.

Portfolio Optimazation Based on 10 US Stocks

The US stock market has recently become volatile because of the increase in interest rates, which arouses investors focus on portfolio optimization. This study selected 10 representative US stocks from different industries listed on NASDAQ and NYSE. This paper applies Monte Carlo simulations to determine the efficient frontier and constructs portfolios with maximum Sharpe Ratio and minimum volatility. The result showed that Lululemon Athletica Inc. possesses the largest proportion of the maximum Sharpe ratio portfolio, and the Coca-Cola Company occupies the greatest weight in the min volatility portfolio. By comparing the cumulative return of the two portfolios with the NASDAQ Composite Index, the max Sharpe ratio portfolio is found to overperform the market benchmark. In the robustness test in which 8 stocks selected from the original group of assets are taken to construct a portfolio, the same result still holds. This result may demonstrate the feasibility of portfolio management for a few investors in that period.

Study on the Applicability of LSTM for Predicting Stock Price when Facing Extreme Events

The analysis of stock price fluctuations holds considerable significance in the field of economics, particularly given the present environment characterized by unpredictability and rapid changes. Previously, the long short-term memory (LSTM) model has been employed effectively in addressing time series problems, including stock market forecasting. However, in the current dynamic landscape, the ability of LSTM to adapt to volatile conditions and provide accurate predictions is an area that merits further investigation. This study gathers stock data from prominent and representative companies, namely Apple, Google, Amazon, and Microsoft, spanning from January 2012 to March 2023. Specifically, two significant events are examined: the impact of the Covid-19 outbreak on the US stock market on February 26, 2020, and the Russia-Ukraine conflict occurring on February 26, 2022. By dividing the stock data surrounding these events into training and test sets, this research aims to evaluate the differential performance of LSTM in scenarios where it possesses no prior knowledge of these events versus situations where it has already assimilated the influence exerted by them.