Major US Stock Research Articles

Exploring the first impression of a company: an empirical analysis of IPO prospectuses applying the impression formation model

PurposeThe present study aims to identify how firms positioned their corporate reputation (i.e. impressiveness vs respectability) in their initial public offering (IPO) communication based on the impression formation model. Further, the study examined whether this presentation of corporate reputation was related to IPO success (i.e. stock price and volume of trading).Design/methodology/approachThe present study analyzed 248 IPO prospectuses that were submitted to the major US stock markets. Specifically, various substantive and symbolic information and cues in IPO prospectuses were content analyzed.FindingsThe results suggest that bigger (in terms of revenue) IPO companies featured more “impressiveness” in their IPO prospectus, leading to greater IPO success. Bigger (in terms of both revenue and number of employees) IPO companies featured more “respectability” impressions in the IPO prospectus, although they did not achieve direct IPO success on the first day of IPO. Different types of industry used different information cues to feature “impressiveness” and/or “respectability,” suggesting that different types of firms view different cues to be important to IPO communication.Practical implicationsThe results also suggest some practical guidelines for the strategic use of contents, tables and illustrations. Using more charts, tables and illustrations in IPO prospectus summaries was associated with a higher volume of trading on the first day. The more illustrations included in the IPO prospectus summaries, the less investors were willing to pay for initial stock prices.Originality/valueIPO communication is a generally understudied area in corporate communication and strategic communication scholarship. The results should help to explain which communicative aspects and PR strategies effectively manage the firm’s impression to maximize the chances of an IPO success as well as initially build the financial reputation of a company. By doing so, the findings contribute to the broader advancement of financial communication within the strategic communications domain.

CEO overconfidence, risk-taking, and firm value: Influence of incentive compensation and financial constraints

By using the data of firms listed on the three major US stock exchanges—the New York Stock Exchange, Nasdaq Stock Exchange, and American Stock Exchange—this study investigated (1) whether CEO overconfidence increases additional risk-taking and affects firm value, (2) whether high incentive compensation for overconfident CEOs increases additional risk-taking behavior and firm value, and (3) whether financial constraints reduce overconfident CEOs’ additional risk-taking and firm value. Our empirical results reveal significant differences in the variable data between the subsamples of firms with an overconfident CEO versus and those with a non-overconfident CEO. The firms with an overconfident CEO had more additional risk-taking, higher firm value, and more financial constraints than those with a non-overconfident CEO did. CEO overconfidence had significant positive associations with additional risk-taking and firm value, indicating that CEO overconfidence has a positive effect on firm overinvestment. Although overconfidence sometimes distorts investment decisions, it also leads to risky but valuable investments. Offering incentive compensation can incentivize CEOs to engage in additional risk-taking behaviors that increase firm value. This result indicates that offering a greater amount of incentive compensation encourages CEOs to take additional risks because they can gain higher compensation from making valuable investments that increase their firm’s value. In this study, incentive compensation was more likely to motivate risk-taking leading to increase firm value in non-overconfident CEOs than in overconfident CEOs. That is, granting non-overconfident CEOs a high percentage of incentive compensation can align their interests with those of shareholders and encourage more rational risk-taking. Compared with firms with overconfident CEOs, those with non-overconfident CEOs that were subject to higher financial constraints exhibited a less significant decrease in additional risk-taking but a more significant decrease in firm value.

Precious Metals Comovements in Turbulent Times: COVID-19 and the Ukrainian Conflict

We examined the evolution of cross-market linkages between four major precious metals and US stock returns, before (Phase I) and after (Phase II) the COVID-19 outbreak. Phase II was also extended to encompass the Ukrainian conflict, which prolonged the period of uncertainty in financial markets. Due to the increase in volatility observed in Phase II, we used a heteroskedasticity-adjusted correlation coefficient to examine the evolution of correlation changes since the COVID-19 outbreak. We also propose a relevant dissimilarity measure in multidimensional scaling analysis that can be used for depicting associations between financial returns in turbulent times. Our results suggest that (i) the correlation levels of gold, silver, platinum, and palladium returns with US stock returns have not changed substantially since the COVID-19 outbreak, and (ii) all precious metal returns exhibit movements that are less synchronized with US stock returns, with palladium and gold being the least synchronized.

Relationship between Total Expenditure on Cosmetic Procedures in the United States and NASDAQ 100 Index.

The authors analyzed annual cosmetic procedure statistics from the American Society of Plastic Surgeons for 2005-2020 against economic indices, including major US stock market indices [NASDAQ 100, S&P 500, Dow Jones Industrial Average (DJIA), Russell 2000 indices from the New York Stock Exchange, gross domestic product (GDP)], median income of US citizens, and the US population from the Federal Reserve Bank of St. Louis. Pearson correlation coefficient and multiple regression analysis were used for the statistical analysis. Total expenditure on cosmetic surgery and minimally invasive procedures (TECP) has more than doubled since 2005 to 2020. TECP revealed statistically significant correlations with all other indicators. TECP had the greatest correlation with DJIA (r = 0.952, P < 0.001). In the multiple regression analysis, the increase in TECP led to the rise of the NASDAQ 100 index (adjusted R2 was 0.790, P < 0.001). There was a statistically significant correlation between the TECP in USA and the major indices of the US stock market. In particular, the increase in TECP led to the rise of the NASDAQ 100 index.

Volatility forecasting with Hybrid‐long short‐term memory models: Evidence from the COVID‐19 period

AbstractVolatility forecasting, a central issue in financial risk modelling and management, has attracted increasing attention after several major financial market crises. In this article, we draw upon the literature on volatility forecasting and hybrid models to construct the Hybrid‐long short‐term memory (LSTM) models to forecast the intraday realized volatility in three major US stock indexes. We construct the hybrid models by combining one or multiple traditional time series models with the LSTM model, and incorporating either the estimated parameters, or the predicted volatility, or both from the statistical models as additional input values into the LSTM model. We perform the out‐of‐sample test of our Hybrid‐LSTM models in volatility forecasting during the coronavirus disease 2019 (COVID‐19) period. Empirical results show that the Hybrid‐LSTM models can still significantly improve the volatility forecasting performance of the LSTM model during the COVID‐19 period. By analysing how the construction methods may influence the forecasting performance of the Hybrid‐LSTM models, we provide some suggestions on their design. Finally, we identify the optimal Hybrid‐LSTM model for each stock index and compare its performance with the LSTM model on each day during our sample period. We find that the Hybrid‐LSTM models' great capability of capturing market dynamics explains their good performance in forecasting.

Dynamic changes in US Financial Markets under the COVID-19 Pandemic

Covid-19 disrupted people’s lives and the world’s economic activities in major ways. The pandemic affected, businesses, companies, and investors in the stock market. This paper aims to how normalized Covid-19 affect the United States stock market by analyzing three major US stock markets: S&amp;P500, NASDAQ, and DJIA. The aim was to examine the effect on stock market’s return and volatility. To analyze the impact of the pandemic, vector autoregression models (VAR) as well as the ARMA-GARCH-X model were used. Impulse response function graph from the VAR model revealed that the pandemic did increase or decrease the stock market returns of either of the indices. However, fluctuations in returns were shown to be higher during the early period but faded with time. The ARMA-GARCH-X model however reported that the pandemic only influenced the volatility of S&amp;P500 returns only while the other two markets were not affected. Conclusion drawn from the study is that the pandemic influenced the stock markets in the early days but its influence tapered down due to normalization of the pandemic in the mind of investors. Government responses to the pandemic as well as the introduction of vaccines could also serve to raise investor’s confidence thus reducing the influence of the pandemic on the stock market.

Domestic liquidity of cross-listed stocks: Evidence from the ADR market

This paper examines the impact of cross-listing in the US on domestic liquidity of the cross-listed stocks. We use a sample of firms from 31 developed and emerging economies that cross-listed their shares on major US stock exchanges through an American Depository Receipt (ADR) program between 1998 and 2020. The study finds that domestic liquidity increases significantly following a US cross-listing for both developed and emerging market firms. Our results suggest that for firms in developed markets, domestic trading activity decreases before and during the cross-listing year. However, it increases significantly in the long-term, suggesting evidence of the ‘flow back’ effect. For firms in emerging economies, the anticipatory, impact, intermediate and long-term effects on domestic liquidity are all significantly positive. We also find evidence for the ‘bonding hypothesis’ suggesting that the improvements in domestic liquidity are greater for cross-listed firms from countries with low rule of law, weak investor protection and bad quality of accounting information long-term.

Segment earnings and managerial incentives: evidence from foreign firms cross-listed in the USA

PurposePrior studies find that US firms with managerial incentives may manipulate the earnings gap to obscure higher performing segments to competitors or to hide underperforming segments from external monitors. The purpose of this study is to complement extant research by examining the association between managerial incentives and segment earnings reporting of cross-listed firms in the USA and the impact of country-level characteristics on this association.Design/methodology/approachThe dependent variable is the earnings gap between firm-level earnings and sum of segment-level earnings. Managerial incentives are proxied by proprietary cost and agency cost. Proprietary cost is measured by the Herfindahl index. Agency cost is measured by inefficient resource transfer activities across segments. Foreign firms in this study are companies listed on major US Stock Exchanges with headquarters outside the USA. Comparable US firms are selected using the Propensity Score Matching procedure as a control group.FindingsThe authors find that 1) proprietary cost motive is not the determinant of earnings gap reporting for cross-listed firms; 2) cross-listed firms motivated by agency costs are more likely to manipulate segment earnings reporting than US firms; and 3) among cross-listed firms motivated by agency costs, firms in weak rule of law countries demonstrate more manipulation in segment earnings than firms in strong rule of law countries.Originality/valueExtant research with regard to segment reporting exclusively focuses on US firms, and little is known about the practice of segment reporting by cross-listed firms originating from different legal regimes. This study fills the gap in the literature by comparing cross-listed firms to US firms in the reporting of segment earnings. The results of this study have implications for regulators and investors who are interested in evaluating the extent of cross-listed firms’ financial reporting quality.

Does Innovation Explain the Skewness of Stock Returns?

This paper investigates the impact of firm-level innovation on the skewness of stock returns. Using data on a broad sample of equities from the major US stock exchanges, we find that innovative companies exhibit strong positive skewness. Our results are robust to both input and output measures of innovation as we find that increases in both firm-level research and development expenditure (R&amp;D), as well as the number of patents, are positively associated with future stock return skewness. Our results hold using both systematic and idiosyncratic measures of skewness while controlling for various stock characteristics, time, and industry-fixed effects.

Did Precious Metals Serve as Hedge and Safe-Haven Alternatives to Equity during the COVID-19 Pandemic: New Insights Using a Copula-Based Approach

We examine the hedging and safe-haven characteristics between four major precious metals (gold, silver, platinum, and palladium) and three major US stock market indices. The metal markets are known for hedging and safety characteristics during financial distress. The paper sheds new insights into the hedging and safe-haven ability of the precious metals under extreme stock market volatility conditions caused by the COVID-19 by drawing empirical estimates using two different copula approaches. The results show that gold outperforms all other precious metals such as silver, platinum, and palladium in hedging abilities under extreme stock market conditions. Likewise, gold remains a safe-haven asset both in calm and turbulent periods, and silver possibly remains the next best alternative, albeit as a weak safe-haven asset.

The uncertainty in extreme risk forecasts from covariate-augmented volatility models

For a GARCH-type volatility model with covariates, we derive asymptotically valid forecast intervals for risk measures, such as the Value-at-Risk or Expected Shortfall. To forecast these, we use estimators from extreme value theory. In the volatility model, we allow for leverage effects and the inclusion of exogenous variables, e.g., volatility indices or high-frequency volatility measures. In simulations, we find coverage of the forecast intervals to be adequate for sufficiently extreme risk levels and sufficiently large samples, which is consistent with theory. Finally, we investigate if covariate information from volatility indices or high-frequency data improves risk forecasts for major US stock indices. While—in our framework—volatility indices appear to be helpful in this regard, intra-day data are not.

The influence of investors’ opinions of human capital and multitasking on firm performance: a knowledge management perspective

PurposeThe purpose of this study is to examine the disruption-adaptation associated with knowledge management (KM) of entrepreneurial multitasking of top strategy and tactics executive (TSTE) succession in positions responsible for both S and T. This provides insight into KM and firm performance during turbulent periods.Design/methodology/approachThe study examines investor’s opinions of human capital in the context of managerial succession. The data was based on 900 publicly available appointment announcements between 2006–2014, allowing for the examination of 459 observations of succession in 51 industries.FindingsThe findings indicate that the relationship between KM of entrepreneurial multitasking and firm performance was more positive for high innovation firms than for low innovation firms. As well, the relationship between investors’ opinions of a top executive manager’s human capital and firm performance is more positive for small firms than for large firms and more positive for high innovation firms than for low innovation firms.Research limitations/implicationsThe study contributes to the literature by systematically examining the announced appointment of executives in one context where KM of entrepreneurial multitasking is prevalent – across marketing strategy and sales tactics (hereafter, S and T) responsibilities – for multiple firms listed at major US stock exchanges across a wide range of industries, using lagged performance data to discern performance outcomes. It highlights important issues related to organizational structure and human capital for firm performance and KM in dynamic environments. Further research could examine the impact on firm performance of a change in structure – from a joint sales and tactics position to a sales or tactics position and vice versa. By studying the impact of change to and from an intertwined position, future scholars can determine the level of risk stemming from coordination uncertainty changes with time.Practical implicationsOf practical relevance, the study shows that vesting dual responsibility for S and T in one executive during managerial succession may not be as universally valuable or adaptive as previously thought. One practical extension of this research may also be that larger firms that are more likely to have clearly defined silos may find that such vesting of multitasking responsibility not as valuable. High innovation and small firms may gain from new executives’ multitasking responsibility for S and T. Thus, firms should think twice before vesting S and T responsibilities with one incoming executive during the leadership change.Social implicationsResponsibility for both S and T compounds ambiguous accountability, frequently leaving the locus of customer-related problems unclear, and therefore unsolved.Originality/valueExtant research has overlooked the relationship between the top management team’s (TMT) abilities to multitask firm performance over time across contexts of external and internal change, operationalized as firm innovation and firm size. Nor have studies explored the firm performance implications of external stakeholders’ opinions of such human capital across these contexts. A novel measure of executive-specific human capital – abnormal returns generated the appointment announcement, is introduced. Understanding the capability of a top executive to simultaneously multitask both S and T responsibilities is a critical component of KM; also relevant are investors’ opinions of their human capital, a particular oversight given the challenge of the “great transformational leader” with servant leadership theory (Carayannis et al., 2017; Gregory Stone et al., 2004).

COVID-19 Effects on the US Stock Index Returns: An Event Study Approach

This paper aims to measure the impact of COVID-19 pandemic on the US stock market.It applies Generalized Autoregressive Conditional Heteroskedasticity (GARCH), Vector Autoregressive (VAR) and Event study Method (ESM) models. The ESM follows three different timelines, such as pre-event, event-day, post-event windows. Data comes from the major US stock indices. We find that the US stock market reacts negatively toward confirmed and death cases due to COVID-19, while death cases have a significant impact on stock market volatility. This study bridges the research gap and adds significant insights to the existing literature.

Topology Data Analysis Using Mean Persistence Landscapes in Financial Crashes

Topological features in high dimensional time series are used to characterize changes in stock market dynamics over time. We explored the daily log returns of four major US stock market indices and 10 ETF sectors between January 2010-June 2020. Topological data analysis and persistence homology were used on two sequences of point cloud data sets the stock indices and the ETF sectors, respectively. Using these sequences, the daily log returns, persistence diagrams, persistence landscapes, and mean landscapes were used to quantify topological patterns in the multidimensional time series. For example, norms of the persistence landscapes were generated to detect critical transitions in the daily log returns. To measure statistical significance, we implemented three permutation tests with a significance level α = 0.05 to determine if topological features change within a particular time frame by comparing sliding windows in the sequence of point cloud data sets. We found that between July 1, 2019 and July 1, 2020, there is evidence of changing structure in the US stock market. Critical transitions are identified by the statistical properties of the norms of the persistence landscape between contiguous daily sliding windows of the stock indices and ETF sector series.

Bitcoin as Hedge or Safe Haven: Evidence from Stock, Currency, Bond and Derivatives Markets

It is crucial for investors to manage their investment risk. This paper examines the dynamic equicorrelation relationship between Bitcoin and four major investment assets, namely, US stock (S&P 500), US dollar, Treasury bonds and gold futures. Our empirical analysis reveals an asymmetric causality between Bitcoin and other asset classes. The results indicate that Bitcoin may be employed as an effective safe haven for investors by providing invaluable information to reduce downside risk, hence strengthening diversification benefits in optimal asset allocation and portfolio risk management.

Extreme Market Value Declines: How Well Do Rating Agency Assumptions Hold?

S&amp;P Global Ratings has been rating leveraged closed-end funds regulated in the United States under the Investment Company Act of 1940 for more than 30 years. The principal source of repayment for these market value securities is the proceeds from liquidating the underlying pledged collateral. In its rating process, S&amp;P applies haircuts to the value of the pledged securities, discounting their value to cover the potential market value decline during the liquidation period. But how appropriate are these haircut assumptions? This article studies the extreme price changes in two major US stock indexes, the Dow Jones Industrial Average and the S&amp;P 500 Index, using extreme value methods. The analysis compares extreme market value declines to the assumptions S&amp;P uses in its market value securities ratings criteria. Moreover, the analysis estimates the likelihood of observing declines larger than those in the criteria (exceedance probabilities) and the average waiting time to observation of declines of various extreme magnitudes (return periods). From the analysis, we find that the likelihood of exceeding the assumptions in the rating methodology is generally in line with S&amp;P’s rating definitions.

Topological data analysis of financial time series: Landscapes of crashes

We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007–2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional time series. Using a sliding window, we extract time-dependent point cloud data sets, to which we associate a topological space. We detect transient loops that appear in this space, and we measure their persistence. This is encoded in real-valued functions referred to as a ’persistence landscapes’. We quantify the temporal changes in persistence landscapes via their Lp-norms. We test this procedure on multidimensional time series generated by various non-linear and non-equilibrium models. We find that, in the vicinity of financial meltdowns, the Lp-norms exhibit strong growth prior to the primary peak, which ascends during a crash. Remarkably, the average spectral density at low frequencies of the time series of Lp-norms of the persistence landscapes demonstrates a strong rising trend for 250 trading days prior to either dotcom crash on 03/10/2000, or to the Lehman bankruptcy on 09/15/2008. Our study suggests that TDA provides a new type of econometric analysis, which complements the standard statistical measures. The method can be used to detect early warning signals of imminent market crashes. We believe that this approach can be used beyond the analysis of financial time series presented here.

Topological Data Analysis of Financial Time Series: Landscapes of Crashes

We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007-2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional time series. Using a sliding window, we extract time-dependent point cloud data sets, to which we associate a topological space. We detect transient loops that appear in this space, and we measure their persistence. This is encoded in real-valued functions referred to as a 'persistence landscapes'. We quantify the temporal changes in persistence landscapes via their Lp-norms. We test this procedure on multidimensional time series generated by various non-linear and non-equilibrium models. We find that, in the vicinity of financial meltdowns, the Lp-norms exhibit strong growth prior to the primary peak, which ascends during a crash. Remarkably, the average spectral density at low frequencies of the time series of Lp-norms of the persistence landscapes demonstrates a strong rising trend for 250 trading days prior to either dotcom crash on 03/10/2000, or to the Lehman bankruptcy on 09/15/2008. Our study suggests that TDA provides a new type of econometric analysis, which goes beyond the standard statistical measures. The method can be used to detect early warning signals of imminent market crashes. We believe that this approach can be used beyond the analysis of financial time series presented here.

Factor Models of Stock Returns: GARCH Errors versus Time‐Varying Betas

This paper investigates the implications of time‐varying betas in factor models for stock returns. It is shown that a single‐factor model (SFMT) with autoregressive betas and homoscedastic errors (SFMT‐AR) is capable of reproducing the most important stylized facts of stock returns. An empirical study on the major US stock market sectors shows that SFMT‐AR outperforms, in terms of in‐sample and out‐of‐sample performance, SFMT with constant betas and conditionally heteroscedastic (GARCH) errors, as well as two multivariate GARCH‐type models. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Generalized Variance‐Ratio Tests in the Presence of Statistical Dependence

This article extends and generalizes the variance‐ratio (VR) statistic by employing an estimator of the asymptotic covariance matrix of the sample autocorrelations. The estimator is consistent under the null for general classes of innovations exhibiting statistical dependence including exponential generalized autoregressive conditional heteroskedasticity and non‐martingale difference sequence processes. Monte Carlo experiments show that our generalized test statistics have good finite sample size and superior power properties to other recently developed VR versions. In an application to two major US stock indices, our new generalized VR tests provide stronger rejections of the null than do competing VR tests.