National Association Of Securities Dealers Automated Quotations Research Articles

Association rules forecasting for the foreign exchange market

Several association rule mining algorithms exist, and among them, Apriori is one of the most commonly used methods for extracting frequent item sets from vast databases and generating association rules to gain insights. In this research, we have applied a data mining technique to implement association rules and explore frequent item sets. Our study introduced a model that employs association rules to uncover associations between the foreign exchange market, the gold commodity, and the National Association of Securities Dealers automated quotations (NASDAQ). We suggested a method that used data mining to identify the good points of buying and selling in the foreign exchange market by utilizing technical indicators such as moving average convergence divergence (MACD) and the stochastic indicator to create association rules. The experimental findings indicate that the proposed model successfully generates strong association rules.

How do corporate governance, artificial intelligence, and innovation interact? Findings from different industries

Research in the field of corporate governance has been exhaustive, and recently many scholars have focused on the relationship between corporate governance attributes and artificial intelligence (AI), corporate governance attributes, and corporate innovation (Asensio-López et al., 2019), however, there are few studies that combine corporate governance, AI, and corporate innovation. This article examines the relationships among corporate governance attributes, AI, and corporate innovation. Adopting a new perspective, we have tried to help resolve this issue using a content-analysis that integrates data from over 50 companies that trade on National Association of Securities Dealers Automated Quotations (NASDAQ) to analyze the relationship between board attributes, the practice of AI and firm innovation for the time 2018–2022. The results suggest that particular aspects of boards, such as board size, board diversity, and ownership concentration show significant correlations with firm AI development and innovation for overall industries, but the levels of associations also vary depending on different innovation measurements and samples considered in specific industries. Corporate governance has more significant variables in the manufacturing and information technology service industries. Moreover, the mediating effects of AI and innovation are examined, respectively. This research offers implications to corporate decision-makers as to how to proceed if the intent is to offer commercialized AI advancements and successful breakthrough innovations.

The Effect of Rise of Federal Funds Rate on US Stock Market: From the Perspective of US Dollar Appreciation

The study examines the impact of the rise of the Federal Funds Rate (FFR) on the stock price returns and volatility of the United States of America. The study extracted the stock price from January 3, 2022, to June 16, 2023, from the Investing finance terminal, including the Standard and Poors (S&P 500), US Dollar Index, and the National Association of Securities Dealers Automated Quotations (NASDAQ) Index. This study uses the Vector Autoregression (VAR) model and the ARMA-GARCH model to evaluate the stock returns and daily return volatility, respectively. The two models inspect the effect of the US Dollar index on both the NASDAQ and S&P 500. The result of this study suggests that the rise in the Federal Funds Rate would cause the stock market price to decline for a relatively long period. However, the volatility of the daily return remains stable. By studying the effect of the rise in the Federal Funds Rate, policymakers can change the stock prices by adjusting the Federal Funds Rate based on the markets needs.

Research on securities price prediction and insurance strategy optimization based on deep learning algorithm

The process of attempting to estimate the future prices of particular stocks by utilizing historical data and various analytical tools, including deep learning algorithms, is called stock price prediction. Insurance providers’ overall approach and decisions to manage their risks, enhance their profitability, and give value to their policyholders are referred to as the insurance strategy. It requires various things to be considered, including underwriting procedures, pricing strategies, product creation, risk analysis, claims administration, and investment choices. This study proposed optimizing an insurance strategy and predicting securities prices using a deep learning algorithm. Initially, the real stock data sources for Microsoft Corporation (MSFT) were gathered from Ping An Insurance Company of China (PAICC) and the Shanghai-based National Association of Securities Dealers Automated Quotation (NASDAQ). Normalization is the procedure used to preprocess data for the raw data. We suggest an Enhanced dragonfly-optimized deep neural network (EDODNN) with stock price forecasting and insurance. The outcomes demonstrate that the proposed model outperforms the current methodology and achieves accuracy, precision, recall, F1 score, R 2, and RMSE. To display the effectiveness of the suggested system, its performance is compared to more established methods to obtain the highest level of efficiency for the research.

Do ESG ratings mediate the relationship between board gender diversity and firm financial performance? Evidence from the U.S. Market

The impact of board gender diversity on firm value is inconclusive. The paper investigates whether a firm’s environmental, social, and governance (ESG) performance mediates the relationship between board gender diversity and firm financial performance. The study employs a sample of 1514 non-financial firms listed on the National Association of Securities Dealers Automated Quotations (NASDAQ) and New York Stock Exchanges (NYSE) from 2016 to 2020. It reveals that a firm improves its ESG ratings and financial performance when there are more female directors on its board. When controlling ESG performance, the significant relationship between board gender diversity and firm performance turns insignificant, and ESG performance has a significant positive impact on a firm’s financial performance. These findings confirm the mediating role of ESG performance in the studied relationship. A firm’s management can refer to these findings to employ more women on its board, which creates additional firm financial value through better ESG practices. Investors may incorporate the factors of board gender diversity and corporate ESG performance into their investment decisions.

Reputation, return and risk: A new approach

Corporate reputation has deserved attention in recent years from firms and researchers given its impact on creating a competitive advantage and on keeping a sustained superior performance. However, the impact of corporate reputation on risk, in addition to being less studied, still presents controversial results. Thus, the purpose of this study is to, simultaneously, analyze the effect of corporate reputation on stock return and risk. A model based on firms' financial market data was assessed through a panel data analysis which included 84,745 firm-year observations, which occurred between January 6th, 2009, and December 31th, 2019, from a full sample of 156 United States firms listed in the New York Stock Exchange (NYSE) and National Association of Securities Dealers Automated Quotations (NASDAQ), 82 of which were listed in Reputation Quotient (RQ).The results show that there are no significant differences between listed and not listed firms in Reputation Quotient concerning firms' abnormal returns and firms' systematic risk. This can be justified because stock prices adjusted instantly to the corporate reputation, which supports the market efficiency hypothesis.This study may provide important insights into the literature: firstly, although the impact of reputation on performance has gained attention in recent years, the firsts studies essentially analyze the reverse impact; secondly, this work aims to, simultaneously, study the effect of corporate reputation on return and risk, being the impact on the risk an area still little explored and with controversial results; thirdly, this study distinguishes itself by using a set of firms listed in a reputation ranking and a set of firms not listed, but with similar characteristics in terms of market capitalization, highlighting the impact of reputation communication through media rankings.

Intelligent candlestick forecast system for financial time-series analysis using metaheuristics-optimized multi-output machine learning

The effective prediction of stock market prices and trends is a critical topic in financial research for investors and stakeholders who wish to increase their return on investment. Motivated by highly unstable stock market targets and the continuous trading system that has been launched by Taiwan’s stock market, this work develops an intelligent candlestick forecast system (ICFS), which combines the advantages of three forecasting techniques — single-point, interval value, and trend forecasting. The ICFS is simple and intuitive, using a sliding-window metaheuristic-optimized multi-output least squares support vector regression hybrid model scheme to forecast simultaneously four types of price information that are represented on a candlestick chart. Three parameter-free metaheuristic algorithms, namely teaching-learning-based optimization (TLBO), symbiotic organisms search (SOS), and forensic-based investigation (FBI), are in turn used to optimize the hyperparameters of the multi-output least squares support vector regression (MLSSVR) model, to increase its accuracy and stability in predicting stock prices. Benchmark data from relevant studies are used to compare the forecast performances of previously developed and the three proposed hybrid models. Among all tested models, the FBI-MLSSVR greatly outperforms the others in forecasting Standard & Poor’s (S&P) and National Association of Securities Dealers Automated Quotations (NASDAQ) indexes, Taiwan’s exchange-traded funds, and major listed construction companies. A floating dollar-cost averaging strategy for maximizing returns while minimizing investment risks is proposed and compared with traditional dollar-cost averaging and the buy-and-hold strategy. The profitability of the predictions made using the proposed system is verified against actual stock market operations. The ICFS is packaged as a visualized expert system and independent application program for the convenience of users, who can intuitively run it and interpret its forecasts. Therefore, this study is highly practical by providing stakeholders with an easy-to-use and accurate predictive tool.

The Cross-Sectional Intrinsic Entropy-A Comprehensive Stock Market Volatility Estimator.

To take into account the temporal dimension of uncertainty in stock markets, this paper introduces a cross-sectional estimation of stock market volatility based on the intrinsic entropy model. The proposed cross-sectional intrinsic entropy (CSIE) is defined and computed as a daily volatility estimate for the entire market, grounded on the daily traded prices—open, high, low, and close prices (OHLC)—along with the daily traded volume for all symbols listed on The New York Stock Exchange (NYSE) and The National Association of Securities Dealers Automated Quotations (NASDAQ). We perform a comparative analysis between the time series obtained from the CSIE and the historical volatility as provided by the estimators: close-to-close, Parkinson, Garman–Klass, Rogers–Satchell, Yang–Zhang, and intrinsic entropy (IE), defined and computed from historical OHLC daily prices of the Standard & Poor’s 500 index (S&P500), Dow Jones Industrial Average (DJIA), and the NASDAQ Composite index, respectively, for various time intervals. Our study uses an approximate 6000-day reference point, starting 1 January 2001, until 23 January 2022, for both the NYSE and the NASDAQ. We found that the CSIE market volatility estimator is consistently at least 10 times more sensitive to market changes, compared to the volatility estimate captured through the market indices. Furthermore, beta values confirm a consistently lower volatility risk for market indices overall, between 50% and 90% lower, compared to the volatility risk of the entire market in various time intervals and rolling windows.

An Exploratory Study on the Complexity and Machine Learning Predictability of Stock Market Data.

This paper shows if and how the predictability and complexity of stock market data changed over the last half-century and what influence the M1 money supply has. We use three different machine learning algorithms, i.e., a stochastic gradient descent linear regression, a lasso regression, and an XGBoost tree regression, to test the predictability of two stock market indices, the Dow Jones Industrial Average and the NASDAQ (National Association of Securities Dealers Automated Quotations) Composite. In addition, all data under study are discussed in the context of a variety of measures of signal complexity. The results of this complexity analysis are then linked with the machine learning results to discover trends and correlations between predictability and complexity. Our results show a decrease in predictability and an increase in complexity for more recent years. We find a correlation between approximate entropy, sample entropy, and the predictability of the employed machine learning algorithms on the data under study. This link between the predictability of machine learning algorithms and the mentioned entropy measures has not been shown before. It should be considered when analyzing and predicting complex time series data, e.g., stock market data, to e.g., identify regions of increased predictability.

The Relationship Between the Chinese Stock Market, the US Stock Market, and Some Other Economic Indexes Under the COVID-19 Pandemic

The COVID-19 outbreak has had a huge impact on all sectors of the world, especially the economy. Among all the economic indicators, the stock market indexes are particularly important, because the stock markets anticipate the future economic change and reflect investor confidence and thus may be the barometer of the overall economy. The Chinese and US stock markets are the most important stock markets. Because New York Stock Exchange (NYSE) and National Association of Securities Dealers Automated Quotations (NASDAQ) have the greatest market cap worldwide. And Shanghai Stock Exchange (SSE) and Hong Kong Stock Exchange (HKSE) can obviously show the conditions of economy in the mainland of China and East Asia. We collected the daily increase or decrease indexes of the U.S. and China stock markets to study their relationships and how they are affected by the pandemic. To study this problem, this paper uses three methods, including Local Similarity Analysis (LSA), Least absolute shrinkage and selection operator (LASSO) and Markov chain. We calculate the correlation between the U.S. stock indexes and China stock indexes with LSA and analyze the possible factors which lead to these correlations. LASSO is used to find out other economic indexes which affect the U.S. and China stock markets and test whether these indexes can predict the trend of future markets. Lastly, Markov chains can be used to analyze the effects of the day to the next day transmission in stock markets. We find that there is a high correlation between the U.S. and the China stock using LSA, especially, from Jan.10 to Feb.10. We find that Shanghai Stock Exchange Composite Index (SSEC) and NASDAQ are correlated to the Standard and Poor’s 500 Index (S&P500) in the US market. S&P500, confirmed cases of COVID-2019 in China (Chinese cases), USD to CNY exchange rate are correlated to the SSEC in the Chinese market. Using these indexes, the future indexes of the U.S. stock market can be predicted at a PCC correlation, the highest is 0.44 and the lowest is 0.24 for the China market. LASSO shows that the U.S. market can be more accurately predicted by the indexes. The estimated Markov models of the two stock markets are pretty similar, i.e. the probability that the value of the U.S. stocks increases two days in a row is 13.40% and the Chinese market is 11.34%. To sum up, we find that the two stock markets are highly correlated under the Covid-19 pandemic despite from some differences.

Global risk factors of NYSE- and NASDAQ-listed shipping companies’ stock returns

PurposeThis paper aims to investigate the impact of global macro and other risk factors of the New York Stock Exchange (NYSE)- and National Association of Securities Dealers Automated Quotation (NASDAQ)-listed shipping companies’ stock returns from January 2001 to December 2019.Design/methodology/approachThe methodological design includes multi-factor regressions for individual companies, augmented versions of these regressions to examine the likely impact of additional factors and finally panel regressions to assess the impact risk factors on all companies simultaneously. Estimations are done via ordinary least squares and the generalized method of moments.FindingsMulti-factor model results showed that some of the US-specific and global macro risk factors surfaced as statistically significant for most of the companies and appeared to exhibit a consistent pattern in the way they affected shipping stocks. Thus, these companies’ exposures emanate mostly from the general US market’s movements and to a lesser extent from other firm-specific factors. Second, from the results of panel specifications, this study observes that domestic risk factors such as unemployment, inflation rates and industrial production growth emerged as significant for the NYSE-listed companies. As regard, the NASDAQ-listed ones, it was found that Libor and the G20 inflation rate were also affecting their stock returns.Research limitations/implicationsCompanies examined are listed only in the US’s NYSE and NASDAQ. Hence, companies listed elsewhere were excluded. It may be concluded that these US exchange-listed companies abide mostly by domestic fundamentals and to some extent to selected global factors.Practical implicationsThe significance of the findings in this study pertains to global investors and shipping companies’ managers alike. Specifically, given the differential sensitivities of the shipping companies to various risk factors (and the global business cycle, in general), it is possible to view the shipping companies’ stocks as a separate, alternate asset class in a global, well-diversified portfolio. Thus, such a broader portfolio would permit investors to earn positive returns and reduce overall risk. Managers of shipping companies would also benefit from the findings in this study in the sense that they should better understand the varying exposures of their companies to changing global and domestic macro conditions and successfully navigate their companies through business cycles.Originality/valueResearch on the global shipping industry has lagged behind and was mainly concentrated on the investigation of the sources of shipping finance and capital structure of shipping companies, investment and valuation, corporate governance and risk measurement and management. Empirical research on the potential micro and macro determinants of the stock returns of shipping companies, however, is scant. This paper fills the gap in the literature of identifying and evaluating the various macroeconomic, US and international risk, factors that affect shipping companies’ stock returns in a highly financially integrated world.

Determinants and Impact of Artificial Intelligence on Organizational Competitiveness: A Study of Listed American Companies

This study investigated the institutional factors affecting the application of Artificial Intelligence (AI) by American companies. The objectives of the study were to: 1) determine the institutional factors affecting AI; 2) evaluate the impact of institutional factors on application level of AI; and 3) assess the impact of AI on organizational competitiveness. This study adopted a survey research design. The population of the study is comprised of all publicly quoted companies on NASDAQ (National Association of Securities Dealers Automated Quotations). Data collection was aided by structured questionnaire. In line with the 10% rule of thumb for large population size, 330 companies were taken as the sample size for the study. The 330 companies were randomly selected, and a copy of questionnaire dispatched to each company, making a total of 330 copies of questionnaire administered. Result shows that the relatively high-ranking institutional factors affecting AI application are Competitors’ activities, Consultants/Professional bodies, actions of Multinational organizations, and the need to satisfy customers. A thematic analysis of the high-ranking factor shows that the mimetic factors (i.e., competitors’ activities and actions of multinational organizations) are the strongest factors driving the application of AI by the companies. The normative factor (i.e., recommendations of consultants/professional bodies/experts) also has appreciable influence on AI application. Result also shows that institutional factors jointly account for an appreciable level of AI application in American companies. Result suggests that the application of AI has a significantly positive impact on organizational competitiveness. This makes it compelling to encourage investment in AI given its significantly positive impact on organizational competitiveness. The study contributes to knowledge by providing empirical evidence on the institutional factors affecting AI in American companies. This study is also one of the earliest empirical studies on the deployment of AI by listed companies in the U.S. The study therefore contributes to the burgeoning literature on AI. The study provides empirical evidence on the applicability of the theory of institutional isomorphism to the adoption of technological innovation.

Understanding the US-China Trade War: Causes, Consequences and Economic Impact

A trade war is a situation in which countries restrict each other’s trade by imposing tariffs or quota on imports. A latest example of a trade war is the one currently going on between the US and China. China with its large manufacturing base has captured the world markets. China’s growth is viewed as a threat to America’s supremacy in world trade. We focus on trade history and explore the trade statistics of China and the United States, the factors that led to the trade war, the journey of China in WTO and America’s appeal to amend WTO rules to ensure fair play. We also outline the outcome of the current war in terms of trade diversion, trade beneficiaries, reaction of NASDAQ (National Association of Securities Dealers Automated Quotations) and Shanghai stock exchange (SSE), & the impact it has on the consumers.

On Hoover’s Scale-Free Forecast Accuracy Metric MAD/MEAN

In this study, we find that Hoover’s scale-free forecast accuracy metric MAD/MEAN is only recommended when the coefficient of variation (c.v.) is small. Using empirical studies, five near identically and independently distributed (i.i.d.) time series from a popular statistics textbook are observed. We find that 100% of real time series chosen in the textbook have a c.v. less than 1, indicating the applicability of Hoover’s metric in most real data analysis. However, under the market efficiency hypothesis, returns for stocks will follow or approximately follow an i.i.d. normal distribution. There are 3173 stocks in the New York Stock Exchange (NYSE), 3182 stocks in the National Association of Securities Dealers Automated Quotations (NASDAQ), and 2044 stocks in the American Stock Exchange (AMEX). In this study, we observe the c.v.’s of monthly returns of 50 stocks chosen to represent these markets—nineteen stocks are randomly drawn from each of the NYSE and the NASDAQ and twelve stocks are randomly drawn from the AMEX. We find that 100% of these series have a c.v. greater than 1, indicating that Hoover’s metric is not applicable to analyzing returns. Further empirical studies about the returns from Minsville, General Electronic, Goodyear, and Owens studied in Fama (J Bus 38(1):34–105, 1965, J Finance 25(2):383–417, 1970) show that Hoover’s MAD/MEAN is not a good accuracy measure to distinguish different MA methods. We conclude that Hoover’s MAD/MEAN has its merit in general real data analysis as shown in a textbook, but that it is not recommended for analyzing time series for returns in economics and finance.

The Effects of Corporate Green Efforts for Sustainability: An Event Study Approach

This study investigates the long-term effects of corporate green efforts on the financial performances of the US public firms by using the ISO 14001 certification. Based on the natural resource based view, the rigorous event study was employed using 174 US public firms on NYSE (New York Stock Exchange) and NASDAQ (National Association of Securities Dealers Automated Quotations) to analyze the pre and post abnormal performances of the certification during the 1996–2010 period. The results indicate that ROI and Tobins’s Q showed the immediate positive response after the firms’ applying for the certification, implying that the market accepts the announcement of ISO 14001 as a positive signal. Asset turnover also showed positive abnormal performances for the short and long term. The results imply that the corporate green efforts are beneficial to the firm by improving sales and profitability, while the capital market does not clearly respond to these efforts in the long run.

EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction

Stock market forecasting is a vital component of financial systems. However, the stock prices are highly noisy and non-stationary due to the fact that stock markets are affected by a variety of factors. Predicting stock market trend is usually subject to big challenges. The goal of this paper is to introduce a new hybrid, end-to-end approach containing two stages, the Empirical Mode Decomposition and Factorization Machine based Neural Network (EMD2FNN), to predict the stock market trend. To illustrate the method, we apply EMD2FNN to predict the daily closing prices from the Shanghai Stock Exchange Composite (SSEC) index, the National Association of Securities Dealers Automated Quotations (NASDAQ) index and the Standard & Poor’s 500 Composite Stock Price Index (S&P 500), which respectively exhibit oscillatory, upward and downward patterns. The results are compared with predictions obtained by other methods, including the neural network (NN) model, the factorization machine based neural network (FNN) model, the empirical mode decomposition based neural network (EMD2NN) model and the wavelet de-noising-based back propagation (WDBP) neural network model. Under the same conditions, the experiments indicate that the proposed methods perform better than the other ones according to the metrics of Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Furthermore, we compute the profitability with a simple long-short trading strategy to examine the trading performance of our models in the metrics of Average Annual Return (AAR), Maximum Drawdown (MD), Sharpe Ratio (SR) and AAR/MD. The performances in two different scenarios, when taking or not taking the transaction cost into consideration, are found economically significant.

Current Market Top Business Scopes Trend—A Concurrent Text and Time Series Active Learning Study of NASDAQ and NYSE Stocks from 2012 to 2017

As information technologies evolve, it has become necessary to examine the changes which have taken place in the top business scopes for both investors and entrepreneurs. To provide an understanding for the trends of the top business scopes in the current market, this article conducts a concurrent text and time series methodology to analyze the stocks in the New York Stock Exchange (NYSE) and the National Association of Securities Dealers Automated Quotations (NASDAQ) from 2012 to 2017. There is evidence that artificial intelligence and blockchains gained increasing importance for companies during that period. The authors contend that their findings in this paper question the status quo of promising business scopes for companies in the U.S. market.

A Generalized Measure for the Optimal Portfolio Selection Problem and its Explicit Solution

In this paper, we offer a novel class of utility functions applied to optimal portfolio selection. This class incorporates as special cases important measures such as the mean-variance, Sharpe ratio, mean-standard deviation and others. We provide an explicit solution to the problem of optimal portfolio selection based on this class. Furthermore, we show that each measure in this class generally reduces to the efficient frontier that coincides or belongs to the classical mean-variance efficient frontier. In addition, a condition is provided for the existence of the a one-to-one correspondence between the parameter of this class of utility functions and the trade-off parameter λ in the mean-variance utility function. This correspondence essentially provides insight into the choice of this parameter. We illustrate our results by taking a portfolio of stocks from National Association of Securities Dealers Automated Quotation (NASDAQ).

Environmental Policy Performances for Sustainable Development: From the Perspective of ISO 14001 Certification

AbstractThis study investigated the financial performances of environmental policy by using the long‐term effect of ISO 14001 certification. Drawing on a natural‐resource‐based view of the firm, this study examined abnormal performances of ISO 14001 certified firms on the New York Stock Exchange (NYSE) and National Association of Securities Dealers Automated Quotations (NASDAQ) in the USA during the period 1996–2010 employing a rigorous event study methodology. The results indicate that the profitability variables showed immediate positive abnormal effects after firms applied for the ISO 14001 certification, while the market benefit variable showed gradual improvements after obtaining the certification. However, ISO 14001 did not show significant effect on the internal process improvement. Overall, ISO 14001 was found beneficial to the firm in the long run from the perspectives of profitability and market benefits. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment

Daily Stock Returns Characteristics and Forecastability

While stock prices and economic activity are interrelated in a nation, they “are not coincident” with each other. Stock prices are a leading economic indicator of the United States of America’s (U.S.A.’s) economy. An economic variable that influences stock market prices is interest rates through an inverse relationship. The changes in stock prices (or stock returns) are generally caused by the demand for stocks. This paper reports on a study that investigates the underlying spectral and time-frequency characteristics of daily Standard and Poor’s (S&P) 500, Dow Jones Industrial Average (DJIA), and National Association of Securities Dealers Automated Quotations (NASDAQ) composite stock returns, and changes in interest rate (namely, inverted 3-month Treasury bill). The study thereafter compared these findings with those obtained in a previous study by Joseph et al, which focused on monthly stock returns and interest rate data. Subsequent to studying stock returns and changes in interest rate that showed relatively similar spectral and frequency-time characteristics, this study investigated the forecastability of stock returns (in S&P 500, DJIA, and NASDAQ composite) by inverted interest rate (in 3-month Treasury bills) over prediction horizons of five and 30 days with the forecasting period covering the last 13 years. The measures of forecast accuracy used were root mean square error and correlation. The forecasts were favorable in all cases even with simpler neural network models.