NASDAQ Stock Research Articles

Studying the Impact of Selected Listing on NASDAQ Stock Exchange on Enterprise Profits: Forecasting the Case of VinFast Manufacturing and Trading Company Limited

Initial Public Offering (IPO) is considered a strategic decision of utmost importance for every business, especially companies that wish to list on the stock exchange. international. Using the Wilcoxon non-parametric test method, the study shows that IPO activities have a positive impact on the profits of businesses listed on the NASDAQ Global Select Market stock exchange in general and of VinFast in particular. At the same time, the author had proposed a number of solutions to increase the number of companies in Vietnam conducting IPOs on the international stock market as well as policies to help increase the profits enterprises listed on the international stock market of listed businesses.

Revisiting the ability of research and development activities to improve value relevance

PurposeThis article aims to evaluate the informativeness of accruals on stock prices. Investors may misinterpret the information contained in accruals and produce accrual anomalies. Accruals accounting improves the quality of financial statements by providing useful information, although it also contains judgments and is less objective than operating cash-flows.Design/methodology/approachWe employ data panel regression analysis to investigate the value relevance of financial information. Our study takes the object of companies listed on the Nasdaq stock exchange (NASDAQ) as the representative of the prominent stock exchange for technology-driven enterprises and entrepreneurs. Expanding the findings, we also use Shanghai Stock Exchange (SSE) data.FindingsOur research finds that accruals are still relevant. However, firms with R&D expenditures reduce the ability of accruals to explain stock prices for non-technology firms and firms without intangible assets. After analyzing only tech firms (and firms with intangible assets), our tests show that R&D expenditures improve the relevancy of accruals. These findings apply to both Nasdaq and SSE.Practical implicationsPractitioners and the investment community get valuable insights into how the recognition and measurement of R&D expenditures affect the value relevance. Information about R&D increases relevancy only in firms with intangible assets and those that operate in the technology industry.Originality/valueOur paper provides benefits by using R&D expenses to affect accruals' informativeness by comparing two countries with different recognition of R&D.

Managerial Performance and Economic Performance in the Technology Sector

The technology (tech) sector outperformed all other sector stock price averages in 2023. This paper addresses two issues regarding the tech sector. First, relative to other firms, are there any managerial areas in which the tech sector outperforms to explain their superior performance relative to all other firms? Second, within the tech sector, what managerial practices lead to higher profits? Four areas of managerial performance are considered: customer satisfaction, employee engagement and development, innovation, and social responsibility. This paper utilizes Bloomberg data and takes the novel approach of utilizing the Drucker Institute Indexes to assess performance in the tech sector. The Drucker Institute has computed a Corporate Effectiveness Index since 2017. The overall effectiveness index is based on performance regarding five dimensions: customer satisfaction, employee engagement and development, innovation, social responsibility, and financial strength. The firms they track are United States companies whose shares are traded on the New York Stock Exchange or Nasdaq Stock Market and meet certain size requirements. Among the four dimensions tested, customer satisfaction is the one dimension that is positive and significant when comparing across tech firms. Second, employee engagement is more important for tech firms than for other firms. The major contribution of this paper’s findings is that while innovation may be the hallmark feature of tech firms, innovation for its own sake does not drive firm performance. A focus on people (customers and employees) is the key to earning higher profits in the tech sector.

An automated decision-making system employing complex networks and blockchain for the decentralized stock market

Stock markets are inherently complex, posing a challenge for investors who must analyze large amounts of data for strategic decision-making. This complexity, coupled with the centralized control and high transaction costs of traditional stock market systems, highlights the need for strategies that simplify analysis and promote informed, cost-effective investment choices. To address this issue, this work proposes real-time decision-making support for the stock market. The system leverages complex networks to enable a deeper understanding and analysis of intricate market trends and behaviors. It also integrates blockchain technology to decentralize traditional stock market infrastructures, reducing transaction costs and increasing transparency for user transactions and asset exchanges. Our system leverages complex networks to analyze multi-source marketplace data, operating on a cloud-computing infrastructure. This approach simplifies the analysis of intricate market dynamics. We evaluated our proof-of-concept prototype using real-world NASDAQ stock market data, demonstrating its ability to aid investor decision-making and enable decentralized transactions with minimal overhead.

Big portfolio selection by graph-based conditional moments method

This paper proposes a new graph-based conditional moments (GRACE) method to do portfolio selection based on thousands of stocks or even more. The GRACE method first learns the conditional quantiles and mean of stock returns via a factor-augmented temporal graph convolutional network, which is guided by the set of stock-to-stock relations as well as the set of factor-to-stock relations. Next, the GRACE method learns the conditional variance, skewness, and kurtosis of stock returns from the learned conditional quantiles via the quantiled conditional moment method. Finally, the GRACE method uses the learned conditional mean, variance, skewness, and kurtosis to construct several performance measures, which are criteria to sort the stocks to proceed the portfolio selection in the well-known 10-decile framework. An application to NASDAQ and NYSE stock markets shows that the GRACE method performs much better than its competitors, particularly when the performance measures are comprised of conditional variance, skewness, and kurtosis.

A study of impact of climate change on the U.S. stock market as exemplified by the NASDAQ 100 index constituents

This paper employs an innovative event study methodology to demonstrate the impact of climate change on the NASDAQ index from the unique perspective of extreme weather events. This is achieved through the application of the event study methodology to a total of 526 biological, climatic, geological, hydrological, and meteorological disasters of climate change occurring in the U.S. during the period of 2000–2019. The results of the study demonstrate that: ① it can be generally observed that the five dimensions of climate change have a significant impact on stock returns. ② Empirical evidence indicates that the impact of different climate change dimensions on the return rate of stocks from NASDAQ stocks varies. In contrast, the biological and hydrological dimensions have a significantly negative impact on the return rate of stocks from the NASDAQ index, while the climate dimension has a significantly positive impact on the return rate of stocks from the NASDAQ index. ③ From the perspective of time, the impact of the five dimensions of climate change on the stock yield exhibits certain non-linear characteristics. This can be observed in the phenomenon of shock reversal, which occurs before and after the event.

Selective genetic algorithm labeling: A new data labeling method for machine learning stock market trading systems

Investing in the stock market involves high risks and equivalent returns. Many investors tend to be loss averse and suffer significant losses due to information asymmetry and emotion-based investing. In order to minimize such losses, various studies are attempting to predict the stock market's future and automate investment decisions. However, the stock market is affected by many factors and poses a challenge in prediction. Also, many researchers who develop stock trading systems are interested primarily in improving prediction model performance. Particularly, most studies set up-down labels in the data labeling phase. However, up-down labeling views all stock price fluctuations as identical, resulting in inefficient model training by including substantial noise in the learning data. Therefore, this study proposes SGA (Selective Genetic Algorithm) labeling to overcome such concerns. A trading system was developed using the proposed method, and a machine learning model and ensemble method supported the label prediction phase. The proposed trading system was analyzed empirically with 38 NASDAQ stocks and 33 KOSPI stocks, and a comparative experiment with up-down labeling method was conducted. In this study, SGA labeling performance exceeded that of up-down labeling. Additionally, instance selection and ensemble methods affected the trading system's performance.

Prediction of Closing Prices for NASDAQ Listed Stocks: A Comparative Study Based on Gradient Boosting Models

The forecasting of trends in U.S. stocks remains a focal point in the financial domain. Within the realm of fine-grained forecasting tasks, predicting price trends with pronounced volatility in the last ten minutes of the closing cross-auction presents a distinctive challenge. This study develops four forecasting models—LightGBM (LGB), XGBoost (XGB), CatBoost (CBT), and a weighted fusion model—utilizing data from the final-market order book and closing auction of 200 NASDAQ stocks. The research aims to conduct a comprehensive comparison of the performance of these four models on the same prediction task. The study results reveal Mean Absolute Error (MAE) values of 5.195, 5.197, 5.200, and 5.202 for the respective models, signifying the integrated model as the most precise predictor. This comparative analysis facilitates a nuanced understanding within the research community regarding the performance of each model, offering a valuable reference for future investigations. Simultaneously, it underscores the inherent value of machine learning in financial forecasting, aiding investors in enhancing decision-making efficiency during critical moments on trading days.

Application of machine learning algorithms in the domain of financial engineering

Financial engineering is crucial for effectively combining finance with quantitative approaches. This study aims to forecast the performance of the Nasdaq stock market by considering numerous factors like wind, hydro, thermal, gas, and nuclear variables. To accomplish this, we utilize sophisticated predictive models, namely adaptive lasso (ALasso), elastic net (Enet), artificial neural network (ANN), convolutional neural network (CNN), and long short-term memory (LSTM). By using these advanced methods, our goal is to offer perceptive and precise predictions, which will enhance comprehension of the complex dynamics within the financial markets. The evidence suggests that the LSTM model has demonstrated superior accuracy in predicting changes in the Nasdaq stock market when compared to ALasso, Enet, ANN, and CNN. While ALasso, Enet, ANN, and CNN exhibit comparable RMSE and MAE values, their performance is slightly less competitive than that of the LSTM model. The marginal differences in RMSE (ALasso: 0.319, Enet: 0.317, ANN: 0.3, CNN: 0.32) and MAE (ALasso: 0.277, Enet: 0.276, ANN: 0.252, CNN: 0.278) emphasize the comparable effectiveness of various methods, but they somewhat drop below the LSTM model in terms of precision. The findings showed the significance of well-known and advanced ML techniques, particularly LSTM, for enhanced accuracy in financial market predictions.

Intelligent risk management system for enhancing performance of stock market applications

This paper proposes an intelligent risk management system in stock markets based on indications of social media platforms. Based on a brief survey, we found that the literature focuses on identifying, assessing and optimizing risks in stock markets using classical data sources as well as utilizing mathematical, statistical and machine learning techniques. So, we suggest a trendy data source that is social media and employing natural language processing as an advanced technique to identify and estimate risks in stock markets. This NLP model can help the investors of stock markets to be aware of the potential risks, its types and magnitudes which can help them to take the best decisions. In addition, we describe this model and some important expressions for risk analysis, risk recognition and risk estimation are driven. Besides, a practical experiment is provided to identify and evaluate the potential risks in NASDAQ stock market. Moreover, several performance measures are used to validate the efficiency of the proposed model such as the mean accuracy for the processes of risk analysis, risk identification and risk assessment is 73.33 %.

The First PCAOB Director of Registration and Inspections George Henry Diacont

ABSTRACT George Diacont was the first Director of Registration and Inspections for the Public Company Accounting Oversight Board (PCAOB). Diacont joined the PCAOB in March 2003, shortly after it commenced operations. His approach was to build a team of highly skilled professionals and encourage them to contribute their best ideas. His leadership style was to support his team, listen to their ideas, and make the best decisions possible without concern for praise or recognition. He also worked to instill a “regulatory attitude” in the former auditors who had become inspectors. This paper describes Diacont’s long and distinguished career in government service beginning with his military service in the U.S. Marine Corps. He spent 24 years at the Securities and Exchange Commission and five years in the Listing Investigation group at the NASDAQ stock market. He served nearly eight years at the PCAOB before he retired in 2011. JEL Classifications: M4.

Impact of Macroeconomics Variables on Stock Market Indices During the period 2010-2020

The primary goal of this research is to look at the influence of numerous macroeconomic variables on stock market indexes, with a particular focus on the NASDAQ. The ARIMA model was utilized to observe the NASDAQ stock price time series and forecast future prices. By analysing data from 2010 to 2020, the research revealed the links between NASDAQ stock prices and macroeconomic factors such as the CPI, interest rates, inflation rates, and unemployment rates. Results indicated a pronounced negative linear relationship between unemployment rates and the NASDAQ closing price, whereas the relationships with other macroeconomic variables were more intricate. Additionally, residual analysis of the model confirmed its commendable fit to the data. The data sources for this study are all from Yahoo Finance and Federal Research Economic Data. The findings contribute to a deeper understanding of how macroeconomic conditions influence stock market behaviour and offer valuable insights for investors, policymakers, and economists. By carefully selecting the model, time frame, and transformation methods, the study presents a well-rounded view of the complex interplay between the stock market and the broader economy.

Empirical Analysis of the Predicting Future Trend in Nasdaq Using ARIMA Model

The paper delves into a detailed empirical analysis leveraging the ARIMA model, with data gleaned from the weekly return trends of the Nasdaq Index. Using diagnostic tools such as ACF, PACF, and the AIC information criteria, the study zeroes in on the optimal lag period for the model. Subsequently, this refined model is employed to forecast potential trajectories for the Nasdaq Index's weekly returns. Through a thorough examination of analytical outcomes, especially AIC values, the study gauges the margin of prediction errors, culminating in the selection of the most proficient forecasting model: ARIMA (2,0,2). Importantly, these analytical insights serve as a valuable compass for investors aiming to capitalize on lucrative opportunities or mitigate potential risks within the Nasdaq stock exchange. Furthermore, it underscores the importance of empirical data analysis in guiding informed decision-making, ensuring optimal returns in the volatile Nasdaq market landscape. This work acts as a beacon for those navigating the intricate nuances of stock market investments.

Intraday Dynamics of NASDAQ Stocks in the Electronic Trading Era: Uncovering Strong U-Shape Patterns in Trading Volume and Bid-Ask Spread

Intraday Dynamics of NASDAQ Stocks in the Electronic Trading Era: Uncovering Strong U-Shape Patterns in Trading Volume and Bid-Ask Spread

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.

Financial Market Linkages: Gold Futures and the NASDAQ Index

Gold futures and the NASDAQ Index are two important asset classes that receive much attention in global financial markets. Gold futures represent a safe haven and a store of value asset that typically perform well in unstable economic and financial environments. In contrast, the NASDAQ index represents the health of the U.S. technology stock market, which is usually affected by technological innovation and economic development. Although the two assets may appear to be completely different, there is a certain linkage between them, meaning that in some cases they may be affected similarly at the same time. In order to provide investors with a better understanding of the gold futures market and the stock market, and to improve investment, this paper found that the yield of gold futures can have a negative impact on the returns of the NASDAQ stock market, and the volatility of the NASDAQ stock market can also affect the volatility of the gold futures market. For investors, both the gold and stock markets have different risk and return characteristics. To ensure that investment strategies align with their risk tolerance and to avoid unnecessary anxiety and risks. Gold is often seen as an asset to resist inflation, so it may be an attractive choice when inflation rises.

Enhancing daily stock trading with a novel fuzzy indicator: Performance analysis using Z-number based fuzzy TOPSIS method

In the dynamic realm of financial markets, developing effective strategies for stock exchange transactions is paramount. This research addresses this critical need by introducing a pioneering indicator for daily stock trading, leveraging a robust fuzzy inference system (FIS). The indicator ingeniously integrates key technical indicators including Moving Average Convergence and Divergence (MACD), Relative Strength Index (RSI), Stochastic Oscillator (SO), and On-Balance-Volume (OBV). The FIS is meticulously constructed based on expert opinions and gleaned fuzzy rules. The fuzzified values then serve as inputs to the FIS, which in turn generates signals indicating optimal actions: buy, hold, or sell stocks. To validate the importance of the FIS, a carefully curated selection of stocks from the NASDAQ stock exchange is employed for experimentation. To prove the efficiency of the FIS, the technical indicators and the fuzzy risk-adjusted returns are considered as alternatives and criteria, respectively. A novel Z-number-based technique for order preference by similarity to the ideal solution (TOPSIS) method is used to rank the technical indicators and the FIS. The comparative results unequivocally demonstrate that the developed FIS surpasses existing indicators, yielding superior returns.

A Regularized High-Dimensional Positive Definite Covariance Estimator with High-Frequency Data

This paper proposes a novel large-dimensional positive definite covariance estimator for high-frequency data under a general factor model framework. We demonstrate an appealing connection between the proposed estimator and a weighted group least absolute shrinkage and selection operator (LASSO) penalized least-squares estimator. The proposed estimator improves on traditional principal component analysis by allowing for weak factors, whose signal strengths are weak relative to idiosyncratic components. Despite the presence of microstructure noises and asynchronous trading, the proposed estimator achieves guarded positive definiteness without sacrificing the convergence rate. To make our method fully operational, we provide an extended simultaneous alternating direction method of multipliers algorithm to solve the resultant constrained convex minimization problem efficiently. Empirically, we study the monthly high-frequency covariance structure of the stock constituents of the S&P 500 index from 2008 to 2016, using all traded stocks from the NYSE, AMEX, and NASDAQ stock markets to construct the high-frequency Fama-French four and extended eleven economic factors. We further examine the out-of-sample performance of the proposed method through vast portfolio allocations, which deliver significantly reduced out-of-sample portfolio risk and enhanced Sharpe ratios. The success of our method supports the usefulness of machine learning techniques in finance. This paper was accepted by Agostino Capponi, finance. Funding: This work was supported by the Research Grants Council, University Grants Committee [Grants 11500119, 11505522, 11505721, and 21504818] and the National Natural Science Foundation of China (NSFC) Basic Scientific Center Project [Grant 71988101], entitled as “Econometric Modelling and Economic Policy Studies”, as well as NSFC [Grants 71803166 and 72173104]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.04138 .

Stock Price Prediction Based on ARIMA and Neural Network

The stock price is affected by many factors and is a very complex nonlinear and non-stationary system. Predicting stock prices is a classic problem. People hope to predict stock prices more accurately, so as to make profits through stocks. This article selects five stocks in the Nasdaq stock market from 2020 to 2023, and tries to use 3 AI models (ARIMA, CNN, LTSM) to predict and analyze their next days closing prices and use the RMSE as the index to analyze the prediction performance. This paper finds that the three models can predict the stock price next day well, among which the ARIMA model and LSTM model have better prediction results, average RMSE for them are about 3.3 and 4.5 while the CNN model has poorer prediction performance with RMSE 7.2. At the same time, paper is found that when the model has a turning point for the stock, all the models predict poorly. In the future, we can consider combining the eigenvalues of more stocks to reduce the impact of turning points on price prediction.

Motivation and Countermeasures of Financial Fraud in Listed Companies A Case Study of Luckin Coffee

The prevalence of financial fraud within publicly traded corporations has emerged as a significant concern within the field of auditing. In recent years, there has been an exacerbation rather than a mitigation of instances of financial fraud. Luckin Coffee is a domestic coffee brand that has emerged in the market, established by Qian Zhiya, who previously served as the CEO of Shenzhou Youchuan Group. Established in the year 2017, the organization achieved a notable milestone by being successfully listed on the NASDAQ stock exchange in 2019. In the year 2020, the disclosure of financial wrongdoing occurred. The discreet and innovative financial fraudulent practices employed by Luckin Coffee can serve as a valuable point of reference for the future advancement of firms. This paper presents an introduction to the fundamental notion of financial fraud and its associated theories. It proceeds to investigate the specific financial fraud process employed by Luckin Coffee. Additionally, it proposes recommendations for mitigating the risk of financial fraud in listed businesses, utilizing the Gone theory.