NASDAQ Stock Research Articles

Statistical Modeling of High Frequency Datasets Using the ARIMA-ANN Hybrid

The core objective of this work is to predict stock market indices’ using autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and their combination in the form of ARIMA-ANN. Financial data are, in fact, trendy, noisy and highly volatile. To tackle their chaotic nature and forecast the three considered stock markets, namely Nasdaq stock exchange, United States, Nikkei stock exchange, Japan, and France stock exchange data (CAC 40 index), we use novel approaches. The data are taken from the Yahoo Finance website for the period from 4 January 2010 to 20 August 2021. To assess the relative predictive effectiveness of the selected tools, the dataset was divided into two distinct subsets: 75% of the data was allocated for training purposes, while the remaining 25% was reserved for testing. The empirical results suggest that ARIMA-ANN produces more accurate forecasts than the separate components of all stock markets. In light of this, it may be inferred that the combining tool is more effective in analyzing financial data and provides a more accurate comparative prediction.

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.

A comparative study of the neural network models for the stock market data classification—A multicriteria optimization approach

The aim of this paper is to explore the potential of the class of recurrent neural networks in developing a classification model for the stock market. For this research, the data that replicates the entire Nasdaq stock market limit order book is used. After extracting order book attributes from the raw dataset, feature selection approaches based on conditional entropy and Stochastic Universal Sampling are proposed, in order to highlight potentially informative features for our data classification task. Furthermore, the performances of nine presented recurrent neural network models for classifying the stock market data vector into one of the labels from the set S = {buy, sell, idle} are examined. In this study, the performances of the models based on different networks, namely the gated recurrent unit, the long short-term memory, and the recurrent neural network, are compared. Using a multi-criteria approach it is concluded that the model based on the GRU topology fed with the features selected using the newly proposed feature selection method outperforms other examined models.

Stable Distribution of Multivariate Data

The main theorem of the paper states that every stable random vector with marginal skewness parameters different from ±1 can be turned into a sub-Gaussian random vector by using an appropriately tailored transformation in multidimensional space. The theorem is used to create a formula on probability density function of stable random vector and to perform a procedure of testing the stable distribution of multivariate data. A dataset collected from the Nasdaq stock market is used to illustrate the proposed procedure.

Diagnosing stock market bubbles with advanced ADF tests

A full-fledged empirical test of the latest methodology in the field of recursive procedures for identifying and dating market bubbles - the GSADF test - has been performed. The use of previous iterations of recursive tests did not allow to accurately determine the presence of a market bubble at the early stages of its formation. The results of this study prove that the GSADF test using a sliding window significantly improves the discriminatory ability of recursive tests, at the early stages of formation it allowed to detect such episodes of stock market collapse as the Japanese economic bubble of 1986-1991 and the dot-com bubble in the USA in 1995-2001. Testing based on current market data demonstrates the formation of a market bubble in the NASDAQ stock index since August 2020.

Capital Market Reactions to The Covid 19 Pandemic

This study aims to determine and analyze the capital market's reaction to the Covid 19 Pandemic in biotechnology companies listed on the Nasdaq. The method used in this study uses a type of quantitative method. This study uses an event study. The sampling method in this study was carried out using a non-probability random sampling approach. The announcement of the COVID-19 pandemic by the WHO and the commitment to produce a COVID-19 vaccine have had a significant impact on abnormal stock returns and trading volume activities of biotech companies listed on the NASDAQ stock market. However, there is no significant effect on the liquidity of biotech company stocks. In addition, the announcement of the COVID-19 pandemic also affected the volatility of biotech company stocks. In contrast, the announcement of a commitment to producing a COVID-19 vaccine did not significantly impact the volatility of biotech company stocks. So, it shows that the NASDAQ stock market reacts sensitively to announcements about the COVID-19 pandemic and efforts to produce COVID-19 vaccines by biotech companies. This shows that the COVID-19 pandemic has significantly impacted the stock market, especially in the biotechnology sector. The event study approach is used to test the market with a semi-strong form of market efficiency by demonstrating that the stock price reflects all published information (all publicly available information).

The expectation-maximization algorithm for autoregressive models with normal inverse Gaussian innovations

In this paper, we study the autoregressive (AR) model with normal inverse Gaussian (NIG) innovations. The NIG distribution is semi heavy-tailed and is helpful in capturing the extreme observations present in the data. The expectation-maximization (EM) algorithm is used to estimate the parameters of the considered AR(p) model. The efficacy of the estimation procedure is shown on the simulated data for AR(2) and AR(1) models. A comparative study is presented, where the classical estimation algorithms are also incorporated, namely, Yule-Walker and conditional least squares methods along with EM method for model parameter estimation. In simulation study, the maximum likelihood estimation (MLE) of NIG distribution by EM algorithm and iterative Newton-Raphson method are also compared. The real-life applications of the introduced model are demonstrated on the NASDAQ stock market index data and US gasoline price data. The studies show that AR(1) model with NIG residuals is good fit for financial data with extreme values as well as for gasoline price data.

Do Firms Round Up Operating Cash Flows?

This study examines the rounding phenomenon in operating cash flows for firms listed in the three major U.S. stock exchanges (i.e., New York Stock Exchange, NASDAQ, and American Stock Exchange) and the U.S. Over-the-Counter (OTC) market. It finds that firms listed in the three major stock exchanges round￾up earnings, but they do not round-up operating cash flows; while firms listed in the OTC market round￾up both earnings and operating cash flows. Overall, the results from this study provide consistent evidence that firms have incentives to round up operating cash flows to paint a rosy picture of the firms’ performance; however, it is more difficult to round-up cash flows than to round-up earnings. Therefore, in an environment where the rules and disclosure requirement is strict (i.e., the three major U.S. stock exchanges), firms do not have the opportunities to round-up cash flows; but in an environment where the rules and disclosure requirement are less strict (i.e., the OTC market), firms actually do round-up cash flows in practice.

The impact of the COVID-19 pandemic on travel cryptocurrency and stock market performances: an event-study approach

COVID-19 大流行对旅游加密货币和股票市场表现的影响：事件研究方法研究目的本文评估了 COVID 大流行在疫情期间很长一段时间对旅游加密货币和股票市场表现的影响。研究设计/方法/途径为 6 种旅行加密货币和纳斯达克证券交易所上市的前 10 大酒店、7 家航空公司和 26 家餐厅股票开发了 GARCH（广义自回归条件异方差）模型。 将事件研究方法应用于新型冠状病毒及其变种 Omicron 的出现。 此外, 还估计了相应资产因此类事件而产生的异常收益。研究发现结果表明, 旅游加密货币市场对疫情初期没有反应, 但纳斯达克酒店、餐厅和航空公司股票在美国出现疫情时出现异常负回报。 在美国正式宣布大流行后, 加密货币和旅游股均出现异常负回报, 但股票中的负回报要远高于加密货币。 相反, 作为对 Omicron 变体的回应, 只有酒店、餐厅和航空股出现异常的负回报。实际意义这些结果表明, 旅行加密货币是一种独立于酒店、餐厅或航空公司股票的金融工具。因此, 采用旅游加密货币可能有助于投资者和企业在 COVID-19 等长期危机期间分散风险。原创性/价值本文是第一份旨在调查 COVID-19 大流行对最近新兴的旅游加密货币市场的影响的实证研究, 并使用事件研究方法来调查它与旅游股票表现有何不同。

Signaling Is Only with Good Intent? Think Again! Introduce A Triangle Model of Signaling Through Research on Short Sale Attacks of NASDAQ Listed Chinese Company

This template explains and demonstrates how to prepare your camera-ready paper for Trans Tech Publications. The best is to read these instructions and follow the outline of this text. Please make the page settings of your word processor to A4 format (21 x 29,7 cm or 8 x 11 inches); with the margins: bottom 1.5 cm (0.59 in) and top 3 cm (1.18 in), right/left margins must be 2 cm (0.78 in). This template explains and demonstrates how to prepare your camera-ready paper for Trans Tech Publications. The best is to read these instructions and follow the outline of this text. Please make the page settings of your word processor to A4 format (21 x 29,7 cm or 8 x 11 inches); with the margins: bottom 1.5 cm (0.59 in) and top 3 cm (1.18 in), right/left margins must be 2 cm (0. The signaling theory has been developed for many years, and applied to various fields. The fraud scandals of three Chinese companies listed on NASDAQ stock exchange were hereby dissected via case analysis, during which process, short sellers were found to prepare their attack on targeted Chinese companies using atypical signaling. Once the signal of fraud research report generated by short sellers was observed by targeted Chinese companies, the companies would also get engaged in signaling to defend their reputation and fend off short sellers. The stock holders of three public listed Chinese companies were marked as signal receiver by short sellers and three Chinese companies. The contradicting signals of two different signalers collided with each other and degenerated into weaker signals. After analyzing the behaviors of three parties, the signaling theory was considered applicable for those cases, which could also be expanded to the research on more complicated scenarios. More importantly, a triangle time line model was formulated to explain a complicated scenario where two signalers targeted at one receiver with their own signaling. This investigation aims to encourage more signaling theory research that expands the research frontier.

On the Determinants of Bitcoin Returns and Volatility: What We Get from Gets?

Since Bitcoin has frequently witnessed price fluctuations and high volatility, the factors influencing its returns and volatility is an important research subject. To accomplish this goal, we applied the Gets reduction method which has a good reputation compared to other competing approaches in terms of the statistical apparatus available for a repeated search to determine the final set of determinants and the consideration of location shifts. We found that the reduced set of explanatory variables that affects Bitcoin returns is composed of Twitter-based economic uncertainty, gold return, the return of the Euro/USD exchange rate, the return of the US Nasdaq stock exchange index, market capitalization, and Bitcoin mining difficulty. In contrast, the volatility of Bitcoin is affected by only lagged terms of the ARCH effect and the volume of this cryptocurrency.

Model and upper–lower bound estimation scheme for portfolio optimisation considering uncertain investment time horizon

In this paper, we study the portfolio selection problem considering the uncertain investment time horizon. We employ the Period Value at Risk (PVaR) to characterise the risk in the special cases and establish a fundamental mixed integer linear programming (MILP) model to obtain the optimal portfolio solutions. Identifying that the symmetric property of PVaR can significantly reduce the computation burden of the CPLEX solver, an enhanced MILP model is proposed. To verify the quality of obtained solutions, we also develop fast lower and upper bound estimation schemes respectively. Using the real-world data sets from NYSE and NASDAQ stock markets, numerical results are provided to show the efficiency of the enhanced model.

Investor base, cost of capital, and new listings on the NYSE

Investor base, cost of capital, and new listings on the NYSE

Research of Manipulation of Stock Assets on the Example of Shares of American High-Tech Companies of the Nasdaq Stock Exchange

The subject of the study is the processes of manipulation of stock asset prices on the example of shares of American high-tech companies of the NASDAQ exchange. The concept of manipulation of stock assets is formulated and the signs of manipulation of them are presented. The main purpose of the work is to identify signs of manipulation of the stock prices of Facebook, Nasdaq and Google companies from the moment of the initial public offering of the company's shares on the stock exchange (IPO) to the present. The methodology of the study includes statistical methods of data analysis: comparative analysis, correlation and regression analysis. Based on the annual quotes and financial reports of Facebook, Google and Nasdaq, a comparative and correlation analysis of the annual values of investment multipliers is carried out. Particular attention is paid to the comparative and statistical analysis of the data of these companies at the initial time stage after their IPO on the NASDAQ exchange. The result of the analysis showed a fairly strong correlation between them on this indicator. At the same time, a significant overvaluation of Facebook shares was established in the first years after its initial public offering on the stock exchange, in relation to the analog companies of Google and Nasdaq, as well as to the companies listed on the S&P500 stock market of the USA. The scientific novelty of the study lies in the fact that the presented methodology allows identifying signs of manipulation with the share prices of companies-analogues of any sufficiently liquid stock market. The topic of the article is of interest to stock market participants, stock analysts, potential investors and purchasers of companies, as well as to stock market regulators dealing with the reliability and security of its processes.

Forecasting Nasdaq stock progressions using classification and deep learning techniques

Stock Market prices have always been unpredictable resulting in a lot of risk for its investors. This proposal uses machine learning techniques (Decision Tree, Random Forest, Adaptive Boosting (Adaboost), eXtreme Support Vector Classifier (SVC), Logistic Regression and deep learning methods such as Long short-term memory (LSTM) to build modules that can be used to predict accurate stock prices reducing the chances of risk and increasing in gains. In this proposal the National Association of Securities Dealers Automatic Quotation System (NASDAQ) stock data is being used which has been extracted from Yahoo Finance to predict and analyze various Stock Progressions.

An efficient real-time stock prediction exploiting incremental learning and deep learning.

Intraday trading is popular among traders due to its ability to leverage price fluctuations in a short timeframe. For traders, real-time price predictions for the next few minutes can be beneficial for making strategies. Real-time prediction is challenging due to the stock market's non-stationary, complex, noisy, chaotic, dynamic, volatile, and non-parametric nature. Machine learning models are considered effective for stock forecasting, yet, their hyperparameters need tuning with the latest market data to incorporate the market's complexities. Usually, models are trained and tested in batches, which smooths the correction process and speeds up the learning. When making intraday stock predictions, the models should forecast for each instance in contrast to the whole batch and learn simultaneously to ensure high accuracy. In this paper, we propose a strategy based on two different learning approaches: incremental learning and Offline-Online learning, to forecast the stock price using the real-time stream of the live market. In incremental learning, the model is updated continuously upon receiving the stock's next instance from the live-stream, while in Offline-Online learning, the model is retrained after each trading session to make sure it incorporates the latest data complexities. These methods were applied to univariate time-series (established from historical stock price) and multivariate time-series (considering historical stock price as well as technical indicators). Extensive experiments were performed on the eight most liquid stocks listed on the American NASDAQ and Indian NSE stock exchanges, respectively. The Offline-Online models outperformed incremental models in terms of low forecasting error.

An Experimental Study and Analysis of Long-Term Multi-Trending Trajectory Forecasting of Stock Indices Using Time Series Inferential Statistical Projection

The focus of this study is on time series inferential statistical projection and analysis for long-term multi-trending trajectory forecast modelling. Inferential statistics was used for projection and analysis of long-term multi-trending trajectory forecasting. The proposed model is trained, tested and validated on three different types of stocks viz. NVIDIA, UNG and IBM taken from the NASDAQ stock market index. In Phase I, the long-term deterministic multi-trending model is assessed and fitted. Model skewed residual series is assessed using the goodness of fit in Phase II. The future stock close prices and long-term multi-trending trajectory paths are simulated and analyzed in Phase III. Finally, in Phase IV, epsilon-skew-normal results and normal distribution assumptions are compared. The experimental result shows that an uptrend trajectory of historical close price simulates an uptrend trajectory in the forecasted close price, a downtrend trajectory of historical close price simulates a downtrend trajectory in the forecasted close price, a sideways or mixed trend trajectory of historical close price simulates a sideways or mixed trend trajectory in the forecasted close price.

An Experimental Study and Analysis of Long-Term Multi-Trending Trajectory Forecasting of Stock Indices Using Time Series Inferential Statistical Projection

The focus of this study is on time series inferential statistical projection and analysis for long-term multi-trending trajectory forecast modelling. Inferential statistics was used for projection and analysis of long-term multi-trending trajectory forecasting. The proposed model is trained, tested and validated on three different types of stocks viz. NVIDIA, UNG and IBM taken from the NASDAQ stock market index. In Phase I, the long-term deterministic multi-trending model is assessed and fitted. Model skewed residual series is assessed using the goodness of fit in Phase II. The future stock close prices and long-term multi-trending trajectory paths are simulated and analyzed in Phase III. Finally, in Phase IV, epsilon-skew-normal results and normal distribution assumptions are compared. The experimental result shows that an uptrend trajectory of historical close price simulates an uptrend trajectory in the forecasted close price, a downtrend trajectory of historical close price simulates a downtrend trajectory in the forecasted close price, a sideways or mixed trend trajectory of historical close price simulates a sideways or mixed trend trajectory in the forecasted close price.

Multi-Timeframe Algorithmic Trading Bots Using Thick Data Heuristics with Deep Reinforcement Learning

This article presents an augmented Artificial Intelligence (AI) algorithmic trading approach that combines Thick Data Heuristic (TDH), with Deep Reinforcement Learning (DRL), to successfully learn trading execution timing policies. Combining the augmented AI human trader's intuition and heuristics with DRL techniques to provide more focused drivers for trading order execution timing is explored in this study. In this research, the goal is to solve the sequential decision-making problem of AI for profitable day and swing trading order timing executions. Enabling trading bots with cognitive intelligence and common-sense heuristics will offer traders including automatic traders an insight to understand the day-to-day swing trading timeframes indicators and arrive at mature trading decision-making. This article examines the performance of bots with Nasdaq and NYSE stocks that have a strong catalyst (info. which increases directional momentum) to find that they outperform benchmark algorithmic trading approaches. The research illustrates to the reader how to combine TDH and Deep Q-networks (DQN) into a TDH-DQN augmented AI trading bot. The bot learns through test data to predict the optimal timing of order executions autonomously on idealized trading time series data.

Pengaruh Indeks Harga Saham Global terhadap Indeks Harga Saham Indonesia Studi Kasus Sebelum dan Selama Periode Covid- 19

This study was conducted to examine the effect of the five largest stock price indices in the world on the Indonesian Composite Stock Price Index in the time series before the COVID-19pandemic and during the COVID-19 pandemic, taken from January 1, 2015 June 30, 2022. This study uses the stepwise regression method. The results in this study indicate that in the period before the COVID-19 pandemic the variables that affected the IHSG were the New York Stock Exchange and Euronext N.V, each of which had a positive effect, while during the COVID-19pandemic the variables that affected the IHSG were New York Stocks. Exchange, Nasdaq Stock Market, Shanghai Stock Exchange, and Euronext N.V with the results of the New York Stock Exchange and Shanghai Stock Exchange having a positive effect, Nasdaq Stock Market and Euronext N.V having a negative effect.