NASDAQ Stock Market Research Articles

Shareholder Value Implications of the Internal Audit Function

SYNOPSIS In 2013, NASDAQ Stock Market LLC (NASDAQ) issued a proposal to mandate the internal audit functions (IAFs) for all listed companies but later withdrew the proposal. In this study, I investigate whether the mandatory IAF adds shareholder value and how the value implications vary across companies. For companies without IAFs before the proposal, I document a positive (negative) price response to the NASDAQ IAF proposal filing (withdrawal). This evidence suggests that, on average, investors perceive mandatory IAF as value enhancing. I find that the positive market reaction is concentrated among firms with lower external audit quality and institutional ownership. Additionally, I document that the return responses are higher for companies with smaller book-to-market ratios, multiple business segments, and lower Altman z-scores. Those results suggest that investors' perceptions of the mandatory IAF are more positive for weakly monitored firms, firms with high-growth prospects and complex business, and firms in financial distress.

Associating Fundamental Features with Technical Indicators for Analyzing Quarterly Stock Market Trends Using Machine Learning Algorithms

The stock market is the primary entity driving every major economy across the globe, with each investment designed to capitalize profit while decreasing its associated risks. As a result of the stock market’s importance, there have been enumerable studies conducted with the goal of predicting the stock market through data analysis techniques including machine learning, neural networks, and time series analysis. This paper uses machine learning algorithms to perform stock market indices classification using fundamental data, while classifying the indices using technical indicators, with data derived from Yahoo Finance on the top 100 indices in the NASDAQ stock market from January 2000 to December 2020.

A nonparametric framework for inferring orders of categorical data from category-real pairs

Given a dataset of careers and incomes, how large a difference of incomes between any pair of careers would be? Given a dataset of travel time records, how long do we need to spend more when choosing a public transportation mode A instead of B to travel? In this paper, we propose a framework that is able to infer orders of categories as well as magnitudes of difference of real numbers between each pair of categories using an estimation statistics framework. Our framework not only reports whether an order of categories exists, but it also reports magnitudes of difference of each consecutive pair of categories in the order. In a large dataset, our framework is scalable well compared with existing frameworks. The proposed framework has been applied to two real-world case studies: 1) ordering careers by incomes from 350,000 households living in Khon Kaen province, Thailand, and 2) ordering sectors by closing prices from 1,060 companies in NASDAQ stock market between years 2000 and 2016. The results of careers ordering demonstrate income inequality among different careers. The stock market results illustrate dynamics of sector domination that can change over time. Our approach is able to be applied in any research area that has category-real pairs. Our proposed Dominant-Distribution Network provides a novel approach to gain new insight of analyzing category orders. A software of this framework is available for researchers or practitioners in an R CRAN package: EDOIF.

An Assessment of the Competitive Effects of the Proposed Merger of the New York Stock Exchange and the Nasdaq Stock Market

In March of 2000 the New York Stock Exchange proposed a merger with The Nasdaq Stock Market. Applying a qualitative assessment to the proposed merger from the organizations' perspective it is argued that the merger would be favorable for both organizations. Applying a quantitative assessment to the proposed merger from an antitrust perspective it is shown that the merger should be disallowed by federal antitrust regulators if the industry is to be treated as any ordinary industry.

Dampak Pandemi Covid-19 terhadap Pasar Modal di Indonesia

This study aims to: (1) examine the influence of a pandemic on the development of the stock market (CSPI) in Indonesia; (2) analyzing the effect of externalities on the dynamics of stock market developments in Indonesia; and (3) examine whether differences in social distancing policies affect the dynamics of Indonesian capital market movements. The research method uses quantitative analysis with a dummy variable multiple regression approach. JCI as a bound variable, while the independent variable is the number of Covid-19 pandemic cases in Indonesia, China and Spain, then the movement of the FTSE100 stock indexes (London), Hangseng (Hong Kong) and NASDAQ (New York), as well as differences in social distancing policies in Indonesia (Indonesia) Task Force, WFH and PSBB). The results of the study concluded: The movement of the composite stock index (CSPI) on the Jakarta Stock Exchange is influenced by internal and external conditions. Internally the condition of the Covid-19 pandemic and social distancing (WFH and PSBB) policies in the country have influenced the dynamics of the stock market (indicated by the movement of the IHSG index on the JSX). Externally, the Covid-19 pandemic in China and Spain also influenced the dynamics of the stock market in Indonesia (IHSG index). Likewise, the dynamics of the stock market in Hong Kong (Hangseng), London (FTSE100) and News York (NASDAQ). The coronavirus pandemic in Indonesia, China, the dynamics of the Nasdaq stock market in New York, and the social dintancing (WFH and PSBB) policies had a negative impact on the movement of the JCI stock index. While the pandemic in Spain, the dynamics of the stock market in Hong Kong (Hangseng) and London (FTSE100) actually had a positive impact on stock market conditions in Indonesia (JSX).

DAMPAK PANDEMI COVID-19 TERHADAP PASAR MODAL DI INDONESIA:

ABSTRACT
 This study aims to: (1) examine the influence of a pandemic on the development of the stock market (CSPI) in Indonesia; (2) analyzing the effect of externalities on the dynamics of stock market developments in Indonesia; and (3) examine whether differences in social distancing policies affect the dynamics of Indonesian capital market movements. The research method uses quantitative analysis with a dummy variable multiple regression approach. JCI as a bound variable, while the independent variable is the number of Covid-19 pandemic cases in Indonesia, China and Spain, then the movement of the FTSE100 stock indexes (London), Hangseng (Hong Kong) and NASDAQ (New York), as well as differences in social distancing policies in Indonesia (Indonesia) Task Force, WFH and PSBB). The results of the study concluded: The movement of the composite stock index (CSPI) on the Jakarta Stock Exchange is influenced by internal and external conditions. Internally the condition of the Covid-19 pandemic and social distancing (WFH and PSBB) policies in the country have influenced the dynamics of the stock market (indicated by the movement of the IHSG index on the JSX). Externally, the Covid-19 pandemic in China and Spain also influenced the dynamics of the stock market in Indonesia (IHSG index). Likewise, the dynamics of the stock market in Hong Kong (Hangseng), London (FTSE100) and News York (NASDAQ). The coronavirus pandemic in Indonesia, China, the dynamics of the Nasdaq stock market in New York, and the social dintancing (WFH and PSBB) policies had a negative impact on the movement of the JCI stock index. While the pandemic in Spain, the dynamics of the stock market in Hong Kong (Hangseng) and London (FTSE100) actually had a positive impact on stock market conditions in Indonesia (JSX).
 Keywords: IHSG, Stock Market, Pandemic Covid-19, Social Distancing

META-ANALYSIS ON AMERICAN PRESIDENTIAL ELECTION - 2016 AND AMERICAN STOCK MARKET

This paper aims to analyze the efficient stock market hypothesis as responsive to American Presidential Election, 2016. The meta-analysis has been done combining content analysis and event study methodology. The all major newspapers, news channels, public polls, literature and five important indices as Dow Jones Industrial Average (DJIA), NASDAQ Stock Market Composit Indexe (NASDAQ-COMP), Standard & Poor's 500 Index (SPX-500), New York Stock Exchange Composite Index (NYSE-COMP) and Other U.S Indexes-Russell 2000 (RUT-2000) are critically examined and empirically analyzed. The findings from content analysis reflect that stunned winning of Mr Trump from Republican Party worked as shock for American stock market. From event study, findings confirmed that all the major indices reflected a decline on winning of Trump and losing of Ms. Clinton from Democratic. The results are supported empirically and practically through the political event like BREXIT that resulted in shock to Global stock index and loss of $2 Trillion.

A Multifactor Fuzzy Time-Series Fitting Model for Forecasting the Stock Index

Fuzzy time series (FTS) models have gotten much scholarly attention for handling sequential data with incomplete and ambiguous patterns. Many conventional time series methods employ a single variable in forecasting without considering other variables that can impact stock volatility. Hence, this paper modified the multi-period adaptive expectation model to propose a novel multifactor FTS fitting model for forecasting the stock index. Furthermore, after a literature review, we selected three important factors (stock index, trading volume, and the daily difference of two stock market indexes) to build a multifactor FTS fitting model. To evaluate the performance of the proposed model, the three datasets were collected from the Nasdaq Stock Market (NASDAQ), Taiwan Stock Exchange Index (TAIEX), and Hang Seng Index (HSI), and the RMSE (root mean square error) was employed to evaluate the performance of the proposed model. The results show that the proposed model is better than the listing models, and these research findings could provide suggestions to the investors as references.

Hierarchical time-varying mixed-effects models in high-dimensional time series and longitudinal data studies

ABSTRACT We propose time-varying coefficient mixed-effects models for continuous multiple time series data and longitudinal data. The challenge is how to simultaneously display serial, clustering, and multivariate attributes of the data set, to which the routinely assumed two-level hierarchical model and univariate response models are not able to apply. Asymptotic properties of the proposed methods are established. We also conduct the model comparison, and find that the proposed methods outperform the traditional univariate response models, nonparametric models, and linear mixed effects models in both predicting the response and estimating the coefficient surface based on simulation studies. Finally, we have applied our methods to a real-world study on the price–volume relationship of NASDAQ stock market data.

The curvilinear effect of manufacturing outsourcing and captive-offshoring on firms' innovation: The role of temporal endurance

This paper aims to contribute to the open debate in the literature on the effect of global sourcing strategies on firm performance by studying the consequences of manufacturing outsourcing and captive-offshoring for the innovation capability of the firm. We grounded our hypotheses based on the outsourcing and offshoring literature and by narrowing our focus to the effects of persisting in their adoption over time. We tested our hypotheses using data from a sample of 368 manufacturing companies listed on NASDAQ stock market. The paper provides theoretical explanations and empirical findings for the inverted U-shaped influence of keeping doing captive-offshoring on new product development performance, in contrast to outsourcing, which shows a negative linear relationship. The conclusion is that managers should be aware of the consequences of outsourcing and captive-offshoring manufacturing, either in a spot manner or over a long period. They should eventually search for equilibrium between adopting captive-offshoring for too few years vs. too many years, by monitoring year by year its effects to avoid adaptation processes and corporation hollowing, and evaluate the opportunity to re-shore manufacturing.

Comment Letters to NASDAQ and NYSE: Time-Based Sunsets and the Problem of Early Unifications of Dual Class Share Structures

I am responding to the Council of Institutional Investors’ (“CII”) request that the NASDAQ Stock Market (“NASDAQ”) and New York Stock Exchange amend its listing standards to require companies seeking to go public with dual class shares (classes of common stock with unequal voting rights) to include in their certificates of incorporation a time-based sunset provision (a forced unification of shares into one share structure with equal voting rights after a certain period of time) that must go into effect no more than seven years after the initial public offering (IPO) unless the minority shareholders vote to extend up to an additional seven years. These letters (both letters have been combined into one document) argue that such a mandatory provision would be extremely unwise and harmful to our most important public companies and their shareholders, current as well as future. As a creation of private ordering, the absence of time-based sunset provisions in dual class share structures serves a significant value enhancing purpose. It prevents the risk that a pre-mature and therefore suboptimal unification of shares may occur. This risk has so far been ignored by those advocating for the implementation of a mandatory time-based sunset provision.

Bооm and crisis of «dot-сoms» in the USA (1995–2000)

Financial crises are always predated by periods of prolonged economic recovery and the speculative boom around the stock of certain innovative companies. When the lift reaches its climax, and the market prices of the shares are maximally inflated, a quick decline and crisis begin. Dot-com boom (companies that have engaged in sales of goods and services via the Internet) was preceded by a long economic upswing of the 1990s, one of the largest lifts of the twentieth century. Information technology was at the forefront of it, especially the mass proliferation of the Internet since the mid- 1990s. A large number of new Internet companies appears, the majority of which wanted to receive money from the issue of shares, regardless of whether a business of selling goods and services via the Internet would be profitable. As is the case with all joint-stock boom that took place earlier, this approach led to the dot-com crisis of March 10, 2000. On the NASDAQ stock market, where most of the «dot-com» shares traded, a rapid drop in their rates began. The huge losses are evidenced by the fact that during 2000, the NASDAQ index fell almost 50 %. If there were over three hundred «dot-coms» in 1999, in 2001, only 76 left. The total capitalization of the «dot-com» shares, part of the Bloomberg US Internet index, fell after the crisis by 1,755 trillion dollars, and the market prices of most stocks declined in 2000 by 80–90 %. Due to the speed of the fall in stock prices, the dot-com crisis can be compared with the «Great crash» of 1929. Although the crisis was short-lived and two years later it was replaced by a new rise, the «dot-com» crisis began the sequence of events that led to large-scale global crisis of 2007–2009. Nobody wanted to hinder the heated market of securities, so the «dotcoms» were superseded by the mortgage boom of the mid-2000s.

Portfolio Selection Using Z-Number Theory: Two Solution Methodologies

The Z-number theory is a new concept in fuzzy logic. This theory describes the uncertainty of information where any Z-number is displayed by a pair of fuzzy numbers. The first component of the Z-number creates a restriction on the uncertain variable through a fuzzy number, while the second component of the Z-number explains the reliability of the first component through another fuzzy number. Because there is no any accurate image of the future of financial markets, the Z-number theory can be widely used in the financial markets widely. In this paper, the theory of Z-number is investigated in portfolio selection problem using the utility function. The Z-number is almost always converted to classical fuzzy number in the existing literature. Although this approach reduces the computational complexity, converting Z-number to classical fuzzy number causes the loss of significant information. Hence, we propose two practical models with Z-number approach and optimize them both with and without converting Z-number to classical fuzzy number. Optimization without converting Z-number causes the information about utility of assets and their reliability to be available in each stage. However, optimization with converting Z-number causes portfolio selection model to be transformed into a linear programming model. Furthermore, a practical method for obtaining the utility of assets in shape of the Z-numbers is introduced. In addition, liquidity constraint and the maximal fraction of the capital allocated constraint are considered in portfolio selection models. Eventually, two numerical examples from NYSE and NASDAQ Stock Market are provided, and the results are compared with each other.

Novel and topical business news and their impact on stock market activity

We propose an indicator to measure the degree to which a particular news article is novel, as well as an indicator to measure the degree to which a particular news item attracts attention from investors. The novelty measure is obtained by comparing the extent to which a particular news article is similar to earlier news articles, and an article is regarded as novel if there was no similar article before it. On the other hand, we say a news item receives a lot of attention and thus is highly topical if it is simultaneously reported by many news agencies and read by many investors who receive news from those agencies. The topicality measure for a news item is obtained by counting the number of news articles whose content is similar to an original news article but which are delivered by other news agencies. To check the performance of the indicators, we empirically examine how these indicators are correlated with intraday financial market indicators such as the number of transactions and price volatility. Specifically, we use a dataset consisting of over 90 million business news articles reported in English and a dataset consisting of minute-by-minute stock prices on the New York Stock Exchange and the NASDAQ Stock Market from 2003 to 2014, and show that price volatility, transaction volumes, and the number of transactions exhibited a significant response to a news article when it was novel and topical.

Analysis of financial time series with model hybridization

Purpose- The aim of this study is to obtain better estimation results by hybridizing models that reveal linear and nonlinear relationships used in the intended financial time series. Methodology- ARIMA and Artificial Neural Networks (ANN) models were used in estimating NASDAQ stock market index values between 03.01.2012 and 30.06.2017 comparison of hybrid model results with different ways of error determination in literature. Findings- ARIMA residues have been tested different models where only residues are used with basic indications, only residues and basic. The calculation of residues was done separately with the addition and multiplication function. These residues were modeled with ANN, and the obtained results are collected and established hybrid model with ARIMA forecasts. When the results obtained at the end of the operations are compared, it is seen that the product function of some of the addition functions gives better results in some models. Conclusion- The hybridization of the ANN and NASDAQ index estimates with the ARIMA method resulted in processing for both addition and multiplication functions. Residues calculated with the addition model showed better results in ANN hybrid. What variables are used to calculate residuals is that the hybrid model gives better estimation results than single models.

FINANCIAL BUBBLE IMPLOSION AND REVERSE REGRESSION

Expansion and collapse are two key features of a financial asset bubble. Bubble expansion may be modeled using a mildly explosive process. Bubble implosion may take several different forms depending on the nature of the collapse and therefore requires some flexibility in modeling. This paper first strengthens the theoretical foundation of the real time bubble monitoring strategy proposed in Phillips, Shi and Yu (2015a,b, PSY) by developing analytics and studying the performance characteristics of the testing algorithm under alternative forms of bubble implosion which capture various return paths to market normalcy. Second, we propose a new reverse sample use of the PSY procedure for detecting crises and estimating the date of market recovery. Consistency of the dating estimators is established and the limit theory addresses new complications arising from the alternative forms of bubble implosion and the endogeneity effects present in the reverse regression. A real-time version of the strategy is provided that is suited for practical implementation. Simulations explore the finite sample performance of the strategy for dating market recovery. The use of the PSY strategy for bubble monitoring and the new procedure for crisis detection are illustrated with an application to the Nasdaq stock market.

Working Capital Management of Small Firms

The purpose of this paper to investigate the efficiency of working capital management and its impact on small firm’s liquidity, profitability and operating cash flows. Small firms have a considerable amount of fund tied up with current assets and faced with difficulties to access external fund from the capital markets, their current liabilities are an important source of their external financing. The relationship between the study variables examined using dynamic panel data analysis for a panel of 87030 U.S. non-financial firms listed in the New York Stock Exchange, American Stock Exchange, NASDAQ Stock Market, and Over the Counter Market. The analysis is applied at the level of the full sample and divisions of the sample by size. I also examined industry effect within small firms. The results suggest that managers of small firms can increase corporate liquidity, profitability, and operating cash flow of their firms by shortening the cash conversion cycle

Current Assets Management of Small Enterprises

The purpose of this paper is to investigate the relationship between firm’s net trade cycle, its size, and liquidity as a measure of efficiency in working capital management. The relation between the firm’s net trade cycle and its liquidity is examined using Generalized Method of Moment System Estimation applied to dynamic panel data for a sample of 5802 U.S. non-financial firms listed on the New York Stock Exchange, American Stock Exchange, NASDAQ Stock Market, and Over the Counter Market for the period 1990-2004 (87030 firm-year observations). The analysis is applied at the levels of the full sample and divisions of the sample by size.The results show a negative and significant relationship between net trade cycle as a comprehensive measure of efficiency in working capital management and liquidity for small firms. Most of the literature available focuses on the large firm’s experience in working capital management. The small firms generally face liquidity problems and have limited access to external capital and studies on their efficiency in working capital management are scant. In this context, the present study investigates the relation between the firm’s net trade cycle and liquidity of small firms.

MULTIFRACTALS IN WESTERN MAJOR STOCK MARKETS HISTORICAL VOLATILITIES IN TIMES OF FINANCIAL CRISIS

In this paper, the generalized Hurst exponent is used to investigate multifractal properties of historical volatility (CHV) in stock market price and return series before, during and after 2008 financial crisis. Empirical results from NASDAQ, S&P500, TSE, CAC40, DAX, and FTSE stock market data show that there is strong evidence of multifractal patterns in HV of both price and return series. In addition, financial crisis deeply affected the behavior and degree of multifractality in volatility of Western financial markets at price and return levels.

Instance Selection Using Genetic Algorithms for an Intelligent Ensemble Trading System

Instance selection is a way to remove unnecessary data that can adversely affect the prediction model, thereby selecting representative and relevant data from the original data set that is expected to improve predictive performance. Instance selection plays an important role in improving the scalability of data mining algorithms and has also proven to be successful over a wide range of classification problems. However, instance selection using an evolutionary approach, as proposed in this study, is different from previous methods that have focused on improving accuracy performance in the stock market (i.e., Up or Down forecast). In fact, we propose a new approach to instance selection that uses genetic algorithms (GAs) to define a set of target labels that can identify the buying and selling signals and then select instances according to three performance measures of the trading system (i.e., the winning ratio, the payoff ratio, and the profit factor). An intelligent ensemble trading system with instance selection using GAs is then developed for investors in the stock market. An empirical study of the proposed model is conducted using 35 companies from the Dow Jones Industrial Average, the New York Stock Exchange, and the Nasdaq Stock Market from January, 2006 to December, 2016.