NASDAQ Market Research Articles

An Advisor Neural Network framework using LSTM-based Informative Stock Analysis

In the past years, the widespread diffusion of Artificial Intelligence (AI) in the finance domain transformed different services, with particular attention to the stock market. Although different AI-based approaches have been proposed for stock forecasting, they are focused on news content or sentiment without considering fundamental features and vice versa. In turn, other approaches rely on handmade rules or ones based on technical indicators for providing advice without considering contextual information that can strongly affect the stock market. In this paper, we propose an Advisor Neural Network framework using Long Short-Term Memory (LSTM)-based Informative Stock Analysis for Daily investment Advice. Specifically, the forecasting unit relies on a LSTM-based model, which combines technical indicators, contextual information, and financial data for stock forecasting. Successively, the advice unit provides next-day advice based on predicted information in conjunction with the proposed Heuristic Stocks Selection algorithm. This framework has been evaluated on the Stock and Cryptocurrencies markets, considering a subset of 417 stocks and 67 cryptocurrencies over three years, respectively. We compared the proposed framework with several state-of-the-art approaches, showing how it outperforms the baseline in both markets. Furthermore, we achieved a financial gain greater than 41%, despite the downward trend of the NASDAQ market in the quarter under review, and we obtained a 39.38% return on investment for the Cryptocurrencies market.

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.

Predicting stock market trends with self-supervised learning

Predicting stock market trends is the basic daily routine task that investors should perform in the stock trading market. Traditional market trends prediction models are generally based on hand-crafted factors or features, which heavily rely on expensive expertise knowledge. Moreover, it is difficult to discover hidden features contained in the stock time series data, which are otherwise helpful for predicting stock market trends. In this paper, we propose a novel stock market trends prediction framework SMART with a self-supervised stock technical data sequence embedding model S3E. Specifically, the model encodes stock technical data sequences into embeddings, which are further trained with multiple self-supervised auxiliary tasks. With the learned sequence embeddings, we make stock market trends predictions based on an LSTM and a feed-forward neural network. We conduct extensive experiments on China A-Shares market and NASDAQ market to show that our model is highly effective for stock market trends prediction. We further deploy SMART in a leading financial service provider in China and the result demonstrates the effectiveness of the proposed method in real-world applications.

A Technical Indicator for a Short-term Trading Decision in the NASDAQ Market

Purpose: The objective is to employ a stochastic model to develop a new technical analysis indicator that could compute the variation of any index. We demonstrate the superiority and applicability of our proposed model and show that our proposed indicator could help investors and market analysts to anticipate the market trend in the short term and make better trading decisions by using our proposed model to analyze the variation of the NASDAQ Composite Index (IXIC). Design/methodology/approach: This study uses a stochastic process without mean-reverting property to develop a stochastic model that could compute the variation of any index. To show the superiority and applicability of our proposed model in computing the variation of any index, we employ our proposed model to compute the daily closing values of the IXIC over 10 years and derive the variation of the IXIC index. Findings: Our findings indicate that, based on the mean absolute percentage error, the calibrated model we proposed provides a more accurate estimate of the short-term index that outperforms both the simple moving average and the MACD in predictive accuracy. It delivers a robust anticipation of the overall market trend by offering a 95% confidence interval for the value of the composite NASDAQ index. Practical Implications: Our proposed indicator could help investors and market analysts to anticipate the market trend in the short term and make better trading decisions. Our proposed model provides market analysts with a forecasting tool by using our proposed technical analysis indicator to anticipate the market trend, which outperforms some traditional indicators of technical analysis, including Simple Moving Averages and Moving Average Convergence Divergence. Originality/value: Our approach, results, and conclusions are original and new in the literature. Our proposed model is a new technical indicator for predicting any index based on a stochastic process, which has been found to outperform some classical indicators. This research makes significant contributions to the field of decision sciences because the indicator we have developed plays a crucial role. It enables better buying and selling decisions based on market trend predictions estimated by using our proposed model. In this way, the indicator offers added value to professionals in making investment decisions. The results of this research work contribute to the development of new technical analysis indicators. Here, the IXIC index is an example, the use of this indicator is wider and could concern any stock market index and any share. So, this work enriches the literature and opens up new avenues for any researcher who wants to use stochastic processes to develop new technical indicators for different financial assets.

The effects of NASDAQ delisting on firm performance

We empirically investigate the effects of involuntary delisting from the NASDAQ on delisted firms. To disentangle the delisting effects from other factors, we use NASDAQ market volatility during a firm’s delisting grace period as an instrumental variable (IV) and compare firms that ultimately delisted and began trading on over-the-counter markets with those that regained compliance and remained on the NASDAQ. Although delisted firms experienced declines in liquidity, access to capital markets, investments, and performance, these declines are not mainly attributable to delisting. Among various liquidity measures, delisting reduces daily trading volume only for firms whose stocks were actively traded prior to delisting. Similarly, delisting reduces equity issuance only if the firm’s stock was actively traded prior to delisting. Overall, our findings indicate that delisting from a stock exchange is not as costly as commonly believed, supporting recent policy initiatives to provide alternative listing and trading venues for small companies.

An examination of impact of gold and oil prices on the stock markets indices during COVID-19

Understanding stock market volatility has remained significantly important because of its diverse implications. The current pandemic COVID-19 has brutally exaggerated the financial market across the board. This study inspects the volatility of Pakistan, India and USA stock markets due to pandemic of COVID-19 using the GARCH model. Furthermore, the price of oil and gold is used as exogenous variables in the conditional mean equation of the GARCH (1,1) model to assess the volatility of stock indices KSE-100 (Pakistan), BSESN (India) and NASDAQ (USA). Moreover, the data covering the period from 1st June 2018 to 31st December 2022 are divided into pre-COVID-19 (1st June 2018 to 30th January 2020) and during-COVID-19 (31st January 2020 to 31st December 2022. Findings reveal that the stock market dependency of NASDAQ experienced oil and gold impact followed by pre-COVID-19 and during-COVID-19 periods, respectively. Whereas, the Indian stock market had a significant impact on gold and oil during the COVID-19 period but the Pakistani stock market was not influenced by oil and gold in the entire considered study period. In addition, NASDAQ market and BSESN have the highest risk-return trade-off and are more persistent followed by pre-COVID-19 and during-COVID-19 periods, respectively.

A Technical Indicator for a Short-term Trading Decision in the NASDAQ Market

A Technical Indicator for a Short-term Trading Decision in the NASDAQ Market

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

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

Multifractal Analysis of the Impact of COVID-19 on NASDAQ, CIOPI, and WTI Crude Oil Market

In this paper, we explore the impact of COVID-19 on auto-correlations and cross-correlations among NASDAQ stock index of the USA, China iron ore price index (CIOPI), and West Texas Intermediate Crude Oil price (WTI). To find out the effect of COVID-19 on financial markets, we divide the investigated data series into two sub-periods, i.e., pre-COVID19 period and post-COVID19 period. First, multifractal detrended fluctuation analysis (MF-DFA) of those series shows a general trend of strong multifractality after COVID-19, indicating lower market efficiency after the pandemic shock. Second, multifractal detrended cross-correlation analysis (MF-DCCA) method is employed to examine cross-correlations among NASDAQ, CIOPI, and WTI. The three cross-correlations all increase in post-COVID19. The correlation between NASDAQ and CIOPI increases the most, becoming the strongest correlation among the three cross-correlations in post-COVID19. The surrogate procedure shows that the post-COVID19 cross-correlation multifractalities are mostly due to fat-tail distribution. Third, we use multi-scale multifractal analysis (MMA) to visualize the dynamic behaviors of correlations among the series. The Hurst surfaces of the three cross-correlations have more fluctuation, both at small and large scale in post-COVID19 than that of pre-COVID19. Particularly, the Hurst surface of cross-correlation between NASDAQ and CIOPI exhibits stronger multifractality during the outbreak of COVID-19 than that in both pre-COVID19 and post-COVID19. The above investigations provide helpful insights of relevant market trends.

IPO Pricing, Investor Behavior, and IPO Underpricing of High‐Tech Companies: Evidence from SSE STAR Market and Nasdaq Market

There is a significant IPO underpricing phenomenon in the stocks of high‐tech companies on China Science and Technology Innovation Board (STAR Market). In order to analyze the causes of high IPO underpricing on STAR Market, from the perspective of investor behavior, this paper uses the Two‐tier Stochastic Frontier Model to decompose IPO underpricing into two parts, the pricing impact of the primary market and the transaction impact of the secondary market, and then studies the differences of the impact of different markets on IPO underpricing. The results show the following: (1) the primary market pricing of most stocks, that is, the difference between the issuance price of stocks and the real value of stocks is small, which shows that the primary market pricing has a weak impact on the IPO underpricing of the STAR Market, which in turn shows that the implementation of the registration system has significantly improved the IPO pricing efficiency of the STAR Market; (2) the closing price of most stocks fell on debut, that is, the trading price of stocks is significantly higher than its real value, which shows that the blind optimism of investors in the secondary market and the irrationality of investor structure are the main factors leading to the IPO underpricing of the STAR Market; and (3) through the variance decomposition and the robustness test of grouping the industry to which the enterprise belongs, the year of listing, and issuance and the size of the enterprise, we find that in different years and different industries, the impact of investor behavior in the secondary market on IPO underpricing is significantly higher than that in the primary market. In addition, the IPO underpricing of enterprises is not affected by the size of enterprises. This shows that the behavior of investors in the secondary market has always been the main means to affect the IPO underpricing of the STAR Market. This paper expands the analysis idea of IPO underpricing, which has a certain reference significance for the reform of STAR Market and the listing and financing of scientific and technological innovation enterprises.

Sparse vector heterogeneous autoregressive modeling for realized volatility

We propose a sparse vector heterogeneous autoregressive (VHAR) model for realized volatility forecasting. As a multivariate extension of a heterogeneous autoregressive model, a VHAR model can consider the dynamics of multinational stock volatilities in a compact manner. A sparse VHAR is estimated using adaptive lasso and some theoretical properties are provided. In practice, our sparse VHAR model can improve forecasting performance and explicitly show the connectivity between stock markets. In particular, our empirical analysis shows that the NASDAQ market had the strongest influence on stock market volatility worldwide in the 2010s.

Copulas based seemingly unrelated quantile regression

We propose a multivariate copulas based seemingly unrelated quantile regression. We add the multivariate copula density function into the likelihood to relax the strong assumption of multivariate normal distribution of the conventional model. The simulation study is conducted to evaluate the performance of our proposed model. Moreover, we apply our proposed model to the Fama-French equation in order to investigate the systematic risk in the three major stocks in NASDAQ market. The results of this study suggest that our proposed model provides a particularly good description of these stock prices at every quantile level.

An Investigation of the Informational Role of Short Interest in the Nasdsaq Market

This paper examines the informational role of short interest in the Nasdaq market. Using the population of monthly short interest data over the period of June 1988 through December 1994 we find that firms with high short interest experience significant negative abnormal returns ranging from -0.76% to -1.13% per month during the period over which they are heavily shorted. In contrast to results in Aitken et al. (1998) that show that information in short interest is incorporated quickly into prices for Australian stocks, we find that information in short interest is incorporated gradually into prices for Nasdaq stocks. The negative excess returns are increasing in the level of short interest indicating that a higher level of short interest is a stronger signal. Consistent with short interest being a bearish signal, we find that the heavily shorted firms get delisted with a higher frequency relative to their size and industry matched control firms.

The Impact of Market Structure on Ex‐Dividend Day Stock Price Behavior

AbstractWe explore the impact of market structure on the ex‐day price anomaly. Measuring the price‐drop ratio (PDR) as the ratio of the price change on the ex‐day to the dividend amount, we find that the average NASDAQ PDR is significantly less than one and significantly less than the New York Stock Exchange (NYSE) PDR. We then investigate a subset of firms that voluntary switch from NASDAQ to the NYSE and find that the PDR significantly increases after the switch, suggesting that market structure affects PDRs. We also create a matched sample and find that the NASDAQ PDR converges toward its matched NYSE counterpart, particularly after the introduction of SuperMontage. Our evidence is consistent with significant NASDAQ market structure changes reducing execution cost differences between the two exchanges and, in turn, reducing the PDR difference. Overall, our results highlight the important role market structure can play in understanding anomalies.

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.

Determinants of Short Interest Trading

Short selling is an investing strategy growing in importance, but with detractors who fear that the strategy can disrupt markets. Despite the volume of research on the effects of short selling, very little work has sought to understand the factors that determine the amount of short selling. This article explores which factors best explain the amount of shorting activity. The authors introduce a new variable that measures the relative price change in a company’s equity. Using NASDAQ company level data for the period from 2007 to 2013, they test the importance of volume, percentage price change, and relative price change to explain the number of shares shorted. All three variables are significant determinants of shorting activity. A large enough increase in industry prices may negate the effect of the increase in a company’s own share price on short-selling activities. If the average industry price change is larger than the change in the company’s share price, short sellers are less attracted to the company. Trifurcation (into high, medium, and low values) of the three primary independent variables yielded additional information on their effects on shorting activities. The authors find size-related monotonic relationships between two of their three primary variables and short selling—volume compared with shares outstanding and percentage price change. Relative price change does not support a monotonic relationship, but the differences between the subcategories are small. They also test the importance on short-selling activities of the rate of change in the overall NASDAQ market. High and low rates of change in overall NASDAQ prices do not affect short selling, but the middle range with a flat market reveals an increase in short selling. Flat markets appear to be the short-sellers’ sweet spot.

Markets without Subjects: Nasdaq and the Financial Interface

This article is a re-evaluation of recent discourses around financial subjectivity and debt, popularised in the wake of the 2008 crash, from the perspective of critical media studies. Through an examination of the history and development of the Nasdaq market - the world’s first electronic stock exchange which came to serve as the engine for the ‘New Economy’ and the dot-com boom of the 1990s - this paper complicates recent theoretical conversations around the way that financial capitalism shapes political subjects in the neoliberal era in order to take into account the machinic and pre-individual operations of financial markets and the computational infrastructures that undergird them. It argues that the Nasdaq offers insight into the unfolding of a unique socio-technical apparatus that defines contemporary capitalism, one not adequately accounted for in either the popular or scholarly discourse on finance written since the crash. Paying particular attention to the role of the Nasdaq in producing the myths of the New Economy, this paper examines how the Nasdaq model was instrumental in an attempt to resolve the contradictions of neoliberal capitalism. It argues that the Nasdaq marks a crucial moment in the integration of financial markets and new mechanisms of control in contemporary capitalism that work on the dividual, always rendered in a subjugated and reactive position to the flow of automated financial information.

Econometric Tests of the CAPM Model for a Portfolio Composed of Companies Listed on Nasdaq and Dow Jones Components

Abstract We tested empirically through econometric methods the classic CAPM model for 15 shares listed on the NASDAQ market in United States of America. The results showed that, for the majority of shares, there is a linear relation between expected return and market return. The shares of the largest companies from sample (AAPL, MSFT, GOOGL, etc. INTC) had a subunitary beta and the shares of smaller companies (ADBE, YHOO, BIDU etc.) had a beta greater than one. Compared with Security Market Line (SML) the shares were found to be overestimated and overstated and using GARCH-VECH model we identified the presence of high correlation between shares and the volatility spillover phenomenon.

Dealers and changing obligations: the case of stub quoting

We examine the liquidity providing behavior of NASDAQ market makers surrounding two periods of changing dealer obligation. The first change in November, 2007 is the relaxation of rule 4613, which required NASDAQ market makers to place two-sided quotes “reasonably related” to the current best bid and offer. The relaxation of this rule permitted NASDAQ market makers to post quotes far away from the prevailing market, a practice frequently referred to as stub quoting. The second is the Securities and Exchange Commission ban on stub quoting in December, 2010, which requires market makers to quote within a predefined distance from market prices. We find evidence that placing restrictions on stub quoting alters market makers’ liquidity providing behavior in both the 2007 and 2010 rule change periods. Stub quoting restrictions increase the time that market makers quote at the NBBO. We also find evidence that the proportion daily volume executed by market makers increases during the 2007 stub quoting restriction. We also find evidence that restrictions on stub quoting narrows spreads and reduces the price impact of trades. However, we find little evidence that stub quoting rules impact the participation of market makers during days with excessive volatility.

Bubbles and the Weibull distribution: was there an explosive bubble in US stock prices before the global economic crisis?

Existing studies on bubbles have been mainly concerned with investigating the stationarity properties of stock prices and market fundamentals. We develop a new method of testing for bubbles that relates the bubble component of stock prices to the probability of bursting in the context of the Weibull distribution. There were several eruptions and subsequent collapses of seeming bubbles over the past three decades: 1987 (Black Monday), 2000 (information technology (IT) boom) and 2007 (housing market boom). Using US monthly data for the S&P 500 and NASDAQ series, we have found that the S&P 500 series contained an explosive bubble only during the boom of the housing market that occurred before the 2007 global economic crisis, and the NASDAQ market contained an explosive bubble during the surge of stock prices peaking in 1987 and 2007, although our stationarity tests fail to detect the bubbles. No bubble was found in both the S&P and NASDAQ series during the 2000 IT boom. Our evidence corroborates the criticism that the traditional unit root and cointegration tests may not be able to detect some important class of bubbles.