Search

The prediction of stock market fluctuations is crucial for decision-making in various financial fields. Deep learning algorithms have demonstrated outstanding performance in stock market index prediction. Recent research has also highlighted the potential of the Transformer model in enhancing prediction accuracy. However, the Transformer faces challenges in multi-step stock market forecasting, including limitations in inference speed for long sequence prediction and the inadequacy of traditional loss functions to capture the characteristics of noisy, nonlinear stock history data. To address these issues, we introduce an innovative transformer-based model with generative decoding and a hybrid loss function, named "Galformer," tailored for the multi-step prediction of stock market indices. Galformer possesses two distinctive characteristics: (1) a novel generative style decoder that predicts long time-series sequences in a single forward operation, significantly boosting the speed of predicting long sequences; (2) a novel loss function that combines quantitative error and trend accuracy of the predicted results, providing feedback and optimizing the transformer-based model. Experimental results on four typical stock market indices, namely the CSI 300 Index, S&P 500 Index, Dow Jones Industrial Average Index (DJI), and Nasdaq Composite Index (IXIC), affirm that Galformer outperforms other classical methods, effectively optimizing the Transformer model for stock market prediction. [ABSTRACT FROM AUTHOR]

This paper aims to assess the stochastic dominance of the extreme downside (negative return) and upside (positive return) risk profiles of three US stock market indices, namely NASDAQ Composite, S&P 500 and Dow Jones Industrial Average (DJIA) based on the block maxima method in extreme value theory. The extreme downside and upside risk profiles were developed using two datasets of 360 monthly minimum and maximum daily log returns respectively (from January 1992 to December 2021). Extreme losses beyond the 80th percentile (corresponding to a tail probability of less than 0.2) of the theoretical extreme risk profiles were adopted to investigate stochastic dominance. Pairwise comparisons show that the DJIA stochastically dominates the other two indices in both extreme negative and positive returns. Moreover, the extreme upside risk profile of the DJIA stochastically dominates its extreme downside risk profile. The paper finds that investment in short positions (encountering upside risk) provides the least extreme risk compared with long positions (encountering downside risk) for the DJIA, as well as both short and long positions for the S&P 500 and NASDAQ Composite. [ABSTRACT FROM AUTHOR]

Expanded feature engineering to handle complex and noisy stock data is not much explored in financial forecasting. Along with feature engineering, hyperparameter tuning is also important. This paper provides a solution to handle these issues by elaborating on Discrete Wavelet Transform (DWT)-based feature engineering and hyperparameter-tuned ensemble model. The Multi-Stage Feature Engineering (MSFE) is proposed in which DWT-based decomposition is utilized to handle the noise. DWT decomposition expands the features; therefore, two-stage feature reduction is proposed in which first the filter method is used, and then the probabilistic method is utilized. Next, hyperparameter tuning of the ensemble model is offered through Particle Swarm Optimization (PSO). The proposed model is named as Wavelet-Particle Swarm Optimization (WPSO). WPSO is tested and evaluated on the three stock indices (NIFTY, NASDAQ, and NYSE), and provided 92.51%, 94.18%, and 87.62% accuracy for NIFTY, NASDAQ, and NYSE, respectively. The WPSO is validated by comparing it with state-of-the-art methods. The performance of the WPSO is statistically analyzed through the Bonferroni–Dunn post hoc test where WPSO positioned at rank 1 for all the evaluation metric and datasets. The WPSO empirically verifies that improving feature quality through MSFE and hyperparameter tuning of ensemble model significantly improves the predictive outcomes. [ABSTRACT FROM AUTHOR]

This article covers the topic of the relationship between the growth of companies and their financial condition when different business profiles are surveyed, such as high-tech, growth potential companies included in the NASDAQ Composite Index and traditional, matured companies included in the Dow Jones Industrial Average (DJIA), commonly known as blue-chip stocks. The hypothesis that the relationship between the Altman Z-score and the growth of enterprises as measured by assets, equity, sales, and earnings per share is positive was tested with Granger and VAR models. The main difference was found to be related to the size of the companies size and dynamics of growth. It was also shown that between NASDAQ enterprises there was no relationship between their growth and Altman Z-score, whereas when the DJIA blue chips were taken into account, a positive relationship was identified. It can be concluded that high-tech enterprises grow in a less predictable way not related to their economic condition, but mature enterprises focused on the growth and their condition. The value added of the article is finding that high-tech companies with growth potential and blue chips are managed in a different way due to their strategies of development. [ABSTRACT FROM AUTHOR]

In the FinTech era, we contribute to the literature by studying the pricing of Bitcoin options, which is timely and important given that both Nasdaq and the CME Group have started to launch a variety of Bitcoin derivatives. We find pricing errors in the presence of market smiles in Bitcoin options, especially for short-maturity ones. Long-maturity options display more of a "smirk" than a smile. Additionally, the ARJI-EGARCH model provides a better overall fit for the pricing of Bitcoin options than the other ARJI-GARCH type models. We also demonstrate that the ARJI-GARCH model can provide more precise pricing of Bitcoin and its options than the SVCJ model in term of the goodness-of-fit in forecasting. Allowing for jumps is crucial for modeling Bitcoin options as we find evidence of time-varying jumps. Our empirical results demonstrate that the realized jump variation can describe the volatility behavior and capture the jump risk dynamics in Bitcoin and its options. [ABSTRACT FROM AUTHOR]

The performance of technology companies listed on NASDAQ significantly impacts larger economic trends. Investors need specific information to navigate market volatility and make informed decisions in an increasingly complex marketplace. Furthermore, amidst the ongoing digital revolution, legislators and regulatory agencies must comprehend the operational dynamics of technology companies to develop frameworks that support innovation while maintaining market stability. Our study assesses the impact on the overall operational efficiency of NASDAQ-listed firms from 2011 to 2023, resulting from the interdependence of critical variables such as selling, general, and administrative expenses (SGA), cost of goods and services sold (COGS), and investments in research and development (R&D). Johansen's cointegration methodology and pairwise Granger causality tests were employed to unveil long-term relationships, equilibrium adjustments, and causal relationships among the considered variables. The results provide critical insights into the strategic management of operational variables by the listed companies. The economic significance of the results obtained underscores the paramount importance of efficiently managing the cost of goods and services sold to achieve superior operating performance among these leading technology firms. [ABSTRACT FROM AUTHOR]

Copyright of Dilemas Contemporáneos: Educación, Política y Valores is the property of Dilemas Contemporaneos: Educacion, Politica y Valores and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accepted by: Giorgio Consigli The passive management approach offers conservative investors a way to reduce risk concerning the market. This investment strategy aims at replicating a specific index, such as the NASDAQ Composite or the FTSE100 index. The problem is that buying all the index's assets incurs high rebalancing costs, and this harms future returns. The index tracking problem concerns building a portfolio that follows a specific benchmark with fewer transaction costs. Since a subset of assets is required to solve the index problem this class of problems is NP-hard, and in the past years, researchers have been studying solution approaches to obtain more practical tracking portfolios. This work brings an analysis, spanning the last three decades, of the advances in mathematical approaches for index tracking. The systematic literature review covered important issues, such as the most relevant research areas, solution methods and model structures. Special attention was given to the exploration and analysis of metaheuristics applied to the index tracking problem. [ABSTRACT FROM AUTHOR]

Analysis of stock prices has been widely studied because of the strong demand among private investors and financial institutions. However, it is difficult to accurately capture the factors that cause fluctuations in stock prices, as they are affected by a variety of factors. Therefore, we used non-harmonic analysis, a frequency technique with at least ${10}^{4}$ to ${10}^{10}$ more accurately than conventional analysis methods, to visualize the periodicity of the Nasdaq Composite Index stock price from January 4, 2010, to September 8, 2023. We classified periods with intense periodic fluctuations as ROI1, ROI2, and ROI3, and conducted a detailed analysis by comparing the results of short-time Fourier transform (STFT), continuous wavelet transform (CWT), and non-harmonic analysis (NHA). The results showed that NHA effectively suppressed side lobes and captured subtle changes in periodic fluctuations, enabling us to decompose the complex state of stock prices into multiple waveforms. Furthermore, we confirmed the potential for these waveforms to correspond to causal relationships arising from events. Moreover, the results of this experiment could be used to understand the relationship between periodic fluctuations and incidents or events and to use deep learning to predict stock prices.

This study proposes a control chart that monitors conditionally heteroscedastic time series by integrating the Huber support vector regression (HSVR) and the one-class classification (OCC) method. For this task, we consider the model that incorporates nonlinearity to the generalized autoregressive conditionally heteroscedastic (GARCH) time series, named HSVR-GARCH, to robustly estimate the conditional volatility when the structure of time series is not specified with parameters. Using the squared residuals, we construct the OCC-based control chart that does not require any posterior modifications of residuals unlike previous studies. Monte Carlo simulations reveal that deploying squared residuals from the HSVR-GARCH model to control charts can be immensely beneficial when the underlying model becomes more complicated and contaminated with noises. Moreover, a real data analysis with the Nasdaq composite index and Korea Composite Stock Price Index (KOSPI) datasets further disclose the validity of using the bootstrap method in constructing control charts. [ABSTRACT FROM AUTHOR]

This study explored the complex interplay and potential risk of financial contagion across major financial indices, focusing on the Bucharest Exchange Trading Investment Funds Index (BET-FI), along with global indices like the S&P 500, Nasdaq Composite (IXIC), and Dow Jones Industrial Average (DJIA). Our analysis covered an extensive period from 2012 to 2023, with a particular emphasis on Romania's financial market. We employed Autoregressive Distributed Lag (ARDL) modeling to examine the interrelations among these indices, treating the BET-FI index as our primary variable. Our research also integrated Exponential Curve Fitting (EXCF) and Generalized Supremum Augmented Dickey–Fuller (GSADF) models to identify and scrutinize potential price bubbles in these indices. We analyzed moments of high volatility and deviations from typical market trends, influenced by diverse factors like government policies, presidential elections, tech sector performance, the COVID-19 pandemic, and geopolitical tensions, specifically the Russia–Ukraine conflict. The ARDL model revealed a stable long-term relationship among the variables, indicating their interconnectedness. Our study also highlights the significance of short-term market shifts leading to long-term equilibrium, as shown in the Error Correction Model (ECM). This suggests the existence of contagion effects, where small, short-term incidents can trigger long-term, domino-like impacts on the financial markets. Furthermore, our variance decomposition examined the evolving contributions of different factors over time, shedding light on their changing interactions and impact. The Cholesky factors demonstrated the interdependence between indices, essential for understanding financial contagion effects. Our research thus uncovered the nuanced dynamics of financial contagion, offering insights into market variations, the effectiveness of our models, and strategies for detecting financial bubbles. This study contributes valuable knowledge to the academic field and offers practical insights for investors in turbulent financial environments. [ABSTRACT FROM AUTHOR]

Investing in stocks is a common choice for financial management. Technical indicators (TIs) assist investors in determining the best trading time to make a fortune. Moving average (MA) and relative strength index (RSI) are the most common TIs. The proposed hybrid technique maximizes the capabilities of these two indicators. This study utilizes the quantum-inspired algorithm to assist effectively in searching for the optimal solution in the vast solution space. The proposed trading system contains four innovative features. First, the traditional usage restriction of MA and RSI is removed to increase their potential effectiveness and identify the most profitable trading strategy. Second, this research proposes an innovative hybrid indicator (HI) that combines MA and RSI to simultaneously achieve both benefits. HI eliminates the restriction of employing a single indicator at once. Third, an efficient quantum-inspired algorithm, the Global-best-guided Quantum-inspired Tabu Search Algorithm with Quantum NOT Gate (GNQTS), effectively and efficiently explores optimized parameters. Fourth, this study proposes 60 sliding windows to determine the optimal period for training and testing. The investment targets include well-known indices: DJIA, S&P 500, NASDAQ Composite Index, and NYA, as well as high-reputation companies on the US stock market: DJIA components. By removing the restrictions imposed by these two indicators and the use of HI, the experiment results demonstrate that GNQTS can discover optimal parameters to generate higher returns than state-of-the-art methods and buy-and-hold (B&H) strategies. The proposed hybrid strategy provides for the promising prospect of quantum-inspired applications and the utilization of multiple indicators. [ABSTRACT FROM AUTHOR]

Functional data analysis has significantly enriched the landscape of existing data analysis methodologies, providing a new framework for comprehending data structures and extracting valuable insights. This paper is dedicated to addressing functional data clustering—a pivotal challenge within functional data analysis. Our contribution to this field manifests through the introduction of innovative clustering methodologies tailored specifically to functional curves. Initially, we present a proximity measure algorithm designed for functional curve clustering. This innovative clustering approach offers the flexibility to redefine measurement points on continuous functions, adapting to either equidistant or nonuniform arrangements, as dictated by the demands of the proximity measure. Central to this method is the "proximity threshold", a critical parameter that governs the cluster count, and its selection is thoroughly explored. Subsequently, we propose a time-shift clustering algorithm designed for time-series data. This approach identifies historical data segments that share patterns similar to those observed in the present. To evaluate the effectiveness of our methodologies, we conduct comparisons with the classic K-means clustering method and apply them to simulated data, yielding encouraging simulation results. Moving beyond simulation, we apply the proposed proximity measure algorithm to COVID-19 data, yielding notable clustering accuracy. Additionally, the time-shift clustering algorithm is employed to analyse NASDAQ Composite data, successfully revealing underlying economic cycles. [ABSTRACT FROM AUTHOR]

The most recent fall of the Silicon Valley (SVB) and Credit Suisse (CS) banks increased the fear of a worldwide banking crisis. We analyse the impacts of their fall on five financial indices. We apply detrended fluctuation analysis, static and with sliding windows. We find a higher impact of the SVB fall on the efficiency dynamic of the studied indices, which revealed fluctuating efficiency and a loss of efficiency during the period of the falls. The fall of both banks contributed to some persistence in stock indices returns. The Nasdaq and STOXX Europe 600 Banks are the most and the least efficient indices, respectively. Despite the apparent evidence of inefficiency, it might not necessarily mean a capacity for abnormal profits. [ABSTRACT FROM AUTHOR]

The task of detecting a structural change in conditional quantiles of time series with time-varying volatilities is vital in the field of financial time series, especially risk management. Therefore, in this study, we aim to construct a change point test to detect a quantile change by hybridizing the cumulative sum of squares test with the support vector quantile regression. Compared to the test employing the standard quantile regression, this approach not only provides more robust property against a high degree of nonlinearity of time series but also exhibits better performance for the datasets contaminated with high-frequency noises, as demonstrated in our simulation study. It also has merits to provide more diverse interpretations and a deeper understanding of financial time series than the volatility change point test, as illustrated in a real data analysis of three financial indices, namely, S&P500, Nasdaq composite index, and the stock price of Apple Inc. [ABSTRACT FROM AUTHOR]

We present a method to estimate price impact in order-driven markets that does not require averaging over executions or scenarios. Given order book data associated with one single execution of a sell metaorder, we estimate its contribution to price decrease during the trade. We do so by modelling the limit order book using a state-dependent Hawkes process, and by defining the price impact profile of the execution as a function of the compensator of the state-dependent Hawkes process. We apply our method to a dataset from NASDAQ, and we conclude that the scheduling of sell child orders has a bigger impact on price than their sizes. [ABSTRACT FROM AUTHOR]

Recent research suggests that long memory can be caused by regime switching and is easily confused with it. However, if the causes of confusion were properly controlled, they could be distinguished. Motivated by this idea, our study aims to distinguish between the long memory and regime switching of financial volatility. We firstly modeled the long memory and regime switching of volatility using the Fractionally Integrated Exponential GARCH (FIEGARCH) and Markov Regime-Switching EGARCH (MRS-EGARCH) frameworks, respectively, and performed a simulation study on their finite-sample properties when innovations followed a non-normal distribution. Subsequently, we demonstrated the confusion between the FIEGARCH and MRS-EGARCH processes using simulations. A recent study theoretically proved that the time-varying smoothing probability series can induce the presence of significant long memory in the regime-switching process. To control for its effect, the two-stage two-state FIEGARCH and MRS-FIEGARCH frameworks are proposed. The Monte Carlo studies showed that both frameworks can effectively distinguish between the pure FIEGARCH and pure MRS-EGARCH processes. When the MRS-FIEGARCH model was further employed to fit series generated with the MRS-FIEGARCH process, it outperformed the ordinary FIEGARCH model. Finally, an empirical study of NASDAQ index return was conducted to demonstrate that our MRS-FIEGARCH model can provide potentially more reliable long-memory estimates, identify the volatility states and outperform both the FIEGARCH and MRS-EGARCH models. [ABSTRACT FROM AUTHOR]

Kim Stoll is chair of Milwaukee Women Inc and vice president of sales and marketing for Badger Meter. In this article, Kim discusses Nasdaq's groundbreaking action to advance board diversity and shares thought‐provoking comments from Toni Townes‐Whitley, independent director on the Nasdaq Board and keynote speaker at the organization's 20th anniversary event. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the relationship between investor sentiment and leading equity market indices from the U.S., Europe, Asia, and globally between January 2020 and June 2022. The methodological approaches utilized are quantile regression and wavelet analysis. The results of quantile regression suggested that Google Search Volume (GSV) and Twitter-based Market Uncertainty Index (TMU) negatively influenced the equity indices at lower quantiles. The wavelet coherence analysis highlighted that, at lower frequency bands, GSV moves in sync with the S&P 500, NASDAQ Composite, Dow Jones Industrials, and FTSE 100 but not with the DAX, CAC 40, TOPIX, Nikkei 225, or MSCI. Nonetheless, when the TMU was used to measure investors’ sentiment, the results revealed that the whole series was out of phase. [ABSTRACT FROM AUTHOR]

Clean energy companies often require more costly investment in technology innovation than traditional energy companies, which poses higher technological risks. Therefore, many inconclusive debates have arisen concerning whether the R&D investment can generate sustained returns for such companies. This paper adopts progressive modeling steps to address the problem by using a modified Cobb-Douglas production function, System Generalized Method of Moments (SYS-GMM) approach, and fixed-effect panel threshold model. The role of R&D investment; the non-linear relationship between revenue, innovation, efficiency, and risk; as well as the corresponding threshold effects in clean and traditional energy companies are analyzed. 840 firm-year observations of clean energy companies from the NASDAQ Clean Edge Index are collected and screened, and compared with 280 firm-year observations of listed traditional energy companies in the U.S. Moreover, four types of clean energy companies, comprising green energy, wind power, water, and smart grid companies, are calculated and summarized separately. The results show that, for clean energy companies, long-term R&D intensity is beneficial to returns, shortening the cash conversion cycle (CCC) value, and reducing the financial leverage can produce a positive effect on return on assets (ROA), while different types of clean energy companies are advised with tailor-made portfolio strategies. For traditional energy companies, controlling the financial leverage while properly increasing CCC can help improve their ROAs. In this context, policy recommendations are provided for stakeholders to optimize their investment strategies in various clean and traditional energy enterprises according to the time-lag effect and threshold effect. [ABSTRACT FROM AUTHOR]

Triggered by COVID-19, one of the most dramatic crashes in the stock market in history occurred in March 2020. The sharp reductions in NASDAQ insurance stock indexes were observed after the occurrence of COVID-19 and in March 2020. In this study, the NASDAQ insurance stock markets (including NASDAQ Insurance Index, Developed Markets Insurance Index, and Emerging Markets Insurance Index) and NASDAQ Composite Index are utilized. The "scissors difference" between the NASDAQ Insurance Index and NASDAQ Composite Index is observed. The dynamic effects of the COVID-19 epidemic and the March 2020 crash on the multifractality of four series are explored. Firstly, the apparent and intrinsic multifractality, the components of multifractality, and the dynamic effects of the COVID-19 epidemic on these indexes are analyzed. Secondly, the multifractal cross-correlation between the NASDAQ Insurance Index and NASDAQ Composite Index is investigated. The dynamic influence of the COVID-19 epidemic on the cross-correlation is examined. The multifractal analysis results reveal that four series both before and after the occurrence of COVID-19 have multifractal characteristics. The stronger multifractal characteristics and the greater multifractal degree are obtained after the occurrence of COVID-19. The intrinsic multifractality of the three indexes ascends largely after the occurrence of COVID-19. The multifractal cross-correlation analysis illustrates that the cross-correlation between two indexes before and after the occurrence of COVID-19 is multifractal. The stronger multifractal cross-correlations and greater multifractal degrees are shown. The contribution of the intrinsic multifractal cross-correlation increased after the occurrence of COVID-19. [ABSTRACT FROM AUTHOR]

Copyright of Mexican Journal of Economics & Finance / Revista Mexicana de Economia y Finanzas is the property of Instituto Mexicano de Ejecutivos de Finanzas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Several well-established benchmark predictors exist for value-at-risk (VaR), a major instrument for financial risk management. Hybrid methods combining AR-GARCH filtering with skewed- t residuals and the extreme value theory-based approach are particularly recommended. This study introduces yet another VaR predictor, G-VaR, which follows a novel methodology. Inspired by the recent mathematical theory of sublinear expectation, G-VaR is built upon the concept of model uncertainty, which in the present case signifies that the inherent volatility of financial returns cannot be characterized by a single distribution but rather by infinitely many statistical distributions. By considering the worst scenario among these potential distributions, the G-VaR predictor is precisely identified. Extensive experiments on both the NASDAQ Composite Index and S&P500 Index demonstrate the excellent performance of the G-VaR predictor, which is superior to most existing benchmark VaR predictors. [ABSTRACT FROM AUTHOR]

In this paper, we consider the mildly explosive autoregression y t = ρ n y t − 1 + u t , 1 ≤ t ≤ n , where ρ n = 1 + c / n ν , c > 0 , ν ∈ (0 , 1) , and u 1 , ... , u n are arithmetically α -mixing errors. Under some weak conditions, such as E u 1 = 0 , E | u 1 | 4 + δ < ∞ for some δ > 0 and mixing coefficients α (n) = O (n − (2 + 8 / δ)) , the Cauchy limiting distribution is established for the least squares (LS) estimator ρ ^ n of ρ n , which extends the cases of independent errors and geometrically α -mixing errors. Some simulations for ρ n , such as the empirical probability of the confidence interval and the empirical density, are presented to illustrate the Cauchy limiting distribution, which have good finite sample performances. In addition, we use the Cauchy limiting distribution of the LS estimator ρ ^ n to illustrate real data from the NASDAQ composite index from April 2011 to April 2021. [ABSTRACT FROM AUTHOR]

Applying deep learning, especially time series neural network, to stock market prediction, has become one of the important applications in the quantitative finance field. However, due to the multi-correlation and volatility of the stock market, how to timely and accurately predict it has become a challenging issue. In order to cope with this challenge, a news-driven stock market index prediction model based on TrellisNet and a sentiment attention mechanism (SA-TrellisNet) is proposed. A sentiment analysis model based on CNN and LSTM is presented to obtain the sentiment index of massive news crawled from authoritative financial websites. Furthermore, a sentiment attention mechanism is designed for data fusion of stock data and news sentiment index as the input of the simple and efficient TrellisNet network for model training and prediction. The performance of our model is systematically evaluated using seven major international stock market indices including S&P500, NYSE, DJI, NASDAQ, FTSE 100, Nikkei 225 and SSE, and comparative experiments demonstrate that SA-TrellisNet is competitive to the other state-of-the-art methods in predicting stock market indices. [ABSTRACT FROM AUTHOR]

In this paper, we propose to analyze multiple cross-correlation between six stock market indexes (Dow Jones Industrial Average, Nasdaq Composite, DAX Performance, FTSE 100, S&P/TSX Composite and Nikkei 225) based on the multiple detrended cross-correlation coefficient, DMC x 2 . Due to its importance and long existence, we will have as the base index, acting as dependent variable, the Dow Jones Industrial Average, which will be compared with the other five indexes in all possible combinations. In addition to calculating the DMC x 2 for all combinations with Dow Jones, we will also infer the relative strength of each index in relation to the whole, and this will be done here through an unprecedented map of relative strength. With this map it will be possible to verify that the Nikkei 225 index has less weight for all time-scales and also all combinations, and that there is better distribution in the Nasdaq and DAX combination. Finally, we present more possibilities for this application throughout the paper. [ABSTRACT FROM AUTHOR]

The shares were to be listed on Nasdaq's MTF,[7] First North Sweden MTF, a market not listed as an SME growth market. Nasdaq's Dominance There are three companies providing SME growth markets in Sweden: Nasdaq, NGM and Spotlight Group AB (Spotlight). Sweden • NASDAQ Abandons Plans to Trade Shares Listed on a Rival Market Place Following an Interim Injunction by the Swedish Competition Authority Nasdaq's SME growth market is by far the largest, with 54 exchange members, compared to those of NGM and Spotlight, listing 25 and 19 members respectively. [Extracted from the article]

Purpose: The survival rate of newly listed firms is low, and there is evidence of a surge of poorly performing new listed firms leading up to the crash of the dot.com bubble. The author investigates this phenomenon and analyzes investors' ability to understand the quality of accounting information and to adjust their expectations. Design/methodology/approach: The author employs the dividend discount model in conjunction with clean surplus accounting discussed by Ohlson (1995) to compare the value relevance of earnings and research and development (R&D) expenditures for short and longer listed National Association of Security Dealer Automated Quotations (NASDAQ) firms between 1980 and 2014. The author also uses univariate tests and logistic regression to analyze both recently listed and short-listed firms. In this analysis, the author compares the differences in investors' expectations for the first five years for both types of firms. Findings: The author provides convincing evidence that markets clearly placed lower valuation weights on accounting earnings and R&D expenditures for short-listed firms on NASDAQ. Market participants originally had high expectations for these ventures. But, they gradually understood the lower quality of accounting information and adjusted their expectations downward. Originality/value: The author's results show that optimistic expectations along with easy equity financing created a surge of new listings. My analysis of the interplay between the quality of accounting information and investors' expectations indicates a negative spillover effect where investors are overoptimistic about firms that rode on waves of new listings backed by liberal financing. The author shows that analysis of Tobin's Q and negative earnings can separate ill-prepared from longer-listed firms. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Expected Shortfall (ES ) is one of the most heavily used measures of financial risk. It is defined as a scaled integral of the quantile of the profit-and-loss distribution up to a certainly confidence level. As such, quantile regression (QR) and the closely related expectile regression (ER) methods are natural techniques for estimating ES . In this paper, we survey QR and ER based estimators of ES and introduce several novel variants. We compare the performance of these methods through simulation and through a data analysis based on four major US market indices: the S&P 500 Index, the Russell 2000 Index, the Dow Jones Industrial Average, and the NASDAQ Composite Index. Our results suggest that QR and ER methods often work better than other, more standard, approaches. [ABSTRACT FROM AUTHOR]

The relative performance analysis and ranking of the financial ratios are highly important for optimal portfolio selection in the stock market. However, the relative performance evaluation of the financial ratios is highly complex and nonlinear. Thus, the main goal of this study is to measure the relative importance of the financial ratios of two groups as Accounting based financial measures (AFM) and Economic value-based financial measures (EFM) through Decision Making Trial and Evaluation Laboratory (DEMATEL) method under the neutrosophic environment. In this regard, one-year data (June 2018-May 2019) has been collected from 8 industries in the IT sector. The AFM and EFM values have been evaluated for each firm through the balance sheet. The obtained values have been given to the two experts: an experienced investor in the NASDAQ exchange and a Professor in Finance. They have given their opinion in terms of linguistic terms. Then, the AFM and EFM have ranked based on the neutrosophic DEMATEL approach. Finally, the neutrosophic DEMATEL approach has compared with the fuzzy DEMATEL and classical DEMATEL approach. The empirical results assist the investor and traders in selecting among the selected stock. [ABSTRACT FROM AUTHOR]

This study compares the results of two completely different models: statistical one (ARIMA) and deep learning one (LSTM) based on a chosen set of NASDAQ data. Both models are used to predict daily or monthly average prices of chosen companies listed on the NASDAQ stock exchange. Research shows which model performs better in terms of the chosen input data, parameters and number of features. The chosen models were compared using the relative metric mean square error (MSE) and mean absolute percentage error (MAPE). Selected metrics are typically used in regression problems. The performed analysis shows which model achieves better results by comparing the chosen metrics in different models. It is concluded that the ARIMA model performs better than the LSTM model in terms of using just one feature – historical price values – and predicting more than one time period, using the p, q parameters in the range from 0 to 2, Adam optimizer, tanh activation function, and 2xLSTM layer architecture. The longer the data window period, the better ARIMA performs, and the worse LSTM performs. The comparison of the models was made by comparing the values of the MAPE error. When predicting 30 days, ARIMA is about 3.4 times better than LSTM. When predicting an averaged 3 months, ARIMA is about 1.8 times better than LSTM. When predicting an averaged 9 months, ARIMA is about 2.1 times better than LSTM. [ABSTRACT FROM AUTHOR]