NASDAQ Index Research Articles

Coarse Graining on Financial Correlation Networks

Community structure detection is an important and valuable task in financial network studies as it forms the basis of many statistical applications such as prediction, risk analysis, and recommendation. Financial networks have a natural multi-grained structure that leads to different community structures at different levels. However, few studies pay attention to these multi-part features of financial networks. In this study, we present a geometric coarse graining method based on Voronoi regions of a financial network. Rather than studying the dense structure of the network, we perform our analysis on the triangular maximally filtering of a financial network. Such filtered topology emerges as an efficient approach because it keeps local clustering coefficients steady and it underlies the network geometry. Moreover, in order to capture changes in coarse grains geometry throughout a financial stress, we study Haantjes curvatures of paths that are the farthest from the center in each of the Voronoi regions. We performed our analysis on a network representation comprising the stock market indices BIST (Borsa Istanbul), FTSE100 (London Stock Exchange), and Nasdaq-100 Index (NASDAQ), across three financial crisis periods. Our results indicate that there are remarkable changes in the geometry of coarse grains.

Equity returns and sentiment

Abstract This paper analyzes approximately 100 Gigabytes of raw text data from Twitter with keywords “AAPL,” “S&P 500,” “FTSE100” and “NASDAQ” to explore the relationship between sentiment and the returns and prices on the Apple stock and the S&P 500, FTSE 100 and NASDAQ indices. The findings point to significant relationship and dependence between sentiment measures and the S&P 500 and FTSE 100 indices’ returns and prices. The econometric analysis of dependence between the aforementioned variables in the paper is presented in some detail for illustration of the methodology employed.

An integrated fuzzy-grey relational analysis approach to portfolio optimization.

This paper combines two approaches (Fuzzy set theory and Grey Relational Analysis) for modelling an investor’s imprecise linguistic expectations and the uncertain returns of assets. We propose a novel maximization-type risk measure capable of incorporating the investor’s individual preferences. The investor provides the expectations of what is considered the “ideal” return from the portfolio. We use Credibility theory to capture the investors’ subjective and imprecise expectations in a precise mathematical form. We construct a portfolio return sequence using the assets’ actual return data and an ideal sequence based on investors’ preferences. Subsequently, we calculate the Grey similitude and the closeness incidence degree between the two sequences. The closer the portfolio return is to the ideal return, the better. In this manner, we develop a new risk measure that can quantify an investor’s perception of risk. This measure is intuitive and easy to calculate. It does not involve estimating many parameters, something which would increase the estimation risk. We use a genetic algorithm to solve the resulting portfolio optimization model. We illustrate this method with two case studies: (i) a case study of 100 assets of the U.S. stock market’s NASDAQ-100 index and (ii) a case study of 50 assets of the Indian stock market’s NIFTY-50 index. We comprehensively analyze the model’s out-of-sample performance and discuss its implications. The portfolios obtained using the proposed approach exhibit healthy growth outside the in-sample period. We also compare the out-of-sample performance of the proposed model with several approaches in the literature to establish its superiority.

The use of autoregressive process AR(1) in the analysis of some finance indices

The article considers an autoregressive process AR(1) with finance application. The solution of many control problems comes down to autoregressive process. We study the first-order autoregressive process AR(1) with application to the S&P500 and NASDAQ indices for 01/01/2017-01/08/2021. The main purpose of the study is to investigate the effects of the Covid-19 pandemic on stock indices. For statistical computing and visualization, R language and environment are used.

Dynamic portfolio optimization using technical analysis‐based clustering

An accurate prediction of asset prices is perhaps the biggest challenge of any study in portfolio optimization. Asset prices are affected by several random and nonrandom factors, which makes them difficult to forecast. This paper proposes a two-phase dynamic portfolio optimization approach. In the first phase, assets are clustered into buy, sell, and hold groups using technical indicators. We provide a methodology to integrate the investor attitude (optimistic, pessimistic, or neutral) during the clustering phase. In the second phase, we input the clustered groups into a portfolio optimization model to obtain the optimum asset allocations. We use coherent fuzzy numbers to model the asset returns to integrate the investor attitude in this phase. The optimization model is solved using a genetic algorithm. The portfolios are rebalanced at regular intervals as new data becomes available. We illustrate the proposed methodology on a 100-asset problem of the US stock market. We analyze the real-world performance of the obtained portfolios. We compare the performance of the proposed approach with the mean–variance model, and other portfolios, such as the naïve portfolio and the NASDAQ-100 index.

An Empirical Risk and Return Analysis of Select Stocks in NASDAQ 100

Stock market indices are considered to be a powerful economic indicator. These indices can be classified based on the methodology of weight allocation for each stock and the rules governing the entry, retention and exit criteria of various stocks in the index. This paper presents a descriptive and an exploratory analysis carried out on the daily returns data of NASDAQ 100 (^NDX) index and shortlist of 20 stocks in the index. Random sampling was conducted at the sector level strata of all stocks that make up the index. This approach was followed to avoid selection bias and that stocks from the varied sectors are represented equally for this analysis. R-squared values and correlation coefficients were used to determine the explain-ability and relationship between the stock returns and the index returns respectively. The paper applied descriptive univariate analysis on daily returns at an individual stock level and at an aggregated sector level. Inter-relationship between stocks and the index returns was carried out by computing Pearson’s correlation coefficient across the different combinations of stocks and index return values. Linear regression was carried out identify the explain ability of the variance in the returns of from the index to the returns from the stocks. All analysis was carried out using the python and the stats-models library. As anticipated, the returns of randomly picked 20 stocks were able to explain ~85 % of the variance of the returns of index. One of the primary focus of the paper was to explore whether NASDAQ-100 index can explain the variability of the technology stocks relatively more than the stocks that belong to other sectors in its portfolio owing to the nature of most stocks that make up the index.

Pandemic-induced economic policy uncertainty and US stock exchanges.

This study investigates the impacts of pandemic‐induced economic policy uncertainties (PIEPU) on the S&P500, Nasdaq‐100, and Dow Jones indexes (stock returns). To this aim, for the first time, newly created IDEMV (the Infectious Disease Equity Market Volatility index (henceforth, PIEPU index) is used. The Autoregressive Distributed Lag (ARDL) model and the Toda and Yamamoto (Journal of Econometrics, 1995, 66, pp. 225–250) causality test are applied for the 2009–2020 period. Empirical findings indicate that rises in the PIEPU index lead to falls of only the S&P500 and Dow Jones indexes. Corporations in the tech‐heavy Nasdaq100 index do not negatively respond to rises in the PIEPU index. Additionally, the negative impacts of the rises in the specifically COVID‐19 based‐constructed PIEPU (DCOVPIEPU) index on the S&P500 and Dow Jones indexes are higher than the negative impacts of the general PIEPU index. This can be interpreted to mean that the larger the magnitude and spread rate of a pandemic, the larger the negative impacts on stock returns. In the sample period of this study, COVID‐19 is the largest and most destructive pandemic compared to H1N1 and Ebola.

PRICE CLUSTERING IN INTERNATIONAL FINANCIAL MARKETS DURING THE COVID-19 PANDEMIC AND ITS IMPLICATIONS

The proliferation of trading strategies in many security markets has led to intense scrutiny of market price movements and their distribution. The increase in trading activities across financial markets around the world has enhanced the likelihood of behavioral biases and the tendency for stock prices to cluster around certain intervals. The purpose of this study was to investigate price clustering and psychological barriers in the NASDAQ Index, CAC 40 Index, DAX Index, JPXNikkei Index 400, SSE Index, and the JSE Index from 2/01/2020 to 31/12/2021, during the height of the COVID-19 pandemic. Using chi-square and a Kolmogorov-Smirnov tests, the findings revealed evidence of price clustering in the JPX-Nikkei 400 and JSE Index, with further evidence of psychological barriers in the form of support and resistance in the JSE Index. This result implies that a retracement entry strategy is suitable for the JPX-Nikkei Index 400 and JSE Index, and a breakout strategy should be used in the NASDAQ Index, CAC 40 Index, DAX Index, and SSE Index. Security markets should actively promote UTP in order to promote price efficiencies.

Analyzing the effects of global oil, gold and palladium markets: Evidence from the Nasdaq composite index

The capital market is one of the most critical pillars of financial markets, which plays an important role in countries' economies. With the increasing development of international trade and the free flow of capital, financial asset prices spread to other assets. The extend is increasing with the expansion of communication networks as well as the close connection of financial markets to each other. Therefore, the state of a market is not only specific to itself but also affects other financial markets. Global oil, gold, and basic metal prices, including palladium, significantly influence the American financial markets. Due to the importance of the Nasdaq index for investors' investment decisions, this study analyzes the impact of oil, gold, and palladium metal prices on the Nasdaq index monthly between 2016 and 2021. Based on the final model, the oil and gold markets significantly impact the Nasdaq index, while palladium has a less significant impact.

Wikipedia pageviews as investors’ attention indicator for Nasdaq

SummaryThe attempt to measure investors’ mood to find an early indicator of financial markets has evolved and developed with the advancement of technology over the years. The first attempts were based on surveys, a long and expensive process. Nowadays, big data has made it possible to measure the investor’s mood accurately and almost entirely online. This paper analyzes the explanatory and predictive capacity of Wikipedia pageviews for the Nasdaq index. For this purpose, two econometric models have been developed. In both models, the explanatory variable is the number of Wikipedia visits, and the endogenous variable is Nasdaq index return. As an alternative to this approach, an algorithmic trading system has been developed. It uses Wikipedia visits as investment signals for long and short positions to check the predictability power of this indicator. It is determined that the volume of queries about Nasdaq companies is a statistically significant variable for expressing the evolution of this index. However, it has no predictive capacity. Keeping in mind the capacity of Wikipedia to exemplify Nasdaq trends, further studies should be conducted to determine how to make this indicator profitable.

BP-CVaR: A novel model of estimating CVaR with back propagation algorithm

We propose a more flexible and useful model, BP-CVaR, to estimate conditional value-at-risk (CVaR) using back propagation (BP) algorithm, which can capture the change of markets and use the information to adjust the next CVaR result. We use three samples including S&P 500 index, Nasdaq index, DIJA index and compare the results by back-testing approaches. We find that (i) BP-CVaR is more reliable and has higher accuracy than Monte Carlo CVaR model and (ii) BP-CVaR can react to the change of markets more quickly than traditional CVaR model.

Relationships among US S&P500 Stock Index, its Futures and NASDAQ Index Futures with Volatility Spillover and Jump Diffusion: Modeling and Hedging Performance

This study takes the US S&P500 stock index cash, futures and NASDAQ stock index futures as the main research objects, and applies the ARJI (autoregressive jump intensity model) VEC GJR-GARCH model to examine the co-integration, volatility spillover, jump behavior and hedge performance of the three markets. With the rapid circulation of new information, the financial market will often fluctuate under the impact of new information. Investors will have different and timely responses to emergencies, and this event will have an impact on the stock market. When the event is unexpected or abnormal, the financial market will have huge fluctuations, and this kind of fluctuation is a jump. The empirical results found that the three markets have linkages and volatility spillover effects, and there are indeed discontinuous jumps. Two-way volatility spillovers between S&P500 index cash and futures, and only one-way volatility spillovers from S&P500 futures to the Nasdaq futures market. International investors need to consider information from their own-market volatility (risk) as well as information on volatility spillovers (risk) from other markets. The jump frequency is not a fixed constant, that is, the jump frequency (strength) generated by abnormal information changes over time. In addition, the results of this research also found that the ARJI VEC GJR-GARCH model can better capture the risk of fluctuations in price discontinuities after adding jump factors to the hedging performance estimated by the ARJI VEC GJR-GARCH model. The hedging performance can be more effective, which is conducive to investors' risk management decisions. Also, the performance of direct hedging that is better than the performance of cross hedging.

Prediction of Stock Market Index Using a Hybrid Technique of Artificial Neural Networks and Particle Swarm Optimization

In this paper we examine the ability of Artificial Neural Network methods (ANN) for predicting the stock market index. We first conduct an ANN analysis and then optimize the ANN model using Particle Swarm Optimization algorithm (PSO) to improve the prediction accuracy. In terms of data, we use NASDAQ index which is one of the most widely followed indices in the United States. Empirical results show that by determining the optimal set of biases and weights using PSO, we can augment the accuracy of the ANN model for this stock market data set.

Portfolio optimization using higher moments in an uncertain random environment

In this paper, a multi-objective portfolio optimization problem is studied in an uncertain random environment using higher moments. We consider a scenario involving an asset universe wherein some assets have sufficient historical return data for modelling as random variables, and others, listed relatively recently, lack historical data. The asset returns of such assets are modelled as uncertain variables. Thus, a hybrid environment involving both uncertain and random variables is considered. We use mean absolute semi-deviation as a risk measure and employ skewness (i.e., third moment) in the portfolio optimization model. The expressions for the mean absolute semi-deviation and skewness of an uncertain random variable have been derived. We show that the derivation of mean absolute semi-deviation is not based on any stipulation and thus is an accurate measure of risk (unlike the variance of an uncertain random variable, which uses stipulation). We propose a hybrid genetic algorithm as a solution methodology and provide empirical proofs to illustrate its advantages. The proposed methodology has been applied to a case study involving 100 assets listed in the NASDAQ-100 index of the U.S. stock market. We do an ex-post analysis to track the performance of our model out-of-sample and illustrate its advantages.

Choosing the frequency of volatility components within the Double Asymmetric GARCH–MIDAS–X model

The Double Asymmetric GARCH–MIDAS (DAGM) model has the advantage of modelling volatility as the product of two components: a slow–moving term involving variables sampled at lower frequencies and a short–run part, each with an asymmetric behavior in volatility dynamics. Such a model is extended in three directions: first, by including a market volatility index as a daily lagged variable in the short–run component (the so-called “–X” term); second, by adding the same variable in the long–run component as variations of data aggregated at any desired frequency; third, by proposing a data-driven method to find the optimal number of lags to be included in the positive and negative parts of the long–run component. The resulting model, labelled as DAGM–X–2K, is extensively evaluated under several alternative configurations, producing satisfactory evidence when applied to the S&P 500 and NASDAQ indices. The out–of–sample results show that the “–X” addition significantly improves the performance, making the proposed DAGM–X–2K model enter the Model Confidence Set, even for large forecasting horizons (for 1 to 60 days).

Financial Market Integration Between Stock Market From North American Free Trade Agreement (NAFTA) Member

Economic recession or crisis could show a higher possibility of financial crisis transmission in an integrated stock market. Integration between financial markets is a channel of spreading the devastating effects of the crisis. The objective of this study is to detect significant interactions among the stock markets of countries that are members of the North American Free Trade Agreement (NAFTA). NAFTA is a regional partnership with members from the United States, Canada and Mexico that are committed to reducing trade and investment barriers between member countries. The methodology of this research with VAR VECM model consists of three stages, the first analysis of the presence impact of the stock market index using the Granger Causality Test. Second, analyze the speed of response of an index to a change / shock in another index using the Impulse Response Function (IRF). The third stage analyzes the impact of changes / shocks from one index to other indices by using Variance Decomposition. From the 5 sets of stock market data for NAFTA countries, the results of the study show that there is only one cointegration. When viewed in the cointegration process of each of the two data series, cointegration occurs between the Nasdaq index with TSE and Nasdaq with MSE. Whereas TSE and MSE did not find any cointegration.

Research on Application of Multifractal Spectrum and Neural Network in Financial Market

With the deepening of the financial market’s reform and innovation, to better explain the market's stock price fluctuations, this article uses the traditional efficient market hypothesis to study further. This study is showed that the Shanghai Stock Exchange Index, the Shenzhen Stock Exchange Index, and the Nasdaq Index have the characteristics of "tip fat tail ", which indicates that the distribution is not normal. Based on the theory of multifractal spectrum, empirical analysis and comparison results of multifractal characteristics, three indexes are carried out to analyze the impact of the maturity of different financial markets on stock price fluctuations. Finally, the multifractal spectrum parameters and daily return rate as input variables, an average accuracy rate of 99.436%, are obtained for the Shanghai Composite Index forecast for the next 30 days, using a 5-layer neural network model. It has particular practical significance for controlling and managing financial risks.

THE IMPACT OF COVID-19 ON MARKET EFFICIENCY: A COMPARATIVE MARKET ANALYSIS

Covid-19 has had severe consequences on the financial systems of many countries and has altered the manner in which most businesses operate. This adverse situation has a spill-over effect on financial markets where the market efficiency may have been altered, hence using preCovid-19 strategies for investment purposes may no longer be applicable. Market efficiency, which is closely linked to informational efficiency, depicts the extent to which financial markets adjust quickly and correctly to new information. It is instrumental for fundamental analysis. The aim of this study is to analyze the market efficiency during the pandemic in five major financial markets around the world. Using the runs tests, the results indicate that the market efficiency in the JSE and JPX-Nikkei 400 has been altered significantly, while the Nasdaq Index, DAX, and CAC 40 have not changed from what it used to be. This was evident in the significant difference between the expected number of runs and the total number of runs, as indicated by the runs test. The implication of this study is that investment professionals in the JSE and JPX-Nikkei 400 should alter their investment strategies, without which they may incur severe losses.

Witching Days and Abnormal Profits in the US Stock Market

This paper examines price effects related to witching days in the US stock market using both weekly and daily data for three major indices, namely the Dow Jones, SP500 and Nasdaq, over the period 2000-2021. First it analyses whether or not anomalies in price behaviour arise from witching by using various parametric (Student’s t-test, and ANOVA) and non-parametric (Mann-Whitney) tests as well as an event study method and regressions with dummies; then it investigates whether or not any detected anomalies give rise to profit opportunities by applying a trading simulation approach. The results suggest the presence of the anomaly in daily returns on witching days which can be exploited by means of suitably designed trading strategies to earn abnormal profits, especially in the case of the Nasdaq index. Such evidence is inconsistent with the Efficient Market Hypothesis (EMH).

Alternative investments in the Fintech era: The risk and return of Non-Fungible Token (NFT)

We utilize one of the earliest and largest NFT collections to investigate the pricing and the risk-return profile of NFTs. In general, we find that NFTs have higher returns than traditional financial assets. Yet, investing in NFTs comes along with extremely high volatility. The average monthly returns on NFTs range from 6.10% to 44.11%. But their standard deviations fluctuate between 44.35% and 74.57%, leading to a Sharpe ratio comparable to the NASDAQ index. NFT prices surge when there is a drastic increase in demand for alternative investments and a search for yield, especially in a low interest rate environment. We also find that the pricing of NFT largely depends on a token’s scarceness and an investor’s aesthetic preference. Hence, conventional asset-pricing models are unlikely to explain NFT returns. Overall, we provide the first comprehensive analysis that NFTs serve as a novel investment vessel in this Fintech era.