NASDAQ Index Research Articles

Comparative Study of Hedging Models in US

This study focuses on the use of three dynamic hedging models in the American stock market, and it computes the dynamic correlation coefficient, dynamic hedging ratio, and hedging effectiveness of three common models (DCC, BEKK, CCC). This paper fills in the gaps in the application and comparison of three hedging models commonly used in the US stock market during the pandemic. The results showed that NASDAQ index and futures were highly correlated and had significant volatility spillover effect before and during the epidemic. The hedging efficiency of the three models before the epidemic was higher than 0.931, and the hedging efficiency of the three models during the epidemic was higher than 0.936. It is proved that futures hedging is a good way to avoid risk no matter before or during the epidemic, and the highest hedging efficiency is dcc-garch.

Modeling Long Memory and Regime Switching with an MRS-FIEGARCH Model: A Simulation Study

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.

Comparison of Different Machine Learning Prediction about Stock based on Multi-Factor Input-Taking the Nasdaq Index as an Example

Stock investment has always been widely concerned, and the prediction of future stock trends is what many investors look forward to. There are numerous techniques that can be used to predict stocks as machine learning advances. The commonly used method is support vector machine, random forest, linear regression, etc. Recurrent neural networks, multi-layer perceptron, single-layer LSTM networks, naive Bayes networks, convolutional neural networks, back propagation networks, etc. are examples of deep learning techniques. In historical studies, researchers have tended to predict directly from stock prices or used time series as an independent variable to build a forecasting model for stock prices. In this study, we propose input training parameters based on stock indicators and build a sliding window of it to predict the future price. Based on LSTM, ANN, support vector machine regression, Linear Regression to make predictions on price, and analyze the differences between them. Our research is based on the Nasdaq and the evaluation values show that the neural network approach is effective for stock return forecasting.

Using the Nasdaq Index to Predict AAPL Price by Linear Regression Analysis

In this project, we want to predict AAPL’s stock price by the NASDAQ index by the regression model. The dependent variable is AAPL’s stock price, and the independent variable is the NASDAQ index. First, we do some descriptive statistics for the two variables and measure the distribution from the central tendency, variation tendency, and distribution to acknowledge the character of distributions. Based on the strong linear relationship between AAPL stock price and the NASDAQ index, we constructed a simple linear regression model. Considering the scale of the two variables, we tried the other three models with log transformation. And then, it shows that the log-log model has the best performance. However, in the residual analysis of the log-log model, it shows an autocorrelation in the residual, then we generate a new variable that is the one-order term for AAPL and add it into the model, and it surprisingly performs very well, whose R square is up to 99.72%. Therefore, we think combining the linear relationship with the market and the autocorrelation itself can construct a good model, and it can apply to predict much other stock's prices in the market.

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, S&P500 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).

The effect of the US Dollar Index and foreign stock indexes on Jakarta Composite Index (JKSE)

Many theories explain that the Jakarta Composite Index (JKSE) can be influenced by stock indices or currencies of other countries. With the recent events of the war between Russia and Ukraine, this might be the perfect opportunity to prove that statement. The purpose of this study is to see whether the United States Dollar Index, Dow Jones Industrial Average (DJIA), NASDAQ 100 Index, Shanghai Composite Index (SSEC), Nikkei 225 Index (N225), Australian All Ordinaries Index (AORD), FTSE Index 100, against the Jakarta Composite Index (JKSE). This study uses the documentation method with secondary data in the form of daily closing prices for foreign stock indices and USD indices spread from 4 January 2022 to 25 February 2022. The hypothesis is tested with a partial significant test (t-test), simultaneous significant test (F test), and coefficient of determination. The test results show that the USD, DJIA, NASDAQ, SSEC, N225, AORD, and FTSE indices do not affect the JKSE. But simultaneously the USD Index, DJIA, NASDAQ, SSEC, N225, AORD, and FTSE affect JKSE. This may be because, during the study period, there were no foreign companies listed on the Indonesian Stock Exchange. Companies listed on the Indonesia Stock Exchange are domestic companies denominated in Rupiah, so the investment that occurs is purely local investment using Rupiah. At this time the world is also in a period of economic recovery after previously declining due to the COVID-19 pandemic. This causes investors from other countries to prefer investing in their own country. So, investment from abroad to Indonesia also decreased. This research is limited to using independent and dependent variables used and the research is limited to only 2 months. For further research, it is recommended to add more varied independent variables and extend the observation period so that it can increase better data distribution.

Research On Portfolio Construction for Information Technology and Communication Service Industries Based on The Monte Carlo Simulation

Attention is paid to the portfolio construction of the information technology and the communication services industries affected by the Covid-19. Firstly, the representative stocks which cover the most varieties of different market values are selected and the preliminary analysis for return performance is performed. Then, the event-study methodology is used to explore the effect of Covid-19 on the stocks chosen by this paper. The Monte Carlo simulation is also used to do the portfolio optimization. In the process of research, the Monte Carlo simulation and event study method are mainly used. Through the former one, the efficient frontier is obtained. Besides, this research also get the minimum volatility portfolio, where AT&T has the largest share of 16.33%, and the maximum Sharpe ratio portfolio, where ZOOM has the largest share of 18.79%. Then through the results of event-study, it is found that most companies generated negative abnormal returns on event date and had negative cumulative returns during the post-event window. Finally, the back test is done by comparing our portfolio with the equal weighted portfolio and NASDAQ index, and our work performs better both in the return and the risk. The results in this paper benefit the investors in the technology and communication service industries, which is especially helpful for them to do invest decision after COVID-19.

Almost exact risk budgeting with return forecasts for portfolio allocation

We revisit the portfolio allocation problem with designated risk-budget. We generalize the problem of arbitrary risk budgets with unequal correlations to one that includes return forecasts and transaction costs while keeping the no-shorting constraint. We offer a convex second order cone formulation that scales well with the number of assets and explore solutions to problem variants - on equity-bond asset allocation problems as well as formulating portfolios using index constituents from the NASDAQ100 index, illustrating the benefits of this approach.

Investigating Stochastic Volatility during Periods of Financial Distress: Evidence from International Financial Markets

Capturing the dynamic properties of financial market volatility has always been very auspicious in asset pricing. The mean reverting properties of financial markets are of paramount importance for investment decision makers with far reaching implications. The purpose of this study was to investigate stochastic volatility in international financial markets during periods of financial distress. Accordingly, a Heston model was used to stochastic volatility in the CAC 40, DAX, JSE, Nasdaq Index and the Nikkei-225 during the 2007-2008 financial crisis and the Covid-19 pandemic. This study used January 1, 2020 to December 31, 2021 and December 1, 2007 to June 30, 2009 as the sample period representing the Covid-19 pandemic and financial crisis respectively. The findings of this study revealed the extortionate stochastic volatility during the Covid-19 pandemic compared to the financial crisis. There will be slightly higher than normal arbitrage opportunities and mispricing in most financial markets as a result of the pandemic.

Option-Strategy Indexes: A Powerful Tool for Improving Portfolios

The majority of multiasset investment portfolios allocate most of their assets to a mix of stocks and bonds, ostensibly relying on the observed negative correlation between the two asset classes for diversification. Unfortunately, the observed negative correlation between equities and fixed income is not guaranteed to continue. Furthermore, although rates are beginning to rise from historic lows, bonds may take some time to provide satisfactory yields. Equity options may be used to reduce the risk of portfolios, generate income, or both. Historically, options strategies have been out of reach for most investors, but a growing number of exchange-traded funds offer access to option strategy indexes that can provide the potential for material improvements in the risk and reward of portfolios. In this article, the authors introduce a set of option strategy indexes linked to the NASDAQ-100 Index that can be accessed via exchange-traded funds and demonstrate how they can add value to multiasset portfolios.

Stock Market Liquidity during Periods of Distress and its Implications: Evidence from International Financial Markets

Traditional market pricing models assume frictionless markets with abundant liquidity. This traditional models also incorporate stock market liquidity as an exogenous cost. However, this paradigm has many shortcomings due to its inability to explain some of the problems associated with security market illiquidity. The aim of this study was to explore the concept of stock market liquidity during periods of financial distress. A Markov switching GARCH model was used to investigate market liquidity in the CAC 40, DAX, JSE, Nasdaq Index and the Nikkei-225 during the 2007-2008 financial crisis and the Covid-19 pandemic. The sample period was January 1, 2020 to December 31, 2021 and December 1, 2007 to June 30, 2009. From the findings, some financial markets where still liquid despite the financial crisis with the exception of the Nasdaq index. Conversely, all the financial markets under consideration displayed strong illiquidity during the covid-19 pandemic. In essence, the level of market depth has significantly decreased from the financial crisis to the covid-19 pandemic which may be attributed to increasing margin requirements and information asymmetry as well as price restrictions. There is an urgent need for regulatory authorities to review some of the trading regulations during financial distress.

VOLATILITY PERSISTENCE IN INTERNATIONAL FINANCIAL MARKETS IN THE POST COVID-19 ERA

The long-term behaviour of stock markets are of significant importance to asset managers and financial experts due to its direct link with security price valuation. Volatility persistence has a significant impact on the returns of security prices due to its time varying properties. However, there is no real meaningful effect of current volatility on future security prices and returns if the volatility is transitory and not persistent. The aim of this study was to explore conditional volatility properties and determine whether the current volatile environment would persist in the JSE, S&P 500, Nasdaq Index, SSE, CAC 40 and DAX markets. Using a GARCH 1.1 model and a Markov switching model, the findings revealed that volatility would persist in the JSE, S&P 500, Nasdaq Index, SSE, CAC 40, and the DAX from their ARCH and GARCH coefficients, as well as the delay parameters. In addition, the effects of past volatility in the Nasdaq, CAC 40, and DAX would remain in the forecast of variance. A diversified and broader investment approach should be used in the JSE, S&P 500, Nasdaq Index, SSE, CAC 40, and DAX indexes to mitigate risk, and portfolio formation should not concentrate on any sector or asset classes.

The digital image and company’s value

Goal: the paper evaluates the impact of a company’s digital image on its value. Methodology: the authors use panel data analysis applied to a large array of information on the news of the 25 largest US medias, the Google Trends web application and the Twitter social network for the period from 2017 to 2022 for companies from the NASDAQ-100 index. Findings: the findings revealed that the popularity of the company on the Internet, the number of tweets, the information polarity in digital channels and the frequency of mentioning Robotics and CRM technologies signifi cantly positively affect company’s value. At the same time, the amount of news and information about Big Data technology has a negative impact on investor expectations. Originality and contribution of the authors: to the best of our knowledge this study is the first one introducing a definition of the company’s digital image and indicators for its measurement. The authors emphasis the specific characteristics of digital channels, such as visibility, information content, involvement, reliability and the sentiment analysis of company’s information in the digital space.

Cryptoasset Returns: Statistical Properties and Implications for Asset Allocations

We examine Bitcoin returns’ statistical properties and check whether these properties are consistent with the well-known stylized facts of asset returns for the period from January 2019 to May 2022. We find that Bitcoin exhibits price behaviors that correspond well to all the stylized facts of asset returns examined in the analyses. We also examine the returns of Tesla stock, NASDAQ index, and S&P500 index for comparison and find that they generally conform to the stylized facts examined. However, Tesla stock does not exhibit an asymmetric volatility feature, and the two indices show significant autocorrelations in low order time lags, which are inconsistent with the well-known stylized facts of asset returns. Based on these findings, we discuss the possible implications of cryptoassets for asset allocation processes. Cryptoassets provide some potential utilities for asset allocations, but only with several simultaneous limitations.

The Russian-Ukraine Conflict, Crude Oil Price Fluctuation and Dynamic Changes in Stock Market: Evidence from the U.S and China

Political struggle is an important factor affecting the pattern of the international oil trade. The escalation of collisions between Russia and Ukraine, and the economic sanctions that the United States and European countries implemented against Russia had a significant impact on Russian oil exports. In this context, it is crucial to study how Chinese and American stock markets react to the burnt of international crude oil prices, in order to understand the phenomenon of risk contagion between markets and prevent risks. This paper selects the settlement prices of the Dow Jones index, S&P 500 index, Nasdaq index, Shanghai index, Shenzhen index, and international crude oil futures prices from three months before the Russian-Ukraine Conflict to the end of May 2022 to illustrate the tolerance to the volatility of two markets, using VAR and ARMA-GARCH model. The empirical results indicated that crude oil price fluctuation has a stronger impact on the US stock market than on the Chinese stock market, and specific stocks act differently during this “black swan incident”.

“Growth Followed by a Pandemic: Patterns in Fourteen Major Stock Exchanges, from 2012 to 2021”

This paper analyzes fourteen major global exchanges during the period 2012 to 2021 in order to evaluate the impact of COVID-19 on previous growth as well as to ascertain the nature of changes in proportion of regional and size-based categories. The primary audience for this analysis are investors in mutual funds or index funds. Drawing on publicly available information from Yahoo! Finance and research in financial news, a variety of conclusions were drawn regarding the above question. Although generally the fourteen chosen stock indexes grew during 2012 to 2020 and shrank in 2021, certain indexes appeared more resilient than others, namely the Nikkei and Nasdaq indices, in weathering the impacts of the COVID-19 recession. Other indexes, such as the Johannesburg and the Swiss, saw their relatively large pre-COVID gains more than wiped out during 2021. Finally, several formerly leading regional exchanges, such as the Hong Kong Hang Seng, lost their formerly dominant regional position. Although COVID-19 caused great havoc in the markets, it appears that some of the relative “winners” of the pandemic might have been the result of strong external factors only partially related to their country’s overall handling of the pandemic.

GCNET: Graph-based prediction of stock price movement using graph convolutional network

The importance of considering related stocks data for the prediction of stock price movement has been shown in many studies; however, advanced graphical techniques for modeling, embedding and analyzing the behavior of inter-related stocks have not been widely exploited for the prediction of stocks price movements yet. The main challenges in this domain are to find a way for modeling the existing relations among an arbitrary set of stocks and to exploit such a model for improving the prediction performance for those stocks. The most of existing methods in this domain rely on basic graph-analysis techniques, with limited prediction power, and suffer from a lack of generality and flexibility. In this paper, we introduce a novel framework, called GCNET that models the relations among an arbitrary set of stocks as a graph structure called influence network and uses a set of history-based prediction models to infer plausible initial labels for a subset of the stock nodes in the graph. Finally, GCNET uses the Graph Convolutional Network algorithm to analyze this partially labeled graph and predicts the next price direction of movement for each stock in the graph.GCNET is a general prediction framework that can be applied for the prediction of the price fluctuations of interacting stocks based on their historical data. Our experiments and evaluations on a set of stocks from the NASDAQ index demonstrate that GCNET improves the performance of the state-of-the-art algorithms in terms of Accuracy and Matthew’s Correlation Coefficient by at least 1.5% and 2%, respectively.

Investigating The Weekend Anomaly and Its Implications: Evidence From International Financial Markets

Purpose: As contended in prior literature, the weekend anomaly is the tendency for financial markets or security prices to be lower on Mondays than on previous Fridays. The aim of this study was to empirically investigate the weekend anomaly in seven international financial markets namely; NASDAQ Index, CAC 40 Index, DAX Index, JPX-Nikkei Index 400, SSE Index, BIST and JSE Index. Methodology: This study made use of the F- statistics test for the most recent 5 years August 22, 2017 to August 22, 2022. Findings: Contrary to the findings in the literature, there is no evidence to support the weekend anomaly. This was evident in the pvalues for the F-statistics test in all the financial markets under consideration to be statistically insignificant. Originality/Value: Although this concept may have existed, it is no longer applicable hence traders and market participants should avoid regular or pure arbitrage strategy as it may result in significant losses. As per the author’s knowledge, this study is the first to empirically investigate the weekend anomaly in seven international markets using the most recent data..

Robust nonparametric estimation for the volatility of financial market

The occurrence of a macroeconomic policy would lead to a jump of financial data and the presence of jump behaviors might make the statistical methods for high-frequency sampling data to face new challenges. This paper will use the threshold function technique to disentangle the continuous part and the jump part from the high frequency financial data. Moreover, in the financial practices, the abnormal observations contained in the data could cause bias from nonparametric estimation based on least squares. The paper will employ the local M estimation to provide a robust estimator for the unknown diffusion coefficient of the diffusion model with jumps under high frequency sampling data. Under certain conditions for the initial values, this paper further considers one-step local M estimation for the unknown diffusion coefficient which can reduce the calculation quantity under the estimation efficiency. The Monte Carlo numerical simulation results verify that compared with the local linear threshold estimator, the threshold one-step local M estimator is more accurate and more robust. Finally, the threshold one-step local M estimator in this paper is applied to the Shanghai composite index of 2015 and 2020 in China and the Nasdaq index of 2020 in USA, which illustrates the method considered in this paper possesses good finite sample properties.

Forecasting BIST100 and NASDAQ Indices with Single and Hybrid Machine Learning Algorithms

Forecasting BIST100 and NASDAQ Indices with Single and Hybrid Machine Learning Algorithms