Dow Jones Industrial Average Research Articles

Pengaruh Indeks Saham Asing Dan Variabel Makroekonomi Terhadap Indeks Harga Saham LQ45

Investment is one kind of country’s income. The capital market, including stocks, is a form of the convenience of modern investment. Investors, policy makers, capital market observers, and financial analysts need information in monitoring the movement of traded stock prices. Stock index is the main information in the stock market. One of the stock indexes which have potential and noticed by the people is the LQ45 stock price index. In this study, researchers tried to determine the effect of foreign stock index the Dow Jones Industrial Average (DJIA) and Straits Times Index (STI) and macroeconomic variables such as the USD/IDR exchange rate, interest rates (BI 7-days (Reverse ) Repo Rate), and inflation on the LQ45 stock price index. This study uses the Error Correction Model (ECM) method with 60 observational data from the research period January 2017 to December 2021. The results show that DJIA and STI have a positive and significant effect in the long and short term, the exchange rate has a negative and significant effect in the long and short term, interest rate has a negative and insignificant effect in the short term while in the long term it has a significant positive effect, while inflation has an insignificant negative effect in the long and short term on the LQ45 stock price index.

Ensemble K-Nearest Neighbors Method to Predict Composite Stock Price Index (CSPI) in Indonesia

The Composite Stock Price Index (CSPI) is a guide for investors to see the movement of stock prices as a whole from time to time. These movements always change from time to time, so it is necessary to use analytical methods to make predictions. The method that can be used to examine this is the K-Nearest Neighbor method. The combination of the results of several K-NN predictions is an effective way to get one final prediction result, namely the method ensemble K-NN. The response variable used in this study is the Composite Stock Price Index (CSPI), while the predictor variables are the gold price, the rupiah exchange rate against the dollar, and the Dow Jones Industrial Average (DJIA) index. The data used are 52 periods. The data used for training are 39 periods and the data used for testing is 13 periods. The prediction results from the ensemble have better results than the K-NN. The prediction results from the ensemble have better results than the single K-NN. The prediction results from the method are ensemble K-NN average of 6078, 634 with a MAPE value of 7,16% including high accuracy

The Cross-Sectional Intrinsic Entropy-A Comprehensive Stock Market Volatility Estimator.

To take into account the temporal dimension of uncertainty in stock markets, this paper introduces a cross-sectional estimation of stock market volatility based on the intrinsic entropy model. The proposed cross-sectional intrinsic entropy (CSIE) is defined and computed as a daily volatility estimate for the entire market, grounded on the daily traded prices—open, high, low, and close prices (OHLC)—along with the daily traded volume for all symbols listed on The New York Stock Exchange (NYSE) and The National Association of Securities Dealers Automated Quotations (NASDAQ). We perform a comparative analysis between the time series obtained from the CSIE and the historical volatility as provided by the estimators: close-to-close, Parkinson, Garman–Klass, Rogers–Satchell, Yang–Zhang, and intrinsic entropy (IE), defined and computed from historical OHLC daily prices of the Standard & Poor’s 500 index (S&P500), Dow Jones Industrial Average (DJIA), and the NASDAQ Composite index, respectively, for various time intervals. Our study uses an approximate 6000-day reference point, starting 1 January 2001, until 23 January 2022, for both the NYSE and the NASDAQ. We found that the CSIE market volatility estimator is consistently at least 10 times more sensitive to market changes, compared to the volatility estimate captured through the market indices. Furthermore, beta values confirm a consistently lower volatility risk for market indices overall, between 50% and 90% lower, compared to the volatility risk of the entire market in various time intervals and rolling windows.

Score-driven location plus scale models: asymptotic theory and an application to forecasting Dow Jones volatility

Abstract We present the Beta-t-QVAR (quasi-vector autoregression) model for the joint modelling of score-driven location plus scale of strictly stationary and ergodic variables. Beta-t-QVAR is an extension of Beta-t-EGARCH (exponential generalized autoregressive conditional heteroscedasticity) and Beta-t-EGARCH-M (Beta-t-EGARCH-in-mean). We prove the asymptotic properties of the maximum likelihood (ML) estimator for correctly specified Beta-t-QVAR models. We use Dow Jones Industrial Average (DJIA) data for the period of 1985–2020. We find that the volatility forecasting accuracy of Beta-t-QVAR is superior to the volatility forecasting accuracies of Beta-t-EGARCH, Beta-t-EGARCH-M, A-PARCH (asymmetric power ARCH), and GARCH for the period of 2010–2020.

The Effect of the World Exchange Indexes (DJIA, SSEC and NIKKEI 225) on the Indonesia Stock Exchange Index (IDX)

This study aims to determine the effect of the Dow Jones Industrial Average (DJIA), Shanghai Stock Exchange Composite (SSEC) and Nikkei 225 Indexes on the movement of the Indonesian Stock Exchange Index (IDX) for the period of November 2016 – November 2021. This study uses non-participant observation methods and the whole population in that time period is used as a sample. Data analysis techniques include descriptive statistics, classical assumption test. The results of the multiple linear regression analysis showed that the DJIA and Nikkei 225 indexes partially had a positive effect on the IDX, while the SSEC Index had no effect on the IDX. However, the value of (R2) shows that only 16.6 percent of the IDX movement can be partially explained by research variables, while 83.4 percent is influenced by factors outside this study.

A New Trend Pattern-Matching Method of Interactive Case-Based Reasoning for Stock Price Predictions

In this paper, we suggest a new case-based reasoning method for stock price predictions using the knowledge of traders to select similar past patterns among nearest neighbors obtained from a traditional case-based reasoning machine. Thus, this method overcomes the limitation of conventional case-based reasoning, which does not consider how to retrieve similar neighbors from previous patterns in terms of a graphical pattern. In this paper, we show how the proposed method can be used when traders find similar time series patterns among nearest cases. For this, we suggest an interactive prediction system where traders can select similar patterns with individual knowledge among automatically recommended neighbors by case-based reasoning. In this paper, we demonstrate how traders can use their knowledge to select similar patterns using a graphical interface, serving as an exemplar for the target. These concepts are investigated against the backdrop of a practical application involving the prediction of three individual stock prices, i.e., Zoom, Airbnb, and Twitter, as well as the prediction of the Dow Jones Industrial Average (DJIA). The verification of the prediction results is compared with a random walk model based on the RMSE and Hit ratio. The results show that the proposed technique is more effective than the random walk model but it does not statistically surpass the random walk model.

Permutation entropy and statistical analysis of the historical evolution of the Mexican stock market index

The objective of this work is to analyze the Indice de Precios y Cotizaciones (IPC), which is the Mexican stock market index, by using several statistical tools in order to study the tendencies that can shed light on the evolution of the IPC towards a more efficient market. The methodology used is to apply the statistical tools to the Mexican index and compare the results with a mature and well-known market index such as the Dow Jones Industrial Average (DJIA). We employ an autocorrelation analysis, and the volatility of the indexes, applied to the daily returns of the closing price on a moving time window during the studied period (1980–2018). Additionally, we perform an order three permutation entropy analysis, which can quantify the disorder present in the time series. Our results show that there is evidence that the IPC has become more mature since its creation and that it can be considered an efficient market since around year 2000. The behavior of the several techniques used shows a similar behavior to the DJIA which is not observed before that year. There are some limitations mainly because there is no high frequency data that would permit a more detailed analysis, specifically in the periods before and after a crisis is located. Our conclusion is that since around the year 2000, the Mexican stock index displays the typical behavior of other mature markets and can be considered as one.

Should You Invest in Stocks of Racially Diverse Companies?

In today's business environment, many firms passionately debate the impact of racial and gender diversity, particularly how a diverse workforce affects analytical thinking and innovation. To learn more, we examined the effects of diversity on the risk-adjusted performance of stocks from the 33 most diverse corporations. Using traditional portfolio performance indicators, we examined risk-adjusted performance from 2000 to 2020. We found that diverse companies outperformed stock market benchmark indices in most cases. We also discovered that an equally-weighted portfolio of the 33 most diverse firms outperformed the risk-adjusted performance of the Dow Jones Industrial Average (DJIA), the Standard & Poor 500 index (S&P500), and the NASDAQ.

Quantifying Time-Frequency Co-movement Impact of COVID-19 on U.S. and China Stock Market Toward Investor Sentiment Index.

The worldwide spread of COVID-19 dramatically influences the world economic landscape. In this paper, we have quantitatively investigated the time-frequency co-movement impact of COVID-19 on U.S. and China stock market since early 2020 in terms of daily observation from National Association of Securities Dealers Automated Quotations Index (NDX), Dow Jones Industrial Average (DJIA), Standard & Poor's 500 Index (SPX), Shanghai Securities Composite Index (SSEC), Shenzhen Securities Component Index (SZI), in favor of spatiotemporal interactions over investor sentiment index, and propose to explore the divisibility and the predictability to the volatility of stock market during the development of COVID-19. We integrate evidence yielded from wavelet coherence and phase difference to suggest the responses of stock market indexes to the COVID-19 epidemic in a long-term band, which could be roughly divided into three distinguished phases, namely, 30–75, 110–150, and 220–280 business days for China, and 80–125 and 160–175 after 290 business days for the U.S. At the first phase, the reason for the extreme volatility of stock market mainly attributed to the sudden emergence of the COVID-19 epidemic due to the pessimistic expectations from investors; China and U.S. stock market shared strongly negative correlation with the growing number of COVID-19 cases. At the second phase, the revitalization of stock market shared strong simultaneous moves but exhibited opposite responses to the COVID-19 impact on China and U.S. stock market; the former retained a significant negative correlation, while the latter turned to positively correlated throughout the period. At the third phase, the progress in vaccine development and economic stimulus began to impose forces to stock market; the vulnerability to COVID-19 diminished to some extent as the investor sentiment indexes rebounded. Finally, we attempted to initially establish a coarse-grained representation to stock market indexes and investor sentiment indexes, which demonstrated the homogenous spacial distribution in the vectorgraph after normalization and quantization, implying the strong consistency when filtering the frequent small fluctuations during the evolution of the COVID-19 pandemic, which might help insights into the prediction of possible status transition in stock market performance under the public health issues, potentially performing as the quantitative references in reasonably deducing the economic influences.

The Effect of Exchange and Dow Jones Industrial Average (DJIA) on the Jakarta Composite Index (JCI) when the Downtrend Happened Due to the Covid-19 Pandemic

This study analyzes the effect of the Exchange Rate and the Dow Jones Industrial Average on the Jakarta Composite Index during a downtrend due to the COVID-19 pandemic. The impulse response function was used to analyze the permanent effects on an exogenous shock variable on one of them. The study results prove a significant effect of the exchange rate on the Jakarta Composite Index. The same result showed that the most the weakening of the exchange rate of the Rupiah, the Jakarta Composite Index tends to fall. Besides, the Dow Jones Industrial Average harms the Jakarta Composite Index. In the long run, the response given by the Jakarta Composite Index showed a negative level. The USD/IDR exchange rate weakening was very influential on the Jakarta Composite Index. Otherwise, the weakening of the Rupiah exchange rate has led to the decrease of the Jakarta Composite Index. Monetary authorities should reckon on the stock price movements to control the stability of monetary exchange. Keywords: Jakarta Composite Index, Dow Jones Industrial Average, exchange rate, COVID-19, monetary policies. DOI: 10.7176/EJBM/13-14-03 Publication date: July 31 st 2021

Correcting for risk premium on Extended Generalised Leland Models: an empirical study on Dow Jones Industrial Average (DJIA) index options

The relative option pricing performance of Extended Generalised Leland models is examined in this study. We generalise the extended Leland models based on the implied adjusted volatility introduced in the models. Non-parametric framework is fitted into parametric option-pricing framework based on the Leland models to achieve a more realistic option pricing. To reflect the real probability measure, the implied adjusted information is corrected in terms of risk premium. This study concentrates mainly in examining the option-implied information produced by the models after correcting for risk-premium. Data extracted from DJIA index options are employed in this study, which covers the period from January 2009 until the end of 2015. We discovered that the option-implied volatility, which is priced using the Extended Generalised Leland models, especially after being corrected for risk-premium factor improves the option valuation accuracy significantly.

Prediction accuracy of volatility using the score-driven Meixner distribution: an application to the Dow Jones

ABSTRACT The score-driven QAR-EGARCH-M (quasi-autoregressive, exponential generalized autoregressive conditional heteroscedasticity-in-mean) model using the Meixner distribution is introduced to improve the prediction accuracy of GARCH. QAR-EGARCH-M extends the recent EGARCH-M model in a statistically innovative way because a new score-driven filter is included in the risk premium. Volatility forecasts of QAR-EGARCH-M, EGARCH-M, and GARCH, all with leverage effects, are compared for the Dow Jones Industrial Average (DJIA). QAR-EGARCH-M is superior to EGARCH-M and GARCH, which is relevant for DJIA options investors at Chicago Mercantile Exchange Globex and Chicago Board Options Exchange.

ANALISIS HARGA SAHAM SYARIAH INDONESIA JAKARTA ISLAMIC INDEX MASA SEBELUM DAN SAAT COVID 19

ABSTRACTThis study aims to test and analyze the Indonesian Jakarta Islamic Index's Islamic stock price which is influenced by 4 macroeconomic fundamentals (inflation, exchange rate $ / IDR, interest rates, stock trading volume) and shares of Indonesia's 5 largest trading partners (Shanghai Composite Index China shares, stocks Japanese Nikkei 225 Index, Singapore Straits Time Index shares, United States Dow Jones Industrial Average shares, India S&P BSE Sensex shares) before and during covid-19. The population in this study were Indonesian Islamic stocks listed on the IDX with the period March 2019 to September 2020. The sampling technique used a purposive sampling method and obtained one sample of the Jakarta Islamic Index (JII) stock index. The analytical method used is multiple linear regression analysis with SPSS application tools. Based on testing using the Simultaneous Model Test (Test F), it is known that the independent variables together (simultaneously) have a significant effect on the Sharia Indonesia Jakarta Islamic Index stock price before and during the Covid-19 period. While the results of the Hypothesis Test (t test) show that, the exchange rate of $ / Rp., China's Shanghai Composite Index (SCI) and the United States Dow Jones Industrial Average (DJIA) Stock Index have a significant effect on Islamic stocks in Indonesia Jakarta Islamic Index during covid-19. .

Advantages of Combining Factorization Machine with Elman Neural Network for Volatility Forecasting of Stock Market

With a focus in the financial market, stock market dynamics forecasting has received much attention. Predicting stock market fluctuations is usually challenging due to the nonlinear and nonstationary time series of stock prices. The Elman recurrent network is renowned for its capability of dealing with dynamic information, which has made it a successful application to predicting. We developed a hybrid approach which combined Elman recurrent network with factorization machine (FM) technique, i.e., the FM-Elman neural network, to predict stock market volatility. In this paper, the Standard & Poor’s 500 Composite Stock Price (S&P 500) index, the Dow Jones industrial average (DJIA) index, the Shanghai Stock Exchange Composite (SSEC) index, and the Shenzhen Securities Component Index (SZI) were used to demonstrate the validity of our proposed FM-Elman model in time-series prediction. The results were compared with predictions obtained from the other two models which are basic BP neural network and the Elman neural network. Some experiments showed that the FM-Elman model outperforms others through different accuracy measures. Furthermore, the effects of volatility degree on prediction performance from different stock indexes were investigated. An interesting phenomenon had been found through some numerical experiments on the effects of different user-specified dimensions on the proposed FM-Elman neural network.

Dynamical Analysis of the Dow Jones Index Using Dimensionality Reduction and Visualization.

Time-series generated by complex systems (CS) are often characterized by phenomena such as chaoticity, fractality and memory effects, which pose difficulties in their analysis. The paper explores the dynamics of multidimensional data generated by a CS. The Dow Jones Industrial Average (DJIA) index is selected as a test-bed. The DJIA time-series is normalized and segmented into several time window vectors. These vectors are treated as objects that characterize the DJIA dynamical behavior. The objects are then compared by means of different distances to generate proper inputs to dimensionality reduction and information visualization algorithms. These computational techniques produce meaningful representations of the original dataset according to the (dis)similarities between the objects. The time is displayed as a parametric variable and the non-locality can be visualized by the corresponding evolution of points and the formation of clusters. The generated portraits reveal a complex nature, which is further analyzed in terms of the emerging patterns. The results show that the adoption of dimensionality reduction and visualization tools for processing complex data is a key modeling option with the current computational resources.

A hybrid approach of adaptive wavelet transform, long short-term memory and ARIMA-GARCH family models for the stock index prediction

Modelling and forecasting the stock price constitute an important area of financial research for both academics and practitioners. This study seeks to determine whether improvements can be achieved by forecasting the stock index and volatility with the use of a hybrid model and incorporating the financial variables. We extend the literature of stock market forecasting by applying a hybrid model which combines adaptive wavelet transform (AWT), long short-term memory (LSTM) and ARIMAX-GARCH family models to predict stock index and combines AWT, LSTM and heterogeneous autoregressive model of realized volatility (HAR-RV) model to predict stock volatility for two major indexes in the U.S. stock market including the Dow Jones Industrial Average (DJIA) and Nasdaq Composite (IXIC). The results indicate an overall improvement in forecasting of stock index using the AWT-LSTM-ARMAX-FIEGARCH model with student’s t distribution as compared to the benchmark models. The robust test proves that this model has a higher prediction accuracy in prediction of different time horizons (1-, 10-, 15-, 20-, 30-, and 60-days ahead) for both stock indexes. Also, AWT-LSTM improves ability of HAR (3) X-RV model in prediction of the stocks realized volatility for mentioned time horizons.

Pengaruh Indeks DJIA, Harga Minyak Dunia, Tingkat Inflasi, dan Nilai Tukar Rupiah terhadap ISSI

Indonesia is a country where the majority of the population is Muslim, so information about the Sharia Stock Index has an important role in understanding the dynamics of the capital market in Indonesia. This study aims to determine the simultaneous and partial influence of Dow Jones Industrial Average (DJIA), World Oil Price, Inflation Rate, and Rupiah Exchange Rate on Indonesia Sharia Stock Index (ISSI). This study uses secondary data in the form of a summary of monthly index reports obtained from financial websites. The population in this study is a time series data that amounts to 60 months and starts from January 2016 to December 2020 by determining samples using saturated samples. The data was analyzed using multiple linear regression analysis techniques. Hypothesis tested using F test and t test. The results showed that simultaneously (Test F) variable Dow Jones industrial Average (DJIA) (X1), World Oil Price (X2), Inflation Rate (X3), and Rupiah Exchange Rate (X4) had a significant effect on the Indonesia Sharia Stock Index (ISSI) on the Indonesia Stock Exchange (IDX). Partially (Test t) DJIA has a significant positive effect on ISSI. Rupiah exchange rate has a significant negative effect on ISSI. Meanwhile, The World Oil Price and Inflation Rate have an insignificant negative effect on ISSI.

Analisis Pengaruh Makroekonomi, Komoditas Dunia, dan Indeks Dunia terhadap Indeks Harga Saham Gabungan (IHSG) pada Periode 2014-2019

This research explains the influence of inflation, Exchange Rate, BI Rate, GDP, World Gold Price, Crude Oil Price, Dow Jones Industrial Average (DJIA), and Nikkei 225 toward Jakarta Composite Index (JCI). Type of research used in causality research with a quantitative approach. The sample was based on daily time series data from 1 January 2014 until 31 December 2019, using a complete sampling method that consists of 2190 samples. This research used a generalized autoregressive conditional heteroskedasticity (GARCH) method. The result of hypothesis testing by the GARCH method shows that the World Gold Price and Dow Jones Industrial Average significant have a positive effect, Then the Nikkei 225 significant have a negative effect, and then the Inflation, Exchange Rate, BI Rate, and GDP have not significant to the Jakarta Composite Index (JCI). The implication of this research provides information to investors who must pay attention to World Gold Price, Dow Jones Industrial Average, and Nikkei225 if they want to invest in Indonesian.

TRADING STOCK MARKET INDICES. A SIMPLE APPROACH

In this article we will present a simple model that can be used by the people that do not have profound financial knowledge and, despite that, could be tempted to try to obtain gains on different financial markets without paying a professional. In fact, the model presented here is developed on some ideas that we presented before in an article on the sports betting industry, adapted to the financial markets. The general idea that we will present in detail in this article is that someone can start and stop trading market indices looking at just one indicator that can be found everywhere: the trading volume. The position that should be taken is, in our opinion, in the opposite way of the last trend that that was ended by the high market volume. In the end, the following article represents just a case study made on the general principles presented here taking into consideration a concrete market. We will try to present the model that we developed on a very well-known and highly traded index, the DJIA (Dow Jones Industrial Average) even if the origin of this index is, as almost every trader knows, american. The reason we will do that is the fact that the traders from the whole world can obtain gains (or losses) on transactions made on DJIA even if they trade from the European Union or other countries that exist in our world so, the gains or losses obtained by the trades we mentioned before can contribute to the value of the traders economy (in our case to the value of EU’s economy).

Quant investing in cluster portfolios

This paper discusses portfolio construction for investing in N given assets, eg, constituents of the Dow Jones Industrial Average (DJIA) or large cap stocks, based on partitioning the investment universe into clusters. The clusters are determined from the trailing correlation matrix via an information theoretic algorithm that uses thresholding of high-correlation pairs. We calculate the principal eigenvector of each cluster from its correlation matrix and the corresponding eigenportfolio. The cluster portfolios are combined into a single N-asset portfolio based on a weighting scheme for the clusters. Various tests conducted on components of the DJIA and a 30-stock basket of large cap stocks indicate that the new portfolios are superior to the DJIA and other mean–variance portfolios in terms of their risk-adjusted returns from 2009 to 2019. We also tested the cluster portfolios for a larger basket of 373 Standard & Poor’s 500 components from 2001 to 2019. The test results provide convincing evidence that a cluster-based portfolio can outperform passive investing.