Dow-Jones Index Research Articles

Impact of Macroeconomic Variables on the Performance of the Indian stock market

This paper analyses the macroeconomic factors influencing performance of Indian stock market index NIFTY 50. The study is unique in nature as it employ both, primary and secondary to derive the conclusion. Initially, nine years monthly data of macroeconomic variables are regressed against monthly performance of NIFTY 50 index using logistic regression. In second stage the outcome of regression analysis is ratified with primary data collected through face to face interview of the stock market expert. The secondary data analysis confirm Dow jones index and exchange rate movement are the main determinants of NIFTY 50 index. However, expert opined that other factors like political stability, economic situation in developed and bilateral relations of India with other countries are also important to forecast the NIFTY 50 index movement. This is the first study in Indian context, to combine domestic and international factors to forecast NIFTY 50 index movement.

Neural Networks for Portfolio Analysis With Cardinality Constraints.

Portfolio analysis is a crucial subject within modern finance. However, the classical Markowitz model, which was awarded the Nobel Prize in Economics in 1991, faces new challenges in contemporary financial environments. Specifically, it fails to consider transaction costs and cardinality constraints, which have become increasingly critical factors, particularly in the era of high-frequency trading. To address these limitations, this research is motivated by the successful application of machine learning tools in various engineering disciplines. In this work, three novel dynamic neural networks are proposed to tackle nonconvex portfolio optimization under the presence of transaction costs and cardinality constraints. The neural dynamics are intentionally designed to exploit the structural characteristics of the problem, and the proposed models are rigorously proven to achieve global convergence. To validate their effectiveness, experimental analysis is conducted using real stock market data of companies listed in the Dow Jones Index (DJI), covering the period from November 8, 2021 to November 8, 2022, encompassing an entire year. The results demonstrate the efficacy of the proposed methods. Notably, the proposed model achieves a substantial reduction in costs (which combines investment risk and reward) by as much as 56.71% compared with portfolios that are averagely selected.

Pengaruh Indeks Dow Jones, Suku Bunga The Fed, Dan Bitcoin terhadap IHSG

This research was conducted to examine the effect of the Dow Jones Index, The Fed Interest Rate, and Bitcoin on the Indonesian Composite Stock Price Index (IHSG). The data used is secondary data taken based on closing prices and monthly fluctuations from financial websites. The population is in the form of time series data for 58 months starting from 1 June 2018 to 31 March 2023 using a saturated sample. Data analysis in the study used the classical assumption test (multicollinearity, heteroscedasticity, autocorrelation, normality), multiple linear regression, determinant coefficient, and T test. The results showed that the Dow jones index had a positive effect on the JCI, The Fed's interest rate had a positive effect on the JCI, and Bitcoin has no effect on the JCI. The test results stated that the Dow Jones Index had a positive influence on the JCI, The Fed's Interest Rate had a positive influence on the JCI, and Bitcoin had no influence on the JCI.
 Keywords: Bitcoins; Dow Jones; IHSG; The Fed

Credit Card Fraud Detection Using Ml Algorithms

Bank credit card default rates are (as predicted by S&P dow Jones index) at all time high for the past few months. This possess a real challenge for banks and other lending financial institutions, as they are now more than ever are in need of a robust risk prediction model to generalize the economic behavior of their current and potential future clients. In real world massive stream of payment requests are coming in every second which is huge challenge to monitor. Machine Learning algorithms help in solving these huge challenges to an extent. These predictive models would not only benefit the lending institutions but also the customer as it would make them more aware of their potential defaulting rate. The goal of this project is to resolve this problem by building and comparing predictive risk models using supervised learning algorithms. We use python libraries to perform various Machine learning algorithms on the credit card dataset and data diagrams to have better visualization of data. Here the credit card fraud detection is done on the basis of some attributes in the dataset like the time factor and the amount factor. The accuracy of the algorithms is also being determined and the most efficient one is said with the highest accuracy obtained..

Sequentially Forecasting Economic Indices Using Mixture Linear Combinations of EP Distributions

This article displays an application of the statistical method motivated by Bruno de Finetti’s operational subjective theory of probability. We use exchangeable forecasting distributions based on mixtures of linear combinations of exponential power (EP) distributions to forecast the sequence of daily rates of return from the Dow-Jones index of stock prices over a 20 year period. The operational subjective statistical method for comparing distributions is quite different from that commonly used in data analysis, because it rejects the basic tenets underlying the practice of hypothesis testing. In its place, proper scoring rules for forecast distributions are used to assess the values of various forecasting strategies. Using a logarithmic scoring rule, we find that a mixture linear combination of EP distributions scores markedly better than does a simple mixture over the EP family, which scores much better than does a simple Normal mixture. Surprisingly, a mixture over a linear combination of three Normal distributions also makes a substantial improvement over a simple Normal mixture, although it does not quite match the performance of even the simple EP mixture. All substantive forecasting improvements become most marked after extreme tail phenomena were actually observed in the sequence, in particular after the abrupt drop in market prices in October, 1987. However, the improvements continue to be apparent over the long haul of 1985-2006 which has seen a number of extreme price changes. This result is supported by an analysis of the Negentropies embedded in the forecasting distributions, and a proper scoring analysis of these Negentropies as well.

A machine learning algorithm for stock picking built on information based outliers

Stock picking based on regularities in time series is one of the most studied topics in the financial industry. Various machine learning techniques have been employed for this task. We build a trading strategy algorithm that receives as input indicators defined through outliers in the time series of stocks (return, volume, volatility, bid-ask spread). The procedure identifies the most relevant subset of indicators for the prediction of stock returns by combining an heuristic search strategy, guided from the Information Gain Criterium, with the Naive-Bayes classification algorithm. We apply the methodology to two sets of stocks belonging respectively to the EUROSTOXX50 and the DOW JONES index. Performance is smoother than in the Buy&Hold strategy and yields a higher risk-adjusted return, in particular in a turbulent period. However, outperformance vanishes when transaction costs (5–15 basis points) are inserted. Asset return and return/volume serial correlation turn out to be the most relevant indicators to build the trading algorithm.

A hybrid algorithm for portfolio selection: An application on the Dow Jones Index (DJI)

With the developing technology, investors have started to direct their investments by using computer software. The fact that these software do not take investor’s views into account causes all investors to achieve the same expected return at the same risk level. In this study, in order to find a solution to this problem, a two-stage portfolio selection model, which takes investment data and expert opinions into consideration, is proposed. In the first stage, the weight of the criteria in the portfolio selection problem was determined by the Constrained Fuzzy Analytic Hierarchy Process method. In the second stage, the fuzzy linear programming (FLP) problem created using the weights of the specified criteria is solved by fuzzy logic approach. These two methods in the literature use triangular fuzzy numbers (TFNs) in the solution process of the problem. In this study, methods are developed by creating mathematical models that makes the triangular fuzzy number-oriented theoretical infrastructures of the two methods used suitable for the use of trapezoidal fuzzy numbers (TrFNs). As a result, a new hybrid portfolio selection algorithm is proposed in which the methods developed for TrFNs in the first and second stage are used together. In the application section, portfolio distribution was made with the proposed hybrid model based on TrFNs by using the return rates of 30 stocks included in the Dow Jones Index, financial ratios of the stocks and decision makers’ opinions between January 1, 2018 and December 31, 2020. In the conclusion section of the study, as a result of the portfolio distributions obtained from each method, the total return amounts obtained when the investment was made in January 2021 according to the rate of return of the stocks were compared.

Covid-19, Dow Jones and equity market movement in ASEAN-5 countries: evidence from wavelet analyses

This study gains insights into what drives the ASEAN-5 equity markets. Using several wavelet approaches, we examine the correlation between the ASEAN-5 equity markets with the daily new Covid-19 cases and the Dow Jones Industrial Average (DowJones), the lead-lag relationships and level of disorder (or randomness) between the ASEAN-5 domestic equity markets and DowJones between February 15 to May 30, 2019 (pre-period) and February 15 to May 30, 2020 (during the pandemic period) respectively. The pandemic period is further divided into three different phases; the beginning (February), mid (March and April), and end (May) of the period. This study finds that Malaysia, Indonesia, and Singapore equity markets react to Covid-19 cases at the beginning of the pandemic phase, whereas, Thailand and the Philippines showed coherency during the mid-period. As the pandemic progresses (mid-period), all ASEAN-5 equity markets exhibited strong coherence with the DowJones Index. However, at the end of the sample period, no coherency was observed among the ASEAN-5 equity markets, local Covid-19 cases, and DowJones index. This study has two main contributions to the literature: First, we provide insights on equity markets’ reactions during an epidemic/pandemic crisis in the emerging markets, specifically, the ASEAN-5 countries, which is a less studied area. Second, examining the impact of the Covid-19 and DowJones Index on the ASEAN-5 equity markets using the wavelet method is a novel approach that captures both the time and frequency dimensions. The results of this study have a significant contribution to investors and regulators, particularly in navigating the new 'normal' and data-driven era.

A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of West Texas Intermediate Oil Prices and the DOW JONES Index

The paper features an examination of the link between the behaviour of oil prices and DowJones Index in a nonlinear autoregressive distributed lag nonlinear autoregressive distributed lag (NARDL) framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a monthly West Texas Intermediate (WTI) crude oil series from Federal Reserve Bank of St Louis (FRED), commencing in January 2000 and terminating in February 2019, and a corresponding monthly DOW JONES index adjusted-price series obtained from Yahoo Finance. Both series are adjusted for monthly USA CPI values to create real series. The results of the analysis suggest that movements in the lagged real levels of monthly WTI crude oil prices have very significant effects on the behaviour of the DOW JONES Index. They also suggest that negative movements have larger impacts than positive movements in WTI prices, and that long-term multiplier effects take about 9 to 12 months to take effect.

Pengaruh Perubahan Nilai Tukar Rupiah, Tingkat Inflasi, dan Indeks Bursa Internasional Terhadap Indeks Harga Saham Gabungan (IHSG) di Bursa Efek Indonesia (BEI)

This research aimed to know how the effect of Rupiah Exchange Rate, inflation rate, and international Exchange Index towards Composite Stock Price Index (CSPI). The independent variables of this study are Rupiah Exchange Rate (X1), Inflation rate (X2), Dow Jones index (X3), Nikkei 225 index (X4), and Hang Seng index (X5). The Sample was based on monthly time series data from January 2014 to December 2018, with documentation data collection technique from Bank Indonesia and Yahoofinance.com publication. Analytical techniques used were linier regression, classical Assumtions, determination coeffisient test (R2), for hypothesis test used F-test and t-test with significance level of 5%. The results of this indicate that the value of cooficient of determination (R2) 0,901 which means independent variables affect the dependent variable 89,2% and the rest is 10,8% influenced by other variables outside this study. The result of t-test shows that the Rupiah Exchange Rate and Nikkei 225 index have not significant and negative effect on Composite Stock Price Index (CSPI), Inflation rate have significant and negative effect on Composite Stock Price Index (CSPI), Dow Jones index and Hang Seng Index have significant and positive effect on Composite Stock Price Index (CSPI). Keywords: composite stock price index (cspi), rupiah exchange rate, inflation rate, dow jones index, nikkei 225 index, and hang seng index

The Factors Affecting the Composite Stock Price Index (CSPI) of Indonesia Stock Exchange

The study examines the influence of The Fed Rate, inflation, Dow Jones Index (DJI), exchange rate, and world oil price on the composite stock price index (CSPI) in Indonesia. The method used in this study was the Vector Error Correction Model (VECM). The research showed that the influence of inflation towards the composite stock price index was negative both in the short term and long term. The influence of the exchange rate towards the composite stock price index was positive both short term and long term. The impact of oil price towards composite stock price index was positive in the short term and long term. However, both the Dow Jones Index (DJI) and The Fed Rate had a negative effect on the composite stock price index. The results of this analysis can be used as a reference for investor and government policymaking

Social Spider Optimization with Tumbling Effect Based Data Classification Model for Stock Price Prediction

In last decade, data classification become more famous which aims to classify the data to a fixed number of classes. The data classification problem is treated as an NP hard problem and different optimization models are presented to resolve it. This paper introduces a social spider optimization (SSO) algorithm with tumbling effect called SSO-T algorithm to solve the data classification problem. First, the SSO algorithm is derived to solve the classification process which is considered as a NP hard problem. Next, to further enhance the exploration process of SSO algorithm, it is modified by the inclusion of tumbling effect, called SSO-T algorithm. For validating the results of the SSO and SSO-T algorithm, a real time problem of stock price prediction (SSP) is employed. For experimentation, the results are validated by testing the SSO and SSO-T algorithms against four datasets such as Dow Jones Index (DJI) dataset, three own datasets gathered from Yahoo finance on the basis of daily, weekly and yearly. The empirical result states that the proposed algorithms perform well and it is noted that the classification performance of the SSO algorithm is increased by the inclusion of tumbling effect.

Social Spider Optimization Algorithm for Effective Data Classification: An Application of Stock Price Prediction

Presently, data classification has become a hot research topic, intends to categorize the data into a predefined number of classes. The data classification problem has been considered as a NP hard problem and various optimization algorithms are introduced to solve it. Social spider optimization (SSO) algorithm is originally developed to resolve continuous and optimization problems. In line with, it has been altered to manage different kinds of optimization process and also to be employed for data investigation. And, some other studies are also investigated the use of SSO algorithm in different domains. In this paper, we introduce new SSO algorithm particularly applicable for data classification process. In order to validate the performance of the SSO algorithm, a real time problem of stock price prediction (SPP) is employed. For experimentation, the results are validated by testing the SSO algorithm against four datasets such as Dow Jones Index (DJI) dataset, three own datasets gathered from Yahoo finance on the basis of daily, weekly and yearly. The empirical result states that the proposed algorithms perform well and it is noted that the classification performance of the SSO algorithm better than the compared methods.

Model Fungsi Transfer Input Ganda untuk Pemodelan Jakarta Islamic Index

Transfer function model with multiple input is a multivariate time series forecasting model that combines several characteristics of ARIMA models by utilizing some regression analysis properties. This model is used to determine the effect of output series towards input series so that the model can be used to analyze the factors that affect the Jakarta Islamic Index (JII). The USD exchange rate against rupiah and Dow Jones Index (DJI) were used as input series. The transfer function model was constructed through several stages: model identification stage, estimation of transfer function model, and model diagnostic test. Based on the transfer function model, the JII was influenced by JII at the period of one and two days before. JII was also affected by the USD exchange rate against rupiah at the same period and at one and two days before. In addition, the JII was influenced by DJI at the same period and also at period of one until five days ago. The Mean Absolute Prencentage Error (MAPE) value of forecasting result was 0.70% and the correlation between actual and forecast data was 0.77. This shows that the model was well performed for forecasting JII.

A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of West Texas Intermediate Oil Prices and the DOW JONES Index

The paper features an examination of the link between the behaviour of oil prices and DowJones Index in a nonlinear autoregressive distributed lag NARDL framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a monthly West Texas Intermediate (WTI) crude oil series from FRED, commencing in January 2000 and terminating in February 2019, and a corresponding monthly DOW JONES index adjusted-price series obtained from Yahoo Finance. Both series are adjusted for monthly USA CPI values to create real series. The results of the analysis suggest that movements in the lagged real levels of monthly WTI crude oil prices have very significant effects on the behaviour of the DOW JONES Index. They also suggest that negative movements have larger impacts than positive movements in WTI prices, and that long-term multiplier effects take about 9 to 12 months to take effect.

Test for tail index constancy of GARCH innovations based on conditional volatility

This study considers the problem of testing whether the tail index of the GARCH innovations undergoes a change according to the values of conditional volatilities. Special attention is paid to power-transformed and threshold generalized autoregressive conditional heteroscedasticity processes that can accommodate the GARCH family. We show that the proposed test asymptotically follows a functional of a standard Brownian motion under some regularity conditions. To evaluate our method, we carry out a simulation study and real data analysis using the return series of the Google stock price and DowJones index.

ANALISA PENGARUH KRISIS GLOBAL FINANCIAL TERHADAP JAKARTA ISLAMIC INDEX (JII) DAN INDEX HARGA SAHAM GABUNGAN (IHSG)

The financial crisis in the United State on September 2008 has created global impact to the world economy, including the performace of capital markets. Most of market indexes around the world fall down significantly, including market indexes in Bursa Efek Indonesia (BEI). There were five market index in BEI, namely IHSG, LQ 45, MBX, DBX, and JII. This research is mainly aim to examine the influence of the US financial crisis to the performace of JII and IHSG. In addition, the relation of JII and IHSG to DowJones Index, Tokyo Index, and Hongkong Index are also examined. To get the aim done, a Vector Autoregressive Model (VAR) is utilized. VAR is an appropriate model to examine a simultaneous relationship with capability to tract impulse response among variables. The research findings show the US financial crisis has created significant shocks toward JII and IHSG during September to October 2008. Both IHSG and JII has constantly decrease during 8 weeks, but IHS was getting recovered quicker than JII. The investment rather than speculation motive of the players in JII cound be the answer for this finding.

An entropy-based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series

ABSTRACTThis article features an analysis of the relationship between the DOW JONES Industrial Average (DJIA) Index and a sentiment news series using daily data obtained from the Thomson Reuters News Analytics (TRNA) provided by SIRCA (The Securities Industry Research Centre of the Asia Pacific). The recent growth in the availability of on-line financial news sources, such as internet news and social media sources provides instantaneous access to financial news. Various commercial agencies have started developing their own filtered financial news feeds which are used by investors and traders to support their algorithmic trading strategies. TRNA is one such data set. In this study, we use the TRNA data set to construct a series of daily sentiment scores for DJIA stock index component companies. We use these daily DJIA market sentiment scores to study the relationship between financial news sentiment scores and the stock prices of these companies using entropy measures. The entropy and mutual information (MI) statistics permit an analysis of the amount of information within the sentiment series, its relationship to the DJIA and an indication of how the relationship changes over time.

Bootstrap forecast intervals for asymmetric volatilities via EGARCH model

Bootstrap forecast intervals are developed for volatilities having asymmetric features, which are accounted for by fitting EGARCH models. A Monte-Carlo simulation compares the proposed forecast intervals with those based on GARCH fittings which ignore asymmetry. The comparison reveals substantial advantage of addressing asymmetry through EGARCH fitting over ignoring it as the conventional GARCH forecast. The EGARCH forecast intervals have empirical coverage probabilities closer to the nominal level and/or have shorter average lengths than the GARCH forecast intervals. The finding is also supported by real dataset analysis of Dow–Jones index and financial times stock exchange (FTSE) 100 index.

The Volatility of Indonesia Shari’ah Capital Market Stock Price Toward Macro Economics Variable

Shari’ah stock market is also affected by many highly interrelated economic, social, political and other factor, same as the conventional stock market, the interaction between macroeconomic variables and Shari’ah stock market creating volatility in the stock price as a response towards several shocks. The sensitivity of Shari’ah stock market towards shocks happened related with the future expectation of micro and macro factor in one country which can be predict or unpredictable. There are six macroeconomic variables that used in this research; inflation, exchange rate, interest rate, dow jones index, crude oil palm price, and FED rate. Using vector error correction model (VECM), the result shows that domestic macroeconomic variables that significantly affect Indonesia Shari’ah compliance for long term, while for international macroeconomic variables the selected variable such as FED rate and Dow Jones Index are not significantly affected Indonesia Shari’ah compliance both in short term and long term.