Industrial Stock Index Research Articles

Indikator Makro Ekonomi dan Pengaruhnya pada Indeks Saham Sektor Infrastruktur Indonesia

This research examines the influence of macroeconomics as measured by exchange rates, interest rates, and inflation on the movement of the Infrastructure industry stock indeX in Indonesia (IDXINFRA).

Study on The Impact of Shanghai Crude Oil Price Volatility and Stock Indices of China's Oil-related Industries

This paper takes Shanghai crude oil futures price and the industry stock index related to it as the research object, and selects the daily data from March 26, 2018 to December 31, 2022, and adopts the BEKK-GARCH model. To explore the correlation between Shanghai crude oil futures price volatility and different segmented industry stock markets. It is found that there is a significant asymmetric volatility spillover effect effect between the vast majority of oil-related industry markets and the Shanghai crude oil market. Based on the relationship between Shanghai crude oil futures prices and oil-related industry stock indexes, investors can make investments based on the fluctuations of Shanghai crude oil futures prices, rationally study the changes in the stock prices of oil-related industries, and do not follow the market panic operations using futures to control risk. And with the oil industry chain related to a higher degree of industry, by increasing the asset allocation method, can effectively reduce risk.

The Impact of Medical and Health Fiscal Expenditures on Pharmaceutical Industry Stock Index in China.

This paper investigates the relationship between fiscal expenditure on health care (FE) and the pharmaceutical industry stock index (SP) by using full-sample and sub-sample rolling-window bootstrap causality tests. It can be observed that there is both a positive and negative relationship between FE and SP. FE will promote the rise of the pharmaceutical stock market, which proves the Keynesian theory, while the result that FE negatively affects SP supports the classical theory. In turn, SP positively impacts FE, which indicates that the development of the pharmaceutical industry and the increase in medical and health expenditures can promote each other. In addition, the negative influence of SP on FE suggests that the impact of the pharmaceutical index on fiscal expenditure needs to be judged in conjunction with other events and market conditions. In complex economic conditions, investors can rationally consider the industry situation of the pharmaceutical market and benefit by optimising their investment portfolios. The government can regulate and guide the pharmaceutical industry by adjusting the fiscal expenditure on health care, thereby promoting the sustainable and stable development of the financial market.

Government responses to COVID-19 and industry stock returns

Governments around the world have responded to the COVID-19 outbreak with a mix of policies. The strictest responses of the New Zealand government are notable, given their abilities to contain and limit the spread of the virus. However, their impacts on stock returns remain unclear. This paper investigates the impact of three policies, namely lockdown, the stimulus package, and the travel ban, on the returns of 14 New Zealand industry stock indices. Using daily data from 1 January 2019 to 25 August 2020, evidence points to a heightened level of integration among the various industry stock indices during the early stages of the pandemic. Only lockdown has had a positive impact on aggregate stock returns, suggesting its ability to raise investors’ confidence in the overall stock market. At the industry level, the impact of the three response policies is generally positive but heterogeneous across industry stock indices. Notably, none of the three adopted policies significantly impact technology, healthcare, and real estate returns.

Quantitative analysis of the “Journal of Finance, Industry and Trade” for 1913: authors and discussed topics

This article conducts historiographical and quantitative analysis of the prime economic media in the Russian Empire of the late XIX – early XX centuries – “Journal of Finance, Industry and Trade”. The journal was issued from 1885 to 1917 being the official publication of the Ministry of Finance. The materials published in the journal contain reference and analytical information. High quality of the analytical articles was ensured by competent authors – prominent national economists of that time. The goal consists in studying the dynamics of reflection of complex economic processes that unfolded in the prewar 1913 in the journal. Characteristic is given to the composition of the team of authors in 1913. Quantitative analysis of the content of all 52 issues is conducted for the first time in accordance with the dynamics of quarterly distribution of the topics of analytical publications. The author suggests possible interpretations of changes in thematic specialty of the journal throughout the year, and formulates a hypothesis on correlation between these changes and the dynamics of industrial stock index developed by L. Borodkin and G. Perelman by analogy with the famous Dow Jones Industrial Average. Decline in journal’s interest to the industrial problems is noticed until the fourth quarter of 1913; then the favorable conjuncture on the oil market explains the frequency of occurrence of the category of “industry” due to the articles dedicated to oil industry.

Weather Effects on Industrial Stock Index Returns and Volatilities: Evidence from Korean Stock Market

Abstract This study empirically investigates weather effects on Korean stock index returns and market volatility based on the GJR-GARCH-X model. We focus on the issue about whether the weather effect is associated with financial deregulation and foreign investors’ transactions, which should not be affected by the weather conditions in Seoul City. To explore the weather effects by controlling for foreign investors’ trading, we employ daily stock index data and weather indicators (cloud cover, precipitation, sunshine hours, snow, and humidity) during the period from 1981 to 2016. Our empirical results suggest that there is little evidence for weather effects on KOSPI and stock returns by industry listed in the Korea Stock Exchange even if Korean financial market deregulations in 1997 increases foreign investors’ trading, but there is some evidence for weather effects on market volatilities.

The impact of geopolitical risks on travel and leisure stocks

The impact of geopolitical risks on tourism has not received scholarly attention in the existing literature. Previous studies focus on the effects of terrorism, which is a subset of geopolitical risks. Our paper fills this gap and contributes to the literature by analyzing the vulnerability of four regional Travel and Leisure (T&L) industry stock indices to geopolitical risks. Employing traditional and quantile regression techniques, our results suggest a greater sensitivity of T&L stock returns to geopolitical risks at times the industry performs poor, except for Asia & Pacific index that is the most resilient to geopolitical events. We find that the negative effects of geopolitical risks are mostly driven by the threat of adverse geopolitical events only during period of falling T&L stock prices. However, the realization of adverse geopolitical events significantly influences T&L stocks at all times. The results provide potential insights and implications for investors and tourism stakeholders.

Volatility Spillover Effect of International Crude Oil Futures and China-Russia Stock Market: A Multivariate BEKK-GARCH Model Based on Wavelet Multiresolution Analysis

The increasingly prominent strategic position of crude oil determines its high impact on macro-economy. The value of crude oil is reflected in the price of crude oil futures. Stock market is the barometer of macro economy. To what extent does international crude oil futures price affect stock market? China and Russia are the biggest importer and exporter of crude oil, respectively. Crude oil is of strategic value to both countries. This study empirically investigates the volatility spillover effect of international crude oil futures and China-Russia stock market from April 24th, 2015 to April 20th, 2018, based on the data of international crude oil futures prices, China-Russia stock market composite index, and industry stock index. The empirical results show that there is a short-term relationship between China-Russia stock market composite index and international crude oil futures price. The international crude oil futures price has a greater explanatory power to Russian RTS index, but a smaller explanatory power to Shanghai composite index. All industry stock indices are cointegrated with international crude oil futures prices. Except for China industry and Russia energy, the adjustment coefficient of international crude oil futures price on stock index volatility of other industries is insignificant. This study mainly studies the relationship between international crude oil futures price and the comprehensive stock index and industry stock index of China and Russia, and compares the impact of international crude oil futures price on the stock market of the largest importer and the largest exporter of crude oil to explore the linkage between crude oil futures price and stock market, and puts forward policy implications based on the empirical results.

Forecasting the sign of U.S. oil and gas industry stock index excess returns employing macroeconomic variables

In this study we propose a method of selecting the macroeconomic variables for forecasting the excess return signs of the U.S. oil and gas industry stock index by combining the Forward Sequential Variable Selection Algorithm and information criteria. We select predictors from a large monthly macroeconomic variable dataset designed by McCracken and Ng (2015). The method can adapt to the updated macroeconomic information and the possible time-varying relationship between the macroeconomic variables and the stock return signs. We also propose a method which can change the threshold value of the probit model automatically for considering the potential time-varying risk aversion level of the market participants. Further, we investigate the investment performance of an active trading strategy based on our forecasting model and compare it with a passive buy-and-hold trading strategy for different time periods.Our study is important for both oil and gas industry investors and U.S. energy policy makers. The method that we used in this study offers a solution to the issue of selecting useful information from large datasets and absorbing updated market information.

거래소 및 코스닥 유통업종의 군집현상 및 정보전달체계에 관한 연구

거래소 및 코스닥 유통업종의 군집현상 및 정보전달체계에 관한 연구

Investor Attention, Market Liquidity and Stock Return: A New Perspective

We propose a new method to measure the investor attention paid to a specific industry using search data from search engine. Instead of taking company names or stock codes as keywords, we select keywords from a corpus of texts concerning a given industry by text-analysis technique such as TextRank algorithm. Two indices were constructed by principal component analysis method, including a positive index and a negative index. The empirical analysis demonstrates that the influence of investor attention on market liquidity is coincident and significant, and the effect on industry stock index return is less significant.

Forecasting the Sign of U.S. Oil and Gas Industry Stock Index Excess Returns Employing Macroeconomic Variables

In this study, we investigate whether we can identify a probit model with macroeconomic variables to forecast the monthly excess return signs of the U.S. oil and gas industry index by mining a big macroeconomic variable dataset designed by McCracken and Ng (2015). Three different information criteria and a Forward Sequential Variable Selection Algorithm are combined to select the most ”important” macroeconomic variables for prediction. A static probit model with 14 monthly macroeconomic predictors is found to predict one-month ahead excess returns of the U.S. oil and gas industry. We also show that the predictors in this model are related to the future performance of the S&P 500 index, the WTI crude oil price and the U.S. foreign exchange rate against the major currencies, which are the key risk factors of the oil and gas industry identified by previous studies. Active trading strategies based on the static probit model can generate higher Sharpe ratios than a buy-and-hold strategy. The forecasting ability of the identified model is found to be robust to different industry classification systems. The empirical results have important implications for investors and policy makers.

Asymmetric determinants of CDS spreads: U.S. industry-level evidence through the NARDL approach

This paper investigates the presence of asymmetries in the short- and long-run relationships between the 5-year CDS index spreads at the U.S. industry level and a set of major macroeconomic and financial variables, namely the corresponding industry stock indices, the VIX index, the 5-year Treasury bond yield and the crude oil price, using the NARDL approach. The empirical results provide significant evidence of both short-run and long-run asymmetries in the linkage between ten industry CDS spreads and the potential driving factors common for all industries, confirming the importance of asymmetric nonlinearity in this context. It is also shown that the industry equity prices, the VIX, the 5-year Treasury bond rate and, to a lesser extent, the crude oil price constitute important asymmetric determinants of these U.S. industry CDS spreads. The findings of this study have relevant implications for investors, speculators, arbitrageurs and policy makers interested in credit risk at the industry level.

A Prediction Model for Taiwan Tourism Industry Stock Index

Investors and scholars pay continuous attention to the stock market, as each day, many investors attempt to use different methods to predict stock price trends. However, as stock price is affected by economy, politics, domestic and foreign situations, emergency, human factor, and other unknown factors, it is difficult to establish an accurate prediction model. This study used a back-propagation neural network (BPN) as the research approach, and input 29 variables, such as international exchange rate, indices of international stock markets, Taiwan stock market analysis indicators, and overall economic indicators, to predict Taiwan’s monthly tourism industry stock index. The empirical findings show that the BPN prediction model has better predictive accuracy, Absolute Relative Error is 0.090058, and correlation coefficient is 0.944263. The model has low error and high correlation, and can serve as reference for investors and relevant industries.

The Effect of Energy Prices on Iranian Industry Stock Returns

While previous studies have provided some evidence that energy prices have impact on stock returns across industries, to the best of our knowledge, it is surprising that no recent work is known about the Iran’s emerging stock market. However, the published daily data on Tehran’s industry stock indices since 2005 make such a study possible. Therefore, our study is considered very beneficial as it provides insights regarding information transmission and volatility estimation of oil and gas prices and Iranian industry equity returns. The purpose of this study is to examine the effect of oil and gas prices on Iranian industry stock returns. To this end, we analyze 35 Iranian industry indices for the period from January 2005 to March 2010. Based on the multifactor model and using time-series regressions, our findings indicate that oil prices are an important determinant of returns in the banks, credit and other financial institutions, basic metals, electric machinery and apparatus and refined petroleum products and nuclear fuel industries. The findings also suggest that gas price movements do not seem to play a role for Iranian industry stock returns except for motor vehicles and auto parts industry. Moreover, consistent with the capital asset pricing model (CAPM), the market portfolio is a significant pricing factor in most of the (studied) industry stock returns. The results of this study help domestic and potential foreign investors to understand the effect of energy price changes on Iranian industry stock returns in order to manage their portfolios effectively.

World War II Events and the Dow-Jones Industrial Index

Abstract This paper investigates which events of World War II (WWII) the US investors (at that time) considered as turning points (structural breaks) of the war. The empirical study employs daily Dow Jones industrial average stock index and volatility from January 1939 to December 1945 and applies structural shift oriented test to determine endogenously the structural breaks during the WWII period. Results show that the majority of the wartime events (on and off the battlefield) labelled important by historians did result in structural breaks in both price movement and stock returns volatility (risk). These results have major implications for investors of the present and future.

Large-Scale Disasters and the Insurance Industry

We investigate the impact of the 20 largest – in terms of insured losses – man-made or natural disasters on various insurance industry stock indices. We show via an event study that insurance sectors worldwide are quite resilient, in a market–value sense, to unexpected losses to capital: our data provide evidence that equity market investors believe that insurance companies will on average be able to make losses back over the foreseeable future, i.e. that the adverse shocks to equity which have resulted from these catastrophes will be compensated by either an outward shift of the demand curve or an ability to raise premiums, or both.

Large-scale disasters and the insurance industry

The authors investigated the impact of the 20 largest – in terms of insured losses – man-made or natural disasters on various insurance industry stock indices. They show via an event study that insurance sectors worldwide are quite resilient, in a market–value sense, to unexpected losses to capital. The data provide evidence that equity market investors believe that insurance companies will on average be able to make losses back over the foreseeable future, i.e. that the adverse shocks to equity which have resulted from these catastrophes will be compensated by either an outward shift of the demand curve or by an ability to raise premiums, or both.

Relationships among U.S. oil prices and oil industry equity indices

The cointegration analysis suggests that the pure oil industry equity system and the mixed oil price/equity index system offers more opportunities for long-run portfolio diversification and less market integration than the pure oil price systems. On a daily basis, in the oil price systems all oil prices with the exception of the 3-month futures can explain the future movements of each other. In the mixed system, none of the daily oil industry stock indices can explain the daily future movements of the New York Mercantile Exchange (NYMEX) futures prices, whereas these prices can explain the movements of independent companies engaged in exploration, refining, and marketing. The spillover analysis of oil volatility transmission suggests that the oil futures market has a matching or echoing volatility effect on the stocks of some oil sectors and a volatility-dampening effect on the stocks of others. The policy implication is that, during times of high oil volatility, traders should choose the S&P oil sector stocks that match their tolerance for volatility and use the right financial derivative to hedge against or profit from this volatility. The day effect for volatility transmission suggests that Friday has a calming effect on the volatility of oil stocks in general. The effect for Monday is not significant.

Federal Reserve Monetary Policy and Industry Stock Returns

Recent studies identify stock return patterns associated with changes in Federal Reserve monetary policy. We find that these return patterns prevail across sixteen industry stock indices. However, significant cross‐industry variation exists as the apparel industry exhibits mean annual returns that are 50% higher under an expansive Fed policy than under a restrictive policy, while the same return difference for the oil industry is only 20%. This cross‐industry variation suggests that monetary conditions may be used by investors to estimate different expected returns across industries. Furthermore, the findings support the view that monetary considerations should be considered in ex ante asset pricing models such as the CAPM.