Relevant bibliographies by topics / Stock price indexes Australia

Academic literature on the topic 'Stock price indexes Australia'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Stock price indexes Australia"

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Hailu, Suadiq Mehammed, and Gamze Vural. "The Impact of COVID-19 Pandemic on Financial Markets: Evidence from Developed and Developing Countries` Stock Markets Indexes." European Journal of Business and Management Research 6, no. 4 (August 30, 2021): 372–77. http://dx.doi.org/10.24018/ejbmr.2021.6.4.1041.

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In this study, we investigate the stock markets’ reaction to the COVID-19 outbreak. For this purpose, we collected daily cumulative confirmed cases, cumulative deaths, and stock index price data from Australia, Germany, Japan, UK, USA, Brazil, China, Malaysia, South Africa, and Turkey over the period from March 11, 2020, to December 31, 2020, and examined using multiple and panel data regression. Findings reveal that the cumulative daily infection cases have a significant negative impact on the entire and first sub-period covering from March 11 to June 30, 2020. However, this negative impact of cumulative infection cases on the stock market was significant only among developed countries. In contrast, the cumulative death rate was not a fundamental factor that explains stock market price changes. The result also indicated that exchange rate has a significant negative impact on both developed and developing countries’ stock markets. The overall findings of the study indicated that COVID-19 outbreak has a negative significant impact on stock markets and this impact continue until the end of the 2020 second quarter and then the impact became insignificant. Besides, the impact of the COVID-19 pandemic was different in developed and developing countries and even different from country to country.
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Madyan, Muhammad, Haka Adila, and Novian Abdi Firdausi. "Keterkaitan Antar Bursa Efek Dunia (Studi Kasus pada Bursa Efek Negara Maju dan Negara Berkembang)." Jurnal Manajemen Teori dan Terapan | Journal of Theory and Applied Management 12, no. 1 (August 8, 2019): 85. http://dx.doi.org/10.20473/jmtt.v12i1.14115.

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This research analyzes the correlation between stock markets worldwide. Developing countries stock exchanges are represented by China and Indonesia, whereas developed countries stock exchanges are represented by Germany, Japan, Australia, Singapore, and the United States. Using stock’s daily close prices as data, then assessed with Vector Error Correction Model and Granger Causality. Analyzed indexes are Shanghai Stock Exchange Composite (SHCOMP), Indeks Harga Saham Gabungan (IHSG), Dow Jones Industrial Average (DJIA), Nikkei225 (NKY), Deutscher Aktien Index (DAX), All Ordinaries Index (AOI), and Strait Times Index (STI). Stock data grouped into two periods, the first period is the Asian Financial Crisis in 1 January 1998-31 December 2003, while second period is the Subprime Mortgage crisis in 1 January 2008-31 December 2013. Research results show correlations between analyzed stock indexes in both long run and short run relationship in the firstperiod as well asthe second period, however the correlation between Singapore’s and Indonesia’s stock exchange in second period is unproven.
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Fisher, Lawrence, Daniel G. Weaver, and Gwendolyn Webb. "International Real Estate Review." International Real Estate Review 15, no. 1 (April 30, 2012): 43–71. http://dx.doi.org/10.53383/100148.

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In this paper, we apply the method for removing the upward bias in returns in equally-weighted return indexes developed by Fisher, Weaver, and Webb (2010) to real estate investment trust (REIT) stocks in the US. While we find significant bias in this index, two trends are evident: first, there is less overall bias than in non-REIT stocks, and second, the bias of REIT stocks has declined over time. These trends are consistent with growing listings of REIT stocks on the New York Stock Exchange (NYSE), as well as with increasingly higher stock prices. They also support the hypothesis that there have been significant improvements in the market micro-structure environment of REIT stocks since the early 1970s. We further apply our methodology to REIT stocks listed in the two countries with the largest number of REITs outside the US: Germany and Australia. The results support the hypothesized relationship between index bias and market micro-structure environment.
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Wolski, Rafal. "Co-Integration Test of Selected Indexes on the Share Market and Index of Housing Real Estate Prices." Real Estate Management and Valuation 28, no. 1 (March 1, 2020): 100–111. http://dx.doi.org/10.1515/remav-2020-0009.

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AbstractThe integration of financial markets is an ongoing process throughout the world. Research shows that, from Australia through Europe to the United States, the capital and real estate markets are integrating, influencing each other. Although this process seems obvious, only research can show whether it actually occurs. Identifying these relationships is important for analyzing the entire market. Many methods, such as estimating the cost of equity, have been developed with the stock market in mind. Meanwhile, real estate valuation requires the cost of equity. Market integration is the rationale for using equity market methods on the real estate market.Aim of the work - the research is aimed at verifying whether there is cointegration between the secondary housing market and the stock market. A research hypothesis was put forward: the stock market and secondary housing market are integrated.Research methodology - the study used co-integration analysis using the Engle-Granger test. The study was conducted in the period from the third quarter of 2006 to the fourth quarter of 2018.Result - The tests carried out showed the existence of co-integration in one out of 36 cases for the explanatory variable - the delayed WIG index and the explained variable in the average price of residential real estate on the secondary market for the 7 largest Polish cities.Originality / Value - demonstrating the co-integration of markets justifies the use of analytical methods developed for stock markets on real estate markets. The research has no equivalent study on the Polish market. Similar analyses were carried out, but not for the stock and real estate market.
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Clements, Kenneth W., H. Y. Izan, and Yihui Lan. "Volatility and stock price indexes." Applied Economics 45, no. 22 (August 2013): 3255–62. http://dx.doi.org/10.1080/00036846.2012.703315.

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ZHOU, FENG, RONGQIU CHEN, and XINPING XIA. "FRACTAL CHARACTER OF STOCK PRICE-VOLUME RELATION AND REGULATION OF STOCK PRICE MANIPULATION." Fractals 11, no. 02 (June 2003): 173–81. http://dx.doi.org/10.1142/s0218348x03001586.

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We construct indexes of the price-volume relation and calculate correlation dimensions, based on the Fractal Market Hypothesis (FMH). According to this result, we propose a new method for the detection of stock price manipulation on the secondary market in China. The result of empirical research indicates: the great change of the stock price alone is insufficient to prove that there is stock price manipulation. However, the drastic fluctuations of correlation dimensions indicate the instability of the secondary market; and especially, the sudden drop of the correlation dimensions always means that there are stock manipulations on the market.
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Robiyanto, Robiyanto. "Performance Evaluation of Stock Price Indexes in the Indonesia Stock Exchange." International Research Journal of Business Studies 10, no. 3 (March 9, 2018): 173–82. http://dx.doi.org/10.21632/irjbs.10.3.173-182.

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Pan, Chung-Lien, and Yu-Chun Pan. "The Index and Stock Price Synchronicity: Evidence from Taiwan." E3S Web of Conferences 198 (2020): 04029. http://dx.doi.org/10.1051/e3sconf/202019804029.

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Research on stock synchronization has always been a topic of concern to scholars and investors. In the past, the focus was mainly on equity concentration, foreign shareholding, audit quality, and other issues, not including indexes. This article uses the monthly data of the Taiwan Stock Exchange Capital Weighted Stock Index (TAIEX) to solve the problem of the index and stock synchronization. And use the technical theory of the gray system to solve the small sample and uncertain problem. The discovery of the synchronization between these indexes and stock prices may provide investors with sufficient reference to make investment decisions.
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Wang, Zijun. "Predicting the rise and fall of Shanghai composite index based on artificial intelligence." E3S Web of Conferences 235 (2021): 03063. http://dx.doi.org/10.1051/e3sconf/202123503063.

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Shanghai composite index reflects the changes of stock prices, and the methods for various models to predict the stock index emerge one after another, and artificial intelligence is also widely used in various fields due to its stability and accuracy. In this paper, artificial intelligence is applied to Shanghai composite index to predict the stock index. A total of 3422 Shanghai composite indexes from January 1, 2005 to January 1, 2019 were collected, including five indexes: opening price, maximum price, closing price, minimum price and trading volume. Then MA, KDJ and MACD were selected as technical indexes, and their application methods and advantages in Shanghai composite index were analyzed in detail. In addition, in this paper, logistic regression and support vector machine (SVM) in artificial intelligence model were adopted to predict the ups and downs. Finally, it indicates that the support vector basis method based on radial basis is more suitable for stock index prediction model. In this paper, a framework of index prediction is provided by combining technical indicators with artificial intelligence.
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Zapata, Hector O., Joshua D. Detre, and Tatsuya Hanabuchi. "Historical Performance of Commodity and Stock Markets." Journal of Agricultural and Applied Economics 44, no. 3 (August 2012): 339–57. http://dx.doi.org/10.1017/s1074070800000468.

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This paper examines two interrelated issues in commodity markets, namely, the cyclical relationship between stocks and commodities and the function of commodity and agribusiness indexes in portfolios. A high negative correlation has existed between stock and commodity prices over the past 140 years. Moreover, the two markets have alternated in price leadership with 29-32-year cycles. The recent price dominance in agricultural commodities started in 2000, a result supported by the empirical results of the portfolio allocation analysis. For a risk-averse investor, irrespective of the period analyzed, placing funds in agribusiness and/or agricultural commodity indexes was sound investing.
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Dissertations / Theses on the topic "Stock price indexes Australia"

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Tilakaratne, Chandima University of Ballarat. "Stock market predictions based on quantified intermarket influences." University of Ballarat, 2007. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/12798.

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This research investigated the feasibility and capability of neural network-based approaches for predicting the direction of the Australian Stock market index (the target market). It includes several aspects: univariate feature selection from the historical time series of the target market, inter-market analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. Previous research on US stock markets and other international markets have shown that the neural network approach is one of most powerful techniques for predicting stock market behaviour. Neural networks are capable of capturing the non-linear stochastic and chaotic patterns in the stock market time series data. This study discovered that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the studied period of about 14 years. Multi-layer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. Two major testing methods: testing with randomly selected test data and forward testing, were examined and compared. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the target market. The architecture of this network consists of 33 input features, one hidden layer with 3 neurons and 4 output neurons. The best input features set includes the relative returns from 1 to 6 days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index.
Doctor of Philosophy
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Tilakaratne, Chandima. "Stock market predictions based on quantified intermarket influences." University of Ballarat, 2007. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/15394.

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This research investigated the feasibility and capability of neural network-based approaches for predicting the direction of the Australian Stock market index (the target market). It includes several aspects: univariate feature selection from the historical time series of the target market, inter-market analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. Previous research on US stock markets and other international markets have shown that the neural network approach is one of most powerful techniques for predicting stock market behaviour. Neural networks are capable of capturing the non-linear stochastic and chaotic patterns in the stock market time series data. This study discovered that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the studied period of about 14 years. Multi-layer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. Two major testing methods: testing with randomly selected test data and forward testing, were examined and compared. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the target market. The architecture of this network consists of 33 input features, one hidden layer with 3 neurons and 4 output neurons. The best input features set includes the relative returns from 1 to 6 days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index.
Doctor of Philosophy
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Tilakaratne, Chandima University of Ballarat. "A neural network approach for predicting the direction of the Australian stock market index." University of Ballarat, 2004. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/12804.

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This research investigated the feasibility and capability of neural network-based approaches for predicting the direction of the Australian Stock market index (the target market). It includes several aspects: univariate feature selection from the historical time series of the target market, inter-market analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. Previous research on US stock markets and other international markets have shown that the neural network approach is one of most powerful techniques for predicting stock market behaviour. Neural networks are capable of capturing the non-linear stochastic and chaotic patterns in the stock market time series data. This study discovered that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the studied period of about 14 years. Multi-layer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. Two major testing methods: testing with randomly selected test data and forward testing, were examined and compared. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the target market. The architecture of this network consists of 33 input features, one hidden layer with 3 neurons and 4 output neurons. The best input features set includes the relative returns from 1 to 6 days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index.
Master of Information Technology by Research
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Tilakaratne, Chandima. "A neural network approach for predicting the direction of the Australian stock market index." University of Ballarat, 2004. http://archimedes.ballarat.edu.au:8080/vital/access/HandleResolver/1959.17/15397.

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This research investigated the feasibility and capability of neural network-based approaches for predicting the direction of the Australian Stock market index (the target market). It includes several aspects: univariate feature selection from the historical time series of the target market, inter-market analysis for finding the most relevant influential markets, investigations of the effect of time cycles on the target market and the discovery of the optimal neural network architectures. Previous research on US stock markets and other international markets have shown that the neural network approach is one of most powerful techniques for predicting stock market behaviour. Neural networks are capable of capturing the non-linear stochastic and chaotic patterns in the stock market time series data. This study discovered that the relative return series of the Open, High, Low and Close prices of the target market, show 6-day cycles during the studied period of about 14 years. Multi-layer feedforward neural networks trained with a backpropagation algorithm were used for the experiments. Two major testing methods: testing with randomly selected test data and forward testing, were examined and compared. The best neural network developed in this study has achieved 87%, 81% 83% and 81% accuracy respectively in predicting the next-day direction of the relative return of the Open, High, Low and Close prices of the target market. The architecture of this network consists of 33 input features, one hidden layer with 3 neurons and 4 output neurons. The best input features set includes the relative returns from 1 to 6 days in the past of the Open, High, Low and Close prices of the target market, the day of the week, and the previous day’s relative return of the Close prices of the US S&P 500 Index, US Dow Jones Industrial Average Index, US Gold/Silver Index, and the US Oil Index.
Master of Information Technology by Research
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Wongbangpo, Praphan. "Dynamic analysis on ASEAN stock markets." access full-text online access from Digital dissertation consortium, 2000. http://libweb.cityu.edu.hk/cgi-bin/er/db/ddcdiss.pl?9982126.

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Eadie, Edward Norman. "Small resource stock share price behaviour and prediction." Title page, contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09CM/09cme11.pdf.

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Yiu, Fu-keung. "Time series analysis of financial index /." Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18003047.

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Tyandela, Luvo. "The construction of All SADC stock market indices." Thesis, Stellenbosch : Stellenbosch University, 2001. http://hdl.handle.net/10019.1/52499.

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Thesis (MBA)--Stellenbosch University, 2001.
This thesis presents a study on : (1) The construction of the SADC All Stock Market Indices, namely the SADIX (SADC Index Including South Africa) and the SADEX (SADC Index Excluding South Africa), which will serve as performance benchmarks for the region, and as indices for tracking the performance of the region excluding the JSE (2) Comparative analysis of the SADC bourses returns (3) Correlation Analysis between the SADC countries The SADC All Stock Market Indices, SADIX & SAD EX are market value, capitalization-weighted indices in which all components are weighted according to the total market value of their outstanding shares. They comprise all equity securities listed on the SADC region excluding Tanzania. Both series are calculated in local currencies and converted to US dollar terms, using end-af-week data with a base value of 1,000 as at 3rd September 1999. The dissertation presents a discussion on the regionalization of the African stock exchanges and how they this will impact the low liquidity levels which is endemic to most of the African Stock Exchanges. The results obtained indicate a significantly high correlation between the individual country indices with the SADe All Stock market Indices. Furthermore, observations are that the SADe stock exchanges show similar reactions to news flow and economic shocks. However, there are negative correlations, which will offer investors a fundamental basis for a diversification strategy in the region. Finally, the thesis concludes that despite the perception that African stock markets are in chaos, there are lucrative SADe markets, smaller in terms of size and market capitalization that will provide good returns.
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Lee, Sang H. "Index inclusion effect growth vs. value /." Diss., Connect to the thesis, 2008. http://hdl.handle.net/10066/1451.

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Chan, Kwei-sang, and 陳貴生. "Hongkong stock index future and portfolio management." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1989. http://hub.hku.hk/bib/B31264232.

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Books on the topic "Stock price indexes Australia"

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Hernesniemi, Hannu. HEX-indeksi =: The Helsinki Stock Exchange index. Helsinki: Elinkeinoelämän Tutkimuslaitos, 1990.

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Arms, Richard W. The Arms index (TRIN): An introduction tothe volume analysis of stock and bond markets. Homewood, Ill: Dow Jones-Irwin, 1989.

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Richard, Hermann-Josef. Aktienindizes: Grundlagen ihrer Konstruktion und Verwendungsmöglichkeiten unter besonderer Berücksichtigung des Deutschen Aktienindex--DAX. Bergisch Gladbach: J. Eul, 1992.

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Davydoff, Didier. Les indices boursiers. Paris: Economica, 1998.

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Birgit, Janssen. Der Deutsche Aktienindex DAX: Konstruktion und Anwendungsmöglichkeiten. Frankfurt am Main: F. Knapp, 1992.

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Beckmann, Thomas. Die Erfassung von Tendenzen des Aktienmarktes: Eine methodisch-statistische Untersuchung. Münster: Lit Verlag, 1989.

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Outpacing the pros: Using indexes to beat Wall Street's savviest money managers. New York: McGraw-Hill, 2001.

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Swee-Hock, Saw. The Malaysian all-share price indices, 1975-1982. [Singapore]: Singapore Securities Research Institute, 1986.

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Mohan, Neeraj. Artificial neural network models for forecasting stock price index in Bombay Stock Exchange. Ahmedabad: Indian Institute of Management, 2005.

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Kleeberg, Jochen M. Die Eignung von Marktindizes für empirische Aktienmarktuntersuchungen. Wiesbaden: Gabler, 1991.

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Book chapters on the topic "Stock price indexes Australia"

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Zarandi, Mohammad Hossein Fazel, Milad Avazbeigi, and Meysam Alizadeh. "A Neuro-Fuzzy Expert System Trained by Particle Swarm Optimization for Stock Price Prediction." In Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition, 633–50. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61350-429-1.ch031.

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In today’s competitive markets, prediction of financial variables has become a critical issue. Especially in stock market analysis where a wrong prediction may result in a big loss in terms of time and money, having a robust prediction is a crucial issue. To model the chaotic, noisy, and evolving behavior of stock market data, new powerful methods should be developed. Soft Computing methods have shown a great confidence in such environments where there are many uncertain factors. Also it has been observed through many experiments that the hybridization of different soft computing techniques such as fuzzy logic, neural networks, and meta-heuristics usually results in better results than simply using one method. This chapter presents an adaptive neuro-fuzzy inference system (ANFIS), trained by the particle swarm optimization (PSO) algorithm for stock price prediction. Instead of previous works that have emphasized on gradient base or least square (LS) methods for training the neural network, four different strategies of PSO are implemented: gbest, lbest-a, lbest-b, and Euclidean. In the proposed fuzzy rule based system some technical and fundamental indexes are applied as input variables. In order to generate membership functions (MFs), a robust noise rejection clustering algorithm is developed. The proposed neuro-fuzzy model is applied for an automotive part-making manufactory in an Asia stock market. The results show the superiority of the proposed model in comparison with the available models in terms of error minimization, robustness, and flexibility.
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Karasiński, Jacek. "The Investigation of a Weak Form of the Efficient Market Hypothesis: Evidence From Stock Markets in the European Union." In Management Challenges in the Era of Globalization, 107–16. University of Warsaw, 2019. http://dx.doi.org/10.7172/978-83-65402-94-3.2019.wwz.3.7.

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The purpose of this article is to examine whether returns of main indexes of selected stock exchanges in the European Union are subject to the random walk model proposed by L. Bacheliere in 1900, which is considered by many researchers to be a synonym of a weak form of the efficient market. The research was conducted for the main indexes of eight selected European stock exchanges representing markets of a different capitalisation. In order to check whether the level of informational efficiency was stable in a whole research period, namely in the years 2000-2017, the research period was divided into three equal six years sub-periods. To test a weak form of the efficient market hypothesis (EMH), four different tests of returns distribution normality were done for daily, weekly, monthly and quarterly intervals. The conducted study allowed for rejecting the null hypothesis saying that returns are subject to the random walk model proposed by L. Bacheliere which leads to normal distribution. Moreover, some significant differences between the research periods occurred. Nonetheless as the random walk model seems to be too strict even for the biggest European markets, it is proposed to test whether the returns can be subject to other stable distributions like the Pareto distribution, which gives higher probability to extremely low and high returns of what resembles actual price fluctuations of financial markets.
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Gatfaoui, Hayette. "Linking U.S. CDS Indexes with the U.S. Stock Market: A Multidimensional Analysis with the Market Price and Market Volatility Channels." In Risk Management for the Future - Theory and Cases. InTech, 2012. http://dx.doi.org/10.5772/33888.

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Uzun, Uğur, and Zafer Adalı. "Investigation for the Role of Oil and Natural Gas in the BIST Sector Indexes in Turkey." In Advances in Environmental Engineering and Green Technologies, 262–83. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8335-7.ch016.

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In this chapter, the authors aim to investigate the association between the primary energy sources' prices involving oil and natural gas and sectors indices operating the Turkey stock market for the period covering 2012M1-2021M3. Regarding energy price indicators, Brent oil and natural gas real-time future prices are preferred in the models, and BIST Industrials (XUSIN), BIST Chem-Petrol Plastic (XKMYA), and BIST Electricity (XELKT) indices are used as financial performance indicators. Fourier unit root tests improved by Becker et al. and Fourier co-integration tests improved by Tsong et al. are employed to investigate the relationship between considered variables. As a result of the models, it is found that the energy prices and financial performance index do not move together in the long run; in other words, change in oil and natural gas prices seem not to have an impact on the sector indexes.
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Conference papers on the topic "Stock price indexes Australia"

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Karcıoğlu, Reşat, Muhammet Özcan, and Ensar Ağırman. "The Relationship of Petroleum Price and BIST Sector Indexes." In International Conference on Eurasian Economies. Eurasian Economists Association, 2017. http://dx.doi.org/10.36880/c08.01878.

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Energy is not only indispensable element of everyday life, but also underlies industrialization and manufacturing. Energy and manufacturing have become integral parts with the importance of mechanization since the Industrial Revolution. As a result of this emerging situation, businesses, have become sensitive energy and energy prices. For this reason, changes in energy prices directly affect businesses and are thought to have effects on fluctuations in stock prices. Changes in the prices of primary energy sources directly or indirectly affect capital markets. In energy importer countries including Turkey, high energy prices cause an increase in current account deficit and decrease in real national income by increasing the amount of energy imports. In addition, high energy prices lead to cost-based inflation increases as they directly affect raw material prices used in production. All these factors indirectly affect capital markets. Direct effect of energy price changes on the capital market is explained by the fact that energy is an indispensable input in industrial production. In cases where the energy price increase is not reflected to the consumer, the profitability of the enterprise is decreasing. A decrease in profitability affects firm's stock price as well. The aim of this study is to reveal the relationship between sector indices in the Stock Exchange Istanbul (BIST) and oil price changes. Weekly data set for the period for 2006:1 - 2016:4 is used. Johannes co-integration method is used to measure long term relationship in the study.
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Dias, Rui, Paulo Alexandre, Paula Heliodoro, Hortense Santos, Ana Rita Farinha, and Márcia C. Santos. "The 2020 Oil Price War Has Increased Integration Between G7 Stock Markets and Crude Oil WTI." In 7th International Scientific Conference ERAZ - Knowledge Based Sustainable Development. Association of Economists and Managers of the Balkans, Belgrade, Serbia, 2021. http://dx.doi.org/10.31410/eraz.s.p.2021.13.

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This paper aims to examine whether the oil price war between Saudi Arabia and Russia has increased integration between the Crude Oil WTI Spot oil index and the G7 stock markets, namely France (CAC 40), Germany (DAX 30), USA (DOW JONES), UK (FTSE 100), Italy (FTSE MID), Japan (Nikkei 225), Canada (S&P TSX), from January 2018 to January 2021. The results show that in the period before the oil price war, the G7 stock markets and the WTI index had 29 integrations (out of 56 possible). The WTI index is integrated with the UK stock markets (FTSE 100), and Japan (NIKKEI 225), and is integrated into the Japanese market. In the period of the oil price war, the G7’s stock markets and the Crude Oil WTI Spot index had 43 integrations (out of 56 possible), namely the WTI, Dow Jones, and Nikkei 225 indexes, with all their peers (7 out of 7 possible). When comparing the period before and during the 2020 oil crash, we found that integrations increased significantly from 29 to 43 (out of 56 possible); we also found that the Crude Oil WTI Spot index is no longer a safe haven for portfolio diversification in G7 stock markets. These findings validate our research issue, i.e., the oil price war between Saudi Arabia and Russia had increased integrations, and this evidence could question portfolio diversification.
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Elmas, Bekir, and Ömer Esen. "Determining a Dynamic Relationship Between Stock Prices and Exchange Rates: An Empirical Study on Eurasia." In International Conference on Eurasian Economies. Eurasian Economists Association, 2010. http://dx.doi.org/10.36880/c01.00168.

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The stock price has a close relationship with some macroeconomic variables. As examples of the main macroeconomic variables can be shown that exchange rates, inflation, interest rate, growth rates. This paper empirically examined the relationship between the local stock market indexes and exchange rate (USD) in six Eurasian countries namely Turkey, Germany, France, Netherlands, Russia, France and India. The paper set out by testing existence of a long-term relationship between considered two variables using the Engle-Granger (1987), Johansen (1988, 1995) and Johansen-Juselius (1990) cointegration methods. Results of Engle- Granger cointegration test showed that there is no cointegration linkage between two variables under consideration. Furthermore, The Johansen cointegration test found that there is a long-term relationship between two variables (variables in the two countries). Under the VAR (Vector Autoregressive) and VEC (Vector Error Correction) models appllied the Granger causality test, revealed an unidirectional casual relationship between two variables in each of the six countries. In addition as regards the relationship While there is a unidirectional causal relationship running from exchange rate to stock market for four countries. However this relation is casual running from stock market to exchange rate for other two countries. According to the direction of the relationship these results that relationship between stock prices and exchange rate in four countries supports for the “Traditional Approach”. Furthermore, this relation also supports for the “Portfolio Approach” for other two countries.
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Bubić, Jasenka, and Luka Bašić. "GEOPOLITICAL RELATIONS WITH OIL AT THE TIME OF COVID-19: WITHOUT OIL THERE IS NO PRESENT, WITHOUT GREEN ENERGY THERE IS NO FUTURE." In NORDSCI Conference Proceedings. Saima Consult Ltd, 2021. http://dx.doi.org/10.32008/nordsci2021/b2/v4/09.

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Oil drives the entire world economy, and it is entirely a geostrategy issue. The strong development of the economy over the past few decades has provided a global stage for those countries that have a stable political establishment while managing enormous amounts of oil. Now, year after year, it is becoming increasingly clear that the importance of oil and gas is falling away, and it is those energy sources that bring about a reduction in the half-life that comes into the scene. Oil and gas are non-renewable energy sources and as such are naturally limited, therefore their reserve will become economically unprofitable in the future, and exploitation will reach its natural end. The aim of this research paper is divided into two structures: the first thesis concerns giving a fresh insight into the state of the oil market from the beginning of the pandemic to the present day. The issue of geopolitical relations between Riyadh and Moscow is to be addressed here and how much of a negative consequence the price war has left on their fiscal calculations, although geopolitical friction has deepened the shock further into financial markets. Thus, the fiscal calculation of both countries suffered revenue shocks, but it also prompted an even deeper decline in stock indexes and temporary stagflation of the global economy. The second thesis refers to a brief review of the analysis of the long-term future of non-renewable and renewable energy sources. The future of cleaner forms of energy is imperative, but also a challenging task, as this means shifting the entire structure of national economies to green and renewable. The focus is on giving insight into why this is a necessity, but also why there could be a dangerous precedent and negative cash flows in some structures of the economy. Currently, and any future planning and fulfillment of climate guidelines, must not lead to an increase in energy poverty and consequently a decrease in living standards, because in all geopolitical games the line is always drawn between rich and poor countries, that is, advanced economies and developing economies. Therefore, the long-term and global leaders in green and renewable energy sources will be those countries that successfully implement public interests in these projects, because only in this way can the goal be met – shifting a certain structure of the economy to cleaner sources while satisfying social utility and increasing employment.
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Reports on the topic "Stock price indexes Australia"

1

Dassanayake, Wajira, Xiaoming Li, and Klaus Buhr. A Revisit of Price Discovery Dynamics Across Australia and New Zealand. Unitec ePress, August 2015. http://dx.doi.org/10.34074/rsrp.039.

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This study re-investigates the price discovery dynamics of selected stocks cross-listed on the Australian Stock Exchange (ASX) and the New Zealand Stock Exchange (NZX) during a bear trading phase from January 2008 to December 2011. A differing price discovery dynamic in a bear market versus a bull market may occur because of variations in investor sentiments and disparities in the role of the stock prices. Using intraday data, we employ the vector error correction mechanism, Hasbrouck’s (1995) information share and Grammig et al.’s (2005) conditional information share methods. Consistent with previous research, we find that price discovery takes place mostly on the home market for the Australian firms and for all but one of the New Zealand firms. However, not seen in existing studies, we show that the NZX has grown in importance for both the Australian and New Zealand firms. This suggests that the NZX is deviating from being a pure satellite market.
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2

Dassanayake, Wajira, Xiaoming Li, and Klaus Buhr. A Revisit of Price Discovery Dynamics Across Australia and New Zealand. Unitec ePress, August 2015. http://dx.doi.org/10.34074/rsrp.039.

Full text
Abstract:
This study re-investigates the price discovery dynamics of selected stocks cross-listed on the Australian Stock Exchange (ASX) and the New Zealand Stock Exchange (NZX) during a bear trading phase from January 2008 to December 2011. A differing price discovery dynamic in a bear market versus a bull market may occur because of variations in investor sentiments and disparities in the role of the stock prices. Using intraday data, we employ the vector error correction mechanism, Hasbrouck’s (1995) information share and Grammig et al.’s (2005) conditional information share methods. Consistent with previous research, we find that price discovery takes place mostly on the home market for the Australian firms and for all but one of the New Zealand firms. However, not seen in existing studies, we show that the NZX has grown in importance for both the Australian and New Zealand firms. This suggests that the NZX is deviating from being a pure satellite market.
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