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1
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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Kurniasari, Florentina, and Juvy Reyes. "DETERMINANTS OF LQ 45 INDEX BANKING STOCK PRICE VOLATILITY." Ultima Management : Jurnal Ilmu Manajemen 12, no. 2 (December 28, 2020): 261–74. http://dx.doi.org/10.31937/manajemen.v12i2.1771.
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As a developing country, Indonesia needs capital flow from investment to support country’s development growth. Capital market is one form of source investment fund. Of the several indexes listed in Indonesia Stock Exchange, the LQ 45 Index is one of the indexes of concern to investors, in which banking is one of financial institution to support country’s economic development. Investors is concerned about the stock price volatility which is influenced by internal and external factors. In this research there are three internal factors and two external factors as independent variable. This research measured the stock price volatility by analyzing the effect of some factors including dividend yield, return on asset, asset growth, interest rate, and exchange rate of banking industries which registered in LQ 45 Index for year 2012 –2019. The data will be analyzed using multiple linear regression analysis model. The research shows that interest rate had positive influence on stock price volatility. While return on asset and exchange rate have negative effect to stock price volatility.
Key Words: Stock Price Volatility, Dividend, ROA, Interest, Exchange Rate
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Anusha, P., B. Dhushanthan, and T. Vinayagathasan. "The Relationship between Exchange Rate and Stock Market Performance: Empirical Evidence from Sri Lanka." Business and Economic Research 12, no. 2 (June 19, 2022): 135. http://dx.doi.org/10.5296/ber.v12i2.19822.
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The stock market is one of the fastest-growing sectors in the world at present. Such a stock market can be seen growing in Sri Lanka as well. The performance of a stock market is affected by various factors. Among those factors, foreign market stock prices, GDP, corporate performance, and the exchange rate are important. Of these, the exchange rate is the crucial one. Data of exchange rate depict the increasing pattern over time while stock market performance shows high fluctuation in Sri Lanka. Thus, this research aims to identify the impact of the exchange rate on the performance of the Colombo Stock Exchange (CSE). For this purpose, we used price index of all stocks, exchange rate, inflation, foreign direct investment, and interest rate as the variables. We employed annual secondary data from Central Bank of Sri Lanka over the period of 1985 – 2018. The Augmented Dickey Fuller and Phillips Perron unit root tests approaches confirmed that none of the variables are I(2) which allows us to examine the long-run relationship between the variables using Auto-Regressive Distributed Lag (ARDL) bounds testing method. AIC is suggested to employ ARDL(1,1,0,4,4) model among the top 20 models. The bounds testing results detected the cointegrating relationship between the variables. Our results also suggest that there is no correlation between exchange rate and all share price indexes in the long run, whereas there is a positive relationship between exchange rate and all share price indexes in the short run. Inflation has a positive impact on all share price indexes in the long run while it does not have significant impact on all share price indexes in the short run. Moreover, the interest rate has a negative and weakly significant impact on all share price indexes both in the long run and in the short run. The Granger causality test indicates that there is a unidirectional causality between the price index of all stocks and the exchange rate. Therefore the results of this research emphasize that the exchange rate can be used as a policy tool to increase stock market performance.
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Sahabuddin, Mohammad, Junaina Muhammad, Mohamed Hisham Dato' HjYahya, Sabarina Mohammed Shah, and Mohammad Mizanur Rahman. "The Co-Movement between Shariah Compliant and Sectorial Stock Indexes Performance in Bursa Malaysia." Asian Economic and Financial Review 8, no. 4 (March 30, 2018): 515–24. http://dx.doi.org/10.18488/journal.aefr.2018.84.515.524.
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Islamic financial market is the growing innovation of global financial market which moves together with conventional and sectorial counterpart in many countries. As the fastest growing investment component, Shariah compliant stock index in Bursa Malaysia has picked up positive momentum and attracted more attention to the investors, policy makers, issuers and researchers. The main objective of this study is to investigate the co-movement between Shariah compliant stock and sectorial stocks indexes performance in Bursa Malaysia using a standard time series techniques. For understanding a long run and short run co-movement among the Shariah compliant stock index, composite stock index and sectorial stock indexes, a co-integration approach, Vector Error Correction Model (VECM) have been applied respectively in this study. In addition, Granger causality test have been adopted to determine the lead-lag relationship. The findings show that in the long run, Shariah compliant index stock price and sectorial indexes stock price move together but in short run, speed of adjustment varies among the variables. Ganger causality test shows that there are bidirectional, unidirectional and no causality relationships between Shariah compliant and sectorial indexes.
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Majewska, Agnieszka. "Using Sectoral Indexes to Discount the Exercise Price of Employee Stock Options." Folia Oeconomica Stetinensia 16, no. 1 (December 1, 2016): 174–85. http://dx.doi.org/10.1515/foli-2016-0010.
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Abstract Employee stock options (ESOs) are an instrument in compensating top management of corporations. In the literature, they are described as a variable component of remuneration of a long-term character (Borkowska, 2012). There are six characteristic elements of the ESO: a grant date, the ESO plan duration, employees entitled to receive options, vesting criteria, a vesting period, and an exercise price. The article refers to the exercise price. The remuneration of employees is determined by the option’s intrinsic value, i.e. the difference between the current stock price and the exercise price. This difference affects the costs incurred by a company in relation with their incentive stock option plan. In this connection, the exercise price of stock options needs to be analysed. The literature shows that usually the strike price is equal to the stock market’s value at the time the option is granted. The options issued with an exercise price equal to the market value of the company’s stock on the date of the grant usually lead to at-the-money options. Walker (2009) mentions that almost all options issued by US firms have been such type of options. Hence, the options with exercise prices less than the prices of the underlying assets have been rarely observed. One of the solutions can be discounting the exercise price by using sectoral indexes, which are sensitive to changes on a particular market. The purpose of this paper is to address several aspects of specifying the exercise price in ESOs. The research shows how sector indexes can be used to discount it. Using sectoral indexes in determining the exercise price can partly limit the unreasonably high profits from the ESO. The literature does not provide ready-made formulas of exercise prices based on specific variables. The aim of the research is to present and apply the formula of the exercise prices in which sectoral indices are used to discount. The data are from the Warsaw Stock Exchange (WSE) and include those companies that revealed the information concerning their incentive programs in 1999–2013. The relevant data come from annual reports, current reports, supervisory boards’ resolutions, and press announcements.
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Mazouz, Khelifa, Nathan Lael Joseph, and Clement Palliere. "Stock index reaction to large price changes: Evidence from major Asian stock indexes." Pacific-Basin Finance Journal 17, no. 4 (September 2009): 444–59. http://dx.doi.org/10.1016/j.pacfin.2008.11.001.
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Borowski, Krzysztof. "Sensitivity of the Art Market to Price Volatility." Finanse i Prawo Finansowe 2, no. 26 (June 30, 2020): 11–36. http://dx.doi.org/10.18778/2391-6478.2.26.02.
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The purpose of the article: The art market becomes very popular among investors, when there is strong turbulence on the stock market. In times of calm, the art market is used by investors to diversify risk and build more efficient investment portfolios according to the Markovitz’s theory. The aim of this paper is to: (i) present the peculiarity of investment on the art market, represented by art market indexes in comparison to traditional investments in other financial market segments (money market, equity indexes and commodity market), (ii) to verify the hypothesis of normality of the distribution of rates of return of the analyzed art market indices as well as (iii) to analyze calendar effects occurrence on the art market.Methodology: Comparison of rates of return on the stock, bond, commodity and money markets with rates on the art market in four different time intervals. For each of the analyzed periods, an income-risk map was presented, taking into account the spectrum of financial instruments, including six art indexes: Old Masters, 19th Century, Modern art, Post War art, Contemporary art and Global art. The hypothesis of normality of the distribution of rates of return of the art market indices for four analyzed periods was verified with the use of Jarque-Bera test.Results of the research: Comparison of rates of return on the stock market and art market leads to the conclusion that their relationship depends on the period chosen. For two of the analyzed periods, the rates of return on the stock market were higher than on the art market, but for others periods, the opposite. The distribution of quarterly rates of return resulted to be a normal distribution for almost all of analyzed indices and time periods. Calendar effects were observed in the case of four analyzed indexes.
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Chiang-Lin, Tsung-Jui, Yong-Shiuan Lee, Tzong-Hann Shieh, Chien-Chang Yen, and Shang-Yueh Tsai. "Study of Asian indexes by a newly derived dynamic model." PLOS ONE 17, no. 5 (May 2, 2022): e0266600. http://dx.doi.org/10.1371/journal.pone.0266600.
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We take the stock prices as a dynamic system and characterize its movements by a newly derived dynamic model, called the new Price Reversion Model (nPRM), for which the solution is derived and carefully analyzed under different circumstances. We also develop a procedure of applying the nPRM to real daily closing prices of a stock index. This proposed procedure brings a different perspective to the study of stock prices based on thermodynamics, and the time varying coefficients in the nPRM offer economic meanings of the stock movements. More specifically, the average of smoothed historical data A in the nPRM, analogous to the environment temperature in the Newton’s law of cooling, represent an implied equilibrium price. The heat transfer coefficient κ is adapted to be either negative or positive, which illustrates the speed of convergence or divergence of stock prices, respectively. The empirical study of ten Asian stock indexes shows that the nPRM accurately characterizes and forecasts the market values.
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Corrado, Carol, David Martin, and Qianfan Wu. "Innovation α: What Do IP-Intensive Stock Price Indexes Tell Us about Innovation?" AEA Papers and Proceedings 110 (May 1, 2020): 31–35. http://dx.doi.org/10.1257/pandp.20201056.
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Patents and other intellectual property (IP) have grown in relative importance in investments and market capitalizations of public firms (e.g., Corrado and Hulten 2010). This paper illustrates the construction of IP-intensive stock price indexes, focusing on a network analysis tool (Martin 2001, Winer et al. 2003, Luse and Martin 2014) that helps pinpoint firms that are most likely to generate value from their intangible assets. The analysis finds that (a) stock price indexes constructed using the tool yield above-average returns and (b) stock prices of US companies in two tech-driven sectors outperform non-US firms despite lower average patent portfolio valuations.
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Arman, Agus, and Yuyun Karystin Meilisa Suade. "Analysis of the Sectoral Stock Price Index on the IDX during the Covid-19." Jurnal Minds: Manajemen Ide dan Inspirasi 9, no. 1 (May 9, 2022): 79–90. http://dx.doi.org/10.24252/minds.v9i1.25451.
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In this study, we want to measure the performance of the average monthly return of sectoral stock indexes in Indonesia by using the composite index (JCI) as a comparison. We want to find the winner, loser, and neutral stock indexes. We use monthly data from March 2020 to February 2021, namely the sectors: agriculture, mining, essential industry, various industries, consumption, property, infrastructure, finance, and trade. We use panel data analysis from the event study to achieve the research objectives, a combination of cross-section data and time-series data. The research results showed six sectors of the stock index as winners: agriculture, mining, essential industry, infrastructure, finance, and trade. There are three loser stock index sectors: the miscellaneous sector, consumption, and property.
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Feng, Yalan, and James Frank Refalo. "The Price Transmission in European Stock Markets." Applied Finance Letters 7, no. 1 (June 15, 2018): 2–12. http://dx.doi.org/10.24135/afl.v7i1.72.
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We investigate the dynamic price relationships among ten major stock indexes in Europe before, during and after the recent financial crisis. Using an error-correction model we find that the stock markets are cointegrated with three cointegrating vectors before the crisis and that the markets are cointegrated with only one cointegrating vector during and after the crisis. We further apply directed acyclic graph (DAG) analysis on the contemporaneous correlations innovation matrix to explore the instantaneous transmission pattern. The results show that France and Spain appear to share leadership roles before the crisis while leadership role is less obvious during and after the crisis. We also find a decreasing number of instantaneous casual relationships between the markets after the crisis, indicating that the markets are becoming more independent.
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Saputro, Nugroho. "The Effect of Indonesian Government’s Debt to the US and Greece on Composite Stock Price Index in ASEAN-5 and Australia." Sebelas Maret Business Review 4, no. 1 (November 27, 2019): 1. http://dx.doi.org/10.20961/smbr.v4i1.36062.
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<p>The global economic crisis has become a nightmare for other countries when the crisis is originated from a multipower country. A financial crisis that hit European countries (Greece) in 2010 and the United States (US) in 2011 can be categorized as a financial crisis caused by a high state’s debt that leads to default. The response to the financial crisis is reflected in capital market players’ reactions, where other countries will respond to a particular endemic financial crisis. The objectives of this research are (1). Analyze the Impulse Response Function (IRF) of the Composite Stock Price Index of the US on Composite Stock Price Index in Indonesia, Malaysia, Singapore, Vietnam, Thailand, and Australia. (2). Analyze the Impulse Response Function (IRF) of the Composite Stock Price Index of Greece on the Composite Stock Price Index in Indonesia, Malaysia, Singapore, Vietnam, Thailand, and Australia. (3). Analyze the Forecasting Error Variance Decomposition (FEVD) of the Composite Stock Price Index of Indonesia on the Composite Stock Price Index of Malaysia, Singapore, Vietnam, Thailand, and Australia. The analysis will be conducted using VAR (Vector Autoregression). The result shows that all variables are responded to the financial crisis that happened in Greece and the US. This is reflected by the shocks created by the financial crisis in ASEAN-5 countries and Australia. On the other hand, the Composite Stock Price Index of Indonesia is also affected by Malaysia and Singapore.</p>
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Hui, GAO, and GAO Tian Chen. "Crude Oil Price Shocks and Stock Market Volatility: Evidence From China." Review of European Studies 14, no. 4 (October 30, 2022): 39. http://dx.doi.org/10.5539/res.v14n4p39.
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As the first international futures variety in China, crude oil futures, its price influence and function play has attracted much attention at home and abroad, from the perspective of market performance, crude oil futures have had a greater impact on the capital market since its launch, and what needs to be further studied is the quantitative degree and complexity of the impact of crude oil futures price fluctuations on stock market fluctuations. The daily data from March 26, 2018 to July 5, 2022 were selected to study the influence of domestic crude oil futures prices on domestic Shanghai and Shenzhen stock index by Granger causality, cointegration test and smooth transition regression model. The study shows that the price and yield of domestic crude oil futures have a one-way guiding effect on the domestic Shanghai and Shenzhen stock indexes and yields, but their guiding effect on the Shenzhen component index is greater than that of the Shanghai Composite Index. Domestic crude oil futures prices and Shanghai and Shenzhen stock indexes have a long-term similar negative cointegration relationship. The positive and negative impact of the domestic crude oil futures price yield on Shanghai and Shenzhen stock index yields is non-linear and asymmetrical, but the mechanism of impact on the two stock markets is different, for the Shanghai stock market, the negative impact of the crude oil futures price yield is greater than the positive shock impact, for the Shenzhen stock market, the positive impact of the crude oil futures price yield is greater than the negative shock impact, and the impact on both stock markets was limited .Therefore, for domestic crude oil futures to become the global crude oil price benchmark, they also need to be continuously improved in terms of national policies, industry supervision, exchange rules and market system construction.
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Rudzkis, Rimantas, Roma Valkavičienė, and Virmantas Kvedaras. "Prediction of Baltic Sectorial Share Price Indices." Lietuvos statistikos darbai 53, no. 1 (December 20, 2014): 53–59. http://dx.doi.org/10.15388/ljs.2014.13894.
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Extending the research started in [31], the paper uses econometric methods for the short-term forecasting of quarterly values of sector indexes of stock prices from the OMX Baltic stock exchange. The ARMA models and modelling methodology that was used to build the statistical models in the previous paper are now augmented with the algorithms of time series aggregation and identification of special features of the series. Here, the search for informative factors relies on the study of related literature. The specification of models is further tailored using the traditional significance (p-value) analysis of regressors and a cross-validation analysis. The latter is implemented in this paper using the Jack-knife approach. The data period analysed covers the years 2000–2013. The results of the analysis indicate that the inclusion not only of recent autoregressive terms but also of some aggregated characteristics (as certain special features of indexes) improves the precision of forecasting substantially. The calculations were performed using the statistical analysis software SAS.
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Hindle, Don, and Julie Newman. "Hospital input price indexes in Australia: Are they worth the effort?" Australian Health Review 19, no. 3 (1996): 28. http://dx.doi.org/10.1071/ah960028.
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This paper summarises aspects of the design and use of hospital input price indexes,and describes four indexes produced in Australia in the last decade. It argues that therewould be some benefit in establishing a routine national index, if it were designedto be low-cost.However, care should be taken to avoid excessive reliance on the results in the resourceallocation and funding context. Input prices contribute relatively little to hospitals?expenditure changes. It is also necessary to monitor and manage changes in thevolumes of inputs, and this is likely to be a more rewarding task.
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Lee, Bi-Juan, Chin Wei Yang, and Bwo-Nung Huang. "Oil price movements and stock markets revisited: A case of sector stock price indexes in the G-7 countries." Energy Economics 34, no. 5 (September 2012): 1284–300. http://dx.doi.org/10.1016/j.eneco.2012.06.004.
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Štifanić, Daniel, Jelena Musulin, Adrijana Miočević, Sandi Baressi Šegota, Roman Šubić, and Zlatan Car. "Impact of COVID-19 on Forecasting Stock Prices: An Integration of Stationary Wavelet Transform and Bidirectional Long Short-Term Memory." Complexity 2020 (July 20, 2020): 1–12. http://dx.doi.org/10.1155/2020/1846926.
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COVID-19 is an infectious disease that mostly affects the respiratory system. At the time of this research being performed, there were more than 1.4 million cases of COVID-19, and one of the biggest anxieties is not just our health, but our livelihoods, too. In this research, authors investigate the impact of COVID-19 on the global economy, more specifically, the impact of COVID-19 on the financial movement of Crude Oil price and three US stock indexes: DJI, S&P 500, and NASDAQ Composite. The proposed system for predicting commodity and stock prices integrates the stationary wavelet transform (SWT) and bidirectional long short-term memory (BDLSTM) networks. Firstly, SWT is used to decompose the data into approximation and detail coefficients. After decomposition, data of Crude Oil price and stock market indexes along with COVID-19 confirmed cases were used as input variables for future price movement forecasting. As a result, the proposed system BDLSTM + WT-ADA achieved satisfactory results in terms of five-day Crude Oil price forecast.
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Wang, Chenyu. "Pattern Classification of Stock Price Moving." Frontiers in Computing and Intelligent Systems 2, no. 2 (December 26, 2022): 32–41. http://dx.doi.org/10.54097/fcis.v2i2.3754.
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The stock is one of the most important instruments of finance. However, the tendency of stock always has a high level of irregularity. In stock market, the stock price moving is considered as a time series problem. Clustering method on stock data is one of the machine learning methods and it is one of the most important analysis methods of technical analysis. The aim of this project is to find an efficient unsupervised learning way to analysis the stock market data to make classification of the patterns on different stock price moving data and get useful information for investment decisions by implementing different clustering algorithms. For this aim, the research objective of this project is to compare several of clustering methods like K-means algorithm, EM algorithm, Canopy algorithm, specify the best number of clusters for each clustering method by several evaluation indexes, show the result of each clustering method and make evaluation on the results of these clustering methods on stock market data of standard S&P 500 stock marketing data. In addition, Weka 3 and Matlab are used to implement the clustering methods and evaluation program. Data visualization shows clearly that those public companies in the same cluster have similar stock price moving pattern. The experiment shows the result that K-means algorithm and EM algorithm perform effectively in stock price moving and Canopy algorithm can be used before K-means algorithm to improve the efficiency.
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WANG, HONG-YONG, HONG LI, and JIN-YE SHEN. "A NOVEL HYBRID FRACTAL INTERPOLATION-SVM MODEL FOR FORECASTING STOCK PRICE INDEXES." Fractals 27, no. 04 (June 2019): 1950055. http://dx.doi.org/10.1142/s0218348x19500555.
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Forecasting stock price indexes has been regarded as a challenging task in financial time series analysis. In order to improve the prediction accuracy, a novel hybrid model that integrates fractal interpolation with support vector machine (SVM) models has been developed in this paper to forecast the time series of stock price indexes. For this, a new method to calculate the vertical scaling factors of the fractal interpolation iterated function system is first proposed and an improved fractal interpolation model is then established. The improved fractal interpolation model and the SVM model are integrated to predict the every 5-min high frequency index data of Shanghai Composite Index. The experimental results show that the hybrid model is suitable for forecasting the stock index time series with fractal characteristics. In addition, a comparison of the prediction accuracy is carried out among the hybrid model and other three commonly used models. The results show that the prediction performance of the hybrid model is superior to that of other three models.
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Mahardika, Alamsyah Noval, and Whinarko Juliprijanto. "ANALYSIS OF FACTORS AFFECTING CHANGES IN JCI VALUE ON THE INDONESIA STOCK EXCHANGE." MARGINAL : JOURNAL OF MANAGEMENT, ACCOUNTING, GENERAL FINANCE AND INTERNATIONAL ECONOMIC ISSUES 2, no. 1 (September 5, 2022): 140–56. http://dx.doi.org/10.55047/marginal.v2i1.369.
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This research aims empirical evidence regarding the impact of the inflation rate, the BI Rate, the dollar exchange rate, and the interest rate on the Composite Stock Price Index (JCI). Composite Stock Price Index (JCI) is one of the indexes that investors frequently consider when making investments on the Indonesia Stock Exchange. Therefore, the authors wish to investigate the factors that influence the CSPI in greater detail. This study's population comprises the overall annual data for the inflation rate, BI Rate, Exchange Rate, and Composite Stock Price Index (JCI) from 1991 to 2020. The sampling method employed is a saturated sample in which the entire population is represented. This study employs the ECM technique. The results indicated that the inflation rate, the BI Rate, and the Dollar Exchange Rate (USD/IDR) partially influenced the Composite Stock Price Index (JCI).
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Tang, Xiaobin, Nuo Lei, Manru Dong, and Dan Ma. "Stock Price Prediction Based on Natural Language Processing1." Complexity 2022 (May 6, 2022): 1–15. http://dx.doi.org/10.1155/2022/9031900.
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The keywords used in traditional stock price prediction are mainly based on literature and experience. This study designs a new text mining method for keywords augmentation based on natural language processing models including Bidirectional Encoder Representation from Transformers (BERT) and Neural Contextualized Representation for Chinese Language Understanding (NEZHA) natural language processing models. The BERT vectorization and the NEZHA keyword discrimination models extend the seed keywords from two dimensions of similarity and importance, respectively, thus constructing the keyword thesaurus for stock price prediction. Furthermore, the predictive ability of seed words and our generated words are compared by the LSTM model, taking the CSI 300 as an example. The result shows that, compared with seed keywords, the search indexes of extracted words have higher correlations with CSI 300 and can improve its forecasting performance. Therefore, the keywords augmentation model designed in this study is helpful to provide references for other variable expansion in financial time series forecasting.
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Su, Zhi, and Bo Yi. "Research on HMM-Based Efficient Stock Price Prediction." Mobile Information Systems 2022 (March 7, 2022): 1–8. http://dx.doi.org/10.1155/2022/8124149.
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Stock market is one of the most important parts of the investment market. Compared with other industries, the stock market not only has a higher rate of return on investment but also has a higher risk, and stock price prediction has always been a close concern of investors. Therefore, the research on stock price prediction methods and how to reduce the error of stock price prediction has become a hot topic for many scholars at home and abroad. In recent years, the development of computer technology such as machine learning and econometric method makes the stock price prediction more reliable. Due to the hidden Markov nature of stock price, this paper proposes a stock price prediction method based on hidden Markov model (HMM). To be specific, since the data of stock price have continuity in time series, it is necessary to extend the discrete HMM to the continuous HMM, and then put forward the up and down trend prediction model based on the continuous HMM. The first-order continuous HMM is extended to the second-order continuous HMM, and the stock price is predicted by combining the prediction method of fluctuation range. As a result, the proposed second-order continuous HMM-based stock price prediction model is simulated on Hang Seng Index (HSI), one of the earliest stock market indexes in Hong Kong. The evaluation results on six months HSI show that the predicted value of the proposed model is very close to the actual value and outperforms three benchmarks in terms of RMSE, MAE, and R2.
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Qin, Peng, and Manying Bai. "Does oil price uncertainty matter in stock market volatility forecasting?" PLOS ONE 17, no. 12 (December 28, 2022): e0277319. http://dx.doi.org/10.1371/journal.pone.0277319.
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We analyze whether oil price uncertainty and U.S. stock uncertainty can simultaneously provide additional information to volatility forecast of six major stock indexes. For model settings, we find not only the uncertainty information of previous day, but that of previous week and month will also provide incremental predictive power for the stock market volatility. Based on that, from in-sample and out-of-sample perspective, the empirical evidences imply separately incorporating oil price uncertainty into the model can significantly improve the stock market volatility forecasting performance, but the improvements vanish after controlling the effects of volatility spillover from U.S. stock market while the effect of U.S. stock uncertainty is nonnegligible and sustainable for stock volatility forecasting. We confirm this finding from average and dynamic perspective. We further proceed the process in longer-horizon volatility forecasting, the evidences cannot overturn our conclusion. This conclusion implies that we should be cautious about the stock volatility predictability based on the oil price uncertainty, which further provide some important implications for researchers, regulators and investors.
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Bukovina, Jaroslav. "The Impact of Economic Agents Perceptions on Stock Price Volatility." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 63, no. 4 (2015): 1229–34. http://dx.doi.org/10.11118/actaun201563041229.
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This paper studies perceptions of economic subjects and its impact on stock prices. Perceptions are represented by stock market indexes and Facebook activity. The contribution of this paper is twofold. In the first place, this paper analyzes the unique data of Facebook activity and proposes the methodology for employment of social networks as a proxy variable which represents the perceptions of information in society related to the specific company. The second contribution is the proposal of potential link between social network principles and theories of behavioral economics. Overall, the author finds the negative impact of Facebook activity on stock prices and the positive impact of stock market indices. The author points the implications of findings to protection of company reputation and to investment strategy based on the existence of undervalued stocks.
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Chu, Chen-Chin, and Edward L. Bubnys. "EMPIRICAL EVIDENCE OF SPOT AND FUTURES PRICE VOLATILITY IN THREE STOCK INDEXES." Financial Review 22, no. 3 (August 1987): 33. http://dx.doi.org/10.1111/j.1540-6288.1987.tb01167.x.
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Guesmi, Khaled, Frederic Teulon, and Amine Lahiani. "Australias Integration Into The ASEAN-5 Region." Journal of Applied Business Research (JABR) 29, no. 6 (October 29, 2013): 1607. http://dx.doi.org/10.19030/jabr.v29i6.8198.
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This paper attempts to evaluate the time-varying integration of Australian stock market in ASEAN-5 region (ASEAN + Australia, Korea, China, India and Japan) by using a conditional version of the international capital asset pricing model (ICAPM) allowing for dynamic changes in the degree of market integration, regional market risk price, currency risk price and domestic market risk price. Main findings are as follows: i) the prices of risk in Australia are extremely sensitive to major international economic and political events such as the different monetary and financial crises in international financial market; ii) the level of market openness and development of the stock market satisfactorily explain the time-varying degree of Australian stock integration.
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Chang, To-Han, Nientsu Wang, and Wen-Bin Chuang. "Stock Price Prediction Based on Data Mining Combination Model." Journal of Global Information Management 30, no. 7 (September 2022): 1–19. http://dx.doi.org/10.4018/jgim.296707.
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Predicting stock indexes is a common concern in the financial world. This work uses neural network, support vector machine (SVM), mixed data sampling (MIDAS), and other methods in data mining technology to predict the daily closing price of the next 20 days and the monthly average closing price of the future expected daily closing price on the basis of the market performance of stock prices. Additionally, by the mutual ratio of weighted mean square error the study achieves the best prediction result. Combining value investment effectively with nonlinear models, a complete stock forecasting model is established, and empirical research is conducted on it. Results indicate that SVM and MIDAS have good results for stock price forecasting. Among them, MIDAS has a better mid-term forecast, which is approximately 10% higher than the forecast accuracy of the SVM model; Meanwhile, SVM is more accurate in the short-term forecast.
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Luxianto, Rizky, Usman Arief, and Muhammad Budi Prasetyo. "Day-of-the-Week Effect and Investors’ Psychological Mood Testing in a Highly Mispriced Capital Market." Journal of Indonesian Economy and Business 35, no. 3 (September 16, 2020): 257. http://dx.doi.org/10.22146/jieb.54377.
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Research Aims: This research examines investors’ psychological moods which cause day-of-the-week anomalies in highly mispriced stock markets. Design/methodology/approach: We use a sample from the Indonesian capital market as, in the Asian region, this country is considered to have a highly mispriced capital market. We decompose the stock price index in Indonesia into speculative, less speculative, and non-speculative indexes. We employ the mean and variance regressions to control the heteroscedasticity and serial correlation. Novelties: Our novelties are two fold. We postulate a method to decompose stock price indexes in Indonesia (the JKSE, LQ 45, and Kompas 100) into speculative, less speculative, and non-speculative indexes. Secondly, we estimate the mean and variance levels simultaneously to get a robust estimation result of the anomaly. Research Findings: We empirically find that the behavior mood hypothesis is supported only during normal periods, when investors tend to be irrational and use their good mood to trade on speculative stocks on a Wednesday and sell them on Monday. In other periods, rationality and psychological effects play a role with Indonesian investors, when their mood is good they are more active in trading less speculative stocks, to avoid higher risks and earn higher returns from those less speculative and non-speculative stocks.
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Azar, Samih Antoine. "Irrelevance of inflation: The Dow stocks." Accounting and Finance Research 9, no. 1 (January 5, 2020): 45. http://dx.doi.org/10.5430/afr.v9n1p45.
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The irrelevance of inflation is a proposition, inherited from corporate finance, which states that inflation is irrelevant for the valuation of nominal and real stock prices. In other terms, Net Present Values (NPVs) and stock returns are independent of the inflation rate. The issue at stake is both theoretical and empirical, although the first came much before the latter. In the empirical realm, stock returns are found to be statistically negatively related to inflation. However, and theoretically, the classical school predicted that they should be related positively one-to-one. Moreover long run analysis, that came later, found that stock prices are positively related to price indexes. This stems from the fact that stocks are claims upon real assets, and, therefore, should be a hedge against inflation with the same one-to-one relation. This paper differs by subjecting all these hypotheses to the individual stocks included in the Dow Jones Industrial Index, and not to returns calculated from stock indexes, which is the usage. The empirical results in this paper support strongly the irrelevance of inflation. This is true whatever the price index, whatever the econometric procedure, whatever the industry to which the stock belongs, and whatever the specification of the model. Hence inflation is neither negatively nor positively related to stock returns, whether nominal or real.
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Sahoo, Aditya Prasad. "An Empirical Study on Movement of Stock Market of BRIC Economies- Are they Co-Integrated?" ComFin Research 9, no. 4 (October 1, 2021): 11–16. http://dx.doi.org/10.34293/commerce.v9i4.4225.
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The major objective of this article is to assist the BRICS nations’ foreign investment decisionmaking process, as well as the creation or changes in policies by these nations’ characteristics. The context is crucial for foreign investors considering diversification advantages internationally, as well as policymakers responding to the aforesaid economies’ growth. This study examines the interconnections between the stock indexes of the BRIC economies. The goal of the research is to look at the long-term link between stock market indexes. From January 2010 to December 2020, the researcher utilized the index’s monthly closing price. To get the ADF at the first-order difference, all of the data is utilized in its raw form. The co-integration method is employed to determine the connection between stock indexes. The causal influence on stock market indices is studied using Granger causality. The sample considers countries such as Brazil, Russia, India, and China. The goal of the research is to look at the long-term link between stock market indexes. It is found that Sensex has the highest return among others, followed by SHCOMP, MOEX and BOVESPA. It is also found that the standard deviation of MOEX is high, followed by SENSEX, SHCOMP and BOVESPA. From the causality analysis, it is found Bi-directional relationship between India and China stock market. Whereas in the case of the other two markets, i.e., Brazil and Russia, the relationship with the Indian stock market are neither Uni-directional nor Bi-directional.
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Lee, Donghyuk, KyungJun Kang, and Hojin Yang. "Logistic regression model and Application using Functional Data Method." Korean Data Analysis Society 24, no. 3 (June 30, 2022): 971–82. http://dx.doi.org/10.37727/jkdas.2022.24.3.971.
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Stock prices around the world dramatically decreased in 2020 due to COVID-19. Although the general trend seemed to be rebounding in 2021, the degree of recovery might vary by industry. This paper intends to investigate the relationship between the type of the industry companies and the pattern of individual stock indexes for 2021. Specifically, we investigate whether the functional pattern of individual stock indexes can be an important factor that can distinguish the type of the industry company. Traditionally, the stock price data has been analyzed using a time series approach. However, with the recent development of automated devices, the interest on the functional data analysis has been increasing. If individual stock prices are assumed to be random functions defined above continuous time, functional data techniques can be applied. Following this approach, we propose to use a functional logistic regression model that can explain the type of the industry company with the pattern of individual stock prices especially for 2021.
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Jariyapan, Prapatchon, Jittima Singvejsakul, and Chukiat Chaiboonsri. "A Machine Learning Model for Healthcare Stocks Forecasting in the US Stock Market during COVID-19 Period." Journal of Physics: Conference Series 2287, no. 1 (June 1, 2022): 012018. http://dx.doi.org/10.1088/1742-6596/2287/1/012018.
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Abstract This paper study the nowcasting and forecasting for the healthcare stock price in the united states during the Covid-19 period including the google trend data information. The data is collected in monthly data from 2015 to 2020 which are five interested stock price indexes in the healthcare sector. Empirically, the finding reveals that the Bayesian structural time series analysis can be used to investigate the stock price indexes with the google trend data is becoming useful for the prediction in term of current movement. In term of the machine learning algorithms, the unsupervised learning k-Mean algorithm is employed to cluster the cycle regimes of the stock market which provided three regimes such as Bull market, Sideways and Bear market. There are twenty-nine months stand for bull market, thirty-seven months are predictively provided sideways market and five months are referred as the bear market. Additionally, the supervised learning algorithms by using the Linear Discriminant Analysis (LDA), k-Nearest Neighbors (kNN) and Support vector machine (SVM) are used to investigate the cycle regimes of healthcare stock in next five year. The results indicated that LDA is chosen by the highest coefficient validation which represented the the regimes of stock in the healcare sector of the unites states of America will stay on the sideways periods in the next five years. Thus, the finding in this paper can be the useful information for investor to manage their portfolio especially, in healthcare sector during the Covid-19 period.
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Li, Hui, Jinjin Hua, Jinqiu Li, and Geng Li. "Stock Forecasting Model FS-LSTM Based on the 5G Internet of Things." Wireless Communications and Mobile Computing 2020 (June 20, 2020): 1–7. http://dx.doi.org/10.1155/2020/7681209.
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This paper analyzed the development of data mining and the development of the fifth generation (5G) for the Internet of Things (IoT) and uses a deep learning method for stock forecasting. In order to solve the problems such as low accuracy and training complexity caused by complicated data in stock model forecasting, we proposed a forecasting method based on the feature selection (FS) and Long Short-Term Memory (LSTM) algorithm to predict the closing price of stock. Considering its future potential application, this paper takes 4 stock data from the Shenzhen Component Index as an example and constructs the feature set for prediction based on 17 technical indexes which are commonly used in stock market. The optimal feature set is decided via FS to reduce the dimension of data and the training complexity. The LSTM algorithm is used to forecast closing price of stock. The empirical results show that compared with the LSTM model, the FS-LSTM combination model improves the accuracy of prediction and reduces the error between the real value and the forecast value in stock price prediction.
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Hamdi, Haykel, and Jihed Majdoub. "Risk-sharing finance governance: Islamic vs conventional indexes option pricing." Managerial Finance 44, no. 5 (May 14, 2018): 540–50. http://dx.doi.org/10.1108/mf-05-2017-0199.
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Purpose Risk governance has an important influence on the hedging performances in option pricing and portfolio hedging in both discrete and dynamic case for both conventional and Islamic indexes. The paper aims to discuss these issues. Design/methodology/approach This paper explores option pricing and portfolio hedging in a discrete and dynamic case with transaction costs. Monte Carlo simulations are applied to both conventional and Islamic indexes in US and UK markets. Simulations show that conventional and Islamic assets do not exhibit the same price and portfolio hedging strategy governance. Findings The authors conclude that Islamic assets show different option price and hedging strategy compared to their conventional counterpart. Originality/value The research question of this paper aims at filling the gap in the empirical literature by exploring option price and hedging structure for both conventional and Islamic indexes in US and UK stock markets.
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Triyono, David, and Robiyanto. "The Effect of Foreign Stock Indexes and Indonesia’s Macroeconomics Variables Toward Jakarta Composite Stock Price Index (JCI)." Advanced Science Letters 23, no. 8 (August 1, 2017): 7211–14. http://dx.doi.org/10.1166/asl.2017.9332.
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Yuwono, Jessie D., and Yanthi Hutagaol Martowidjojo. "Stock Price Synchronicity, Earnings Quality, Foreign Ownership: Evidence of ASX200 Firms." 13th GLOBAL CONFERENCE ON BUSINESS AND SOCIAL SCIENCES 13, no. 1 (June 16, 2022): 1. http://dx.doi.org/10.35609/gcbssproceeding.2022.1(80).
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The issue of stock price synchronicity has been a debate for more than a decade. The question is central to what information that is more reflected in market stock price. One stream suggests that firm-specific information is, relatively, more reflected in its stock price, while the other streams argues that market (industry)-specific information is more relevant in stock price formation (Zhou, 2007). Studies about stock price synchronicity have been ubiquitous in emerging markets (e.g. Gul et al., 2010; Li et al., 2020; Vo & Chu, 2019; Farooq & Aktaruzzaman, 2016; Lyimo, 2014). Price synchronicity studies are also found in mature markets but majorly in US (e.g. Zhou, 2007; Gul et al., 2011; Kan & Gong, 2018). To the author knowledge, the only study of stock price synchronicity in Australia is by Bissessur & Hodgson (2012) that investigate the impact of IFRS adoption. Generally, prior studies found that IFRS implementation increases financial reporting quality. Specifically, this study focuses on the relationship between earnings quality and price synchronicity, since earnings is the mostly accounting information used by investors to price the stocks in the market. Keywords: ASX200,Conservatism, Foreign ownership, Stock price synchronicity, Timeliness, Value relevance
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Zheng, Hongying, Hongyu Wang, and Jianyong Chen. "Evolutionary Framework with Bidirectional Long Short-Term Memory Network for Stock Price Prediction." Mathematical Problems in Engineering 2021 (October 5, 2021): 1–8. http://dx.doi.org/10.1155/2021/8850600.
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As an important part of the social economy, stock market plays an important role in economic development, and accurate prediction of stock price is important as it can lower the risk of investment decision-making. However, the task of predicting future stock price is very difficult. This difficulty arises from stocks with nonstationary behavior and without any explicit form. In this paper, we propose a novel bidirectional Long Short-Term Memory Network (BiLSTM) framework called evolutionary BiLSTM (EBiLSTM) for the prediction of stock price. In the framework, three independent BiLSTMs correspond to different objective functions and act as mutation individuals, then their respective losses for evolution are calculated, and finally, the optimal objective function is identified by the minimum of loss. Since BiLSTM is effective in the prediction of time series and the evolutionary framework can get an optimal solution for multiple objectives, their combination well adapts to the nonstationary behavior of stock prices. Experiments on several stock market indexes demonstrate that EBiLSTM can achieve better prediction performance than others without the evolutionary operator.
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Lv, Jiehua, Chao Wang, Wei Gao, and Qiumin Zhao. "An Economic Forecasting Method Based on the LightGBM-Optimized LSTM and Time-Series Model." Computational Intelligence and Neuroscience 2021 (September 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/8128879.
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Stock price prediction is very important in financial decision-making, and it is also the most difficult part of economic forecasting. The factors affecting stock prices are complex and changeable, and stock price fluctuations have a certain degree of randomness. If we can accurately predict stock prices, regulatory authorities can conduct reasonable supervision of the stock market and provide investors with valuable investment decision-making information. As we know, the LSTM (Long Short-Term Memory) algorithm is mainly used in large-scale data mining competitions, but it has not yet been used to predict the stock market. Therefore, this article uses this algorithm to predict the closing price of stocks. As an emerging research field, LSTM is superior to traditional time-series models and machine learning models and is suitable for stock market analysis and forecasting. However, the general LSTM model has some shortcomings, so this paper designs a LightGBM-optimized LSTM to realize short-term stock price forecasting. In order to verify its effectiveness compared with other deep network models such as RNN (Recurrent Neural Network) and GRU (Gated Recurrent Unit), the LightGBM-LSTM, RNN, and GRU are respectively used to predict the Shanghai and Shenzhen 300 indexes. Experimental results show that the LightGBM-LSTM has the highest prediction accuracy and the best ability to track stock index price trends, and its effect is better than the GRU and RNN algorithms.
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Sheikh, Mohammad javad, Mohsen Nazem Bokaei, Hadi Alijani, Mohammad Saadatmand, Sayed Mojtaba Hosseini Fard, and Ismaeil Chezani Sharahi. "INVESTIGATING RELATIONSHIP BETWEEN CONSUMER PRICE INDEX AND PRODUCER PRICE INDEX AND DIVIDEND PER SHARE." Australian Journal of Business and Management Research 01, no. 07 (February 10, 2012): 121–28. http://dx.doi.org/10.52283/nswrca.ajbmr.20110107a13.
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Managers of economic institutions should possess some criteria in the stock exchange so that they can evaluate their performance and economic plans. Criteria for performance evaluation accounting can be used for evaluating performance and economic plans. One of the main criteria for performance evaluation accounting is reported accounting profit or dividends. This criterion is one of the main indexes for evaluating managers’ performance, and it is also a main criterion for decision-making on approval or rejection of economic plans. This index is influenced by different factors such as price index, especially consumer price index and producer price index. In this paper, relationship between producer price index and consumer price index is investigated in accepted firms in Tehran stock exchange. Dicky- Fuller Test is used for time series reliability and Pearson correlation coefficient and Granger-causality tests are used for investigating the relationship between variables. Finally, it was concluded that consumer price index has an inverse correlation with dividend per share (first hypothesis), and producer price index has a direct correlation with dividend per share (second hypothesis).
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Brandi, Vinicius Ratton. "Predictability of stock market indexes following large drawdowns and drawups." Brazilian Review of Finance 19, no. 1 (March 6, 2021): 1–23. http://dx.doi.org/10.12660/rbfin.v19n1.2021.81140.
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The efficient market hypothesis is one of the most popular subjects in the empirical finance literature. Previous studies of the stock markets, which are mostly based on fixed-time price variations, have inconclusive findings: evidence of short-term predictability varies according to different samples and methodologies. We propose a novel approach and use drawdowns and drawups as triggers, to investigate the existence of short-term abnormal returns in the stock markets. As these measures are not computed within a fixed time horizon, they are flexible enough to capture subordinate, time-dependent processes that could drive market under- or overreaction. Most estimates in our results support the efficient market hypothesis. The underreaction hypothesis receives stronger support than does overreaction, with higher prevalence of return continuations than reversals. Evidence for the uncertain information hypothesis is present in some markets, mainly after lower-magnitude events.
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Prasetyo, Dian Angga, and Rofikoh Rokhim. "Indonesian Stock Price Prediction using Deep Learning during COVID-19 Financial Crisis." International Journal of Business, Economics, and Social Development 3, no. 2 (May 6, 2022): 64–70. http://dx.doi.org/10.46336/ijbesd.v3i2.273.
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This research paper aims to use the deep learning model Long Short-Term Memory (LSTM) for the stock prediction model under the financial crisis of COVID-19. The financial impact of the COVID-19 has brought many of the world's indexes down. The impact of the financial crisis is even riskier for an emerging country such as Indonesia where foreign investors tend to take out their investments in emerging countries in financial crisis events. The application of deep learning in financial time series applications such as stock price prediction has been researched extensively. This study used the (Bidirectional LSTM) BiLSTM model which is a variation of the LSTM model to predict stock closing price. The stock prediction is applied to a selected company from the Indonesian stock market using historical prices. The model is then evaluated using metrics Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE). A graphical comparison between the actual price and predicted price of the stock is charted to study the stock price movement. To study the impact during COVID-19 on the stock prices, an intervention analysis is conducted along with the Wilcoxon model. The stock price prediction model can forecast the price of stocks before and during the financial crisis with minimal error. The intervention analysis result showed that health sectors have a positive effect while other sectors such as transportation, finance, information technology, and entertainment have a negative effect during the financial crisis of COVID-19. Being able to analyze and study the stock price movement of stocks is beneficial to investors in understanding the impact of the financial crisis on some industries and the behavior of certain stocks or industries under the circumstances which can lead to alternate investment strategies and decision making.