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Journal articles on the topic 'Stock prices'
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1
HSM, Zani Anjani Rafsanjani. "ANALISA LAJU PERUBAHAN HARGA SAHAM LQ45 MENGGUNAKAN PERSAMAAN DIFERENSIAL." Jurnal Riset Akuntansi Politala 3, no. 2 (December 29, 2020): 60. http://dx.doi.org/10.34128/jra.v3i2.68.
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The stock price movement is a very interesting discussion today. Dynamic price changes every time requires deep analysis to determine trends and stock price predictions in the future. There have been many methods used to analyze and predict stock prices. This paper will analyze the acceleration of stock price changes using a mathematical approach, known as a second-order differential equation. The benefit of this research is to obtain a coefficient of change in stock prices that can be used to predict stock prices in the future. Stock prices that will be observed are stocks including the LQ45 category. Furthermore, program analysis is carried out using Matlab software. At the end of the study, the coefficient of price change for LQ45 stocks was generated through provided historical data.
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Danuarta Santosa Suryadi, Gede Kurniawan, and I. Made Dana. "PENGARUH PROFITABILITAS, PRICE TO BOOK VALUE, BOOK VALUE PER SHARE TERHADAP HARGA SAHAM PERUSAHAAN PERBANKAN." E-Jurnal Manajemen Universitas Udayana 12, no. 1 (January 31, 2023): 69. http://dx.doi.org/10.24843/ejmunud.2023.v12.i01.p04.
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Composite Stock Price Index (JCI) during 2017-2020 has increased due to the increasing number of investors and also reflected in the high stock prices of banks. Increase of the Bank stock price is result of investor purchases influenced by supply and demand. Study based on data of banking companies listed on the Indonesia Stock Exchange (IDX). There are nine bank stocks on the Indonesia Stock Exchange (IDX) 80 index used as a sample in this study, The Sample is selected using saturation sampling. This study aims to determine the effect of Return on Equity (ROE), Price to Book Value (PBV), and Book Value Per Share (BVPS) on stock pricesin bankingcompanies at IDX 2017-2020. Data analysis used multiple linear regression. Results showed PBV and BVPS had positive significant effect on stock prices. Partially ROE variable hasa negative effect on stock prices in banking companies on the IDX, but partially PBV and BVPS each have positive significant effect on stock prices. This research implies that PBV and BVPS can considered as one the determinants of stock prices, but ROE shows there is no effect on stock prices in banking companies on the IDX 2017-2020.
Keywords: Profitability, Book Value Per Share, Price to Book Value, Stock Price, Banking Stocks.
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Citra Asmara, Tegar, Desmintari Desmintari, and Indri Arrafi Juliannisa. "Faktor–Faktor yang Mempengaruhi Indeks Harga Saham Gabungan." Jurnal Indonesia Sosial Sains 3, no. 5 (May 29, 2022): 822–34. http://dx.doi.org/10.36418/jiss.v3i5.590.
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The Composite Stock Price Index is an indicator of stock price movements and also to measure the combined performance of all stocks listed on the Indonesia Stock Exchange, as well as being a guide in investing for investors. Many indicators can affect the Composite Stock Price Index, such as inflation, interest rates, gold prices, and world oil prices. This study aims to determine the effect of inflation, interest rates, gold prices, and also world oil prices on the Composite Stock Price Index. This study uses monthly data from 2012 – 2019. The method used in this study is a multiple linear regression analysis model using the OLS method. The results of multiple regression analysis show that (1) there is no influence between inflation on the Composite Stock Price Index (2) there is no influence between interest rates on the Composite Stock Price Index (3) there is an influence between the gold price on the Composite Stock Price Index (4) there is no there is an influence between world oil prices on the Composite Stock Price Index.
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Gyamerah, Samuel Asante, Bright Emmanuel Owusu, and and Ellis Kofi Akwaa-Sekyi. "Modelling the mean and volatility spillover between green bond market and renewable energy stock market." Green Finance 4, no. 3 (2022): 310–28. http://dx.doi.org/10.3934/gf.2022015.
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<abstract><p>In this paper,we investigate the mean and volatility spillover between the price of green bonds and the price of renewable energy stocks using daily price series from 02/11/2011 to 31/08/2021. The unrestricted trivariate VAR-BEKK-GARCH model is employed to examine potential causality,mean,and volatility spillover effects from the green bond market to the renewable energy stock market and vice-versa. The results from the VAR-BEKK-GARCH model indicate that there exists a uni-directional Granger causality from renewable energy stock prices to green bond prices. While the price of green bonds is positively influenced by its own lagged values and the lagged values of renewable energy stock prices,only the past price value of renewable energy stocks has a positive effect on the current price value. We identified a uni-directional volatility spillover from renewable energy stock prices to green bond prices. However,there was no shock spillover from both sides of the market. This research shows that investors in the green bond market should always consider information from the renewable energy stock market because of the causal link between renewable energy stocks and green bonds.</p></abstract>
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Fang, Fei. "Stock Return Autocorrelation and Individual Equity Option Prices." Journal of Business Theory and Practice 9, no. 1 (February 14, 2021): p51. http://dx.doi.org/10.22158/jbtp.v9n1p51.
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This study demonstrates empirically the impact of stock return autocorrelation on the prices of individual equity option. The option prices are characterized by the level and slope of implied volatility curves, and the stock return autocorrelation is measured by variance ratio and first-order serial return autocorrelation. Using a large sample of U.S. stocks, we show that there is a clear link between stock return autocorrelation and individual equity option prices: a higher stock return autocorrelation leads to a lower level of implied volatility (compared to realized volatility) and a steeper implied volatility curve. The stock return autocorrelation is more important in explaining the level of implied volatility curve for relatively small stocks. The relation between stock return autocorrelation and option price structure is more pronounced when market is volatile, especially during financial crisis. The stock return autocorrelation is more important in explaining the level of implied volatility curve for relatively small stocks. Thus, stock return autocorrelation can help differentiate the price structure across individual equity options.
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Shackman, Joshua, Paul Lambert, Phoenix Benitiez, Nathan Griffin, and David Henderson. "Maritime Stock Prices and Information Flows: A Cointegration Study." Transactions on Maritime Science 10, no. 2 (October 21, 2021): 496–510. http://dx.doi.org/10.7225/toms.v10.n02.018.
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In this study, the issue of how global maritime stock prices influence the stock prices of large transportation companies in the U.S. and other large markets is examined. Maritime stocks are chosen because they are central in global trade and thus may be good indicators of future global stock market and economic trends. Maritime companies are often owned by families or governments and are traded in stock markets with lower standards of accountability, hence information flows from maritime stocks may be slower than flows from other stocks. Cointegration and vector error-correction analysis is used to analyze the short-term and long-term relationships between maritime stocks, rail stocks, and trucking stocks. Evidence is found of a gradual diffusion of information from maritime stock prices to large rail or trucking stocks. This suggests that price changes in maritime stocks may help predict changes in prices in non-maritime transportation stocks.
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Sunaryo and Denny Kurniawan. "PENGARUH KURS, HARGA CPO (CRUDE PALM OIL) DAN PROFITABILITAS TERHADAP RISIKO SISTEMATIS DAN IMPLIKASINYA TERHADAP HARGA SAHAM." Kinerja 2, no. 02 (August 12, 2020): 45–67. http://dx.doi.org/10.34005/kinerja.v3i01.924.
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The purpose of this research was to determine the influence of Exchange Rate, CPO Price, Profitability on the systematic risk and its implications on stock price. This research sample is the oil palm sub-sector shares listed on the Indonesia Stock Exchange (IDX) period 2007-2016 by using purposive sampling method. There were 6 stocks selected as samples. The analytical method used is Path Analysis, the development of panel data regression with common effects. The results of the study showed found partially that the Exchange Rate, CPO Prices and Profitability has a significant and negative influence on the Systematic Risk. Exchange rates, CPO prices and Profitability partially has a significant positive effect on stock prices. Systematic Risk has a significant negative effect on Stock Prices. The path analysis results show that Systematic Risk mediates the effect of Exchange Rate and Profitability on Stock Prices. However, Systematic Risk does not mediate the effect of CPO Prices on Stock Prices.
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Ivanovski, Zoran, Zoran Narasanov, and Nadica Ivanovska. "Performance Evaluation of Stocks’ Valuation Models at MSE." Economic and Regional Studies / Studia Ekonomiczne i Regionalne 11, no. 2 (June 1, 2018): 7–23. http://dx.doi.org/10.2478/ers-2018-0011.
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Abstract Subject and purpose of work: The main task of this paper is to examine the proximity of valuations generated by different valuation models to stock prices in order to investigate their reliability at Macedonian Stock Exchange (MSE) and to present alternative “scenario” methodology for discounted free cash flow to firm valuation. Materials and methods: By using publicly available data from MSE we are calculating stock prices with three stock valuation models: Discounted Free Cash Flow, Dividend Discount and Relative Valuation. Results: The evaluation of performance of three stock valuation models at the MSE identified that model of Price Multiplies (P/E and other profitability ratios) offer reliable stock values determination and lower level of price errors compared with the average stocks market prices. Conclusions: The Discounted Free Cash Flow (DCF) model provides values close to average market prices, while Dividend Discount (DDM) valuation model generally mispriced stocks at MSE. We suggest the use of DCF model combined with relative valuation models for accurate stocks’ values calculation at MSE.
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Darsono, Susilo Nur Aji Cokro, Wing-Keung Wong, Tran Thai Ha Nguyen, and Dyah Titis Kusuma Wardani. "The Economic Policy Uncertainty and Its Effect on Sustainable Investment: A Panel ARDL Approach." Journal of Risk and Financial Management 15, no. 6 (June 7, 2022): 254. http://dx.doi.org/10.3390/jrfm15060254.
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This study examines the effect of economic policy uncertainty (EPU) on sustainable investment returns by using panel data of stock market returns and the EPU index from twelve countries for the period from April 2015 to December 2020. In addition, precious metal prices, energy prices, and cryptocurrency prices are used as control variables. To do so, we investigate the impact of EPU, gold prices, oil prices, and Bitcoin prices on stock market returns by using the panel autoregressive distributed lag (ARDL) model to examine both the long-run correlation and short-run effect. Our findings show that EPU, gold prices, oil prices, and Bitcoin prices have a time-varying significant impact on sustainable stock market returns. We discovered that EPU has a significantly negative impact on the returns of the sustainable stocks in the markets over the long run. In contrast, the rise of the gold price, oil price, and Bitcoin price have a significantly positive impact on the returns of the sustainable stocks in the twelve sustainable markets in the long run. On the other hand, EPU in Singapore, Spain, the Netherlands, and Russia has a significant short-run impact on market returns in each country. Based on the findings, managers and investors in the sustainable stock markets are highly recommended to pay more attention to the volatility of EPU, gold prices, oil prices, and Bitcoin prices in the short run to control the risk of returns in the sustainable stock market. Furthermore, policymakers must closely monitor the movement of the EPU index, as it is a major driver of sustainable stock market returns.
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Jasiniak, Magdalena. "Stock Prices and the Rate of Return Analysis: The Case of Warsaw Stock Exchange." Financial Assets and Investing 9, no. 1 (May 31, 2018): 21–34. http://dx.doi.org/10.5817/fai2018-1-2.
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The main aims of this article are to verify whether rates of return might be determined by stock prices and to evaluate low price anomaly on the example of Warsaw Stock Exchange. The author states that cheap assets characterized by nominally lower prices are more attractive to buy and bring higher profits in comparison to assets described as expensive. In order to verify the hypothesis, database of 13789 quotations from 1.07.1999 to 30.12.2013 was created. The sample was divided into three groups – cheap, average, and expensive stocks. Finally, the statistical analysis was conducted using 2924 records including only cheap and expensive units. Statistical analysis confirms that low–priced assets generate higher profits and lower losses.
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Sadorsky, Perry. "A Random Forests Approach to Predicting Clean Energy Stock Prices." Journal of Risk and Financial Management 14, no. 2 (January 24, 2021): 48. http://dx.doi.org/10.3390/jrfm14020048.
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Climate change, green consumers, energy security, fossil fuel divestment, and technological innovation are powerful forces shaping an increased interest towards investing in companies that specialize in clean energy. Well informed investors need reliable methods for predicting the stock prices of clean energy companies. While the existing literature on forecasting stock prices shows how difficult it is to predict stock prices, there is evidence that predicting stock price direction is more successful than predicting actual stock prices. This paper uses the machine learning method of random forests to predict the stock price direction of clean energy exchange traded funds. Some well-known technical indicators are used as features. Decision tree bagging and random forests predictions of stock price direction are more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging and random forests methods produce accuracy rates of between 85% and 90% while logit models produce accuracy rates of between 55% and 60%. Tree bagging and random forests are easy to understand and estimate and are useful methods for forecasting the stock price direction of clean energy stocks.
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Sufyati HS, Hanifah Hanifah, and Sofia Maulida. "FUNDAMENTAL FACTORS OF INDONESIAN SHARIA SHARE PRICE IN THE FINANCIAL SECTOR." International Journal of Social Science 2, no. 5 (February 2, 2023): 2261–68. http://dx.doi.org/10.53625/ijss.v2i5.4965.
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Islamic stocks are securities in the form of shares that have and do not conflict with Islamic principles in the capital market. Investment in Islamic stocks is currently experiencing quite significant developments, so investors are looking at it from the side of the growth of their stock prices. One of the factors that affect stock prices is fundamental factors. The fundamental factors in this study are described as independent variables, namely the Debt-to-Equity Ratio (DER), Net Profit Margin (NPM), and Earning Per share (EPS), while the dependent variable is the Indonesian Sharia Stock Price in the Financial Sector which is listed in the Indonesian Sharia Stock Index (ISSI). This study aims to analyze the effect of DER, NPM, and EPS on Indonesian Sharia Stock Prices in the financial sector listed in the ISSI Index. The research method used is quantitative descriptive with multiple linear regression data analysis. The results of this study show that the Debt-to-Equity Ratio has a negative and insignificant effect on Sharia Stock Prices. Net Profit Margin has a positive but not significant to Islamic Stock Prices. Earning Per-share has a negative and not significant to Islamic Stock Prices. The ability of the DER, NPM, and EPS variables to explain variations in changes in the stock price variable is only 10.5%, whereas the remaining 80.5% can be influenced by other variables not examined. The implication of this study is that investors in investing do not only consider fundamental factors but other factors that are very significant to fluctuations in Islamic stock prices.
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Cao, Mengya. "Predicting the Link between Stock Prices and Indices with Machine Learning in R Programming Language." Journal of Mathematics 2021 (December 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/1275637.
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This paper provides an in-depth analysis machine study of the relationship between stock prices and indices through machine learning algorithms. Stock prices are difficult to predict by a single financial formula because there are too many factors that can affect stock prices. With the development of computer science, the author now uses many computer science techniques to make more accurate predictions of stock prices. In this project, the author uses machine learning in R Studio to predict the prices of 35 stocks traded on the New York Stock Exchange and to study the interaction between the prices of four indices in different countries. Further, it is proposed to find the link between stocks and indices in different countries and then use the predictions to optimize the portfolio of these stocks. To complete this project, the author used Linear Regression, LASSO, Regression Trees, Bagging, Random Forest, and Boosted Trees to perform the analysis. The experimental results show that the MRDL deep multiple regression model proposed in this paper predicts the closing price trend of stocks with a mean square error interval [0.0043, 0.0821]. Additionally, 80% of the proposed DMISV, KDJSV, MACDV, and DKB stock buying and selling strategies have a return greater than 10%. The experimental results validate the effectiveness of the proposed buying and selling strategies and stock price trend prediction methods in this paper. Compared with other algorithms, the accuracy of the algorithm in this study is increased by 15%, and the efficiency of prediction is increased by 25%.
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Safitri, Yunita Dewi, and Robiyanto Robiyanto. "KORELASI DINAMIS ANTARA PERGERAKAN HARGA MINYAK DUNIA DAN INDEKS HARGA SAHAM SEKTORAL DI BURSA EFEK INDONESIA." Jurnal Ekonomi Bisnis dan Kewirausahaan 9, no. 3 (December 28, 2020): 188. http://dx.doi.org/10.26418/jebik.v9i3.42949.
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Changes in the situation that move very quickly on the commodity market have an impact on financial markets, one of which is the stock market in Indonesia. Therefore this study aims to examine the dynamic correlation between the movement of world oil prices and the Sectoral Stock Price Index listed on the Indonesia Stock Exchange (IDX). The data used is obtained from secondary data in the form of daily closing price data for world oil prices and Sectoral Stock Price Index from January 2017 to June 2020. The analysis technique used is Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity (DCC-GARCH), due to previous studies mostly using a static approach. The results of this study show that the DCC-GARCH value between world oil prices (Brent and WTI) and Sectoral Stock Price Index tends to be very weak. A negative dynamic correlation was also found in the Consumer Goods Sector. This research can be a reference for investors who want to invest stocks in Indonesia by looking at the correlation between world oil prices and the Sectoral Stock Price Index.
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Putra, Ariswandi Sang. "PENGARUH EARNING PER SHARE (EPS), DIVIDEND PER SHARE (DPS) DAN FINANCIAL LEVERAGE (FL)TERHADAP HARGA SAHAM PERUSAHAAN MANUFAKTUR DI BURSA EFEK INDONESIA." Jurnal Ecogen 1, no. 4 (December 21, 2018): 169. http://dx.doi.org/10.24036/jmpe.v1i1.4736.
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The ups and downs of stock prices depend on the attractive tug strength between demand and supply of stocks in the capital market. Understanding of stock prices and the factors that affect the changes is very important because it can provide information for investors or prospective investors in investing in the form of shares. For investors information on Earning Per Share, Dividend Per Share and Financial Leverage become a very basic requirement in decision making needs. Therefore, the researcher is interested in doing research about the effect of: 1) earning per shere on stock price, 2) dividend per share to stock price, 3) financial leverage to share price of a company. The population of this study are all manufacturing companies listed on the Indonesia Stock Exchange period 2011-2015. The method of selecting the sample using purposive sampling, samples obtained 53 companies period 2011-2015, so that 265data obtained in this study. The variables in this study consisted of EPS, DPS and FL as independent variable and stock price as dependent variable. Methods of data collection using documentation to obtain data on EPS, DPS, FL and stock prices. Data analysis used multiple regression analysis. The results of this study indicate that earnings per shere have a significant positive effect on stock prices, dividend per share positively insignificant to stock prices, and financial leverage positively insignificant effect on stock prices.Keyword: EPS ,DPS,FL, Stock Price
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BIKSHAM, V., B. VISHAL KUMAR, C. RAHUL, G. VENU, and M. BHARGAV SAI. "STOCK PRICE PREDICTION." YMER Digital 21, no. 05 (May 2, 2022): 1–6. http://dx.doi.org/10.37896/ymer21.05/01.
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Machine learning has many important applications in the stock price prediction. Here, we will discuss about predicting the returns on stocks. This has uncertainties and it is a very complex task. This project will be developed into two parts: First, we will learn how to predict stock price using the Long Short-Term Memory neural networks. Predicting stock market prices involves human-computer interaction. For stock market analysis, conventional batch processing methods cannot be utilized efficiently due to the correlated nature of stock prices. We suggest an algorithm that utilizes a kind of recurrent neural network (RNN) called Long Short-Term Memory (LSTM), where using stochastic gradient descent the weights are adjusted for individual data points
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Potto, Otniel Syebastian Agusto, and Robiyanto Robiyanto. "THE DYNAMIC CORRELATION BETWEEN OIL PRICES AND THE INDONESIAN OIL COMPANIES’ STOCK PRICE." Image : Jurnal Riset Manajemen 10, no. 1 (July 2, 2021): 28–43. http://dx.doi.org/10.17509/image.v10i1.31283.
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This study aims to analyze the correlation between oil prices and the Indonesian oil companies’ stock price during the pandemic of COVID-19 by testing the effect of oil price changes on companies' stock return and stock volatility. Also, by considering the dynamic correlation between oil prices and the Indonesian oil companies’ stock prices. The data were collected from secondary data at www.finance.yahoo.com, Oil prices, and the Indonesian oil companies’ stock price period from January 2020 to June 2020 during the pandemic of COVID-19. Further, the data were analyzed by using a GARCH method to examine the effect of changes in oil prices for stock return and stock volatility. Also, the DCC-GARCH method was used to see the dynamic correlation between oil prices and the Indonesian oil companies’ stock price. The result showed that changes in oil prices have a significant effect on stock price volatility and the presence of a positive dynamic correlation between oil prices and the Indonesian oil companies’ stock price. This research can be used as a reference for investors for their investments by looking at the relationship between the oil price and The Indonesian oil companies’ stock price.
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Warisman, Widya, and Andi Yudha Amwila. "Pengaruh Struktur Modal Dan Kebijakan Dividen Terhadap Nilai Perusahaan Dan Dampaknya Terhadap Harga Saham Pada Sektor Pertambangan Periode 2016-2020." Jurnal Ilmiah Manajemen Kesatuan 10, no. 2 (August 10, 2022): 89–100. http://dx.doi.org/10.37641/jimkes.v10i2.1442.
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The capital market is a facility that is prepared to trade stocks, bonds and various other types of securities. Before buying a stock, investors must conduct an analysis that is expected to affect the stock price. In addition, it must have a benchmark by investing in a company that will get the quality as expected. One of the factors that affect stock prices is the fundamental condition seen from the company's financial condition.
This study uses a comparative descriptive method with a quantitative approach. The data used are in the form of company financial statements through document studies. The sampling technique used is purposive sampling with predetermined criteria in order to obtain 10 samples of companies. Data analysis used path analysis with SPSS as the analysis tool. The purpose of the study was to determine the effect of capital structure and dividend policy on firm value and stock prices.
The results of this study indicate that the capital structure has no effect on firm value dividend policy has a significant positive effect on firm value. Meanwhile, capital structure has a significant negative effect on stock prices, dividend policy has no effect on stock prices and firm value has a significant positive effect on stock prices. Firm value is not able to mediate capital structure on stock prices while firm value is able to mediate policy on stock price dividends.
Keywords : Capital Structure, Dividend Policy, Firm Value, Stock Price.
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Wahyudi, Rahmat. "Analisis Faktor Fundamental dan Risiko Sistematik terhadap Harga Saham dengan Profitabilitas sebagai Variabel Intervening." Journal of Business and Economics (JBE) UPI YPTK 7, no. 3 (September 25, 2022): 388–94. http://dx.doi.org/10.35134/jbeupiyptk.v7i3.189.
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This study aims to determine the effect of Earning Per Share (EPS), Current Ratio (CR), Exchange Rate and Interest Rate Risk on stock price movements with Return On Assets (EPS) as an intervening variable on property stocks listed on the Indonesia Stock Exchange 2017 -2021. The sample in this study was taken by purposive sampling method on property stocks listed on the Indonesia Stock Exchange 2017-2021. The number of samples used as many as 158 companies. The analytical method of this research is using multiple linear regression analysis method. The results of this study indicate that partially Earning per Share (EPS), has a significant effect on Return On Assets (ROA), Current Ratio (CR) has a significant effect on Return On Assets (ROA), Exchange Rates have a significant effect on Return On Assets (ROA)., Interest Rate Risk has a significant effect on Return On Assets (ROA), Earnings per Share (EPS) has a significant effect on Stock Prices, Current Ratio (CR) has a significant effect on Stock Prices, Exchange, Risk Interest has a significant effect on stock prices, Return on Assets (ROA) has a significant effect on stock prices, Earning per Share (EPS) has no significant effect on stock prices through Return On Assets (ROA) as an intervening variable, Current Ratio (CR) has no effect significant effect on stock prices through Return On Assets (ROA) as an intervening variable, exchange rate has an effect s significant no effect on stock prices through Return On Assets (ROA) as an intervening variable and Interest Rate Risk has no significant effect on stock prices through Return On Assets (ROA) as an intervening variable on property stocks listed on the Indonesia Stock Exchange 2017-2021.
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Santos, Leandro da Rocha, and Roberto Marcos da Silva Montezano. "Value and growth stocks in Brazil: risks and returns for one - and two-dimensional portfolios under different economic conditions." Revista Contabilidade & Finanças 22, no. 56 (August 2011): 189–202. http://dx.doi.org/10.1590/s1519-70772011000200005.
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For empirical purposes, value stocks are usually defined as those traded at low price-to-earnings ratios (stock prices divided by earnings per share), low price-to-book ratios (stock prices divided by book value per share) or high dividend yields (dividends per share divided by stock prices). Growth stocks, on the other hand, are traded at high price-to-earnings ratios, high price-to-book ratios or low dividend yields. Academic research so far produced, international and Brazilian alike, shows that value stocks outperform growth stocks, challenging the Efficient Market Hypothesis, which states that the market prices of traded stocks are the best estimate of their intrinsic values. Most studies use a single ratio to sort stocks on percentiles; risks (generally defined as beta or standard deviations) and returns are then calculated for the resulting value and growth portfolios. In the present paper, we aim to further contribute to the growing literature on the field by applying a method not previously tested on the Brazilian market. We build portfolios sorted by the price-to-earnings and price-to-book ratios alone and by a combination of both in order to assess value and growth stocks' risks and returns on the Brazilian stock market between 1989 and 2009. Furthermore, our risk analysis may be regarded as the paper's main contribution, since its approach departs from conventional risk concepts, as we not only test for beta: portfolios' returns are measured under different economic conditions. Results support a pervasive value premium in the Brazilian stock market. Risk analysis shows that this premium holds under every economic condition analyzed, suggesting that value stocks are indeed less risky. Beta proved not to be a satisfactory risk measure. Portfolios sorted by the price-to-earnings ratio yielded the best results.
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Ligocká, Marie, Tomáš Pražák, and Daniel Stavárek. "The Effect of Macroeconomic Factors on Stock Prices of Swiss Real Estate Companies." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 64, no. 6 (2016): 2015–24. http://dx.doi.org/10.11118/actaun201664062015.
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Stock values of companies listed on stock exchanges could be influenced by many factors. The aim of this article is to examine existence and character of relationship between stock prices of selected Swiss real estate companies and macroeconomic fundamentals (GDP, interest rate, price level). The existence of long-run equilibrium relationship between stock prices and macroeconomic fundamentals is tested with the Johansen cointegration. The short run dynamics between the variables is examined by Vector Error Correction modelling and the Granger causality test. During the period 2005 – 2014 we revealed a long‑run equilibrium for five of the six analyzed stocks. We also confirmed that macroeconomic variables and the interest rate in particular, can explain a long-run behavior of stock prices. By contrast, macroeconomic variables are usually short in explanation of short‑run dynamics of stock prices. However, the results differ substantially among the stocks and, hence, they prevent us from drawing any general conclusion for the entire real estate sector in Switzerland.
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Wafula, Lugongo Maurice, and Dr Sifunjo E. Kisaka. "AN EMPIRICAL STUDY OF PRICE CLUSTERING ON THE NAIROBI SECURITIES EXCHANGE." International Journal of Finance and Accounting 2, no. 2 (February 14, 2017): 23. http://dx.doi.org/10.47604/ijfa.295.
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Purpose: The purpose of this study was to empirically investigate price clustering phenomenon on the Nairobi Securities Exchange for the period 2009 to 2013.Materials and methods: The study used secondary sources of data obtained from the Nairobi Securities exchange. The study revealed that there has been a preference by investors for stock whose prices end with the digit 5 and this accounted for 67.88 percent of all the stocks examined and was followed by stocks whose prices ended with the digit 0 which accounted for 4.55 percent. In order to establish the determinants of this observed behavior a multivariate regression model used by Harris (1991) was adopted where price clustering was regressed against stock volatility, number of trades, market capitalization, and own stock price.Results: The regression results indicated that the number of trades as well as Market Capitalization was positive and significantly related to price clustering. The study also found the stock price to be negative and significantly related to price clustering. On the other hand, Stock volatility was established to be an insignificant predictor of price clustering. The multivariate regression model was found to be significant in explaining the observed relationship and that 15.4 percent of the variance in price clustering was explained by number of trades, stock volatility, own stock price and the market capitalization. The study finds that there is a tendency of prices to cluster around certain numbers as evidenced by the 67.88 percent of numbers clustering around the number 5 and that price clustering is positively related to number of tradesRecommendations: It is thus recommended that if firms are to increase the number of trades of their shares they should consider pricing their shares according to the preferences of investors who prefer shares or stocks whose prices ends with 5 or 0.
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Heny Sidanti and Annisa Istikhomah. "The Effect Of Stock Price, Share Return, Share Trading Volume, And Return Variant On Bid-Ask Spread On Textile And Garment Companies Listed On The Indonesia Stock Exchange, 2019-2020." International Journal of Science, Technology & Management 2, no. 4 (July 23, 2021): 1357–66. http://dx.doi.org/10.46729/ijstm.v2i4.269.
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This study aims to obtain empirical evidence of the effect of Stock Price, Stock Return, Stock Trading Volume, and Return Variant on the Bid-Ask Spread of Stocks in Textile and Garment Companies Listed in Indonesia Stock Exchange in 2019-2020. The stock price used is the stock price recorded at the end of each closing period (closing price), stock returns are measured using the difference between returns on the research day and before the study divided by returns on the day before the study, stock trading volume is measured by the number of shares traded at the time of the study. t is divided by the number of shares outstanding at the time of the study, the variance of stock returns is measured using the standard deviation, and the bid-ask spread is measured by the difference between the selling price and the purchase price divided by the difference between the selling price and the purchase price divided by two. The population in this study is 17 textile and garment companies listed on the IDX. Based on the purposive sampling method, a sample of 16 companies was obtained with 309 data. This research data is obtained from the company's monthly data from 2019 to 2020. The results of the analysis show that stock prices and stock trading volumes affect the bid-ask spread, while stock returns and return variances do not affect the bid-ask spread. Meanwhile, simultaneously, stock prices, stock returns, stock trading volume, and return variance affect the bid-ask spread. This research data is obtained from the company's monthly data from 2019 to 2020. The results of the analysis show that stock prices and stock trading volume affect the bid-ask spread, while stock returns and return variances do not affect the bid-ask spread. Meanwhile, simultaneously, stock prices, stock returns, stock trading volume, and return variance affect the bid-ask spread. This research data is obtained from the company's monthly data from 2019 to 2020. The results of the analysis show that stock prices and stock trading volumes affect the bid-ask spread, while stock returns and return variances do not affect the bid-ask spread. Meanwhile, simultaneously, stock prices, stock returns, stock trading volume, and return variance affect the bid-ask spread.
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KUDRYAVTSEV, Andrey, Shosh SHAHRABANI, Aviad DIDI, and Eyal GESUNDHEIT. "DIFFERENTIAL EFFECTS OF TARGET PRICE RELEASES ON STOCK PRICES: PSYCHOLOGICAL ASPECTS." Theoretical and Practical Research in the Economic Fields 5, no. 2 (December 31, 2014): 153. http://dx.doi.org/10.14505/tpref.v5.2(10).03.
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In the present study, we attempt to shed light on potential factors affecting how investors react to target price announcements made by security analysts. More specifically, the study focuses on cross-sectional differences between the magnitude of reactions for stocks whose prices have increased and reactions for stocks whose prices decreased immediately prior to such announcements. Employing a sample of target price announcements classified as "buy" (positive) recommendations for Israeli stocks, we document their significantly positive effect on stock prices both on the day of the announcement and during a short period following the announcement. The effect of target price releases is also found to be significantly stronger for smaller stocks. Moreover, we document that those stocks that have experienced positive cumulative abnormal returns prior to target price releases yield significantly higher abnormal returns on average, both on the event day and during a short subsequent period. We explain this finding by the effect of the availability heuristic on investors' perceptions and decisions. Namely, we suggest that investors may expect target price releases to have a stronger effect on stock prices if these releases are preceded by stock returns of the same sign as the recommendation itself (making the recommendation more available, or in other words, subjectively more informative).
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Bhavanagarwala, Mustafa Shabbir, Nagarjun K N, Tanzim Abbas Charolia, Vishal M, and Ashwini M. "STOCK AND CRYPTOCURRENCY PREDICTION." International Journal of Innovative Research in Advanced Engineering 9, no. 8 (August 12, 2022): 182–86. http://dx.doi.org/10.26562/ijirae.2022.v0908.06.
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In our project, the point is to anticipate long term esteem of the money related stocks of a company and crypto coins individually with fine precision. The future prices of stock and cryptocurrency are predicted by using the past available values. “Buy low, sell high" is a good saying but it is not a good choice for making speculations. Investment is best stock or crypto currency in awful time can have bad results, while investment in best stock or cryptocurrency at right time can have best benefits. Prediction for long term values is easy as compared to day-to-day basis as prices fluctuate a lot. So, our model predicts the price of stocks and cryptocurrencies, which helps the investors to invest in appropriate stocks and cryptocoins. The dataset used is taken from yahoo finance and twelve data using web scraping. The dataset retrieved is in raw format. It consists of collection of values of stock market data of various companies, and also data of various cryptocurrencies. First, raw data is converted into processed data, which is done using feature extraction. Then the dataset is splitted into training and test sets. We use the training dataset to train the model, and use test dataset to predict the future prices of stocks and cryptocurrencies. Now user can gain best knowledge about stock price trends of various companies and also cryptocurrency price trends, and can decide on for best investments in respective fields and gain best benefits.
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Sari, Ratna. "Analysis of the Effect of Earnings per share, Price earning ratio and Price to book value on the stock prices of state-owned enterprises." Golden Ratio of Finance Management 1, no. 1 (March 31, 2021): 25–32. http://dx.doi.org/10.52970/grfm.v1i1.117.
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This study aims to (1) determine the effect of earnings per share on stock prices, (2) determine the effect of price-earnings ratio on stock prices, (3 determine the effect of price to book value on stock prices, (4) find out whether earnings per share, price earning ratio, and price to book value have a simultaneous effect on stock prices. This study uses secondary data through data sources and the Indonesian stock exchange that have been published by companies, as many as nine companies within five years. This research was conducted within the research period. From August to October 2020. Methods: the analysis used is multiple linear regression analysis, classical assumption test, partial test, simultaneous test, and coefficient of determination. The data were analyzed using the Statistical Product and Service Solution (SPSS) 23 software program. The results of this study state that (1) Earning Per Share has a positive and significant effect on stock prices, (2) price earning ratio has a positive and significant effect on stock prices, (3) price to book value has a positive and significant effect on stock prices, (4) Earning Per Share, price earning ratio, debt to equity, price to book value simultaneously affects the stock price. Therefore, construction companies are expected to improve their performance to increase stock prices further.
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Ditasari, Prilia, Embay Rohaeti, and Isti Kamila. "Aplikasi Geometric Brownian Motion dengan Jump Diffusion dalam Memprediksi Harga Saham Liquid Quality 45." Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi 10, no. 1 (June 10, 2022): 111–19. http://dx.doi.org/10.34312/euler.v10i1.14655.
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At the beginning of 2021, stock LQ45 experienced a significant spike in stock prices, making the stock price difficult to predict and the profits obtained by investors uncertain. The purpose of this study is to predict the stock price of LQ45 using the Geometric Brownian Motion model with Jump Diffusion and determine investment by comparing the expected return and return of stock price prediction results. This study uses a Geometric Brownian Motion model with Jump Diffusion to predict stock prices because it can predict when stock prices experience price spikes and fluctuations. Based on the results of the study, of the 45 stocks listed on LQ45 stock, only nine stock companies were selected that had a normally distributed return whose stock price was predicted by applying the Geometric Brownian Motion model with jump-diffusion and each MAPE value obtained was in the very good category. There are six stock companies with investment decisions in the form of buying shares, including Ace Hardware Indonesia Tbk, Bank Central Asia Tbk, Charoen Pokphand Indonesia Tbk, Indofood CBP Sukses Makmur Tbk, Vale Indonesia Tbk and Indah Kiat Pulp Paper Tbk. Three stock companies with investment decisions in the form of selling shares including, Bank Rakyat Indonesia (Persero) Tbk, Jasa Marga (Persero) Tbk and Mitra Keluarga Karyasehat Tbk.
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Amalia, Farah, and Nindi Riyana Saputri. "Does Investor Sentiment Affect Islamic Stock Prices? Evidence From Indonesia." Jurnal Riset Ekonomi Manajemen (REKOMEN) 5, no. 2 (April 29, 2022): 117–27. http://dx.doi.org/10.31002/rn.v5i2.5609.
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The Islamic finance industry in Indonesia has grown rapidly in the last decade, one of which is marked by the number of sharia stocks. Sharia stocks, based on the underlying principle, prohibit the involvement of investor sentiment which is often used as a consideration in investment decisions because there are elements of tadlees in it. This study examines the influence of investor sentiment on islamic stock prices index. This study aims to analyze whether Islamic stock price indices are influenced by investor sentiment. The representation of Islamic stock price indices are Indonesia Sharia Stock Index (ISSI) and Jakarta Islamic Index (JII). This study utilizes ARCH/GARCH analysis to determine whether there is an influence of investor sentiment on Islamic stock prices. The statistical tool used is e-views 12.0 program. The research findings stated that investor sentiment influences Jakarta Islamic Index (JII) but doesnt influence Indonesia Sharia Stock Index (ISSI). The difference in the results between the two Islamic stock indices can be explained by the different constituents and criteria for selecting Islamic stocks
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Megawati, Resmawan, Boby Rantow Payu, and Amanda Adityaningrum. "Prediksi Pergerakan Saham Menggunakan Metode Simulasi Monte Carlo untuk Pembentukan Portofolio Optimal dengan Pendekatan Model Markowitz." Jurnal Statistika dan Aplikasinya 6, no. 1 (June 30, 2022): 86–95. http://dx.doi.org/10.21009/jsa.06108.
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Stock movements that follow a stochastic process move randomly at certain times, have led stock prices challenging to predict. For this reason, the monte carlo simulation method is used to get the possibilities of stock prices in the future. This case study focused on the shares listed on the Jakarta Islamic Index 70 in 2018, by simulating 10 times the daily closing price data, thus, the possible stock prices in 2019 were obtained. Portfolio optimization was then carried out using the markowitz model approach from the predicted data. Based on the prediction data, there are 20 stock have a positive expected return. The stocks that has the largest weight is ICBP.JK (Indofood CBP Sukses Makmur Tbk) stocks, with 0.1396, while the stocks with the smallest weight is INAF.JK (Indofarma (Persero) Tbk) at 0.0053. Histirical simulations calculate the Value a Risk of 20 stocks that provide optimal returns, if investors invest Rp. 100,000,000.00 the maximum risk or loss that will be obtained is Rp. 2,910,410.00 for 1 year.
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Tampubolon, Vienna Agatha, and Muhammad Hasyim Ibnu Abbas. "Pengaruh nilai tukar dan ekspor terhadap harga saham perbankan sebelum dan setelah pengumuman covid-19." Fair Value: Jurnal Ilmiah Akuntansi dan Keuangan 4, no. 8 (March 25, 2002): 3534–47. http://dx.doi.org/10.32670/fairvalue.v4i8.1458.
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Covid-19 hampered most businesses and caused a banking crisis that triggered uncertainty so that economic growth declined. Banks can dampen these economic shocks by providing much-needed funding for companies, especially those affected by Covid-19. The bank can obtain these funds by selling stocks to the public. However, some factors affect stock prices, such as exchange rates, exports, and unexpected conditions such as Covid-19. Based on existing research, stock prices in general after Covid-19 have fallen drastically. Therefore, it is hoped that this research can help the Indonesian government control exchange rates and exports so that the price of banking stocks does not experience a drastic decline when unexpected situations such as Covid-19 occur. This study uses a panel data analysis technique. The results of this study are that the exchange rate and exports have a significant effect on stock prices, but conditions before and after Covid-19 have no significant effect on stock prices. It is expected that investors and the government can consider the factors that affect stock prices.
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Fathi, Asmaa Y., Ihab A. El-Khodary, and Muhammad Saafan. "Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (September 1, 2022): 851. http://dx.doi.org/10.11591/ijai.v11.i3.pp851-858.
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<span>The main objective of stock market investors is to maximize their gains. As a result, stock price forecasting has not lost interest in recent decades. Nevertheless, stock prices are influenced by news, rumor, and various economic factors. Moreover, the characteristics of specific stock markets can differ significantly between countries and regions, based on size, liquidity, and regulations. Accordingly, it is difficult to predict stock prices that are volatile and noisy. This paper presents a hybrid model combining singular spectrum analysis (SSA) and nonlinear autoregressive neural network (NARNN) to forecast close prices of stocks. The model starts by applying the SSA to decompose the price series into various components. Each component is then used to train a NARNN for future price forecasting. In comparison to the autoregressive integrated moving average (ARIMA) and NARNN models, the SSA-NARNN model performs better, demonstrating the effectiveness of SSA in extracting hidden information and reducing the noise of price series.</span>
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Al-Hasnawi, Salim Sallal, and Laith Haleem Al-Hchemi*. "CLOSING PRICE PREDICTION OF STOCK LISTED ON THE IRAQ STOCK EXCHANGE USING ANN-LSTM." JURISMA : Jurnal Riset Bisnis & Manajemen 12, no. 2 (October 30, 2022): 173–85. http://dx.doi.org/10.34010/jurisma.v12i2.8103.
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Financial markets are highly reactive to events and situations, as seen by the very volatile movement of stock values. As a result, investors are having difficulties guessing prices and making investment decisions, especially when statistical techniques have failed to model historical prices. This paper aims to propose an RNNs-based predictive model using the LSTM model for predicting the closing price of four stocks listed on the Iraq Stock Exchange (ISX). The data used are historical closing prices provided by ISX for the period from 2/1/2019 to 24/12/2020. Several attempts were conducted to improve model training and minimize the prediction error, as models were evaluated using MSE, RMSE, and R2. The models performed with high accuracy in predicting closing price movement, despite the Intense volatility of time series. The empirical study concluded the possibility of relying on the RNN-LSTM model in predicting close prices at the ISX as well as decisions making upon.
Keywords: Stock, LSTM, Prediction, ANN, RNN, ISX
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Kreidl, Felix. "Stock-Market Behavior on Ex-Dates: New Insights from German Stocks with Tax-Free Dividend." International Journal of Financial Studies 8, no. 3 (September 21, 2020): 58. http://dx.doi.org/10.3390/ijfs8030058.
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We examine stock prices and the number of stocks traded around ex-dividend dates of German stocks with tax-free dividend. Tax-free dividends are temporarily tax-exempt, as they reduce the initial purchasing price of a stock. With our analysis of this particular group of German stocks, we can make clear predictions regarding ex-date prices and analyze the number of stocks traded around ex-dates, doing so without the systematic bias of cum-ex trades over time. For XETRA, our empirical results indicate that ex-date prices decline, on average, by the amount of the dividend. We do not find a significant relationship between a stock’s price-drop ratio and dividend yield. Further, the empirical analysis suggests that there is no significant correlation between an abnormal number of a stock being traded and its dividend yield. These results are most consistent with tax-motivated reasoning. However, our volume analysis reveals no consistency regarding the abnormal number of stocks traded for multilateral trading facilities.
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Gregoriou, Andros, Jerome Healy, and Jairaj Gupta. "Determinants of telecommunication stock prices." Journal of Economic Studies 42, no. 4 (September 14, 2015): 534–48. http://dx.doi.org/10.1108/jes-06-2013-0080.
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Purpose – The purpose of this paper is to analyze the determinants affecting the stock prices of telecommunications firms in both developed and developing countries around the world. Design/methodology/approach – The empirical analysis is performed using panel data from 160 countries and 45 companies, covering the time period from 2000 to 2011. To identify the significant factors, company level firm-specific financial and non-financial factors have been analyzed that are expected to bear significant impact on price volatility of telecommunications stock. Findings – The test results reveal that capital expenditure and book value are the most significant factors. Dividends and debt levels only affect prices significantly in specification tests with either time-series or cross-sectional effects, whereas firms’ earnings and numbers of mobile internet subscribers do not contribute to the explanatory power of telecommunication stock price variability. Practical implications – The study sheds light to the potential investors in evaluating the risk associated with investment in stocks of telecommunications firms and take informed investment decisions. Originality/value – This is the first study that presents a comprehensive analysis of determinants affecting the stock prices of telecommunications firms in both developed and developing countries around the world.
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Jotanovic, Vera, and Rita Laura D’Ecclesia. "Do Diamond Stocks Shine Brighter than Diamonds?" Journal of Risk and Financial Management 12, no. 2 (May 3, 2019): 79. http://dx.doi.org/10.3390/jrfm12020079.
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This paper addresses two practical investment questions: Is investing in the diamond equity market a more feasible and liquid alternative to investing in diamonds? Additionally, is diamond equity affected by polished diamond prices? We assemble an original database of diamond mining stock prices traded on main stock exchanges in order to assess their relationship with diamond prices. Our results show that the market of diamond-mining stocks does not represent a valid investment alternative to the diamond commodity. Diamond equity returns are not driven by diamond price dynamics but rather by local market stock indices.
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Shengjie, Pan, Song Yinqiu, and Zhang Hongyan. "A Study of China’s Hybrid Monetary Policy on the Stock Market." Asian Economic and Financial Review 12, no. 3 (March 15, 2022): 183–93. http://dx.doi.org/10.55493/5002.v12i3.4442.
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China's monetary policy has been progressively modified and transformed. In this study, the correlation between Chinese monetary policy and the stock market is investigated. The CSI 300 index, the trading volume of constituent stocks of the CSI 300 index, and M2 and R007 were selected to represent the stock market, quantitative monetary policy and price monetary policy, respectively. The transmission mechanism and regulatory effects of China's hybrid monetary policy on the stock market were studied. Stock prices and trading volume will rise when the People’s Bank of China (PBOC) puts money into the market; if the central bank raises interest rates, stock prices and trading volume will decline, and the regulatory effect of price-based monetary policy will be more significant than that arising from quantitative monetary policy. China's hybrid monetary policy has a dampening effect on stock prices and trading volume and effectively restrains the bubble of the financial market.
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Lin, Chun-Feng, and Sheng-Chih Yang. "Taiwan Stock Tape Reading Periodically Using Web Scraping Technology with GUI." Applied System Innovation 5, no. 1 (February 18, 2022): 28. http://dx.doi.org/10.3390/asi5010028.
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Stock tape reading involves surveilling stock prices once in a while and recording stock prices. The method of observing stock prices may be television or stock exchange. The time step for recoding stock prices is every stock user’s experience and their theory, perhaps 3 min or 2 h and so on. As an example, the Taiwan stock market starts at 9:00 a.m. to 13:30 p.m. It will have a 4 h operating time. Splitting the 4 h operating time for tape reading is the skill of stock users. The stock price sequence generated by tape reading can be predicted by stock users, but finally, it is the stock user’s experience. Therefore, the meaning of tape reading is to record the stock price, but its concept should have no prediction purpose. This study used thread technology and proposed a tape-reading method with web scraping. This method can periodically scrape stock prices and generate a stock price sequence to Excel file. This application can satisfy the demand of these stock users, who are called day trading users. Because these day trading users want to gain stock price sequences minute by minute, rather than the stock exchange format day by day, and also ones which are better than the those provided by the stock website service, because its stock sequence format is limited and not normalized, these day trading users think that minute-by-minute stock price sequences are very clear to forecast. This study implemented the prior scheme and designed the GUI to query a company’s stock price and its stock news, even per second, etc., and how long it took to update the stock price, and the GUI also included a time-up feature to stop scraping stock prices if users just wanted to scrape stock prices periodically.
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Akbulaev, Nurkhodzha, Basti Aliyeva, and Shehla Rzayeva. "Analysis of the Influence of the Price of Raw Oil and Natural Gas on the Prices of Indices and Shares of the Turkish Stock Exchange." Pénzügyi Szemle = Public Finance Quarterly 66, no. 1 (2021): 151–66. http://dx.doi.org/10.35551/pfq_2021_1_8.
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This article is a review on the impact of prices and their dependence on the cost of oil and natural gas on the world stock markets. The main studies and results achieved in the field of the impact of prices on both the stock index and industrial stocks and the dependence on the level of oil prices are presented. The paper presents an econometric study on the choice of offers on the securities market that allows us to identify the main specifics of changes in prices for the stock index and industrial shares in the daily period from 13. 05. 2012 to 01. 12. 2019. The article uses methods for estimating the impact of the price of natural gas and WTI crude oil using the Gretl statistical program, taking into account the selection of the main correlation features of the price matrix. Of the 13 proposed research models, only one model showed its statistical insignificance. A paired linear model of the CocaCola share price dependence and its dependence on NGFO prices was presented and analyzed in detail. Based on the results of econometric modeling, linear regression models were constructed for the dependence of stock prices on the NGFO and WTISPOT prices. The Gretl environment allows you to evaluate the situation in the econometric environment and make a forecast based on the obtained models of the dependence of stock prices and make appropriate conclusions.
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Lee, Ming-Te, Chyi Lin Lee, Ming-Long Lee, and Chien-Ya Liao. "Price linkages between Australian housing and stock markets." International Journal of Housing Markets and Analysis 10, no. 2 (April 3, 2017): 305–23. http://dx.doi.org/10.1108/ijhma-05-2016-0037.
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Purpose The purpose of this study is to examine the linkages between Australian house prices and stock prices under the Toda and Yamamoto test framework. Specifically, it investigated whether there is a capital switching effect between house prices and stock prices. Design/methodology/approach This study examined the linkages between house prices and stock prices under the Toda and Yamamoto test framework. To accommodate the impact of the global financial crisis (GFC), a sub-period analysis was undertaken. To assess the impact of investor structure, the tests were also performed for small cap stocks and large cap stocks individually. Findings The empirical results reveal a negative lead–lag relationship between house prices and stock prices in Australia, suggesting the existence of capital switching activities between housing and stocks. The impact of the GFC on the lead–lag relationship between house prices and stock prices is also documented. Before the crisis, a causality transmission was running from house prices to stock prices, whilst stock prices appeared to lead house prices after the crisis. The capital switching activities between housing and stocks are more evident for small cap stocks. Originality/value This study is the first to examine the linkages between house prices and stock prices under the Toda and Yamamoto test framework. This is the first study to explore the impacts of the GFC on the lead–lag relationship between the two asset prices under the capital switching framework. This study is also the first to provide empirical evidence regarding the existence of capital switching activities between housing and stocks. In addition, the impact of investor structure on the interrelationship between the two asset prices is examined for the first time under the capital switching framework.
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Salim, Muhammad Agus, and Pardiman Pardiman. "The Role of Dividend Policy as Intervening Variables on The Effect of Earning Per Share, Debt Equity Ratio and Price Book Value on Stock Price." Jurnal Bisnis dan Manajemen 9, no. 1 (May 31, 2022): 77–86. http://dx.doi.org/10.26905/jbm.v9i1.7602.
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The purpose of this study is to analyze the role of dividend policy in mediating the effect of earnings per share, debt equity to ratio and price book value on stock prices. The sample used is a manufacturing company listed on the Indonesian stock exchange with complete financial statements for the 2015-2020 period. Data were analyzed using path analysis method using SPSS version 25 software. The results of this study indicate that earnings per share have no effect on dividend policy. Debt equity to ratio has no effect on the company's dividend policy. Price book value has no effect on the company's dividend policy. Dividend performance has no effect on stock prices. Earning per share has an effect on stock prices. Debt equity to ratio has an effect on stock prices. Price book value has an effect on stock prices. Dividend policy is not successful in mediating the effect of earnings per share on stock prices. Dividend policy also failed to mediate the effect of debt euqity to ratio on stock prices. Furthermore, the dividend policy also failed to mediate the effect of price book value on stock prices
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a, Oleh. "ANALYSIS OF COMPANY PERFORMANCE AS ISSUERS BASED ON THE COMPASS 100 INDEX ON MARKET PRICES." International Journal of Advanced Research 9, no. 5 (May 31, 2021): 1279–87. http://dx.doi.org/10.21474/ijar01/12968.
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The background of this research is the desire of investors and issuers so that the individual stock price index continues to increase in the Indonesian stock market, in fact, fluctuates where investment stores and previous studies have stated the influence between company performance variables, investor expectations, investment risk on market prices and their impact on the index individual share price The research objective is to give investors confidence to invest in the Indonesian stock market by analyzing the effect of company performance, investor expectations, investment risk on market prices, and their impact on the individual stock price index. The sampling technique used is a nonprobability sampling from the Indonesia Stock Exchange data from September 2015- May 2018 with the Kompas Index 100 as an index of 100 liquid companies which is one of the references for investors to invest, the Analysis Technique uses Structural Equation Modeling (SEM) with the approach WarpPLS The research results concluded Company performance has a direct effect on market prices and has an indirect effect on the individual stock price index through market prices, investor expectations do not have a direct effect on market prices but have an indirect effect on the individual stock price index through market prices, investment risk does not directly affect market prices but indirect effect on the individual stock price index through market prices and market prices have a direct effect on the individual stock price index.
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Kim, Yun-Yeong. "Analysis of Changes in the Soundness of Korean Stocks after Capital Market Opening: Focusing on Role and Identification of Long-term Equilibrium Variables." Korean Journal of Financial Studies 51, no. 5 (October 31, 2022): 523–41. http://dx.doi.org/10.26845/kjfs.2022.10.51.5.523.
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This study examines and evaluates changes in the soundness of Korean stock prices by classifying the financial and global financial crises after the 1990s, when the capital market was first opened. The criteria for judging soundness include whether stock prices are determined by long-term equilibrium variables, whether there is a trend among non-long-term equilibrium variables, and whether long-term equilibrium variables of overseas stocks are identified during the Korean stock price determination process. Further, a co-integration test is performed using the VAR model which comprises the stock prices from Korea and the United States, and their long-term economic equilibrium variables. Furthermore, a transformation error correction model with a VAR type consisting of differences and cointegration errors in Korean stock prices is derived. Accordingly, the trend of long-term equilibrium variables is estimated using the Beveridge-Nelson decomposition of Korean stock prices. Furthermore, a test for its presence is performed. According to the empirical analysis conducted using monthly Korean data; the influence of the long-term equilibrium variable trend in the Korean economy tends to increase the stock prices following a financial crisis.
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Naelufar, Yuyun, Anita Wijayanti, and Rosa Nikmatul Fajri. "FAKTOR YANG MEMPENGARUHI HARGA SAHAM PADA PERUSAHAAN MANUFAKTUR SUB SEKTOR OTOMOTIF." Jurnal Akuntansi dan Pajak 22, no. 1 (July 30, 2021): 296. http://dx.doi.org/10.29040/jap.v22i1.1899.
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The purpose of this study was to iexamine and analyze the effect of EPS, ROE, PER, inflation and interest rates on stock prices of manufacturing companies in the automotive sub-sector listed on the Indonesian Stock Exchange. The sample of this research is the automotive sub-sector manufacturing companies ilisted on the Indonesia Stock Exchange during 2016-2019. The samples this study were 9 companies with total of 36 dats. The sampling technique used purposive side. The data analysis methode used in this study is multiple linear regresion analiysis using SPSS 21 software’s. The result showed that the effect of EPS, ROE, PER, inflationiand interes rates has a effect on stocke prices by 80.3% and the remaining 19.7. % influencedi by other factor not examined. From the partial test, EPS has an effeck on stocks prices, while the variables ROE, PER, inflation and interest rates have no effection stock prices.
Keywords: EPS, ROE, PER, Inflation, Interest Rates, Stock Prices
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Sri Herianingrum, Annisa M. Zaimsyah, Alvira A. Ayun, Khofidlotur Rofi’ah,. "Pengaruh Variabel Makroekonomi Terhadap Index Harga Saham Syariah." Jurnal Ekonomi 25, no. 1 (March 11, 2020): 1. http://dx.doi.org/10.24912/je.v25i1.623.
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The purpose of this paper to investigate macroeconomic indicators affects sharia stock prices in Asia Muslim majority countries. This research used inflation, interest rates and exchange rate in Indonesia, Malaysia, Saudi Arabia and Bangladesh. This research use data panels and multiple linear regression analyses using Ordinary Least Square (OLS) method as parameter. The results shows inflation and exchange rates have no effect in sharia stock prices, while interest rates have negative effect. Implications for investors who want to invest in stocks to attention macroeconomic variables that affect movement of stock price, information that related macroeconomic conditions, information that predict fluctuation stock price. This study has limitations only focus in four countries in Asia and only use three most dominant macro variables. Future research can improved other macroeconomic variables such world oil prices and gross domestic income. Novelty of this research examines the Sharia stock market in Muslim majority countries in Asia.
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Serbin, V., and U. Zhenisserov. "ANALYSIS OF MACHINE LEARNING METHODS FOR PREDICTIONS OF STOCK EXCHANGE SHARE PRICES." Scientific Journal of Astana IT University, no. 5 (July 27, 2021): 94–100. http://dx.doi.org/10.37943/aitu.2021.47.22.009.
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Since the stock market is one of the most important areas for investors, stock market price trend prediction is still a hot subject for researchers in both financial and technical fields. Lately, a lot of work has been analyzed and done in the field of machine learning algorithms for analyzing price patterns and predicting stock prices and index changes. Currently, machine-learning methods are receiving a lot of attention for predicting prices in financial markets. The main goal of current research is to improve and develop a system for predicting future prices in financial markets with higher accuracy using machine-learning methods. Precise predicting stock market returns is a very difficult task due to the volatile and non-linear nature of financial stock markets. With the advent of artificial intelligence and machine learning, forecasting methods have become more effective at predicting stock prices. In this article, we looked at the machine learning techniques that have been used to trade stocks to predict price changes before an actual rise or fall in the stock price occurs. In particular, the article discusses in detail the use of support vector machines, linear regression, and prediction using decision stumps, classification using the nearest neighbor algorithm, and the advantages and disadvantages of each method. The paper introduces parameters and variables that can be used to recognize stock price patterns that might be useful in future stock forecasting, and how the boost can be combined with other learning algorithms to improve the accuracy of such forecasting systems.
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Marjuni, Aris. "Peramalan Harga Saham Serentak Menggunakan Model Multivariate Singular Spectrum Analysis." JURNAL SISTEM INFORMASI BISNIS 12, no. 1 (August 24, 2022): 17–25. http://dx.doi.org/10.21456/vol12iss1pp17-25.
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Stock price fluctuations in the stock market are widely influenced by financial environment changes in both micro and macro that are usually unpredictable and can not be controlled by stock players. On the other side, stock price information is very essential and much needed for both buyers and traders. Stock price forecasting is one of the analytical techniques to obtain stock price prediction based on the previous historical stock prices. The open and close prices are important information in stock trading. The opening price can influence the movement towards the closing price, and the closing price becomes important for the next day's opening price. In technical analysis, the relationship between the two stock prices can be parametric or non-parametric. This study discusses the stock price prediction or forecasting through the non-parametric approach using a multivariate singular spectrum analysis method with the consideration that open and close prices are simultaneously working in the same system and time. Performance evaluation using Mean Absolute Percentage Error shows that the multivariate singular spectrum analysis method can produce predicted open and close prices with an error rate of 3.18% and 3.21%, respectively. Hence, this method can be used as an alternative for stock price forecasting simultaneously.
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Talati, Drashti, Dr Miral Patel, and Prof Bhargesh Patel. "Stock Market Prediction Using LSTM Technique." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1820–28. http://dx.doi.org/10.22214/ijraset.2022.43976.
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Abstract: One of the most intricate machine learning problems is the share value prediction. Stock market prediction is an activity in which investors need fast and accurate information to make effective decisions. Moreover, the behavior of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. This leads to the research of finding the most effective prediction model that generates the most accurate prediction with the lowest error percentage. Prices of stocks are depicted by time series data and neural networks are trained to learn the patterns from trends in the existing data. This system employed algorithm using LSTM to improve the accuracy of stock price prediction.
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Gokmenoglu, Korhan, and Siamand Hesami. "Real estate prices and stock market in Germany: analysis based on hedonic price index." International Journal of Housing Markets and Analysis 12, no. 4 (August 5, 2019): 687–707. http://dx.doi.org/10.1108/ijhma-05-2018-0036.
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PurposeReal estate and stocks are two major asset types in an investor’s portfolio. Therefore, this paper aims to investigate the relationship between these two markets to provide a valuable insight into the process of portfolio optimization and security selection.Design/methodology/approachThis study examines the long-run relationship between residential real estate prices and stock market index in the case of Germany for the period of 2005-2017 by applying time series econometrics techniques. To this aim, this study uses Hedonic House Price Index as a proxy for real estate prices and DAX30 as a proxy for stock prices. Moreover, three additional variables, namely, consumer confidence, credit availability and supply of mortgage loans, are incorporated as control variables to assess the robustness of the results.FindingsObtained empirical results indicate a long-run relationship between stock prices and real estate prices which suggests that in long-run, there is no diversification benefit from allocating stock and real estate assets in a portfolio. This finding is especially important for long-term investors such as pension funds.Originality/valueTo the authors’ best knowledge, this is the first study that empirically investigates the relationship between the real estate market and stock prices using the Hedonic Price Index for the case of Germany.
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Stephen Harlan and Henryanto Wijaya. "Pengaruh ROA, ROE, EPS, & PBV terhadap Stock Price dan Stock Return." Jurnal Ekonomi 27, no. 03 (March 4, 2022): 202–23. http://dx.doi.org/10.24912/je.v27i03.873.
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The purpose of this study is to determine the effect of Return on Assets (ROA), Return Equity (ROE), Earning per Share (EPS), and Price per Book Value (PBV) on stock prices and stock returns in manufacturing companies registered on the Indonesia Stock Exchange in 2018-2020. This study used 73 manufacturing companies as the sample and using panel data regression analysis. The results indicate that ROA has negative and insignificant effect on stock prices, and has positive and significant effect on stock returns; ROE has insignificant positive effect on stock prices, and has significant and negative effect on stock returns; EPS has negative and insignificant effect on stock prices, and has positive and insignificant effect on stock returns; PBV has negative and insignificant effect on stock prices, and has positive and insignificant effect on stock returns. ROA, ROE, EPS, and PBV simultaneously have significant effect on stock price and stock return.
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Khoir, Nidaul, Di Asih I. Maruddani, and Dwi Ispriyanti. "PREDIKSI HARGA SAHAM MENGGUNAKAN GEOMETRIC BROWNIAN MOTION WITH JUMP DIFFUSION DAN ANALISIS RISIKO DENGAN EXPECTED SHORTFALL (Studi Kasus: Harga Penutupan Saham PT. Waskita Karya Persero Tbk.)." Jurnal Gaussian 11, no. 1 (May 13, 2022): 153–62. http://dx.doi.org/10.14710/j.gauss.v11i1.33989.
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Investment is an activity that is quite popular among investors in recent years. One of the forms of investment in the financial sector is investing in the capital market by buying stocks in a company. The level of profit from stock investment activities can be seen from the value of stock returns. Factors that can affect the value of stock returns are stock prices. However, stock prices often experience unpredictable changes so that they experience fluctuating movements with increasing time and developing situations, therefore a stock price model is needed to predict stock prices in the future period. The Geometric Brownian Motion with Jump Diffusion’s method is more appropriate to be used in predicting stock prices if there is a jump in stock price data. Predicted stock prices can be used as a basis for measuring the value of investment risk. The results of data processing indicate that the stock return data of PT. Waskita Karya Persero Tbk has a kurtosis value > 3 which means there is a jump in stock return data so that it is more accurately modeled using the Geometric Brownian Motion with Jump Diffusion’s method. The prediction results have a good level of accuracy based on the MAPE value of 18,733%. Furthermore, in order to measure the investment risk of the predicted stock price of PT. Waskita Karya Persero Tbk used the Expected Shortfall Historical Simulation’s method with a significance level of α = 5%, the results were 0,10939, and for the significance level α = 10%, the results were 0,07596. The calculation results show that the greater the trust level used, the greater the risk borne by investors.Keywords: Jump Diffusion Process, Expected Shortfall, Risk, Extreme Value