Journal articles on the topic 'Stocks – Prices'
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1
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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Zhao, Hang, and Yucan Liu. "A study of the impact of investor attention on stock prices ——take new energy concept stocks as an example." BCP Business & Management 35 (December 31, 2022): 768–76. http://dx.doi.org/10.54691/bcpbm.v35i.3398.
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In recent years, "new energy" has been the focus of national attention. Under such circumstances, this paper takes new energy concept stocks as an example to explore the impact of investor attention on stock prices in the current period. Among them, there are many indicators in the stock market, and this article measures stock prices from the perspective of stock prices with yield and stock price volatility. The proxy variable of investor attention is Baidu index of new energy concept stocks to explore the impact of investor attention on the price of new energy concept stocks. The study finds that there is a positive relationship between investor attention and the yield and stock price volatility of new energy concept stocks in the current period.
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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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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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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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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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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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Ma, Yixuan. "The Relationship between Stock Prices and Silver Future Prices Based on VAR Model." Highlights in Business, Economics and Management 7 (April 5, 2023): 490–95. http://dx.doi.org/10.54097/hbem.v7i.7022.
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Despite recent dramatic increases in the prices of coal, oil, natural gas, and other fossil energy futures, some of which have hit successive record highs, related stocks have seen substantial decreases. The price trend of commodities is typically driven by commodity futures, which serve as price discoverers. There is a relationship between futures and stocks in the market, meaning that when the price of the futures contract for a particular commodity rises, so will the price of the stock of the company that produces the commodity because investors anticipate rising earnings. But does the price of futures vary when the stock price does? Based on the findings of the VAR model and Granger causality test, this study concludes that the stock price, whether it be the current price or the historical earnings, does not significantly affect the futures price. Futures prices have a significant impact on future stock prices. This would facilitate investment decision-making.
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Zhao, Yinuo. "Research on momentum strategy and contrarian strategy in AI stock prediction." Applied and Computational Engineering 29, no. 1 (December 26, 2023): 125–32. http://dx.doi.org/10.54254/2755-2721/29/20231207.
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The emergence of ChatGPT has significantly enhanced the recognition and acceptance of artificial intelligence concept stocks within the Chinese stock market. Nevertheless, the short- and long-term fluctuations in the prices of AI companies remain uncertain. Therefore, the purpose of this research is to determine optimal strategy for evaluating the suitability of the contrarian strategy versus the momentum strategy in predicting the stock prices of AI concept stocks in the Chinese stock market. Based on a cross-comparison of the Chinese financial data sources iFinD and Wind Economic Database (EDB), this study collects the price data of AI concept stocks over the past six months, starting from the date of ChatGPT's publication. This study employ Python to model stock price movements for both the momentum and reversal strategies. The goodness of fit is evaluated by comparing the modeled stock prices with the actual stock prices. This study demonstrates that the momentum strategy exhibits greater explanatory power than the contrarian strategy, accurately predicting 84.21% of artificial intelligence concept stocks. However, other studies suggest that while AI concept stocks continue to rise, momentum strategies remain effective, whereas when market sentiment cools down, contrarian strategies become more suitable for Chinese AI concept stocks. Hence, in China, the effectiveness of these strategies may vary depending on the prevailing market conditions.
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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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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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Abrahamson, Martin. "Offer Price and Post-IPO Ownership Structure." Journal of Risk and Financial Management 17, no. 2 (February 6, 2024): 61. http://dx.doi.org/10.3390/jrfm17020061.
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In an initial public offering (IPO) the firm can set the offer price of its shares, based on the valuation of the firm, by changing the number of shares. This study uses stock ownership records and hand-collected IPO data to analyze the offer prices, the underpricing of IPO shares (measured as the initial return, IR) and the relationship with the post-IPO ownership structure. Specifically, the paper focuses on individual IPO investors. The results show that for the lowest priced IPOs the IR is significantly higher priced IPOs. Furthermore, for the low-priced IPOs, there is a negative relationship between offer price and breadth of ownership. This implies that stocks with a low price can attract more investors than stocks with higher offer prices. However, for high-priced IPOs the relationship is positive, suggesting that also the IPOs with highest price attract more investors. Overall, this study shows that the offer price of an IPO firm may have a moderate effect on its post-IPO ownership structure.
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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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Atanta, Alfikranta, Sofyan Syahnur, and Taufiq C. Dawood. "Dynamic Relationship Among Crude Oil Price, Stock Price, and Exchange Rates In Indonesia." International Journal of Quantitative Research and Modeling 4, no. 4 (December 29, 2023): 191–99. http://dx.doi.org/10.46336/ijqrm.v4i4.376.
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This study aimed to examine the causal relationship among oil prices, JCI stock prices, and exchange rates in Indonesia. Observational data were from the period 2015M01-2020M06. The research analysis model used was Toda-Yamamoto (1995) causality test. The results showed a two-way causal relationship between exchange rates and oil and a one-way relationship between exchange rates and stocks. There was a one-way effect of stocks on oil. Stock shocks occurred due to the influence of the stocks themselves—only 10 percent of the exchange rates and 0.62 percent of the oil price. Meanwhile, oil prices experienced shocks from stocks of 35.95 percent and exchange rates of 20.87 percent. Changes were found in the exchange rates because stocks were 57.21 percent and oil prices were 11.27 percent. It is recommended to control the exchange rates so that the economy becomes stable, explore oil in the country or use renewable energy technology to break away from dependence on fossil oil, and maintain the value of stocks to be strong and stable.
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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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Liao, Xinyu. "Stock Price Prediction Based on ARIMA and Neural Network." Advances in Economics, Management and Political Sciences 56, no. 1 (December 1, 2023): 163–71. http://dx.doi.org/10.54254/2754-1169/56/20231102.
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The stock price is affected by many factors and is a very complex nonlinear and non-stationary system. Predicting stock prices is a classic problem. People hope to predict stock prices more accurately, so as to make profits through stocks. This article selects five stocks in the Nasdaq stock market from 2020 to 2023, and tries to use 3 AI models (ARIMA, CNN, LTSM) to predict and analyze their next days closing prices and use the RMSE as the index to analyze the prediction performance. This paper finds that the three models can predict the stock price next day well, among which the ARIMA model and LSTM model have better prediction results, average RMSE for them are about 3.3 and 4.5 while the CNN model has poorer prediction performance with RMSE 7.2. At the same time, paper is found that when the model has a turning point for the stock, all the models predict poorly. In the future, we can consider combining the eigenvalues of more stocks to reduce the impact of turning points on price prediction.
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Machtra, Catona, Muhammad Shabri Abdul Majid, and Taufiq Carnegie Dawood. "Does Foreign Interest Rate Determine Islamic Stock Prices?" Amwaluna: Jurnal Ekonomi dan Keuangan Syariah 7, no. 1 (January 31, 2023): 1–10. http://dx.doi.org/10.29313/amwaluna.v7i1.7001.
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This paper aims to investigate whether foreign interest rate determines Islamic stock prices vis-à-vis domestic interest rate. The method applied is the Autoregressive Distributed Lag (ARDL) model to Indonesian data from January 2008 to June 2018. The study found a long-run negative relationship between foreign interest rate with both Islamic and conventional stock prices. Additionally, foreign interest rate is more important than domestic interest rate in determining the price of both types of stocks. Although Islamic stocks are affected by foreign interest rate dynamics, they are less responsive to changes of the Libor than conventional equities. In addition, it is found that GDP, money supply and real exchange rate has a long-run positive relation with both stock price. Thus, although Islamic equity prices are not insulated against foreign interest rates, they are less responsive to international financial markets movements than conventional stocks. For policy, authorities should pay close attention to foreign interest rate dynamics. While policy makers and fund managers in a dual capital market can use Islamic stocks to provide cushion against dynamics of international funds market.
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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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Wang, Qiming. "Evolution of integer price clustering of IPOs in the aftermarket." Nankai Business Review International 5, no. 4 (October 28, 2014): 365–81. http://dx.doi.org/10.1108/nbri-01-2014-0008.
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Purpose – The purpose of this paper is to, using a large sample of NASDAQ initial public offerings (IPOs), examine the evolution of integer price clustering of IPOs in the aftermarket trading. Design/methodology/approach – Consistent with Harris’s (1991) costly negotiation hypothesis, clustering on integer prices is a positive function of price level and various stock valuation uncertainty proxies, and it is a negative function of trading activities for IPOs and seasoned stocks. Findings – It was found that, after controlling for price level, daily return volatility, number of trades, trading volume, number of market makers and the effect of price support, the integer price frequency of IPOs converge to that of seasoned stocks immediately, and whether IPOs have integer offer prices does not affect their integer price clustering in the aftermarket trading after the effect of price support is controlled for. Originality/value – These results suggest that the IPO pricing process significantly reduce the differences between integer priced IPOs and non-integer priced IPOs in pre-offering valuation uncertainty.
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Yang, Zongye. "The impact of investors attention on stock price." Advances in Economics, Management and Political Sciences 6, no. 1 (April 27, 2023): 124–35. http://dx.doi.org/10.54254/2754-1169/6/20220164.
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From the perspective of investor attention to market stocks, this paper analyzes investors' visitor volume of certain stocks and sentiments of stock indexes. I compare the search vol-ume and stock prices of Apple and Tesla to specifically observe the relationship between stock prices and investor attention in individual companies. Then I examine the search vol-ume and index of SP500 and SSEC to explain the significant disparity in the impacts of in-vestor attention in different regions. It shows that increased attention is positively related to stock price volatility, i.e., positive emotional signals (negative emotional signals) will lead to higher (lower) stock prices.
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Salisu, Afees Adebare, Raymond Swaray, and Tirimisiyu Oloko. "US stocks in the presence of oil price risk: Large cap vs. Small cap." Economics and Business Letters 6, no. 4 (March 18, 2018): 116. http://dx.doi.org/10.17811/ebl.6.4.2017.116-124.
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This study queries the act of making generalization about the dynamics of returns and volatility spillovers between oil price and U.S. stocks by merely considering only large cap stocks. It argues that this kind of generalization may be misleading, as the reactions of large cap, mid cap and small cap stocks to change in oil prices are not expected to be uniform. Our findings show that it is correct to make generalization about oil-U.S. stock relationship with large cap stocks when analysing returns spillovers, but the generalization is incorrect when considering stock caps returns volatility spillovers, particularly under falling and relatively stable oil prices.
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Yu, Junjie, Wenjia Sang, and Yiqian Tang. "Analysis of Apple Stock - Based on R." Frontiers in Business, Economics and Management 15, no. 3 (July 11, 2024): 427–30. http://dx.doi.org/10.54097/sjaw2e59.
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With the continuous development of China's economy, the stock market is becoming increasingly mature, and stocks play a crucial role in the economic life. The development and changes in stocks can measure the economic development of enterprises. Meanwhile, stock investment has become a means for people to obtain economic benefits. The development of stocks is closely related to economic development. The fluctuation of stock prices can reflect the implementation of national economic policies and also comprehensively reflect the living conditions of residents. With the continuous improvement of the stock market, accurately analyzing the trend of stock prices has become an important research topic. Accurate analysis of stock price changes is of great significance for the regulation of macroeconomic policies and making optimal choices for investors. The fluctuation of stock prices is a complex nonlinear dynamic process, and traditional linear models cannot accurately describe and analyze its development and changes. Time series models can effectively fit curve data, so they are of great importance in the analysis and description of stock price changes. In this study, we collected all trading days' stock data of Apple Inc. from January 1, 2021, to June 29, 2021, and used a time series model to analyze the changes in stock prices. In this paper, we first conducted a simple descriptive statistical analysis of the changes in Apple's stock prices and found that the price fluctuations of Apple's stock did not revolve around a specific value. Secondly, through the observation and preprocessing of stock prices, it was found that the sequence was non-stationary. This paper used the first-order difference method to achieve stationarity and fitted the data using the ARIMA model. For different parameters of the ARIMA model, the optimal model was determined based on the AIC criterion and the modified AIC coefficient. Furthermore, the abnormal values of the stock prices were processed, enabling effective prediction of the future trend of Apple's stock prices. Through research analysis, the conclusion drawn in this paper is that the price of Apple's stock is not only influenced by random factors but also significantly affected by the lagged 5-period stock prices. Overall, the trend of Apple's stock price changes is relatively stable.
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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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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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Bin, Zhiyuan, Yunfan Ge, Rui Huang, and Haisheng Xu. "Stock price volume leap principle based on investor sentiment contagion model." SHS Web of Conferences 181 (2024): 02024. http://dx.doi.org/10.1051/shsconf/202418102024.
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The rise and fall of the Chinese stock market has been the subject of many studies since its inception. However, the recurring uncertainty of the broad market and individual stocks has overturned many people’s perceptions of the financial market. This paper explores the principles of the rise and fall of individual stocks under certain conditions, starting from Samuelson’s phenomenon of backward bending of the price supply curve for oil and labour, and proposes a methodology for stock market investment based on this principle. The results of the study show that the rise of stock prices is often characterized by a rapid jump. An important factor related to this is the interest rate of private lending. The rise and fall of private lending rates affect the expected return on stock market investment funds and has an important impact on stock market investment. Therefore, in the absence of a qualitative change in the stock’s texture, the medium-term rate of increase is the key to judging the stock price’s later trend. The results of this study provide a new direction to think about when judging the short- and medium-term stock price movements.
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Febiyanti, Dewi, Nonong Amalita, Dony Permana, and Tessy Octavia Mukhti. "Backpropagation Neural Network Application in Predicting The Stock Price of PT Bank Rakyat Indonesia Tbk." UNP Journal of Statistics and Data Science 1, no. 5 (November 30, 2023): 441–48. http://dx.doi.org/10.24036/ujsds/vol1-iss5/113.
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Investors often make mistakes when making stock transactions even though having chosen good company stocks. The thing that needs to be considered in making stock transactions is to see the movement of stock prices. The movement of the stock price in PT Bank Rakyat Indonesia Tbk has changed in the form of a decrease or increase. An increasing stock price will provide benefits for investors by selling stocks. But, investors actually decide to make stock purchases. The existence of stock purchase transactions causes investors to take a high risk because stock prices fluctuate. To anticipate the occurrence of high risk to investor, stock price predictions are made using a Backpropagation Neural Network (BPNN). BPNN can adapt quickly and is able to predict nonlinear data such as stock prices and produce a high level of accuracy. The results of this study obtained the best BPNN model, namely the BP(5,3,1) model with a Mean Absolute Percentage Error (MAPE) of 0,8193%. These results show that the model has good network performance so that it can predict stock prices well because it gets a small prediction error.
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Gomathi, M., and Dr S. Nirmala. "Analysis of Nifty Movement on Share Prices of Selected Construction Companies." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 1, no. 3 (September 27, 2012): 59–65. http://dx.doi.org/10.24297/ijmit.v1i3.1421.
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This study aims at analyzing and predicting the price movements of construction companies stocks contributing to the NIFTY50 Index. To analyze the volatility of telecom stock and understand the behavior of stock prices in construction sector stocks i.e. (JP ASSOCIATES LIMITED, DLF LIMITED, GAMMON INDIA LIMITED, PUNJ LLOYD LIMITED, HCC LIMITED). The data for these stocks are collected from magazines, newspaper and websites. The stocks are analyzed by monitoring their respective price movements using technical tools. The technical tools used in this study are Exponential moving average, Relative strength index, Rate of change, MACD. Using these tools the trend over the recent past was deciphered. The expected trend in the immediate future was also predicted. Technical Analysis studies the price and volume movement in the market and predicts the future. It helps in identifying that the best time to buy and sell equity. Technical Analysis is a method of evaluating equities by analyzing the statistics generated by market activity, such as past prices and volume.
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SAFIRA, ICHA WINDA DIAN, KOMANG DHARMAWAN, and DESAK PUTU EKA NILAKUSMAWATI. "PENENTUAN KEPUTUSAN INVESTASI SAHAM MENGGUNAKAN CAPITAL ASSET PRICING MODEL (CAPM) DENGAN PENAKSIR PARAMETER STOKASTIK." E-Jurnal Matematika 10, no. 4 (November 30, 2021): 251. http://dx.doi.org/10.24843/mtk.2021.v10.i04.p351.
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CAPM is a method of determining efficient or inefficient stocks based on the differences between individual returns and expected returns based on the CAPM’s positive value for efficient and negative value for inefficient stocks. The move to share prices in the process can influence investors's decisions in investing funds, so that it can be formulated in stochastic differential equations that form the Geometric Brownian Motion model (GBM). The purpose of the study is to determine return value using the CAPM based on share estimates and historical stock prices. The study uses secondary data that data a monthly closing of stock prices from December 2017 to December 2020. The GBG model's estimated stock price is used to determine the expected value return using the CAPM. In this case, it is called CAPM-Stochastic. Then the results of the CAPM-Stochastic was compared to the results of the CAPM-Historical to define efficient stocks and inefficient stocks. The results of research using CAPM-Stochastic obtained that HMSP, ICBP, KLBF, and WOOD shares are efficient stock while UNVR shares are inefficient. The results of CAPM-Historical obtained that HMSP, ICBP, KLBF, and UNVR shares are inefficient stocks and WOOD is an efficient stocks.
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Kumar, Dr Vishal, and Ritu Rani. "Performance Evaluation of Selected Banking Stocks Listed on Bombay Stock Exchange During Pre & Post Covid-19 Crisis." International Journal of Innovation and Economic Development 7, no. 3 (August 2021): 53–61. http://dx.doi.org/10.18775/ijied.1849-7551-7020.2015.73.2005.
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Investing in the stock market has always been regarded as risky. Market sentiment is a factor that influences stock prices. The purpose of this study is to assess the performance of selected banking stocks based on risk and excess return generated by them during the study period. The study also determines the effect of certain financial variables on sample banking stocks during the time crisis of Covid’19. Economic variables such as the BSE Sensex, rate of exchange, variation in FII (Foreign Institutional Investors), and coupon rate of Government Sector (G-Sec) were analysed in conjunction with the analysis of banking stocks. The regression and correlation tests are used to determine the significance of variables using SPSS. Following the BSE’s performance provides insight into the future modifications throughout the price levels of bank shares. Following a sharp decline in the market, private sector bank stock prices are correct, but not public sector bank stock prices. Throughout the first part of the research, there is a direct relationship between the BSE, Sensex, and the selected stocks, but only a weak correlation with FII, G-Sec coupon rate, and the exchange rate. Along the second part of the research, the relationship between stock prices and economic variables varies widely between banks.
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Li, Yung-Chen, Hsiao-Yun Huang, Nan-Ping Yang, and Yi-Hung Kung. "Stock Market Forecasting Based on Spatiotemporal Deep Learning." Entropy 25, no. 9 (September 12, 2023): 1326. http://dx.doi.org/10.3390/e25091326.
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This study introduces the Spacetimeformer model, a novel approach for predicting stock prices, leveraging the Transformer architecture with a time–space mechanism to capture both spatial and temporal interactions among stocks. Traditional Long–Short Term Memory (LSTM) and recent Transformer models lack the ability to directly incorporate spatial information, making the Spacetimeformer model a valuable addition to stock price prediction. This article uses the ten minute stock prices of the constituent stocks of the Taiwan 50 Index and the intraday data of individual stock on the Taiwan Stock Exchange. By training the Timespaceformer model with multi-time-step stock price data, we can predict the stock prices at every ten minute interval within the next hour. Finally, we also compare the prediction results with LSTM and Transformer models that only consider temporal relationships. The research demonstrates that the Spacetimeformer model consistently captures essential trend changes and provides stable predictions in stock price forecasting. This article proposes a Spacetimeformer model combined with daily moving windows. This method has superior performance in stock price prediction and also demonstrates the significance and value of the space–time mechanism for prediction. We recommend that people who want to predict stock prices or other financial instruments try our proposed method to obtain a better return on investment.
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Patterson, Douglas M., and Vivek Sharma. "THE INCIDENCE OF INFORMATIONAL CASCADES AND THE BEHAVIOR OF TRADE INTERARRIVAL TIMES DURING THE STOCK MARKET BUBBLE." Macroeconomic Dynamics 14, S1 (March 12, 2010): 111–36. http://dx.doi.org/10.1017/s1365100509991143.
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This paper investigates the possible formation of informational cascades by traders in a random sample of 8,000 NYSE stock-days experiencing extreme price changes during 1998–2001. Our cascade measure is designed to detect informational cascades in high-frequency stock market prices. First, we find evidence of cascades on approximately 12% of the days when the NYSE experiences, on average, large price increases. This percentage increases to about 20% on days experiencing large price decreases, on average. Second, we find evidence that the interarrival times of trades in those stocks exhibiting significant informational cascades are generated by a nonlinear stochastic process. Third, the evidence supporting cascades is largely confined to smaller stocks and to stocks followed by fewer security analysts. Last, the occurrence of cascades appears to correlate with the incorporation of fundamental information into security prices.
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Mishra, Shambhavi, Tanveer Ahmed, Vipul Mishra, Sami Bourouis, and Mohammad Aman Ullah. "An Online Kernel Adaptive Filtering-Based Approach for Mid-Price Prediction." Scientific Programming 2022 (February 16, 2022): 1–13. http://dx.doi.org/10.1155/2022/3798734.
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The idea of multivariate and online stock price prediction via the kernel adaptive filtering (KAF) paradigm is proposed in this article. The prediction of stock prices is traditionally done with regression and classification, thereby requiring a large set of batch-oriented and independent training samples. This is problematic considering the nonstationary nature of a financial time series. In this research, we propose an online kernel adaptive filtering-based approach for stock price prediction to overcome this challenge. To examine a stock's performance and demonstrate the work's superiority, we use ten different KAF family of algorithms. In this paper, we take on this challenge and propose an approach for predicting stock prices. To analyze a stock's performance and demonstrate the work's superiority, we use ten distinct KAF algorithms. Besides, the results are analyzed on nine-time windows such as one day, sixty minutes, thirty minutes, twenty five minutes, twenty minutes, fifteen minutes, ten minutes, five minutes, and one minute. We are the first to experiment with several time windows for all fifty stocks on the Indian National Stock Exchange, to the best of our knowledge. It should be noted here that the experiments are performed on stocks making up the main index: Nifty-50. In terms of performance and compared to existing methods, we have a 66% probability of correctly predicting a stock's next upward or downward movement. This number clearly shows the edge that the proposed method has in actual deployment. Furthermore, the experimental findings show that KAF is not only a better option for predicting stock prices but that it may also be used as an alternative in high-frequency trading due to its low latency.
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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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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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Zhao, Kevin. "Analyst Downgrades, Short Sale Constraints, And Intra-Day Stock Price Efficiency." Journal of Applied Business Research (JABR) 31, no. 4 (July 10, 2015): 1343. http://dx.doi.org/10.19030/jabr.v31i4.9322.
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This paper studies the impact of short sale constraints on stock price efficiency upon arrival of analyst downgrades. Examining the speed of which stock price response to analyst downgrades for pilot (short sale non-constrained) stocks and control (short sale constrained) stocks in an intra-day setting, I find evidence supporting the hypothesis that short sale constrains hamper intra-day stock price efficiency. For after-hours downgrades, pilot stocks respond quickly, with virtually all of the price response incorporated by the following open, while control stocks take an extra five minutes after opening to fully reflect the new information. For during-hour downgrades, the negative information is partially incorporated into pilot stock prices up to two hours before the recommendation is released, while control stocks take up to an hour and a half after the release to impound the information into stock price, confirming that short sale constraints lower stock price efficiency.
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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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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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Basak, Suleyman, and Anna Pavlova. "Asset Prices and Institutional Investors." American Economic Review 103, no. 5 (August 1, 2013): 1728–58. http://dx.doi.org/10.1257/aer.103.5.1728.
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We consider an economy populated by institutional investors alongside standard retail investors. Institutions care about their performance relative to a certain index. Our framework is tractable, admitting exact closed-form expressions, and produces the following analytical results. We find that institutions tilt their portfolios towards stocks that compose their benchmark index. The resulting price pressure boosts index stocks. By demanding more risky stocks than retail investors, institutions amplify the index stock volatilities and aggregate stock market volatility and give rise to countercyclical Sharpe ratios. Trades by institutions induce excess correlations among stocks that belong to their benchmark, generating an asset-class effect. (JEL G12, G23)
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Hani'ah, Mamluatul, Moch Zawaruddin Abdullah, Wilda Imama Sabilla, Syafaat Akbar, and Dikky Rahmad Shafara. "Google Trends and Technical Indicator based Machine Learning for Stock Market Prediction." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 22, no. 2 (March 31, 2023): 271–84. http://dx.doi.org/10.30812/matrik.v22i2.2287.
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The stock market often attracts investors to invest, but it is not uncommon for investors to experience losses when buying and selling shares. This causes investors to hesitate to determine when to sell or buy shares in the stock market. The accurate stock price prediction will help investors to decide when to buy or sell their shares. In this study, we propose a new approach to predicting stocks using machine learning with a combination of features from stock price features, technical indicators, and Google trends data. Three well-known machine learning algorithms such as Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Multiple Linear regression are used to predict future stock prices. The test results show that the SVR outperformed the MLP and Multiple Linear Regression to predict stock prices for Indonesian stocks with an average MAPE is 0.50%. The SVR can predict the stock price close to the actual price.
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Fernanda, Adeliya, and Najmah Rizqya Maliha Putri. "Optimizing Investment Strategies: A Case Study on JPMorgan Chase & Co. Stock Options Using the Black-Scholes Model and What-If Analysis in Excel." International Journal of Mathematics, Statistics, and Computing 2, no. 1 (February 1, 2024): 25–31. http://dx.doi.org/10.46336/ijmsc.v2i1.63.
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This research focuses on applying the Black-Scholes Model to evaluate European options on JPMorgan Chase & Co. stocks. This model has been a critical foundation in evaluating financial instruments, especially options, since its development in 1973 by Fisher Black, Myron Scholes, and Robert Merton. The study utilizes secondary data from some sources to obtain current information regarding stock prices, strike prices, expiration time, volatility, and relevant risk-free interest rates for option valuation as of December 19, 2023. Through this approach, our aim is to gain a better understanding of how the Black-Scholes Model is used as a framework in determining option prices for these stocks. The research methodology involves What-If analysis, exploring variations in key variables such as current stock price, strike price, expiration time, stock price volatility, and risk-free interest rates to assess how these changes affect the prices of both call and put options. Additionally, the study presents graphs representing stock prices, strike prices, interest rates, time, and volatility to visually support the research findings. The analysis results reveal that the prices of both call and put options are highly responsive to changing market conditions. An increase in the current stock price tends to raise the call option price while reducing the put option price. Conversely, an increase in the strike price has the opposite effect. Moreover, variations in the risk-free interest rates influence the option prices, with rising rates increasing the call option price and decreasing the put option price. Furthermore, as the expiration time approaches or stock price volatility increases, both call and put option prices tend to rise. These findings provide a comprehensive understanding of the dynamics of JPMorgan Chase & Co.'s stock option pricing, offering a foundation for investors to make informed and adaptable investment decisions amid constantly evolving financial markets. Sensitivity to changes in key variables is an essential aspect to consider in designing effective investment strategies in the face of ever-changing financial markets.
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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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Samsuar, Alfan, and Pardomuan Sihombing. "DETERMINANT ANALYSIS IN PROPERTY STOCKS INDEX AT INDONESIA STOCK EXCHANGE." Dinasti International Journal of Management Science 2, no. 2 (November 17, 2020): 255–67. http://dx.doi.org/10.31933/dijms.v2i2.453.
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This research aims to determine those influence of inflation, interest rates, exchange rates, world oil prices and world gold prices against the property sector stock index which registered In Indonesia Stock Exchange. These population of research were all activities from monthly movement of property sector stock index, inflation, exchange rates, BI interest rates, world oil prices and world gold prices. The sample chosen method by purposive sampling where the researcher gathered its data based on proficiency strategies or personal considerations, selecting data based on these following criteria: 1) Availability of macro economic data that affects shares from property sector during January 2016 to December 2019; and 2) Availability of property stock index data from January 2016 till December 2019. The model used in this research was the Vector Error Correction Model (VECM). With The results showed that: 1) ISP responsiveness to inflation movements where stumbled or shocks that occur on inflation had positive influence towards ISP movements; 2) Responsiveness of ISP to instability or shocks that occur in exchange rates will negatively affect ISP movements; 3) Those responsiveness of ISP to the BI rate movement was responded positively; 4) Based on these results from research conducted, the ISP responded negatively on stumbled or shocks towards oil price movements; and 5) ISP responsiveness to movements or shocks to gold price had been responded positively by the ISP.
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Heradhyaksa, Bagas, Rahma Oktaviani, Suparman Syukur, Hangrengga Berlian, and Ahmad Wahyudi. "Indonesia Sharia Stock Investment During Covid-19: Based on Islamic Economic Law Review." Jurnal IUS Kajian Hukum dan Keadilan 11, no. 3 (December 29, 2023): 512–27. http://dx.doi.org/10.29303/ius.v11i3.1066.
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The development of technology and financial literacy has increased the number of Indonesians who use stocks as an investment instrument. This also has an impact on the popularity of Islamic stocks. This is because many people are interested in Islamic investment instruments. However, the COVID-19 pandemic has had a significant impact on stock price movements. Sharia stock prices have also experienced very volatile movements due to the COVID-19 pandemic. This phenomenon raises the question of whether investing in Islamic stocks during the COVID-19 pandemic is against Islamic economic law. This is because Islamic stock prices seem to be filled with uncertainty and have experienced a very significant price decline. Moreover, due to the COVID-19 pandemic, the number of stock investors is increasing rapidly. This article aims to analyze Islamic stock investment during the COVID-19 pandemic through the perspective of Islamic economic law. To analyze this issue, this article collects data through library research. The data were analyzed using qualitative methods. In the end, this article finds that investing in Islamic stocks during the COVID-19 pandemic does not contradict the principles of Islamic economic law. Instead, this article suggests that the public can take advantage of a certain momentum to start investing in Islamic stocks.
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Astawinetu, Erwin Dyah, I. Istiono, and Erma Yuliaty. "ANALISIS THE EFFECT OF STOCK BUYBACKS ON STOCK PRICES IN COMPANIES LISTED IN INDONESIA STOCK EXCHANGE DURING THE COVID-19 PANDEMIC PERIOD IN 2020." JMM17 : Jurnal Ilmu ekonomi dan manajemen 9, no. 02 (September 29, 2022): 155–68. http://dx.doi.org/10.30996/jmm17.v9i02.7046.
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Stock repurchase is a corporate action carried out to withdraw part of the outstanding stocks in the market. There are several purposes of this action, including to increase the market price of the company's stocks. This research was to analyze the effect of stock buybacks carried out by corporations on the stock prices of companies listed on the IDX during the Covid-19 pandemic period 2020. There are 21 companies selected to be the object of this study. The collected stock price data was tested for normality and found that it was not normally distributed, so the statistical tools used were non-parametric statistics. The mean difference test is used to test the effect of the corporate action on the stock price. The results of the mean difference test show that the stock repurchase action was able to increase the stock price, although it has not matched the stock prices before the implementation of this corporate action.
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Suhardi, Deddy A. "MEDIASI BETA SAHAM DALAM MODEL PERTUMBUHAN HARGA SAHAM DENGAN PROFITABILITAS DAN SUKU BUNGA." Media Riset Bisnis & Manajemen 9, no. 1 (April 13, 2009): 91–112. http://dx.doi.org/10.25105/mrbm.v9i1.1075.
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Understanding the empirical description of the behavior and role of beta is fundamental to portfolio risk management. This study evaluates the behavior of beta under the influence of stock prices' factors, and evaluates the role of beta affects the stock prices. Path analytical model was designed with beta as the intermediate variable where the interest rate factor and return on asset profitability factor were exogenous, and the average growth rate of stock prices movement became an endogenous variable.This model had been estimated by using the data of 32 property stocks listing at Indonesia Stock Exchange (BEI). Daily prices for the stocks, BEI Composite Index, profitabil4ty, and interest rate were obtained from BEI and Bank Indonesia tapes for January 2000 to December 2004 period.This analysis indicates that beta would become an effective intermediate variable for transmitting effect of the factors toward the stock prices. Stocks prices influence by beta which it influence by factors (interest rate and profitability), on the other hand, stocks prices influence by factors via beta. Here, if interest rate lead to set down or profitability grows up, of ceteris paribus, beta would forces up, and then the stocks prices would move up. The mediation of beta in this model has shown how the investors decide a stock be yet in their portfolio. The positive relationship of beta with stock prices show that investors like more sensitive stock on following market (higher beta). This decision forces most dominant by leading the economical setting (interest rate) rather than growing the firm's fundamental (ROA profitability). In this fact, the decision of investor is rational and indicated closely more interest minded.Keywords : Beta, Intermediate, Stock prices, Profitability, Interest rate
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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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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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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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Cao, Jie, Bing Han, and Qinghai Wang. "Institutional Investment Constraints and Stock Prices." Journal of Financial and Quantitative Analysis 52, no. 2 (March 9, 2017): 465–89. http://dx.doi.org/10.1017/s0022109017000102.
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We test the hypothesis that investment constraints in delegated portfolio management may distort demand for stocks, leading to price underreaction to news and stock return predictability. We find that institutions tend not to buy more of a stock with good news that they already overweight; they are reluctant to sell a stock with bad news that they already underweight. Stocks with good news overweighted by institutions subsequently significantly outperform stocks with bad news underweighted by institutions. The impact of institutional investment constraints sheds new light on asset pricing anomalies such as stock price momentum and post–earnings announcement drift.
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Khan,, Er Reshma, Gagan Ajit Singh, Shubham Behal, Kartik Sharma, and Saurabh Kumar. "Stock Prediction Using Machine Learning Methods." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (November 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27220.
Full textAbstract:
The volatility and non-linear nature of the financial stock markets make it incredibly difficult to estimate stock market returns efficiently.. Investors need rapid access to precise information while trading stocks to make intelligent selections. Programable prediction approaches have demonstrated to be increasingly successful in forecasting stock prices with the introduction of artificial intelligence and better computing capacity. However, several variables impact the decision- making process as a stock market trades multiple stocks. Furthermore, it is impossible to forecast the behavior of stock prices. All of these elements make stock price prediction, both vital and tricky. This drives research into the most accurate prediction model that creates the fewest mistakes in its projections. This research studies machine learning approaches and algorithms in an effort to increase the accuracy of stock price prediction. Keywords— Machine Learning, Linear Regression, LSTM, SVM, Decision Tree, Random Forest