Relevant bibliographies by topics / Stock prices

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Stock prices'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Stock prices"

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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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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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Dissertations / Theses on the topic "Stock prices"

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Li, Rong-Jen. "Combined Leverage and the Volatility of Stock Prices." Thesis, North Texas State University, 1985. https://digital.library.unt.edu/ark:/67531/metadc331340/.

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Much has been written during the past decade to explain the relationship between financial and operating leverage and stock-price volatility. However, the relationship between combined leverage and stock-price volatility has yet to be fully explored. Mandelker and Rhee's (MR) recent study uses both operating and financial leverage in a regression (equivalent to the traditional total leverage—DTL) and shows that both types of leverage are positively associated with common stock betas. Huffman recently demonstrated that there are interactions between operating leverage and financial leverage. Therefore, MR's model could be oversimplified. This study examines the relationship between firms' combined leverage and their stock-price volatility. The study also examines industry and industry growth to see if the relationship is influenced by these factors. The question is whether DOCL is a better risk measure than DTL and whether there is an interaction between operating and financial leverage. The inferences that can be drawn from the study's results are as follows: (a) Stock risk is a function of combined leverage; (b) Industry significantly influences the relationship between stock risk and DOCL; (c) High growth increases the relationship between stock risk and DOCL; (d) Combined leverage (DOCL) is a better risk measure than total leverage (DTL). Further, the problem with the traditional total leverage measure is the omission of the interaction between DOL and DFL. This is consistent with Huffman's theory and suggests Mandelker and Rhee's model is oversimplified.
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Wang, Hanfeng, and 王漢鋒. "Essays on stock trading volume, volatility and information." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38826185.

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Rahou, Amar A. M. "A generalised framework for modelling & forecasting share prices : a field study on modelling and forecasting the share prices from the banking sector." Thesis, University of South Wales, 2009. https://pure.southwales.ac.uk/en/studentthesis/a-generalised-framework-for-modelling--forecasting-share-prices(10fcca19-ff9a-4497-a0be-55f3e980cbed).html.

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Modelling and forecasting the stock market remains a challenge because of the high volatilities in individual stock prices and the market itself. Hence, this topic has received much attention in the literature since forecast errors represent the systematic risk faced by investors. Therefore, the ability to reliably forecast the future values of the shares would provide essential help in reducing that risk to those investors. The main aim of this research is to develop and calibrate a framework that can be used to model the daily share prices of the companies from the banking sector and hence produce informative and reliable one step-ahead forecasts using an adaptive BPNN. To this end, a novel forecasting algorithm is proposed. This algorithm proposes six steps that, when followed, possibly will lead to obtaining superior forecasting models for the share prices from the banking sector. In addition, novel technical indicators, and further information reflecting market knowledge were developed in this research so as to improve the modelling and forecasting share prices for the banking sector, alongside a novel application of the correctly identified turning points which provided an accurate assessment of the performance of the forecasting models. Furthermore, a selection of a set of inputs that are salient to financial data was identified. The research was to inform and improve share price forecasts of the banking sector. The historic open share prices for HSBC, Lloyds TSB, RBS and Barclays were used as case studies and the results give evidence to conclude that useable forecasting models can be obtained by employing the developed framework to the share prices from the banking sector in terms of the correctly identified turning points and the direction of the shares which are achieved more than 70% of the time. The empirical results show that using the market knowledge as input generally improved the modelling and forecasting of the share prices from the banking sector.
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Ho, Yueh-Fang. "Three essays on seasoned equity offerings /." Philadelphia, Pa. : Drexel University, 2003. http://dspace.library.drexel.edu/handle/1860/251.

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Parsa, Sahar. "Investors' horizon and stock prices." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65491.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 140-150).
This dissertation consists of three essays on the relation between investors' trading horizon and stock prices. The first chapter explores the theoretical relation between the horizon of traders and the negative externality generated by their activity on the information revealed by stock prices. The last two chapters focus on the empirical relation between institutional investors trading frequency and stock prices behaviour. The first chapter examines how short term trading impacts the aggregation of information in financial markets. I develop a model where short-term traders, in an attempt to learn about the average beliefs of future market participants, make the price relatively more noisy. This typically introduces a negative informational externality on long-term investors. I show that (i) as the horizon of the informed traders decreases, the price becomes relatively less precise; (ii) an inflow of informed traders in the market can decrease the informativeness of the price when the traders have a relatively short horizon or the market is expected to be thin in the future; (iii) finally, as rational informed short-term traders have access to an extra source of information about the future price, they end up creating more noise and a decrease in the informativeness of the price might result. Thus, paradoxically, more informed trading could lead to a less informative price. Among scholars, practitioners and policy makers, investor short-termism and high frequency trading have been associated with excess volatility in financial markets and with a disconnect between asset prices and fundamentals. Motivated by this observation, in Chapter 2 I construct a novel measure of the intrinsic frequency of trading for each of the large US institutional investors (13-F institutions) using Thomson-Reuters Institutional Holdings quarterly data for the period 1980-2005. This measure controls for the market and portfolio characteristics and identifies an investor-specific fixed effect in the frequency of trading. I then study how the composition of these fixed effects impacts stock price behavior through their forecasting role in explaining the return and the return on equity (cash flow of a company) in the short run as well as the long run. I show that (i) the securities in which investors exhibit higher intrinsic trading frequency exhibit higher volatility, but (ii) this volatility is mainly driven by the cashflow component of the security prices. Further, (iii) the prices of the securities held by investors with a higher intrinsic trading frequency do not forecast the long-run return as opposed to the securities held by investors with a lower intrinsic trading frequency. As such, the prices mainly respond to the long-run return on equity. Overall, the results challenge the view that higher frequency of trading-a commonly used proxy for investor short-termnism-causes a disconnect between asset prices and fundamentals. Finally, in Chapter 3 (co-auhtored with Fernando Duarte) we show a novel relation between the institutional investors' intrinsic trading frequency-a commonly used proxy for the investors's investment horizon- and the cross-section of stock returns. We show that the 20$ of stocks with the lowest trading frequency earn mean returns that are 6 percentage points per year higher than the 20% of stocks that have the highest trading frequency. The magnitude and predictability of these returns persist or even increase when risk-adjusted by common indicators of systematic risks such as the Fama-French, liquidity or momentum factors. Our results show that the characteristics of stockholders affect expected returns of the very securities they hold, supporting the view that heterogeneity among investors is an important dimension of asset prices.
by Sahar Parsa.
Ph.D.
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Wong, Sau-shing Pierre, and 黃守誠. "A study of the correlation of share price movements of Taiwan listed companies with cross holdings." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1997. http://hub.hku.hk/bib/B31268390.

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Pu, Hansong. "An Analysis of Preferred Equity Redemption Cumulative Stock." Thesis, University of North Texas, 1994. https://digital.library.unt.edu/ark:/67531/metadc277588/.

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This dissertation examines whether Percs, Preferred Equity Redemption Cumulative Stocks, are properly priced regarding to the relevant securities, such as the underlying common stock, the long-term call option of the stock, and so on. Test results indicate that Percs were overpriced with respect to the equivalent packages composed of the relevant securities. Further tests on arbitrage restrictions show that transaction costs would prevent arbitrage profits. This dissertation also examines the market reactions to Percs offerings. Test results reveal that the market reactions to the announcement of Percs offering and the actual issuance are both significantly negative. Compared to the market reaction on common stock offering announcement, the market reaction on Percs offering announcement is weaker. The overpricing of Percs and the weaker reaction of the market suggest that Percs may have advantages in transaction costs, taxes and some corporate finance issues.
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Mullins, Mark Robert. "Stock market prices : determinants and consequences." Thesis, London School of Economics and Political Science (University of London), 1990. http://etheses.lse.ac.uk/1192/.

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This thesis concludes that aggregate stock market prices are significantly linked to the real economy. The thesis does, however, find a number of instances of non-efficient market behaviour, in terms of unexplained stock returns prior to financial crises, the predictability of the equity premium, and, possibly, the weak statistical relationship between stock market prices and corporate investment. Chapter I examines stock price behaviour prior to the stock market crash of 1987. Using data from 23 stock markets, there is little support for the view that the recent crash was caused by a bursting bubble. However, there is evidence that equity prices have recently moved in a non-random manner on some of these exchanges. Chapter II investigates the movements of stock prices in the United Kingdom from 1700 to 1987. A strong nominal interest rate effect on excess returns is found for the entire period, but it appears that inflation has a consistent, negative effect only after 1950. Chapter III analyzes major British financial crises since 1700. Using efficient and non-efficient market models, it is found that fluctuations in macroeconomic variables account for up to one half of equity price variation. As well, relatively few crises have been preceded by the excessive positive returns consistent with rational bubbles. Chapter IV finds that Tobin's Q in OECD countries is inappropriately modelled within a static framework but is improved markedly using a dynamic error correction model. The Q measures are also superior to real stock prices as predictors of investment. Chapter V compares the effects of equity prices on corporate investment and output in Japan, West Germany, the United Kingdom and the United States. It seems that the effect of the equity market is greater in the latter two countries for various institutional reasons associated with managerial autonomy.
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Huang, Lin. "On excess volatility of stock prices." Thesis, University of Essex, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442742.

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Takahashi, Yutaka. "A study of Japanese stock prices." Thesis, Massachusetts Institute of Technology, 1991. http://hdl.handle.net/1721.1/13401.

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Books on the topic "Stock prices"

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How the major stock indexes work: From the Dow to the S&P 500. New York: Rosen Pub., 2013.

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Trading on volume. New York: McGraw-Hill, 2002.

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Cutler, David M. What moves stock prices? Cambridge, Mass: Dept. of Economics, Massachusetts Institute of Technology, 1988.

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Cheryl, Pickerell, ed. Negotiating stock photo prices. Rockville, Md: Stock Connection, 1997.

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Bernstein, Jacob. Momentum stock selection. New York: McGraw-Hill, 2001.

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N, Gregoriou Greg, ed. Stock Market Volatility. Boca Raton, Fl: CRC Press, 2009.

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Kelly, Morgan. Do noise traders influence stock prices? Dublin: University College Dublin, Department of Economics, 1996.

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Hess, Martin. The Determinants and the forecastability of Swiss stock prices. Bern: Studienzentrum Gerzensee, 2001.

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Pástor, Lubos̆. Technological revolutions and stock prices. Cambridge, Mass: National Bureau of Economic Research, 2005.

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Pástor, Lubos̆. Stock prices and IPO waves. Cambridge, Mass: National Bureau of Economic Research, 2003.

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Book chapters on the topic "Stock prices"

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Draze, Dianne. "Stock Prices." In The Stock Market Game, 11–14. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003238935-4.

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Sarkar, Dipanjan, Raghav Bali, and Tushar Sharma. "Forecasting Stock and Commodity Prices." In Practical Machine Learning with Python, 467–97. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3207-1_11.

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Lykkesfeldt, Poul, and Laurits Louis Kjaergaard. "The Formation of Stock Prices." In Investor Relations and ESG Reporting in a Regulatory Perspective, 11–19. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05800-4_2.

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Pepper, Gordon. "The Regulation of Stock Markets." In Money, Credit and Asset Prices, 271–79. London: Palgrave Macmillan UK, 1994. http://dx.doi.org/10.1057/9780230375932_19.

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Hamori, Shigeyuki. "Stock Prices and Effective Exchange Rates." In An Empirical Investigation of Stock Markets, 61–81. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9208-6_4.

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Semmler, Willi. "Macro Factors and the Stock Market." In Asset Prices, Booms and Recessions, 89–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20680-1_7.

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Semmler, Willi. "New Technology and the Stock Market." In Asset Prices, Booms and Recessions, 97–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20680-1_8.

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Vasyaeva, Tatyana, Tatyana Martynenko, Sergii Khmilovyi, and Natalia Andrievskaya. "Stock Prices Forecasting with LSTM Networks." In Communications in Computer and Information Science, 59–69. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30763-9_5.

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Wiedmann, Marcel. "Money and Stock Prices: Economic Theory." In Contributions to Economics, 19–32. Heidelberg: Physica-Verlag HD, 2011. http://dx.doi.org/10.1007/978-3-7908-2647-0_3.

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Orosel, Gerhard O. "Stock Prices When Risk Attitudes Fluctuate." In Beiträge zur Mikro- und zur Makroökonomik, 331–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56606-6_28.

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Conference papers on the topic "Stock prices"

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Ildırar, Mustafa, and Erhan İşcan. "The Interaction between Stock Prices and Commodity Prices: East Europe and Central Asia Countries." In International Conference on Eurasian Economies. Eurasian Economists Association, 2015. http://dx.doi.org/10.36880/c06.01350.

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The sharp increase in commodity prices since 2000s has important effects on many economic variables. Especially the upward trend in commodity prices had substantial effects on stock prices. The literature has continuing and growing interest to the dynamics of commodity price and their significant impact on economic and financial developments. There is growing evidence that commodity prices, stock prices moved together, and that the correlations between them have increased. Many studies investigated the interaction between stock prices and real and commodity prices and find strong interaction for developed countries. However, the effect of the commodity prices on stock markets in relatively less investigated for ECA countries. The purpose of this study is to investigate the long-run relationship between commodity prices and stock prices in ECA countries can by using a panel cointegration test.
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Tekin, Bilgehan, and Seda Nur Bastak. "The Relationship of Stock Prices and Stock Market Performance Ratios in Companies Trading on Borsa Istanbul: An Application in Companies with the Highest Trading Volume." In International Conference on Eurasian Economies. Eurasian Economists Association, 2021. http://dx.doi.org/10.36880/c13.02599.

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In this study, the effect of certain ratios that investors pay attention to on stock prices in Borsa Istanbul is examined. For this purpose, 30 of the stocks with which the investors traded the most were taken as a sample. In the study, 30 companies with the highest average trading volume in the analysis period were selected according to their transactions in Borsa Istanbul. The study covers the period between 2010: 1Q-2019: 4Q. Variables included in the study are stock market price, P/E ratio, trading volume, market to book ratio, beta, free float percentage. In this study, it has been tried to understand at what level the stock market prices of companies' publicly traded stocks are affected by the indicators that emerge as a result of the transactions realized in the stock exchange, rather than the ratios discussed within the scope of financial analysis and ratio analysis, examples of which are very common in the literature. Panel regression analysis was performed in the study. Before proceeding to the panel regression analysis, preliminary tests were carried out and the model was tried to be given its most suitable form. For this purpose, multicollinearity tests, cross section dependency test, second generation unit root tests, varying variance test, panel regression model selection were made. The model created in the last stage was estimated. As a result of the study, it was seen that the Price/Earnings, Transaction Volume, Market Value/Book Value and Beta variables were significantly effective on the stock market prices of the companies' stocks. Among these variables, BETA affects negatively, while other variables affect positively. The variable with the highest effect on the share price is the negative BETA coefficient and the positive direction is the trading volume.
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"Real Estate Prices, Rents and Property Stock Prices." In 6th European Real Estate Society Conference: ERES Conference 1999. ERES, 1999. http://dx.doi.org/10.15396/eres1999_203.

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Madaleno, Mara, and Alfredo Marvao Pereira. "Clean energy firms' stock prices, technology, oil prices, and carbon prices." In 2015 12th International Conference on the European Energy Market (EEM). IEEE, 2015. http://dx.doi.org/10.1109/eem.2015.7216628.

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ALEKNEVIČIENĖ, Vilija, and Asta BENDORAITYTĖ. "LONG-TERM DRIVERS OF WHEAT AND MAIZE COMMODITIES PRICES." In Rural Development 2015. Aleksandras Stulginskis University, 2015. http://dx.doi.org/10.15544/rd.2015.129.

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In the last decades prices of agro-food commodities have a tendency of explosive growth. This increase quite usually is related with biofuels development. However, the prices of commodities are influenced by the set of different variables, i.e. supply and demand factors. In order to provide appropriate policy recommendations for agro-culture there is a need to evaluate the factors and their impact on agro-food commodities. This paper uses the multi regression model in order to analyze long-term drivers of annual world wheat and maize commodities prices. Analysis involves both demand (direct: biofuels production, commodities stock in the end of the period; indirect: the exchange rate, the interest rate, gross domestic product) and supply (price of crude oil) factors. The empirical results indicate that the main price drivers of wheat are crude oil prices, exchange rate and stock of wheat lagged one period. While the main maize price drivers are crude oil price and stock of maize lagged one period.
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Bai, Muqing, and Yu Sun. "An Intelligent and Social-Oriented Sentiment Analytical Model for Stock Market Prediction using Machine Learning and Big Data Analysis." In 8th International Conference on Artificial Intelligence and Applications (AI 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121819.

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In an era of machine learning, many fields outside of computer science have implemented machine learning as a tool [5]. In the financial world, a variety of machine learning models are used to predict the future prices of a stock in order to optimize profit. This paper preposes a stock prediction algorithm that focuses on the correlation between the price of a stock and its public sentiments shown on social media [6].We trained different machine learning algorithms to find the best model at predicting stock prices given its sentiment. And for the public to access this model, a web-based server and a mobile application is created. We used Thunkable, a powerful no code platform, to produce our mobile application [7]. It allows anyone to check the predictions of stocks, helping people with their investment decisions.
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Harnphattananusorn, Supanee. "The Relationship between Thailand Stock Prices andCrude Oil Prices." In International Conference on Advanced Research in Social Sciences. Acavent, 2019. http://dx.doi.org/10.33422/icarss.2019.03.86.

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Wahyuni, Wulan, and Nilda Tartilla. "ANALISIS PENGARUH RASIO KEUANGAN TERHADAP HARGA SAHAM PADA PERBANKAN YANG TERDAFTAR DI BURSA EFEK INDONESIA." In Seminar Ilmiah Sistem Informasi Manajemen dan Akuntansi. Goodwood Conferences, 2022. http://dx.doi.org/10.35912/sisima.v1i1.5.

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The purpose of this study is to test, analyze how big the effect of financial ratios on stock prices in banks listed on the Indonesia Stock Exchange, how the influence of Return On Assets (ROA), Debt Equity Ratio (DER), Price Book Value (PBV) and Net Profit Margin (NPM) against stock prices. This research method uses a descriptive method, which is a method to describe and analyze research results but is not used to make broader conclusions. The population of this study are manufacturing companies listed on the Indonesia Stock Exchange (IDX) with the banking sector. The sample was determined using purposive sampling method. The analytical method used in this research is multiple regression with SPSS version 25 program, hypothesis testing is done using multiple linear regression method. The result of this research is partially PBV has a positive effect on stock prices with a significance value of 0.000, while ROA does not have a positive effect on stock prices with a significance value of 0.272. DER has no effect on stock prices with a significance value of 0.936 and NPM has no effect on stock prices with a significance value of 0.518. Simultaneously ROA, DER, PBV and NPM affect stock prices in banks listed on the Indonesia Stock Exchange. The limitations of this study are the limitations of the data processed and the limitations of the variables used. This research is expected to provide benefits for the community, one of which is for investors or investors as a factor of consideration in investing in the company's capital.
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Costa, Thiago F., Elizabeth F. Wanner, Flávio V. C. Martins, and André R. da Cruz. "A Methodology for Definition and Refinement of a LSTM Stock Predictor Architecture using iRace and NSGA-II." In Brazilian Workshop on Artificial Intelligence in Finance. Sociedade Brasileira de Computação, 2022. http://dx.doi.org/10.5753/bwaif.2022.222869.

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This paper presents a novel methodology aiming to define and refine a LSTM architecture applied to predict stock market prices. The methodology, dubbed STOCK-PRED: THE LSTM PROPHET OF THE STOCK MARKET, uses iRace and NSGA-II algorithms. The LSTM is built in two steps: (i) initially, iRace determines a robust set of hyperparameters using a compound objective function; afterwards, (ii) one of the best structures is used to define a tiny search space for the NSGA-II populations. In this step NSGA-II optimizes, simultaneously, the Mean Squared Error, in relation to price prediction, and the Accumulated Accuracy Rate for a time horizon of seven days, relative to growth price tendency. The methodology is tested considering stock market tickers from USA and Brazil. Even in a challenging scenario surrounded by possibly turbulent events, the Stock-Pred predicts the prices based on historical data and machine learning techniques. Since the optimization problem is noisy and the objective functions have a high computational cost, we consider a low budget in relation to the number of fitness evaluations. The analysis of the non-dominated solution indicates that the proposed methodology is promising, achieving a MAPE of 1.279%, 1.564% and 2.047% for BVSP, IBM and AAPL stocks respectively.
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Yong, Lin, and Tong Xin. "Fractal Fitting Research on Stock Prices." In 2008 Congress on Image and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/cisp.2008.752.

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Reports on the topic "Stock prices"

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Cohen, Lauren, Karl Diether, and Christopher Malloy. Legislating Stock Prices. Cambridge, MA: National Bureau of Economic Research, August 2012. http://dx.doi.org/10.3386/w18291.

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Cutler, David, James Poterba, and Lawrence Summers. What Moves Stock Prices? Cambridge, MA: National Bureau of Economic Research, March 1988. http://dx.doi.org/10.3386/w2538.

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Campbell, John, and Tuomo Vuolteenaho. Inflation Illusion and Stock Prices. Cambridge, MA: National Bureau of Economic Research, February 2004. http://dx.doi.org/10.3386/w10263.

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Diba, Behzad, and Herschel Grossman. Rational Bubbles in Stock Prices? Cambridge, MA: National Bureau of Economic Research, October 1985. http://dx.doi.org/10.3386/w1779.

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Pastor, Lubos, and Pietro Veronesi. Stock Prices and IPO Waves. Cambridge, MA: National Bureau of Economic Research, July 2003. http://dx.doi.org/10.3386/w9858.

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Downs, Thomas, and Patric Hendershott. Tax Policy and Stock Prices. Cambridge, MA: National Bureau of Economic Research, December 1986. http://dx.doi.org/10.3386/w2094.

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Pastor, Lubos, and Pietro Veronesi. Technological Revolutions and Stock Prices. Cambridge, MA: National Bureau of Economic Research, December 2005. http://dx.doi.org/10.3386/w11876.

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Beaudry, Paul, and Franck Portier. Stock Prices, News and Economic Fluctuations. Cambridge, MA: National Bureau of Economic Research, June 2004. http://dx.doi.org/10.3386/w10548.

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Campbell, John, and Robert Shiller. Stock Prices, Earnings and Expected Dividends. Cambridge, MA: National Bureau of Economic Research, February 1988. http://dx.doi.org/10.3386/w2511.

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French, Kenneth, and James Poterba. Were Japanese Stock Prices Too High? Cambridge, MA: National Bureau of Economic Research, March 1990. http://dx.doi.org/10.3386/w3290.

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