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Academic literature on the topic 'Stocks – Prices'

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Published: 4 June 2021
Last updated: 28 July 2024

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Journal articles on the topic "Stocks – Prices"

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Kreidl, Felix. "Stock-Market Behavior on Ex-Dates: New Insights from German Stocks with Tax-Free Dividend." International Journal of Financial Studies 8, no. 3 (September 21, 2020): 58. http://dx.doi.org/10.3390/ijfs8030058.

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We examine stock prices and the number of stocks traded around ex-dividend dates of German stocks with tax-free dividend. Tax-free dividends are temporarily tax-exempt, as they reduce the initial purchasing price of a stock. With our analysis of this particular group of German stocks, we can make clear predictions regarding ex-date prices and analyze the number of stocks traded around ex-dates, doing so without the systematic bias of cum-ex trades over time. For XETRA, our empirical results indicate that ex-date prices decline, on average, by the amount of the dividend. We do not find a significant relationship between a stock’s price-drop ratio and dividend yield. Further, the empirical analysis suggests that there is no significant correlation between an abnormal number of a stock being traded and its dividend yield. These results are most consistent with tax-motivated reasoning. However, our volume analysis reveals no consistency regarding the abnormal number of stocks traded for multilateral trading facilities.
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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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Dissertations / Theses on the topic "Stocks – Prices"

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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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Acree, E. Bryan. "Volatility spillovers in international equity markets." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/30969.

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Sabherwal, Sanjiv. "Price discovery for dually traded securities : evidence from the US-Listed Canadian stocks." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/29160.

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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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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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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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鄧梅君 and Mui-kwan Gina Tang. "The relationships between money supply and equity price." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1985. http://hub.hku.hk/bib/B44569531.

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Kemerer, Kevin L. "Accounting variables, stock splits and when-issued trading." Diss., Virginia Tech, 1990. http://hdl.handle.net/10919/39702.

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When-issued trading, the contractual agreement for the sale and purchase of shares to be issued in the future (when-issued securities), typically occurs after stock split announcements. Curiously, when-issued trading does not always exist for a stock-splitting firm's shares even though the shares are eligible for when-issued trading. Although stock splits have been the subject of a large number of studies, intriguing questions concerning these events remain unanswered. In particular, academia has yet to explain adequately the positive average abnormal returns associated with stock split announcements. These two peculiar phenomena are examined. A major objective of this dissertation is to determine whether there are systematic differences between those stock-splitting firms whose shares are traded on a when-issued basis and those whose shares arc not. A logistic regression model was constructed, using information with respect to nine accounting variables, to determine if there are systematic differences in accounting information that are useful in classifying stock-splitting firms as being associated with when-issued trading. The classification accuracy of the logistic regression model was significantly better than a random walk model, but was not better than the maximum chance model. The results of the final model indicate that size of the stock-splitting firm is the most significant factor affecting the probability that a stock-splitting firm's shares are traded on a when-issued basis. The probability that a stock-splitting firm's shares will be traded on a when-issued basis increases with firm size. The presence/absence of when-issued trading indicates that investors do not react to stock splits in a consistent manner. Therefore, the stock price behavior around the stock split announcements was examined and the difference in the reaction to announcements of when-issued traded and non-when-issued traded firms was tested for statistical significance. The results indicate that the market responds more favorably to the stock split announcements made by non-when-issued traded firms. The variation in the stock price behavior over a two-day stock split announcement period was analyzed cross-sectionally to determine whether the market reaction displayed through stock prices is related to selected accounting variables. Again, size was the most significant factor. In this case, size was negatively related to the stock price behavior suggesting that stockholders of larger firms earn lower abnormal returns. Another interpretation would be that stock splits are viewed more favorably if authorized by smaller firms. Overall, the results of this study suggest that all stock-splitting firms are not similar and that the market does not react consistently to the announcement of stock splits of all firms. It seems that the larger the firm, the more likely its shares will be traded on a when-issued basis after the stock split is announced. Furthermore, the market does not react as positively to stock split announcements of larger firms as it does to announcements of smaller firms. My conclusion is that larger firms are more efficiently valued and, accordingly, the announcements of stock splits by larger firms are less informative than for smaller ones.
Ph. D.
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Books on the topic "Stocks – Prices"

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

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Point and figure charting: The essential application for forecasting and tracking market prices. New York: Wiley, 1995.

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Plessis, Jeremy Du. The definitive guide to point and figure: A comprehensive guide to the theory and practical use of the point and figure charting method. Petersfield, Hampshire: Harriman House, 2012.

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Point and figure charting: The essential application for forecasting and tracking market prices. 2nd ed. New York: John Wiley, 2001.

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O'Brien, Thomas J. A simple binomial no-arbitrage model of the term structure with applications to the valuation of interest-sensitive options and interest-rate swaps. New York, N.Y: Salomon Brothers Center for the Study of Financial Institutions, 1991.

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Haskamp, Clemens Heinrich. Aktienkursprognose auf Grundlage der Identifikation von Trend- und Saisonkomponente: Eine empirische Untersuchung. Krefeld: Marchal und Matzenbacher, 1985.

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Rischbieth, Nick. Zur Eignung von Finanz-Kennzahlen für die Prognose von wesentlichen Ausschüttungsänderungen: Eine empirische Untersuchung anhand der Jahresabschlüsse börsennotierter Aktiengesellschaften in der Bundesrepublik Deutschland. Frankfurt am Main: P. Lang, 1987.

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Comissão Nacional de Bolsas de Valores (Brazil), ed. IBA, Indice brasileiro de ações. Rio de Janeiro, RJ: Comissão Nacional de Bolsas de Valores, 1986.

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Comissão Nacional de Bolsas de Valores (Brazil), ed. IBA, Indice brasileiro de ações. Belo Horizonte, MG: Comissão Nacional de Bolsas de Valores, 1993.

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O'Brien, Thomas J. A simple binomial no-arbitrage model of the term structure with applications to the valuation of interest-sensitive options and interest-rate swaps. New York, N.Y: Salomon Brothers Center for the Study of Financial Institutions, 1991.

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Book chapters on the topic "Stocks – Prices"

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Bukenya, James O. "Do Fluctuations in Wine Stocks Affect Wine Prices?" In Commodity Modeling and Pricing, 136–66. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118267905.ch8.

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Islam, Mohammad Tauhidul, Rezaul Karim, Sumi Khatun, and Mohammad Shamsul Arefin. "Developing a Framework for Trend Prediction of Stocks Prices." In Advances in Intelligent Systems and Computing, 594–606. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51859-2_54.

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Lowry, Mark Newton. "Futures prices and hidden stocks of refined oil products." In International Commodity Market Models, 263–73. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3084-4_14.

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Affonso, Felipe, Thiago Magela Rodrigues Dias, and Adilson Luiz Pinto. "A Method for Clustering and Predicting Stocks Prices by Using Recurrent Neural Networks." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 30–40. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50072-6_3.

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Arratia, Argimiro, Gustavo Avalos, Alejandra Cabaña, Ariel Duarte-López, and Martí Renedo-Mirambell. "Sentiment Analysis of Financial News: Mechanics and Statistics." In Data Science for Economics and Finance, 195–216. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_9.

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AbstractThis chapter describes the basic mechanics for building a forecasting model that uses as input sentiment indicators derived from textual data. In addition, as we focus our target of predictions on financial time series, we present a set of stylized empirical facts describing the statistical properties of lexicon-based sentiment indicators extracted from news on financial markets. Examples of these modeling methods and statistical hypothesis tests are provided on real data. The general goal is to provide guidelines for financial practitioners for the proper construction and interpretation of their own time-dependent numerical information representing public perception toward companies, stocks’ prices, and financial markets in general.
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Mandelbrot, Benoit B. "The variation of the prices of cotton, wheat, and railroad stocks, and of some financial rates." In Fractals and Scaling in Finance, 419–43. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2763-0_15.

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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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Locarek-Junge, Hermann, and Stefan Albers. "The Rhythm of the Night: Some Anomalies in Open and Close Prices of Polish and German Blue-Chip Stocks." In Contemporary Trends and Challenges in Finance, 3–20. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43078-8_1.

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Schoenmaker, Dirk, and Willem Schramade. "Capital Market Adaptability, Investor Behaviour, and Impact." In Springer Texts in Business and Economics, 395–428. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35009-2_14.

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AbstractThe efficient markets hypothesis states that stock prices incorporate all relevant information instantaneously. However, investor behaviour is not always fully in line with theoretical predictions. The mechanism behind efficient markets is that a sufficient number of analysts pay attention to newly arriving information, judge it value relevant, and trade on that information. In that way, the new information gets priced in. But there is evidence that learning takes time and that adaptive markets are a better description than efficient markets. In particular, it seems that analysts have been slow to pick up sustainability-related information.Moreover, stock prices only reflect the effects of (sustainability-related) information on the financial value of companies. There is no ‘market’ (yet) for the diffusion of information on the social and environmental value (impact) of companies. New regulations, scientific research, non-governmental organisations (NGOs), and ratings agencies do produce information on companies’ social and environmental impact. They create implicit markets on impact information and price-setting that are continuously evolving. These markets can be used to determine the willingness to pay for impact (and thus derive prices for impact). At the same time, a new breed of impact investors is emerging. These investors look for financial return (profit) as well as impact and may be willing to sacrifice some part of their financial return for higher impact.
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Sisodia, Dilip Singh, and Sagar Jadhav. "Machine Learning Models for Forecasting of Individual Stocks Price Patterns." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 111–29. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3870-7.ch008.

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Stock investors always consider potential future prices before investing in any stock for making a profit. A large number of studies are found on the prediction of stock market indices. However, the focus on individual stock closing price predictions well ahead of time is limited. In this chapter, a comparative study of machine-learning-based models is used for the prediction of the closing price of a particular stock. The proposed models are designed using back propagation neural networks (BPNN), support vector regression (SVR) with SMOReg, and linear regression (LR) for the prediction of the closing price of individual stocks. A total of 37 technical indicators (features) derived from historical closing prices of stocks are considered for predicting the future price of stock in a time window of five days. The experiment is performed on stocks listed on Bombay Stock Exchange (BSS), India. The model is trained and tested using feature values extracted from the past five-year closing price of stocks of different sectors including aviation, pharma, banking, entertainment, and IT.
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Conference papers on the topic "Stocks – Prices"

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Tabaček, Jakub. "Attention and Volatility in Renewable Energy Stocks." In EDAMBA 2022: 25th International Scientific Conference for Doctoral Students and Post-Doctoral Scholars. Bratislava: University of Economics in Bratislava, 2023. http://dx.doi.org/10.53465/edamba.2022.9788022550420.470-480.

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Under the Efficient Market Hypothesis stock prices should reflect only the fundamental information relevant to the company in question. If other, such as behavioural factors affect the stock price, then this discrepancy should be resolved by the means of arbitrage traders. In our study we look at the effect of retail trader attention on the volatility of renewable energy companies’ stocks. We find that attention, measured by Google Trends, is a good in-sample predictor of next day volatility for a given company’s stock. We later try to explore this anomaly in an out-of-sample study.
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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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Jacob, Sona Susan, Sankha Patra, Kapinesh G, and Thanikaiselvan V. "Monitoring of Stocks using LSTM Model and Prediction of Stock Prices." In 2022 International Conference on Edge Computing and Applications (ICECAA). IEEE, 2022. http://dx.doi.org/10.1109/icecaa55415.2022.9936204.

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Liu, Zimo, Lin Yang, and Tami Takada. "Predicting Stock Prices Using Tweet Frequency and AI: Leveraging Social Media Insights to Forecast Tomorrow's Market Trends." In 12th International Conference on Digital Image Processing and Vision. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131314.

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I use the frequency of the tweet counts of the stock ticker to predict the stock price [1]. Using AI to predict the price with today’s tweet count and the highest and lowest stock price to predict tomorrow’s stock price [2]. The benefits of predicting stock prices are minimizing losses and getting a better idea about making money. But since the price of stocks is hard to predict, analyzing the number of price tickers discussed in social media can help people to know more precisely trends. “They analyzed the activity of 150 companies chosen at random from the S&P 500 Index and noticed a correlation between the number of tweets they sent and their share price [3]. Having observed this, the researchers devised a mathematical model, applied it to an imaginary portfolio and outperformed other financial strategies based on financial analysis by as much as 11%” [4]. This indicates the reliability of predicting stock prices with social media. Compared with some popular methods such as Auto-Regressive Conditional Heteroscedastic or Generalized Auto-Regressive Moving Average, using social media to predict price can consider the effect of social trends on the stock price [5]. Using social media to predict the stock price is not subjective and is much cheaper to computerize.
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Akşehir, Zinnet, Erdal Kılıç, Sedat Akleylek, Mesut Döngül, and Burak Coşkun. "Stocks Prices Prediction with Long Short-term Memory." In 5th International Conference on Internet of Things, Big Data and Security. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009351602210226.

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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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Hudnurkar, Shilpa, Akul S. Rajeevan, Shrey Choudhary, Partho Dipankar Mukherjee, Hemant Kumar Jaiswal, and Kalyani Kadam. "Forecasting of Cryptocurrency and Stocks Prices using Machine Learning." In 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2023. http://dx.doi.org/10.1109/icses60034.2023.10465415.

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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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Sizykh, Natalia, and Dmitry Sizykh. "The Application Efficiency of the Hurst Exponent for the Stocks Prices Forecast." In 2022 15th International Conference Management of large-scale system development (MLSD). IEEE, 2022. http://dx.doi.org/10.1109/mlsd55143.2022.9934235.

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Tasevska, Ivona. "EMPIRICAL RESEARCH ON THE INFORMATION EFFICIENCY OF THE MACEDONIAN STOCK EXCHANGE." In Economic and Business Trends Shaping the Future. Ss Cyril and Methodius University, Faculty of Economics-Skopje, 2022. http://dx.doi.org/10.47063/ebtsf.2022.0027.

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One of the basic hypotheses in modern finance that defines financial markets is the Efficient Market Hypothesis. The existence of information efficient markets, where all information is incorporated in the price of financial instruments is the basis of rational economic theory. There may be an upward or downward trend in the financial markets, but after the inclusion of new information in the financial instruments, they would stabilize until the next new information. In addition to the definition of efficient markets, the hypothesis of random walk has a significant application, which explains that the market cannot be beaten and that prices and returns move in a random upward or downward direction. The paper includes two methodologies to confirm the efficiency of the financial markets. The first research was conducted in order to confirm the hypothesis of a random walk implementing a coefficient of variance test. The test was conducted using a large series of data of the returns’ movement of stock exchange indices on the Macedonian, Belgrade, Zagreb, Sofia and Ljubljana Stock Exchange, as well as the American S&P500 index. The second research which is including the model of market multipliers was conducted for the most liquid stocks on the Macedonian Stock Exchange and selected stocks from the US Stock Exchange Markets, in order to show the underestimation or overestimation in relation to the market value of stocks, thus to show the sentiment that investors have when trading a certain type of stock. The results of the research show that the regional financial markets, as well as the domestic ones, do not follow the random walk, giving an opportunity to the possibility of using alternative behavioral approaches to explain the reasons for the deviation. For the second survey, where significant differences in the fundamental and market value of the stocks appear, the reason for the deviation is the expectations of investors.
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Reports on the topic "Stocks – Prices"

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Barsky, Robert. Why Don't the Prices of Stocks and Bonds Move Together? Cambridge, MA: National Bureau of Economic Research, October 1986. http://dx.doi.org/10.3386/w2047.

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Barberis, Nicholas, and Ming Huang. Stocks as Lotteries: The Implications of Probability Weighting for Security Prices. Cambridge, MA: National Bureau of Economic Research, February 2007. http://dx.doi.org/10.3386/w12936.

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

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This study re-investigates the price discovery dynamics of selected stocks cross-listed on the Australian Stock Exchange (ASX) and the New Zealand Stock Exchange (NZX) during a bear trading phase from January 2008 to December 2011. A differing price discovery dynamic in a bear market versus a bull market may occur because of variations in investor sentiments and disparities in the role of the stock prices. Using intraday data, we employ the vector error correction mechanism, Hasbrouck’s (1995) information share and Grammig et al.’s (2005) conditional information share methods. Consistent with previous research, we find that price discovery takes place mostly on the home market for the Australian firms and for all but one of the New Zealand firms. However, not seen in existing studies, we show that the NZX has grown in importance for both the Australian and New Zealand firms. This suggests that the NZX is deviating from being a pure satellite market.
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Dassanayake, Wajira, Xiaoming Li, and Klaus Buhr. A Revisit of Price Discovery Dynamics Across Australia and New Zealand. Unitec ePress, August 2015. http://dx.doi.org/10.34074/rsrp.039.

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This study re-investigates the price discovery dynamics of selected stocks cross-listed on the Australian Stock Exchange (ASX) and the New Zealand Stock Exchange (NZX) during a bear trading phase from January 2008 to December 2011. A differing price discovery dynamic in a bear market versus a bull market may occur because of variations in investor sentiments and disparities in the role of the stock prices. Using intraday data, we employ the vector error correction mechanism, Hasbrouck’s (1995) information share and Grammig et al.’s (2005) conditional information share methods. Consistent with previous research, we find that price discovery takes place mostly on the home market for the Australian firms and for all but one of the New Zealand firms. However, not seen in existing studies, we show that the NZX has grown in importance for both the Australian and New Zealand firms. This suggests that the NZX is deviating from being a pure satellite market.
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Belaid, Fateh. The Implications of Soaring Gas and Coal Prices on Europe’s Energy Poverty Trap. King Abdullah Petroleum Studies and Research Center, December 2021. http://dx.doi.org/10.30573/ks--2021-ii08.

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With the recovery of the world economy following the easing of restrictions designed to contain COVID-19, energy demand has surged even as natural gas stocks were dangerously low. This triggered one of the first significant energy shocks of the green era and exposed the fragilities of the ongoing process to green the energy system.
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Derbentsev, V., A. Ganchuk, and Володимир Миколайович Соловйов. Cross correlations and multifractal properties of Ukraine stock market. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1117.

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Recently the statistical characterizations of financial markets based on physics concepts and methods attract considerable attentions. The correlation matrix formalism and concept of multifractality are used to study temporal aspects of the Ukraine Stock Market evolution. Random matrix theory (RMT) is carried out using daily returns of 431 stocks extracted from database time series of prices the First Stock Trade System index (www.kinto.com) for the ten-year period 1997-2006. We find that a majority of the eigenvalues of C fall within the RMT bounds for the eigenvalues of random correlation matrices. We test the eigenvalues of C within the RMT bound for universal properties of random matrices and find good agreement with the results for the Gaussian orthogonal ensemble of random matrices—implying a large degree of randomness in the measured cross-correlation coefficients. Further, we find that the distribution of eigenvector components for the eigenvectors corresponding to the eigenvalues outside the RMT bound display systematic deviations from the RMT prediction. We analyze the components of the deviating eigenvectors and find that the largest eigenvalue corresponds to an influence common to all stocks. Our analysis of the remaining deviating eigenvectors shows distinct groups, whose identities correspond to conventionally identified business sectors. Comparison with the Mantegna minimum spanning trees method gives a satisfactory consent. The found out the pseudoeffects related to the artificial unchanging areas of price series come into question We used two possible procedures of analyzing multifractal properties of a time series. The first one uses the continuous wavelet transform and extracts scaling exponents from the wavelet transform amplitudes over all scales. The second method is the multifractal version of the detrended fluctuation analysis method (MF-DFA). The multifractality of a time series we analysed by means of the difference of values singularity stregth (or Holder exponent) ®max and ®min as a suitable way to characterise multifractality. Singularity spectrum calculated from daily returns using a sliding 250 day time window in discrete steps of 1. . . 10 days. We discovered that changes in the multifractal spectrum display distinctive pattern around significant “drawdowns”. Finally, we discuss applications to the construction of crushes precursors at the financial markets.
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Martner, Ricardo. Fiscal Indicators in Latin-American Countries. Inter-American Development Bank, May 2005. http://dx.doi.org/10.18235/0012270.

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The purpose of this document is to provide a comparative analysis of Latin-American government finance statistics including public expenditures, income, overall balances, and debt stocks. The paper explores some of the problems that arise in country comparisons and in regional harmonization of fiscal targets, an important issue when considering common goals of overall balances and public debt. The paper also discusses some of the new initiatives such as: applying accrual accounting and registering all variations of public net worth; including economic cycle and relative prices fluctuations in the estimation of overall balances and public debt; seeking to protect investment in strategic areas; and establishing priority areas in social expenditures.
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Dassanayake, Wajira, Chandimal Jayawardena, Iman Ardekani, and Hamid Sharifzadeh. Models Applied in Stock Market Prediction: A Literature Survey. Unitec ePress, March 2019. http://dx.doi.org/10.34074/ocds.12019.

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Stock market prices are intrinsically dynamic, volatile, highly sensitive, nonparametric, nonlinear, and chaotic in nature, as they are influenced by a myriad of interrelated factors. As such, stock market time series prediction is complex and challenging. Many researchers have been attempting to predict stock market price movements using various techniques and different methodological approaches. Recent literature confirms that hybrid models, integrating linear and non-linear functions or statistical and learning models, are better suited for training, prediction, and generalisation performance of stock market prices. The purpose of this review is to investigate different techniques applied in stock market price prediction with special emphasis on hybrid models.
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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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Rivadeneira, Alex. Public Transportation and Consumer Prices: Chain Stores, Street Vendors and Mom and Pop Stores. Banco de México, May 2024. http://dx.doi.org/10.36095/banxico/di.2024.02.

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Improving public transport infrastructure changes local market conditions. In this paper, I examine the impact of the construction and operation of "Metrobus", Mexico City's Bus Rapid Transit (BRT) system on consumer prices in chain stores, street vendors, and small family-owned (mom and pop) stores. I do so through a panel event study design. I consider the construction and operation of BRT as two different phenomena; while the former is associated to street closures, the latter reduces transportation costs. I show that only prices in mom and pop stores respond to changes in local market conditions produced by the introduction of BRT. For these businesses, construction pressures prices downwards; in contrast, operation is associated with partial price recoveries. I cannot reject a null effect in prices from chain stores or street vendors.
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