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Market capitalization

About: Market capitalization is a research topic. Over the lifetime, 3583 publications have been published within this topic receiving 77288 citations. The topic is also known as: market cap & market value.


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Journal ArticleDOI
TL;DR: The primary purpose of this study was to develop an intelligent framework with the capability of predicting the direction in which stock market prices will move based on financial time series as inputs, and the CNN-LSTM model showed a superior performance compared with the single deep learning LSTM and existing systems in predictingStock market prices.
Abstract: The creation of trustworthy models of the equities market enables investors to make better-informed choices. A trading model may lessen the risks that are connected with investing and make it possible for traders to choose companies that offer the highest dividends. However, due to the high degree of correlation between stock prices, analysis of the stock market is made more difficult by batch processing approaches. The prediction of the stock market has entered a technologically advanced era with the advent of technological marvels such as global digitization. For this reason, artificial intelligence models have become very important due to the continuous increase in market capitalization. The novelty of the proposed study is the development of the robustness time series model based on deep leaning for forecasting future values of stock marketing. The primary purpose of this study was to develop an intelligent framework with the capability of predicting the direction in which stock market prices will move based on financial time series as inputs. Among the cutting-edge technologies, artificial intelligence has become the backbone of many different models that predict the direction of markets. In particular, deep learning strategies have been effective at forecasting market behavior. In this article, we propose a framework based on long short-term memory (LSTM) and a hybrid of a convolutional neural network (CNN-LSTM) with LSTM to predict the closing prices of Tesla, Inc. and Apple, Inc. These predictions were made using data collected over the past two years. The mean squared error (MSE), root mean squared error (RMSE), normalization root mean squared error (NRMSE), and Pearson’s correlation (R) measures were used in the computation of the findings of the deep learning stock prediction models. Between the two deep learning models, the CNN-LSTM model scored slightly better (Tesla: R-squared = 98.37%; Apple: R-squared = 99.48%). The CNN-LSTM model showed a superior performance compared with the single deep learning LSTM and existing systems in predicting stock market prices.

13 citations

04 Nov 2010
TL;DR: In this paper, the authors compared the determinants of stock returns in the 1987 and 2008 stock market meltdowns with the multivariate regression analysis technique and found that technical insolvency risk and bankruptcy risk were significant determinants in the 2008 market meltdown.
Abstract: In this paper, we study and compare the determinants of stock returns in the 1987 and 2008 stock market meltdowns with the multivariate regression analysis technique. We find that technical insolvency risk and bankruptcy risk were significant determinants of stock returns in the 2008 market meltdown. Investors were also somewhat concerned with bankruptcy risk in the 1987 market meltdown. However, technical insolvency risk was not a significant determinant of stock returns in the 1987 meltdown. Our findings indicate that stocks with higher betas, larger market cap, and greater return volatility lost more value in both meltdowns. We find the market-to-book ratio to be a significant determinant of stock returns in the 2008 meltdown but not in the 1987 meltdown. We find stock illiquidity to be a significant determinant of stock returns in the 1987 meltdown but not in the 2008 meltdown. With data for two most important stock market meltdowns in U.S. history since the Great Depression, we test several extant theories related to the determinants of stock returns.

13 citations

Journal ArticleDOI
TL;DR: In this paper, an expiration-specific weighted implied standard deviation (WISD) was proposed to detect ex-post stock return variances that differ between small and large market capitalization firms.
Abstract: Rogalski-Tinic have reported a monthly pattern in ex post stock return variances that differs between small and large market capitalization firms. Maloney-Rogalski find that option prices reflect these monthly patterns ex ante. This study extends Maloney-Rogalski's work by devising an expiration-specific weighted implied standard deviation (WISD). It is found that: i) the monthly patterns in one-month WISDs are basically similar to the monthly patterns in ex post variances detected by Rogalski-Tinic for both large and small size firms, and ii) use of expiration-specific WISDs, as opposed to standard composite WISDs, results in improved performance of option pricing models.

13 citations

Posted Content
17 Apr 2012
TL;DR: In this article, a Two-Stage-Least-squares Instrumental Variable methodology was used to assess the influence of political institutions on stock market performance dynamics in Africa, and found that the role of sound political institutions is crucial for financial development in Africa.
Abstract: Purpose – This paper assesses the incidence of political institutions on stock market performance dynamics in Africa. Design/methodology/approach – The estimation technique used is a Two-Stage-Least Squares Instrumental Variable methodology. Channels of democracy, polity and autocracy are instrumented with legal-origins, religious-legacies, income-levels and press-freedom qualities to account for stock market performance dynamics of capitalization, value traded, turnover and number of listed companies. To ensure robustness of the analysis, the following checks are carried out: (1) usage of alternative indicators of political institutions; (2) employment of two distinct interchangeable sets of moment conditions that engender every category of the instruments; (3) usage of alternative indicators of stock market performance; (4) account for the concern of endogeneity; (5) usage of Principal Component Analysis(PCA) to reduce the dimensions of stock market dynamics and political indicators and then check for further robustness of findings in the regressions from resulting indexes. Findings – Findings broadly demonstrate that democracy improves investigated stock market performance dynamics. Practical implications – As a policy recommendation, the role of sound political institutions is crucial for financial development in Africa. Democracies have important effects on both the degree of competition for public office and the quality of public offices that favor stock market development in the African continent. Originality/value – To the best of our knowledge this is the first paper to assess the incidence of democracy on stock market performance in an exclusive African context. Political strife has plagued many African countries and continue to pose a significant threat to financial market development.

12 citations

Journal ArticleDOI
TL;DR: In this paper, the authors analyzed the degree of integration of Russian stocks grouped into five size and five industry portfolios using the GMM methodology and conditional asset pricing model and found that the strength of integration is higher for those portfolios that have more firms which cross-list their stocks on foreign exchanges and/or sell their output internationally.
Abstract: While there is a significant amount of research on integration differences across countries, the integration variations across industry or market capitalization groups within a single country have been largely unexplored. The degree of integration, however, varies widely cross-sectionally. In this paper, we analyze the degree of integration of Russian stocks grouped into five size and five industry portfolios using the GMM methodology and conditional asset pricing model. In line with economic intuition, the estimates of average degrees of integration show a noticeable downward trend with a decrease in the portfolio size and are also smaller for less diversified industries. The strength of integration is higher for those portfolios that have more firms which cross-list their stocks on foreign exchanges and/or sell their output internationally.

12 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023151
2022279
2021154
2020187
2019196
2018186