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Stock (geology)

About: Stock (geology) is a research topic. Over the lifetime, 31009 publications have been published within this topic receiving 783542 citations.


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Journal ArticleDOI
TL;DR: In this article, the authors used an improved measure of a common stock's annualized dividend yield to show that risk-adjusted NYSE stock returns increase in dividend yield during the period from 1963 to 1994.
Abstract: Using an improved measure of a common stock's annualized dividend yield, we document that risk-adjusted NYSE stock returns increase in dividend yield during the period from 1963 to 1994. This relation between return and yield is robust to various specifications of multifactor asset pricing models that incorporate the Fama–French factors. The magnitude of the yield effect is too large to be explained by a “tax penalty” on dividend income and is not explained by previously documented anomalies. Interestingly, the effect is primarily driven by smaller market capitalization stocks and zero-yield stocks.

176 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigate the effect of stock split announcements on stock prices in perfect capital markets and find that stock prices increase on split announcements, and they interpret their findings as supportive of the liquidity explanation of the stock split announcement effects.

176 citations

Journal ArticleDOI
TL;DR: In this paper, the role of retail investors in stock pricing using a database uniquely suited for this purpose is analyzed, and both aggressive and passive net buying positively predict firms' monthly stock returns with no evidence of return reversal.
Abstract: We analyze the role of retail investors in stock pricing using a database uniquely suited for this purpose. The data allow us to address selection bias concerns and to separately examine aggressive (market) and passive (limit) orders. Both aggressive and passive net buying positively predict firms' monthly stock returns with no evidence of return reversal. Only aggressive orders correctly predict firm news, including earnings surprises, suggesting they convey novel cash flow information. Only passive net buying follows negative returns, consistent with traders providing liquidity and benefitting from the reversal of transitory price movements. These actions contribute to market efficiency.

176 citations

Journal ArticleDOI
TL;DR: This paper used the degree of accessibility of foreign investors to emerging stock markets, or investibility, as a proxy for the extent of foreign investments, to assess whether investibility has a significant influence on the diffusion of global market information across stocks in emerging markets.
Abstract: Using the degree of accessibility of foreign investors to emerging stock markets, or investibility, as a proxy for the extent of foreign investments, we assess whether investibility has a significant influence on the diffusion of global market information across stocks in emerging markets. We show that greater investibility reduces price delay to global market information where the price delay is measured as the proportion of stock returns explained by the lagged world market returns in the regression of stock returns on contemporaneous and lagged world and local market returns. We also find that returns of highly investible stocks lead those of non-investible stocks because they incorporate global information more quickly. These results are consistent with the idea that financial liberalization in the form of greater investibility yields informationally more efficient stock prices in emerging markets.

176 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction, which combines the temporal evolution and relation network of stocks.
Abstract: Stock prediction aims to predict the future trends of a stock in order to help investors make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice for stock prediction. However, most existing deep learning solutions are not optimized toward the target of investment, i.e., selecting the best stock with the highest expected revenue. Specifically, they typically formulate stock prediction as a classification (to predict stock trends) or a regression problem (to predict stock prices). More importantly, they largely treat the stocks as independent of each other. The valuable signal in the rich relations between stocks (or companies), such as two stocks are in the same sector and two companies have a supplier-customer relation, is not considered. In this work, we contribute a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. Our RSR method advances existing solutions in two major aspects: (1) tailoring the deep learning models for stock ranking, and (2) capturing the stock relations in a time-sensitive manner. The key novelty of our work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution, which jointly models the temporal evolution and relation network of stocks. To validate our method, we perform back-testing on the historical data of two stock markets, NYSE and NASDAQ. Extensive experiments demonstrate the superiority of our RSR method. It outperforms state-of-the-art stock prediction solutions achieving an average return ratio of 98% and 71% on NYSE and NASDAQ, respectively.

176 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202237
20211,825
20201,882
20191,697
20181,539
20171,706