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Stock exchange

About: Stock exchange is a research topic. Over the lifetime, 39566 publications have been published within this topic receiving 612044 citations.


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Journal ArticleDOI
TL;DR: In this paper, the authors examined the relation between high frequency quotation and the behavior of stock prices between 2009 and 2011 for the full cross-section of securities in the U.S. On average, higher quotation activity is associated with price series that more closely resemble a random walk, and significantly lower cost of trading.
Abstract: We examine the relation between high frequency quotation and the behavior of stock prices between 2009 and 2011 for the full cross-section of securities in the U.S. On average, higher quotation activity is associated with price series that more closely resemble a random walk, and significantly lower cost of trading. We also explore market resiliency during periods of exceptionally high low-latency trading: large liquidity drawdowns in which, within the same millisecond, trading algorithms systematically sweep large volume across multiple trading venues. Although such large drawdowns incur trading costs, they do not appear to degrade the price formation process or increase the subsequent cost of trading. In an out-of-sample analysis, we investigate an exogenous technological change to the trading environment on the Tokyo Stock Exchange that dramatically reduces latency and allows co-location of servers. This shock also results in prices more closely resembling a random walk, and a sharp decline in the cost of trading.

138 citations

Journal ArticleDOI
TL;DR: A novel financial time series-forecasting model by evolving and clustering fuzzy decision tree for stocks in Taiwan Stock Exchange Corporation (TSEC) is established and the proposed GAFDT model has the best performance when compared with other approaches on various stocks in TSEC.
Abstract: Stock price predictions have always been a subject of interest for investors and professional analysts. Nevertheless, determining the best time to buy or sell a stock remains very difficult because there are many factors that may influence the stock prices. This paper establishes a novel financial time series-forecasting model by evolving and clustering fuzzy decision tree for stocks in Taiwan Stock Exchange Corporation (TSEC). This forecasting model integrates a data clustering technique, a fuzzy decision tree (FDT), and genetic algorithms (GA) to construct a decision-making system based on historical data and technical indexes. The set of historical data is divided into k sub-clusters by adopting K-means algorithm. GA is then applied to evolve the number of fuzzy terms for each input index in FDT so the forecasting accuracy of the model can be further improved. A different forecasting model will be generated for each sub-cluster. In other words, the number of fuzzy terms in each sub-cluster will be different. Hit rate is applied as a performance measure and the proposed GAFDT model has the best performance of 82% average hit rate when compared with other approaches on various stocks in TSEC.

138 citations

Journal ArticleDOI
TL;DR: The results suggest that diversifying the knowledge base of financial expert systems can benefit from data captured from nontraditional experts like Google and Wikipedia, and combining disparate online data sources with traditional time-series and technical indicators for a stock can provide a more effective and intelligent daily trading expert system.
Abstract: A financial expert system for predicting the daily stock movements.Knowledge base captures both traditional and online data sources.The inference engine uses three artificial intelligence techniques.Prediction accuracy of 85% is higher than the reported results in the literature.The system is hosted online and freely available for investors and researchers. There are several commercial financial expert systems that can be used for trading on the stock exchange. However, their predictions are somewhat limited since they primarily rely on time-series analysis of the market. With the rise of the Internet, new forms of collective intelligence (e.g. Google and Wikipedia) have emerged, representing a new generation of crowd-sourced knowledge bases. They collate information on publicly traded companies, while capturing web traffic statistics that reflect the publics collective interest. Google and Wikipedia have become important knowledge bases for investors. In this research, we hypothesize that combining disparate online data sources with traditional time-series and technical indicators for a stock can provide a more effective and intelligent daily trading expert system. Three machine learning models, decision trees, neural networks and support vector machines, serve as the basis for our inference engine. To evaluate the performance of our expert system, we present a case study based on the AAPL (Apple NASDAQ) stock. Our expert system had an 85% accuracy in predicting the next-day AAPL stock movement, which outperforms the reported rates in the literature. Our results suggest that: (a) the knowledge base of financial expert systems can benefit from data captured from nontraditional experts like Google and Wikipedia; (b) diversifying the knowledge base by combining data from disparate sources can help improve the performance of financial expert systems; and (c) the use of simple machine learning models for inference and rule generation is appropriate with our rich knowledge database. Finally, an intelligent decision making tool is provided to assist investors in making trading decisions on any stock, commodity or index.

137 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the association between intellectual capital disclosure levels in prospectuses of 444 IPOs listing on the Singapore Stock Exchange between 1997 and 2006, and three potential explanatory determinants: (1) ownership retention; (2) proprietary costs; and (3) corporate governance structure.
Abstract: Intellectual capital is recognised as the new economic era’s pivotal factor underlying value creation. Deficient and inconsistent intellectual capital reporting is escalating information asymmetry between informed and uninformed investors. This provides fertile ground for informed investors to extract higher abnormal returns and higher wealth transfers from uninformed investors, particularly during a firm’s initial public offering (IPO). This study investigates the association between intellectual capital disclosure levels in prospectuses of 444 IPOs listing on the Singapore Stock Exchange between 1997 and 2006, and three potential explanatory determinants: (1) ownership retention; (2) proprietary costs; and (3) corporate governance structure. Statistical analysis supports our conjecture of a positive association between intellectual capital disclosure and ownership retention. We also find, consistent with expectations, a negative influence of proprietary costs on the positive intellectual capita...

137 citations

Journal ArticleDOI
TL;DR: In this article, the authors investigate how the growth of stock option programs has affected corporate payout policy and find that stock option granted to top executives affect payout policy differently than do stock options granted to other employees and that the larger the executives holding of stock options, the more apt the firm is to retain more earnings and curtail cash distributions.
Abstract: This paper investigates how the growth of stock option programs has affected corporate payout policy Given that earnings per share (EPS) is widely used in equity valuation, some corporations may opt to repurchase shares to avoid the dilution of EPS that results from past stock option grants Executives may also prefer distributing cash by repurchasing shares or retaining more earnings, as opposed to increasing dividends, to enhance the value of their own stock options This paper tests the importance of these two hypotheses using crosssectional and panel data on stock option programs I find that stock options granted to top executives affect payout policy differently than do stock options granted to other employees Option grants in general are associated with increased share repurchases and increased total payouts However, the larger is the executives’ holding of stock options, the more apt the firm is to retain more earnings and curtail cash distributions Analysis of panel data for a sample of large firms suggests that firms conduct an ongoing repurchase of shares over the life of an option that undoes much of the dilution to EPS that results from past stock option grants

137 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20232,414
20225,944
20211,840
20202,645
20192,535
20182,413