Topic
Algorithmic trading
About: Algorithmic trading is a research topic. Over the lifetime, 6718 publications have been published within this topic receiving 162209 citations. The topic is also known as: algotrading & Algorithmic trading.
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TL;DR: In this paper, the authors reexamine these findings by adjusting the daily returns from trading portfolios of Dow stocks for both dividends and the interest earned on the proceeds from short sales, and show that estimates of trading profits based on the true closing levels of the index are not significantly different from buy and hold returns.
57 citations
TL;DR: In this paper, the authors examined the profitability of variable and fixed moving averages as well as trading range breakout (TRB) on nine popular daily Asian market indices from 1st January 1988 to 31st December 2003.
57 citations
TL;DR: In this paper, the authors investigated the impact of salient political and economic news on the intraday trading activity, namely, the stock return volatility, stock price volatility, the number of shares traded, and the trading frequency.
Abstract: This paper investigates the impact of salient political and economic news on the intraday trading activity, namely, the stock return volatility, the stock price volatility, the number of shares traded, and the trading frequency. Using transactions data on 33 constituent stocks of the Hang Seng Index in the Stock Exchange of Hong Kong (SEHK), we find that political news has a distinct impact on market activity when compared with economic news. We argue that the observed phenomenon is related to the precision of signals associated with these two types of news and investors' perceptual biases.
57 citations
TL;DR: A special database allowing to track the trading activity of individual investors of the stock Nokia is investigated and many statistically detected clusters of investors show a very high degree of synchronization in the time when they decide to trade and in the trading action taken.
Abstract: We use statistically validated networks, a recently introduced method to validate links in a bipartite system, to identify clusters of investors trading in a financial market. Specifically, we investigate a special database allowing to track the trading activity of individual investors of the stock Nokia. We find that many statistically detected clusters of investors show a very high degree of synchronization in the time when they decide to trade and in the trading action taken. We investigate the composition of these clusters and we find that several of them show an over-expression of specific categories of investors.
57 citations
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TL;DR: The authors showed that status concerns lead households, especially those living in affluent areas, to demand these stocks to track their neighbors' wealth, and this demand varies pro-cyclically with the stock market's value and generates household trading.
Abstract: We show that Keeping-up-with-the-Joneses preferences can explain several puzzling retail investor behaviors, including the excessive trading of small local stocks. Status concerns lead households, especially those living in affluent areas, to demand these stocks to track their neighbors' wealth. This demand varies pro-cyclically with the stock market's value and generates household trading. Using Chinese data on local stock turnover, stock message boards and brokerage account trading, we test and confirm this hypothesis by exploiting the uneven rise of affluence across Chinese cities between 1998 and 2012.
57 citations