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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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TL;DR: In this article, the stochastic long memory in the Greek stock market, an emerging capital market, was tested using the spectral regression method, and significant and robust evidence of positive long-term persistence was found in the stock market.
Abstract: Tests are made of the stochastic long memory in the Greek stock market, an emerging capital market. The fractional differencing parameter is estimated using the spectral regression method. Contrary to findings for major capital markets, significant and robust evidence of positive long-term persistence is found in the Greek stock market. As compared to benchmark linear models, the estimated fractional models provide improved out-of-sample forecasting accuracy for the Greek stock returns series over longer forecasting horizons.
173 citations
TL;DR: In this article, a review of the state of knowledge of the existing building stock and potential advances in that knowledge is provided, focusing on those that are most relevant to the SEMBE goals of sustainable energy management across the whole building stock.
173 citations
TL;DR: In this article, the authors developed a model to explain why stock returns are positively cross-autocorrelated and showed that both own-and cross-auto-correlations are higher when market movements are larger.
Abstract: I develop a model to explain why stock returns are positively cross-autocorrelated. When market makers observe noisy signals about the value of their stocks but cannot instantaneously condition prices on the signals of other stocks, which contain marketwide information, the pricing error of one stock is correlated with the other signals. As market makers adjust prices after observing true values or previous price changes of other stocks, stock returns become positively crossautocorrelated. If the signal quality differs among stocks, the cross-autocorrelation pattern is asymmetric. I show that both own- and cross-autocorrelations are higher when market movements are larger.
173 citations
TL;DR: In this article, the authors provide global evidence supporting the Low Volatility Anomaly: that low risk stocks consistently provide higher returns than high risk stocks, and that this anomaly is caused primarily by agency issues, namely the compensation structures and internal stock selection processes at asset management firms.
Abstract: This article provides global evidence supporting the Low Volatility Anomaly: that low risk stocks consistently provide higher returns than high risk stocks. This study covers 33 different markets during the time period from 1990-2011. (Two previous studies by Haugen & Heins (1972) and Haugen & Baker (1991) show the same negative payoff to risk in time periods 1926-1970 and 1970-1990.) The procedure for our study is intentionally simple, transparent and easily replicable. Our samples include non-survivors. We look at an international universe of stocks beginning with the first month of 1990 until December 2011; we compute the volatility of total return for each company in each country over the previous 24 months. Stocks in each country are ranked by volatility and formed into deciles. In the total universe and in each individual country low risk stocks outperform, the relationship with respect to Sharpe ratios is even more impressive. We believe this anomaly is caused primarily by agency issues, namely the compensation structures and internal stock selection processes at asset management firms which lead institutional investors on average to hold more volatile stocks. The article also addresses the implications for how corporate finance managers make capital investment decision in light of this evidence. The evidence presented here dethrones both CAPM and the Efficient Market Hypothesis.
173 citations
TL;DR: This paper showed that relatively cloudier days increase perceived overpricing in individual stocks and the Dow Jones Industrial Index, and increase selling propensities of institutions, and investor pessimism negatively impacts stock returns, mostly amongst stocks with higher arbitrage costs.
Abstract: This study shows that weather-based indicators of mood impact perceptions of mispricing and trading decisions of institutional investors. Using survey and disaggregated trade data, we show that relatively cloudier days increase perceived overpricing in individual stocks and the Dow Jones Industrial Index, and increase selling propensities of institutions. We introduce stock-level measures of investor mood; investor pessimism negatively impacts stock returns, mostly amongst stocks with higher arbitrage costs, and stocks experiencing similar changes in weather-induced mood exhibit return comovement. These findings complement existing studies on how weather impacts stock index returns, and identify another channel through which it can manifest.
173 citations