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We study the cross-sectional returns of the firms connected by news articles. A conservative algorithm is proposed to tackle the type-I error in identifying firm tickers and the well-defined directed news networks of S&P500 stocks are formed based on a modest assumption. After controlling for many other effects, we find strong evidence for the comovement effect between news-linked firms stock returns and reversal effect from lead stock return on 1-day ahead follower stock return, however, returns of lead stocks provide only marginal predictability on follower stock returns. Furthermore, both econometric and portfolio test reveals that network degree provides robust and significant cross-sectional predictability on monthly stock returns, and the type of linkages also matters for portfolio construction.
We show how to reduce the problem of computing VaR and CVaR with Student T return distributions to evaluation of analytical functions of the moments. This allows an analysis of the risk properties of systems to be carefully attributed between choices
The composition of natural liquidity has been changing over time. An analysis of intraday volumes for the S&P500 constituent stocks illustrates that (i) volume surprises, i.e., deviations from their respective forecasts, are correlated across stocks,
We highlight a very simple statistical tool for the analysis of financial bubbles, which has already been studied in [1]. We provide extensive empirical tests of this statistical tool and investigate analytically its link with stocks correlation structure.
The success of a cross-sectional systematic strategy depends critically on accurately ranking assets prior to portfolio construction. Contemporary techniques perform this ranking step either with simple heuristics or by sorting outputs from standard
We show how text from news articles can be used to predict intraday price movements of financial assets using support vector machines. Multiple kernel learning is used to combine equity returns with text as predictive features to increase classificat