Alliances and negotiations: an incomplete information example

We study a bargaining game between an individual and an ‘alliance’ in the sense of Manzini and Mariotti (J Econ Theory 121:128–41, 2005), in which the opponent of the alliance is incompletely informed about the relative strengths of its members. The best equilibrium outcome for the alliance under a unanimity rule is not attainable with a non-unanimity rule. However, unlike in the complete information model, less than optimal outcomes and delays may occur with positive probability even under unanimity, depending on the prior beliefs and the preferences of the agents. We are grateful to a careful referee for comments. We wish to thank Clara Ponsati for useful comments.     

ON THE LONG-TERM EFFECTS OF FISCAL POLICY IN THE UNITED KINGDOM: THE CASE FOR A GOLDEN RULE

This paper uses the SVAR methodology to investigate the effects of public investment on GDP and, more specifically, the effects of the introduction of a golden rule of public finance. We extend the existing literature by estimating a model for the British economy that takes into account long-run factors such as public debt accumulation and policy interactions. We find that in such a long-run framework, public investment has a significant and permanently positive effect on GDP; this result runs counter to the most recent literature on the topic using SVAR, which was limited to a short-run specification. We further find, by comparing different subsamples, that the introduction of the golden rule in 1997 strengthened the positive effect of public investment.     

Big Data Algorithmic Trading Systems Based on Investors’ Mood

Traditional automated trading systems use rules and filters based on Chartism to send orders to the market, aiming to beat the market and obtain positive returns in bullish or bearish contexts. However, these systems do not consider the investors’ mood that many studies have demonstrated its effects over the evolution of financial markets. The authors describe 2 \"big data\" algorithmic trading systems over Ibex 35 future. These systems send orders to the market to open long or short positions, based on an artificial intelligence model that uses investors’ mood. To measure the investors' mood, the authors use semantic analysis algorithms that qualify as good, bad, or neutral any communication related to Ibex 35 made on social media (Twitter) or news media. After 1.5 years of research, conclusions are: First, the authors observe positive returns, demonstrating that investors’ mood has predictive capacity on the evolution of the Ibex 35. Second, these systems have beaten the Ibex 35 index, showing the imperfect efficiency of the financial markets. Third, big data algorithmic trading systems numbers are better in Sharpe ratio, success rate, and profit factor than traditional trading systems on the Ibex 35, listed in the Trading Motion platform.     

Studio delle eliminazioni da una collettività per più case con il metodo simulativo

Vengono messe in luce alcune problematiche che si presentano quando si applichi il metodo della simulazione stocastica allo studio dell'evoluzione nel tempo di collettività chiuse ed unitarie esposte a più cause di eliminazione. Viene poi proposta un'impostazione che unifica i diversi punti di vista adottati in recenti lavori sull'argomento.

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Summary We investigate some problems that are met when studying by stochastic simulation the evolution over time of a (closed and homogeneous) collectivity underlying to more than one cause of elimination, and propose a unifying viewpoint on the argument.