Correlated Trading and Returns

A German broker's clients place similar speculative trades and therefore tend to be on the same side of the market in a given stock during a given day, week, month, and quarter. Aggregate liquidity effects, short sale constraints, the systematic execution of limit orders (coordinated through price movements) or the correlated trading of other investors who pick off retail limit orders do not fully explain why retail investors trade similarly. Correlated market orders lead returns, presumably due to persistent speculative price pressure. Correlated limit orders also predict subsequent returns, consistent with executed limit orders being compensated for accommodating liquidity demands.     

Volume,Volatility, and New York Stock Exchange Trading Halts

Trading halts increase, rather than reduce, both volume and volatility. Volume (volatility) in the first full trading day after a trading halt is 230 percent (50 to 115 percent) higher than following “pseudohalts”: nonhalt control periods matched on time of day, duration, and absolute net-of-market returns. These results are robust over different halt types and news categories. Higher posthalt volume is observed into the third day while higher posthalt volatility decays within hours. The extent of media coverage is a partial determinant of volume and volatility following both halts and pseudohalts, but a separate halt effect remains after controlling for the media effect.     

Do Central Bank Forecasts Influence Private Agents? Forecasting Performance versus Signals

Focusing on a set of central banks that publish inflation forecasts in real time, this paper aims to establish whether central bank inflation forecasts influence private inflation forecasts. The response is positive in the five countries studied: Sweden, the United Kingdom, Canada, Switzerland, and Japan. Three hypotheses may explain this central bank influence: central bank forecasts are more accurate than private ones, are based on different information sets, and/or convey signals about future policy decisions and policymakers’ preferences and objectives. We provide evidence that the source of these central banks’ influence is not linked to their forecasting performance.     

On the comparison of dollar-unit and stratified mean-per-unit estimators

Abstract. The paper discusses the findings reported by Smieliauskas (1986) and expands on them. It focuses on the dominance criterion for cost efficiency and on the usefulness of power curves for stratified mean-per-unit estimators. The analyses lead to the conclusions that: (1) while an audit plan which dominates another is associated with lower cost, a plan with lower cost does not necessarily dominate: (2) the distribution function of the t statistic becomes normal for sufficiently large samples, but the approach to normality is not necessarily uniform as sample size increases; (3) large sample size is required for nominal confidence levels to be a good approximation to the true confidence level, and the true confidence level can move temporarily farther from the nominal level as the sample size increases; and (4) the lack of reliability of ex ante power curves for the stratified mean-per-unit estimator makes it difficult to decide which power curve dominates. Résumé. Cet article discute des résultats publiés par Smielauskas (1986) et les extrapole. Il met l'accent sur le critère de dominance pour l'efficacité des coûts et sur l'efficacité des courbes de puissance pour les estimateurs de moyenne-par-unité stratifiée. Les analyses portent à conclure que: (1) même si un programme de vérification dominant par rapport à un autre est associé à un coût inférieur, un programme à coût inférieur n'est pas nécessairement dominant; (2) la fonction de distribution de la statistique t tend vers la normale pour des échantillons suffisamment grands, mais ce rapprochement vers la normalité n'est pas nécessairement uniforme à mesure que la taille de l‘échantillon devient plus grande; (3) un échantillon de grande taille est requis afin que les seuils de confiance “nominaux” constituent une bonne approximation du seuil de confiance “réel”, et le seuil de confiance “réel” peut s’éloigner temporairement du seuil de confiance “nominal” à mesure que s'accroît la taille de l‘échantillon; et (4) l'absence de fiabilité des courbes de puissance ex ante pour l'estimateur de moyenne-par-unité stratifiée rend ardue la sélection de la courbe de puissance dominante.