Verification of LeadLag Effect in Financial Markets by the Adaptive Elastic Net Regression The anomalies of returns have been reported against efficient market hypothesis. Momentums and reversals have been investigated and verified intensively, related to various trading strategies. Leadlag effect, unlike the momentums and reversals, is a financial phenomena, in which price action between different assets shows delay with a time lag originated from information asymmetry. Previous researches have reported the leadlag effects can be investigated in both inter and cross industrial sectors of the US stock market. In this study, we verify whether the leadlag effect can be observed in global stock markets by means of adaptive elastic net regression. Experimental studies for major world indices reveal that longshort portfolios based on the leadlag effect generate positive returns on both weekly and monthly rebalances.

VariableLag Granger Causality for Time Series Analysis Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. However, the assumption of the fixed time delay does not hold in many applications, such as collective behavior, financial markets, and many natural phenomena. To address this issue, we develop variablelag Granger causality, a generalization of Granger causality that relaxes the assumption of the fixed time delay and allows causes to influence effects with arbitrary time delays. In addition, we propose a method for inferring variablelag Granger causality relations. We demonstrate our approach on an application for studying coordinated collective behavior and show that it performs better than several existing methods in both simulated and realworld datasets. Our approach can be applied in any domain of time series analysis.