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Abnormal Returns from Takeover Prediction Modelling: Challenges and Suggested Investment Strategies
Author(s) -
Danbolt Jo,
Siganos Antonios,
Tunyi Abongeh
Publication year - 2016
Publication title -
journal of business finance and accounting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.282
H-Index - 77
eISSN - 1468-5957
pISSN - 0306-686X
DOI - 10.1111/jbfa.12179
Subject(s) - leverage (statistics) , market liquidity , ex ante , profitability index , investment strategy , economics , market timing , monetary economics , econometrics , anticipation (artificial intelligence) , financial economics , investment (military) , business , finance , initial public offering , computer science , machine learning , artificial intelligence , macroeconomics , politics , law , political science
While takeover targets earn significant abnormal returns, studies tend to find no abnormal returns from investing in predicted takeover targets. In this study, we show that the difficulty of correctly identifying targets ex ante does not fully explain the below‐expected returns to target portfolios. Target prediction models’ inability to optimally time impending takeovers, by taking account of pre‐bid target underperformance and the anticipation of potential targets by other market participants, diminishes but does not eliminate the potential profitability of investing in predicted targets. Importantly, we find that target portfolios are predisposed to underperform, as targets and distressed firms share common firm characteristics, resulting in the misclassification of a disproportionately high number of distressed firms as potential targets. We show that this problem can be mitigated, and significant risk‐adjusted returns can be earned, by screening firms in target portfolios for size, leverage and liquidity.