000			02025nab a2200205 4500
999			_c199662 _d199662
003			OSt
005			20200226102053.0
008			200129s2016 xxu|||||r|||| 00| 0 eng d
040			_aCO-BoUGC _cCO-BoUGC
100	1		_aHenry, Elaine _9178470
245	1		_aMeasuring qualitative information in capital markets research _bcomparison of alternative methodologies to measure disclosure tone _cElaine Henry & Andrew J. Leone
300			_aPáginas 153 a la 178
520	3		_aThis study evaluates alternative measures of the tone of financial narrative. We present evidence that word-frequency tone measures based on domain-specific wordlists—compared to general wordlists—better predict the market reaction to earnings announcements, have greater statistical power in short-window event studies, and exhibit more economically consistent post-announcement drift. Further, inverse document frequency weighting, advocated in Loughran and McDonald (2011), provides little improvement to the alternative approach of equal weighting. We also provide evidence that word-frequency tone measures are as powerful as the Naïve Bayesian machine-learning tone measure from Li (2010) in a regression of future earnings on MD&A tone. Overall, although more complex techniques are potentially advantageous in certain contexts, equal-weighted, domain-specific, word-frequency tone measures are generally just as powerful in the context of financial disclosure and capital markets. Such measures are also more intuitive, easier to implement, and, importantly, far more amenable to replication.
650	1	4	_991036 _aContabilidad _vPublicaciones seriadas
650	2	4	_aMercado de capitales _vPublicaciones seriadas _9177692
650	2	4	_aInformación financiera _vPublicaciones seriadas _9176633
700	1		_aLeone, Andrew J. _9178471
773	0		_082265 _9372813 _aThe accounting review 2016 V.91 No. 1 (Jan) _o0000002030250 _x0001-4826 (papel) _h26 páginas _nIncluye tablas, figuras, referencias bibliográficas y apéndices
942			_2ddc _cART