TY - JOUR
T1 - A note of techniques that mitigate floating-point errors in PIN estimation
AU - Ke, Wen Chyan
AU - Chen, Hueiling
AU - Lin, Hsiou Wei William
N1 - Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2019/12
Y1 - 2019/12
N2 - This study aims at the estimation of the probability of informed trading (PIN), which may fail for stocks with high levels of trading activities due to a computer's floating-point exception (FPE). In this paper, we discuss two solutions of adopting scaled trade counts and reformulating the likelihood to estimate PIN for actively traded stocks. This study shows that, although scaled data mitigates the impact of the FPE, the effectiveness of scaled data, however, appears to underperform when users adopt the unsuitable expression of the likelihood function. In contrast, the remedy of reformulating the likelihood is more stable.
AB - This study aims at the estimation of the probability of informed trading (PIN), which may fail for stocks with high levels of trading activities due to a computer's floating-point exception (FPE). In this paper, we discuss two solutions of adopting scaled trade counts and reformulating the likelihood to estimate PIN for actively traded stocks. This study shows that, although scaled data mitigates the impact of the FPE, the effectiveness of scaled data, however, appears to underperform when users adopt the unsuitable expression of the likelihood function. In contrast, the remedy of reformulating the likelihood is more stable.
KW - Floating-point exception
KW - Maximum likelihood
KW - PIN
KW - Scaled trade counts
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U2 - 10.1016/j.frl.2018.12.017
DO - 10.1016/j.frl.2018.12.017
M3 - Article
AN - SCOPUS:85059096571
SN - 1544-6123
VL - 31
SP - 458
EP - 464
JO - Finance Research Letters
JF - Finance Research Letters
ER -