  Extracting implied information, like volatility and/or dividend, from
observed option prices is a challenging task when dealing with American
options, because of the computational costs needed to solve the corresponding
mathematical problem many thousands of times. We will employ a data-driven
machine learning approach to estimate the Black-Scholes implied volatility and
the dividend yield for American options in a fast and robust way. To determine
the implied volatility, the inverse function is approximated by an artificial
neural network on the computational domain of interest, which decouples the
offline (training) and online (prediction) phases and thus eliminates the need
for an iterative process. For the implied dividend yield, we formulate the
inverse problem as a calibration problem and determine simultaneously the
implied volatility and dividend yield. For this, a generic and robust
calibration framework, the Calibration Neural Network (CaNN), is introduced to
estimate multiple parameters. It is shown that machine learning can be used as
an efficient numerical technique to extract implied information from American
options.
