 Hutchinson, Lo and Poggio raised the question that if learning works can learn the Black-Scholes formula, and they proposed the network mapping the ratio of underlying price to strike S_t/K and the\to  time to maturity \tau directly into the ratio of option priceto strike C_t/K . In this paper we propose a novel descision function and study the network mapping S_t/K and%DIFDELCMD < \tauinto %%% the ratio of time value to strike\to V_t/K . Time values'
<coherence>  Hutchinson, Lo and Poggio raised the question that if learning works can learn the Black-Scholes formula, and they proposed the network mapping the ratio of underlying price to strike S_t/K and the\to  time to maturity \tau directly into the ratio of option priceto strike C_t/K . In this paper we propose a novel descision function and study the network mapping S_t/K and%DIFDELCMD < \tauinto %%% the ratio of time value to strike\to V_t/K . Time values'
The mathematical theory of neural networks is ``meant to tell us what is possible and, sometimes equally importantly, what is not." This paper contributes a case that fits into this principle. We propose that "option price or time value" is a natural hyperparameter in the design of neural network option models. Hutchinson, Lo and Poggio raised the question that if learning works can learn the Black-Scholes formula, and they proposed the network mapping the ratio of underlying price to strike S_t/K and the\to time to maturity \tau directly into the ratio of option priceto strike C_t/K . In this paper we propose a novel descision function and study the network mapping S_t/K and%DIFDELCMD < \tauinto %%% the ratio of time value to strike\to V_t/K . Time values'