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This paper presents a novel way to predict options price for one day in advance, utilizing the method of Quasi-Reversibility for solving the Black-Scholes equation.The Black-Scholes equation solved forwards in time with Tikhonov regularization as an ill-posed problem allows for extrapolation of option prices. This provides high-accuracy results, which can be further improved by applying Neural Network Machine Learning to the solution of the Black-Scholes equation as well as initial and boundary conditions and implied volatility.Using historical option and stock price data the results obtained from the method of Quasi-Reversibility and Machine Learning method are compared in terms of accuracy, precision and recall.It is shown that these methods can be applied to the real-world trading within a variety of trading strategies.

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Tm3+-Tb3+-Eu3+ co-doped oxyfluoride glasses, doped with about 3.0 mol. % TmF3, 0.25 mol. % TbF3, and 0.25 mol. % EuF3, have been prepared by melt quenching technique. Under excitation at commercial 365 nm, the rare-earth co-dopants are all directly excited and emit in the blue, green, and red, respectively, without appreciable energy transfer amongst the co-dopants. Tint of the white luminescence can be adjusted by changing the ratio of the co-dopants. Properties of the glass host promote excellent dissolution of the co-dopants and low non-radiative decay rate. The white emission at 365 nm excitation is suitable for light emitting diodes applications.