Neural networks have been used as a nonparametric method for option pricing and hedging
since the early 1990s. Far over a hundred papers have been published on this topic. This …

The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal
and external environmental variables. Artificial intelligence (AI) techniques can detect such …

The adaptability of the traditional GM (1, 1) model is poor because it is a rigorous
homogenous exponent model with a single fixed structure. To improve the adaptability of the …

This study compares the performances of neural network and Black-Scholes models in
pricing BIST30 (Borsa Istanbul) index call and put options with different volatility forecasting …

During the recent decades, option pricing became an important topic in computational
finance. The main issue is to obtain a model of option prices that reflects price movements …

This study constructs a network model to rank the alternatives for implementing the sorting
process of reverse logistics (RL) in the downstream photovoltaic industry. The RL procedure …

Although a previous study found that neural network forecasts were more accurate than time
series models for predicting Latin American stock indexes, the forecasting accuracy of …

This study applies backpropagation neural network for forecasting TXO price under different
volatility models, including historical volatility, implied volatility, deterministic volatility …

This study aims to use financial variables, corporate governance variables, and cash flow
variables to construct financial information fraud warning models for the manufacturing …

The thesis addresses the question of how option pricing can be improved using machine
learning techniques. The focus is on the modelling of volatility, the central determinant of an …