Two-tier machine learning ensemble model for option price forecasting using salp swarm optimization

Abstract

Options and derivative products are complicated financial tools. Because of the risk involved in
options trading, trade support systems are in high demand to help clients manage and mitigate
their volatility. The major issue of option price forecasting is the non-linearity and nonstationarity
of option characteristics. The key job in options trading is to calculate the realistic
price option, which may be used to assess which options are now inexpensive and which are
currently expensive. To address these issues in option price forecasting, a Two-Tier Machine
Learning Ensemble model (TTMLE) using Salp Swarm Optimization (SSO) has been proposed.
In the TTML model, Arbitrary Mode Disintegration (AMD) and Composition Search Algorithm
(CSA) has been integrated into tier 1 and tier 2, respectively to forecast option pricing. The salp
swarm optimization technique has been used to teach the TTMLE model and improve the
weights of the model, resulting in the TTMLE-SSO model, which enhances forecasting
accuracy. Other models such as the Non-linear Neural Network (NNN), Support Vector
Regression (SVR), and Long Short-Term Memory (LSTM) network have been compared to the
suggested model. The new approach beats previous methods, and predicting accuracy is
substantially improved, according to empirical data.