Two-tier machine learning ensemble model for option price forecasting using salp swarm optimization (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2022)
Options and derivative products are complicated financial tools. Because of the risk involved inoptions trading, trade support systems are in high demand to help clients manage and mitigatetheir volatility. The major issue of option price forecasting is the non-linearity and nonstationarityof option characteristics. The key job in options trading is to calculate the realisticprice option, which may be used to assess which options are now inexpensive and which arecurrently expensive. To address these issues in option price forecasting, a Two-Tier MachineLearning 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 salpswarm optimization technique has been used to teach the TTMLE model and improve theweights of the model, resulting in the TTMLE-SSO model, which enhances forecastingaccuracy. Other models such as the Non-linear Neural Network (NNN), Support VectorRegression (SVR), and Long Short-Term Memory (LSTM) network have been compared to thesuggested model. The new approach beats previous methods, and predicting accuracy issubstantially improved, according to empirical data.