Multi-Modal Data Driven Algorithm for Efficient Trade Market Prediction DOI
Komal Batool, Ubaida Fatima

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 24, 2025

Abstract Financial market prediction is an attractive research area for the researchers as it helps participators to make decisions accordingly. However, forecasting of financial not easy task movement stochastic in nature and affected by several controllable uncontrollable factors. In this research, S&P 500 index NASDAQ predicted using five machine learning models including support vector regression, random forest, linear k nearest neighbour LSTM. Three different datasets are used daily closing price order check sensitivity towards Firstly, historical data along with macroeconomic factors design a model. Second dataset sentiment features extracted from web news. Lastly, hybrid developed combining first two datasets. LSTM model outperformed other both markets. It also observed that our most efficient one based on gives minimum RMSE.

Язык: Английский

Multi-Modal Data Driven Algorithm for Efficient Trade Market Prediction DOI
Komal Batool, Ubaida Fatima

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 24, 2025

Abstract Financial market prediction is an attractive research area for the researchers as it helps participators to make decisions accordingly. However, forecasting of financial not easy task movement stochastic in nature and affected by several controllable uncontrollable factors. In this research, S&P 500 index NASDAQ predicted using five machine learning models including support vector regression, random forest, linear k nearest neighbour LSTM. Three different datasets are used daily closing price order check sensitivity towards Firstly, historical data along with macroeconomic factors design a model. Second dataset sentiment features extracted from web news. Lastly, hybrid developed combining first two datasets. LSTM model outperformed other both markets. It also observed that our most efficient one based on gives minimum RMSE.

Язык: Английский

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