A Hybrid Model to Predict Stock Closing Price Using Novel Features and a Fully Modified Hodrick–Prescott Filter

Abstract

Forecasting stock market prices is an exciting knowledge area for investors and traders. Successful predictions lead to high financial revenues and prevent investors from market risks. This paper proposes a novel hybrid stock prediction model that improves prediction accuracy. The proposed method consists of three main components, a noise-filtering technique, novel features, and machine learning-based prediction. We used a fully modified Hodrick–Prescott filter to smooth the historical stock price data by removing the cyclic component from the time series. We propose several new features for stock price prediction, including the return of firm, return open price, return close price, change in return open price, change in return close price, and volume per total. We investigate traditional and deep machine learning approaches for prediction. Support vector regression, auto-regressive integrated moving averages, and random forests are used for conventional machine learning. Deep learning techniques comprise long short-term memory and gated recurrent units. We performed several experiments with these machine learning algorithms. Our best model achieved a prediction accuracy of 70.88%, a root-mean-square error of 0.04, and an error rate of 0.1.

Stock Market Prediction Using Generative Adversarial Networks (GANs): Hybrid Intelligent Model

“…Utilizing Bi-LSTM and GRU networks, the proposed model outperforms other models. In [17], a hybrid stock prediction model was presented. The model comprises a noise-filtering approach, unique features, and a prediction based on machine learning.…”

Unraveling the Impact of the Federal Reserve Rate Hike Policy on Amazons Stock Price Based on ARIMA Model

“…For example, Wanjawa & Muchemi have suggested that Artificial Neural Networks are capable of predicting stock prices on typical markets [5]. Ilyas et al argue that a method combining noise filtering technology, new features, and machine learning-based prediction can achieve low error and high accuracy in predicting stock closing prices [6]. Additionally, according to Hossain et al, by combining technical analysis with Belief Rule-Based Expert Systems (BRBES) and the Bollinger Band concept, people can predict stock prices for the next five days [7].…”