A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting

Abstract

The primary purpose of trading in stock markets is to profit from buying and selling listed stocks. However, numerous factors can influence the stock prices, such as the company's present financial situation, news, rumor, macroeconomics, psychological, economic, political, and geopolitical factors. Consequently, tremendous challenges already exist in predicting noisy stock prices. This paper proposes a hybrid model integrating the singular spectrum analysis (SSA) and the backpropagation neural network (BPNN) to forecast daily closing prices in stock markets. The model first decomposes the stock prices into several components using the SSA. Then, the extracted components are utilized for training BPNNs to forecast future prices. Compared with the BPNN, the hybrid SSA-BPNN model demonstrates a better predictive performance, indicating the SSA's ability to extract hidden information and reduce the noise effect of the original time series.

Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting

“…The SSA decomposes signals using singular value decomposition (SVD) and generates singular values containing information regarding the original time series [23]. The SSA approach is divided into two stages, decomposition and reconstruction, each of which consists of two steps [24].…”

Projection of PT Aneka Tambang Tbk Share Risk Value Based on Backpropagation Artificial Neural Network Forecasting Result

“…High price, low price, and close price were three categories that constitute stock prices in the capital market. A company's high revenue or profit will result in a high stock price (Fathi et al, 2021). The fluctuation of stock prices is crucial for investors because if a company's stock price consistently rises, investors or potential investors might perceive the company as successful in managing its business and vice versa (Ardana et al, 2019).…”

“…The stock market allows investors to put money into businesses to gain profits from purchasing and selling listed stocks (Agustini et al, 2018;Fathi et al, 2021;Utami, Haris, & Wasono, 2023). Besides offering profit potential, such investments also entail significant risks.…”

“…Moreover, BPNN features efficient learning, generalization, and parallel processing capabilities in solving complex problems. Therefore, BPNN was appropriate for modeling time series data with notable deviations and irregularities (Fathi et al, 2021). The pattern of relationships between neurons and the initialization of weight values determined the processing ability of BPNN.…”

“…Stock data forecasting using BPNN was conducted by (Fathi et al, 2021), predicting 20 stock prices. The research finding revealed that BPNN, with ten neurons in the input layer, 21 neurons in the hidden layer, and one neuron in the output layer, could forecast stock prices effectively, as evidenced by the Mean Absolute Percentage Error (MAPE) averaging less than 10%.…”