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https://doi.org/10.62836/iaet.v1i1.1009
Qian Zhang, Jiarui Rao, & Zong Ke. (2022). Enhancing Financial Forecasting Models with Textual Analysis: A Comparative Study of Decomposition Techniques and Sentiment-Driven Predictions. Innovations in Applied Engineering and Technology, 1(1), 1–6. https://doi.org/10.62836/iaet.v1i1.1009
Volume 1, Issue 1

Enhancing Financial Forecasting Models with Textual Analysis: A Comparative Study of Decomposition Techniques and Sentiment-Driven Predictions

Financial time series data are inherently complex, encompassing various components such as trends, seasonal patterns, and irregular fluctuations. This paper presents a comprehensive analysis of decomposition techniques applied to financial time series, aiming to disentangle these components for more accurate forecasting and risk management. We begin by reviewing the traditional methods of decomposition, such as classical decomposition and trend-ratio decomposition, highlighting their limitations in capturing the dynamic nature of financial data. Subsequently, we explore more sophisticated techniques like the Hodrick-Prescott filter, which is widely used for extracting cyclical components, and the Baxter-King filter, which is designed to separate trend and cyclical components while accounting for potential business cycles. The paper then delves into state-of-the-art methods, including the use of autoregressive integrated moving average (ARIMA) models and exponential smoothing state space models, which are capable of capturing both linear and nonlinear patterns in financial time series. We also discuss the application of machine learning algorithms, such as EEMD and long short-term memory (LSTM) networks, which have shown promise in handling the non-stationarity and volatility clustering present in financial data. Empirical analysis is conducted using historical stock prices and exchange rates, demonstrating the effectiveness of these decomposition techniques in isolating the underlying components of financial time series. The results indicate that a combination of traditional and modern methods yields the most robust decomposition, providing a clearer picture of the market dynamics and informing better investment decisions. In conclusion, this paper contributes to the literature by providing a comparative analysis of various decomposition methods and their applicability to financial time series. It underscores the importance of selecting the appropriate technique based on the specific characteristics of the data at hand. The insights gained from this study can be instrumental for financial analysts and economists in developing more effective models for forecasting and risk assessment.

financial time series; decomposition techniques; trend analysis; seasonality; forecasting; risk management

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Supporting Agencies

  1. Funding: Not applicable.