Forecasting Financial Fortunes: Unveiling the Secrets of Stock Prediction Models

Yuanchun (Justin) Chang

doi:10.54097/zwr3hf77

Authors

Yuanchun (Justin) Chang

DOI:

https://doi.org/10.54097/zwr3hf77

Keywords:

Stock Market Prediction, Mathematical Models, Machine Learning, Time Series Analysis

Abstract

Stocks are the most representative area of modern financial markets, serving as a cornerstone of investment strategies for individuals and institutions. Over the years, analysts have devoted substantial effort to developing methodologies for predicting stock price movements more accurately. For seasoned and ordinary investors, buying and selling stocks represents a significant avenue for generating additional income. However, the inherent volatility of stock markets, driven by many economic, political, and psychological factors, poses a persistent challenge to achieving precise predictions. Mathematical models play a crucial role in forecasting stock price trends in this context. These models range from traditional statistical techniques like linear regression and Autoregressive Integrated Moving Average (ARIMA) to advanced machine learning frameworks such as Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks. Each approach offers distinct advantages yet also exhibits limitations when applied to different market conditions, particularly in the face of sudden, unpredictable events. This paper examines the performance of various predictive models, focusing on their strengths and weaknesses in capturing trends across diverse stock types. This research aims to illuminate the potential and limitations of predictive modeling in stable and volatile market environments by analyzing its outcomes. It highlights the importance of aligning forecasting techniques with specific market dynamics and stock characteristics, offering valuable insights for analysts seeking to refine their strategies in the ever-evolving landscape of financial markets.

Downloads

Download data is not yet available.

References

[1] H ochreiter S., & Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9 (8): 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135.

[2] Box G. E. P., Jenkins G. M., & Reinsel G. C. Time series analysis: forecasting and control. 4th ed. Wiley, 2008.

[3] Cristianini N., & Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, 2000.

[4] Draper N. R., & Smith H. Applied regression analysis. 3rd ed. John Wiley & Sons, 1998.

[5] Stock J. H., & Watson M. W. Introduction to econometrics. 4th ed. Pearson, 2019.

[6] Goodfellow I., Bengio Y., & Courville A. Deep learning. MIT Press, 2016.

[7] Kim K. Financial time series forecasting using support vector machines. Neurocomputing, 2003, 55 (1–2): 307–319. https://doi.org/10.1016/S0925-2312(03)00372-2.

[8] Zhang G. P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 2003, 50:159–175. https://doi.org/10.1016/S0925-2312(01)00702-0.

[9] Fischer T., & Krauss C. Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 2018, 270 (2): 654–669. https://doi.org/10.1016/j.ejor.2017.11.054.

[10] Nelson D. M., Pereira A. C., & de Oliveira R. A. Stock market's price movement prediction with LSTM neural networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN), 2017, pp. 1419–1426. https://doi.org/10.1109/IJCNN.2017.7966019.

[11] Yahoo Finance. Historical data for [specific stock, e.g., AAPL, TSLA, NVDA, etc.]. Retrieved from https://finance.yahoo.com.

[12] Bloomberg. NVIDIA stock rises on AI optimism. Retrieved from https://www.bloomberg.com.