2019/05/12 by Jessie Sun, Sun, Jessie
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #FOS: Computer and information sciences #FOS: Economics and business #Mathematical Finance (q-fin.MF) #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE) #Portfolio Management (q-fin.PM) #Stock Market Forecasting Methods #Time Series Analysis and Forecasting #cs.NE #q-fin.MF #q-fin.PM
paper · pdf · doi:10.48550/arxiv.1905.04842
10 pages, 3 figures, 3 tables
arxiv created 2019/05/13 · openalex publication_date 2019/05/13 · arxiv updated 2019/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Long-term investors, different from short-term traders, focus on examining the underlying forces that affect the well-being of a company. They rely on fundamental analysis which attempts to measure the intrinsic value an equity. Quantitative investment researchers have identified some value factors to determine the cost of investment for a stock and compare different stocks. This paper proposes using sequence prediction models to forecast a value factor-the earning yield (EBIT/EV) of a company for stock selection. Two advanced sequence prediction models-Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are studied. These two models can overcome the inherent problems of a standard Recurrent Neural Network, i.e., vanishing and exploding gradients. This paper firstly introduces the theories of the networks. And then elaborates the workflow of stock pool creation, feature selection, data structuring, model setup and model evaluation. The LSTM and GRU models demonstrate superior performance of forecast accuracy over a traditional Feedforward Neural Network model. The GRU model slightly outperformed the LSTM model.