A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning

Abstract

Stock price prediction has always been an important application in time series predictions. Recently, deep neural networks have been employed extensively for financial time series tasks. The network typically requires a large amount of training samples to achieve high accuracy. However, in the stock market, the number of data points collected on a daily basis is limited in one year, which leads to insufficient training samples and accordingly results in an overfitting problem. Moreover, predicting stock price movement is affected by various factors in the stock market. Therefore, choosing appropriate input features for prediction models should be taken into account. To address these problems, this paper proposes a novel framework, named deep transfer with related stock information (DTRSI), which takes advantage of a deep neural network and transfer learning. First, a base model using long short-term memory (LSTM) cells is pre-trained based on a large amount of data, which are obtained from a number of different stocks, to optimize initial training parameters. Second, the base model is fine-tuned by using a small amount data from a target stock and different types of input features (constructed based on the relationship between stocks) in order to enhance performance. Experiments are conducted with data from top-five companies in the Korean market and the United States (US) market from 2012 to 2018 in terms of the highest market capitalization. Experimental results demonstrate the effectiveness of transfer learning and using stock relationship information in helping to improve model performance, and the proposed approach shows remarkable performance (compared to other baselines) in terms of prediction accuracy.

Multimodal Multi-Task Financial Risk Forecasting

“…4a shows the improvements (for n=3, 7: < 0.005, and for n=30: < 0.05 in MCC) for the classification task, when the text and audio aligned encoders are initialized with the weights of the encoders trained for realized volatility regression. These findings are in line with and expand on the claim supporting the generalizability across heterogeneous related tasks in the financial domain [67]. Such weight sharing based ensemble learning boosts overall performance on classification in comparison to individual isolated learning.…”

“…Until recently, multimodal MTL has not been explored in the finance except for [94], which concentrates on the homogeneous tasks of stock volatility regression across various durations. We build upon their homogeneous approach to a heterogeneous multitask [95] ensemble approach with the introduction of stock price classification, owing to the relatedness of financial tasks [67].…”

“…Financial tasks, such as predicting a stock's price movement and volatility, are often strongly correlated, thus making multi-task learning a promising modeling choice for financial forecasting. Though homogeneous multi-task approaches [67,94] have been studied for volatility and price movement prediction, there has been no research on heterogeneous Multi-Task Learning (MTL). Here strongly related tasks on the same feature space differ based on being classification and regression problems [95,98].…”

Comparative Study on Supervised Learning Models for Productivity Forecasting of Shale Reservoirs Based on a Data-Driven Approach

“…This is generally more time consuming than other methods, such as simple train/test partitioning. However, it is a popular method because it prevents overfitting and does not result in biased or non-optimistic assumptions about model technology [32][33][34].…”

A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction

“…Therefore, a holistic fusion of several quantitative and qualitative stock-related data sources to predict the future stock price is a potential way to improve prediction accuracy [2,10,[46][47][48], which remains an open research area. Hence, this study put-forward a novel multi-source data-fusion stock market predictive framework built on a deep hybrid neural network architecture (CNN and stacked LSTM) named IKN-ConvLSTM, to produce a more reliable and accurate stock price prediction.…”