# A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning
## 7 Conclusions  
In this paper, a novel framework called DTRSI is proposed to predict stock price movements by using an insufficient amount of data, yet with satisfactory performance. The framework takes full advantage of parameter transfer learning and a deep neural network. Moreover, we leverage relationships between stocks into constructing effective inputs to enhance the prediction model performance. Applied to the Korean stock market and the US stock market, experimental results show  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Stock Name** & \(m=3\) & \(m=5\) & \(m=7\) \\ \hline Samsung Electronics Co. & 59.42 & 58.14 & 52.39 \\ SK Hynix Inc & 60.06 & 56.56 & 53.66 \\ Celltrion & 54.94 & 60.70 & 55.59 \\ Hyundai Motor Co. & 55.26 & 57.83 & 64.22 \\ LG Chem & 53.33 & 56.21 & 56.87 \\ \hline Boeing Company & 60.19 & 57.41 & 56.48 \\ Apple Inc. & 58.96 & 53.09 & 52.78 \\ Amazon.com Inc. & 62.04 & 61.11 & 57.41 \\ Johnson \& Johnson & 57.79 & 58.33 & 53.70 \\ JPMorgan Chase \& Co. & 57.72 & 56.79 & 56.17 \\ \hline Average & 57.97 & 57.62 & 55.93 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average accuracy for different numbers of similar-field stocks over all study periods.  
that the proposed approach performs better than other baselines (e.g., SVM, RF, KNN, and the model from [20]) in terms of average prediction accuracy. Moreover, stocks related to the COI stock are more useful in financial time series prediction tasks. Among the considered relationships, the prediction model using the returns of companies with a field similar to the COI achieved the highest performance.  
To the best of our knowledge, this is the first study using a combination of transfer learning and long short-term memory in financial time series prediction tasks. Although the proposed integrated system has satisfactory prediction performance, it still has some drawbacks. First, our model has a large number of parameters by the nature of deep neural network. Therefore, the training process needs considerable time and computational resources, compared to other approaches. Second, finding a suitable number of stocks chosen in obtaining source data for the base model should be considered in order to enhance predictive accuracy. In addition, we use only the one-day returns of stocks, whereas market sentiment also helps make profitable models. In particular, several pre-trained models for the sentiment analysis task are being employed extensively. Therefore, our future task will be to design an optimized trading system that uses multiple types of source data, including numerical data (e.g., returns) and sentiment information.  
Conceptualization, S.Y. and T.-T.N.; methodology, T.-T.N. and S.Y.; software, T.-T.N.; validation, T.-T.N. and S.Y.; formal analysis, T.-T.N. and S.Y.; investigation, T.-T.N. and S.Y.; resources, S.Y.; data curation, T.-T.N.; writing--original draft preparation, T.-T.N. and S.Y.; writing--review and editing, T.-T.N. and S.Y.; visualization, T.-T.N. and S.Y.; supervision, S.Y.; project administration, S.Y.; funding acquisition, S.Y. This research was funded by the 2019 Research Fund of University of Ulsan Conflicts of Interest: The authors declare no conflict of interest