# A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning
## 6 Experimental Results
### Effectiveness of Different Types of Input Features  
In this subsection, the performance of our approach is examined using six different types of input features for the top five companies on the KOSPI 200 (i.e., Samsung Electronics Co. ( Suwon, Korea), SK Hynix Inc. (Icheon-si, Korea), Celltrion (Incheon, Korea), Hyundai Motor Co. (Seoul, Korea), and LG Chem (Seoul, Korea)) and the top five companies from five popular sectors on the S&P 500 (i.e., Apple Inc. (Cupertino, CA, USA), Amazon.com Inc. (Seattle, WA, USA), Boeing Company (Chicago, IL, USA), Johnson & Johnson (New Brunswick, NJ, USA), and JPMorgan Chase & Co. (New York, NY, USA)). Note that Table 5 uses COI, CI, CI+CS, CI+SF, CI+HMC, and CI+RND as the names of input features \(s_{t}^{CI}\), \(s_{t}^{CI}\), \(s_{t}^{CI+CS}\), \(s_{t}^{CI+SF}\), \(s_{t}^{CI+HMC}\), and \(s_{t}^{CI+RND}\), respectively. Recall that CI is defined as model inputs formed based on features of the COI stock and the index; CS, SF, HMC, and RND are represented the relationship between other stocks and the COI stock such as the highest cosine similarity, similar field, the highest market capitalization, and randomly chosen, respectively. As seen in Table 5, the average prediction accuracy with CI+SF is the highest over all the study periods. Specifically, CI+SF obtains the highest average accuracy at 60.64%, compared to 53.74% and \begin{table}
\begin{tabular}{c c} \hline \hline
**Base Model** & **Prediction Model** \\ \hline \(\mathbb{X}_{1}\) & \(\mathbb{SI}_{1}\) \\ \(\mathbb{X}_{2}\) & \(\mathbb{SI}_{2}\) \\ \(\mathbb{X}_{3}\) & \(\mathbb{SI}_{3a}\), \(\mathbb{SI}_{3b}\), \(\mathbb{SI}_{3c}\), \(\mathbb{SI}_{3d}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Specification of the training data fed to each model. 56.35% for COI and CI, respectively. In addition, our model with CI+CS, CI+HMC and CI+RND achieves 58.91%, 58.59%, and 55.87%, respectively. This indicates that similar-field companies help to significantly improve the prediction accuracy of the model, compared to other considered factors. Overall, the prediction model with CI+SF obtains the highest performance for almost all of the stocks. In particular, the prediction model with CI+SF effectively predicts Hyundai Motor Co. with a 64.54% average accuracy and Amazon.com Inc. with a 62.65% average accuracy over all study periods. However, in case of Apple Inc., CI attains 60.49% accuracy, which is slightly better than the 59.88% accuracy from CI+SF. One reason may be that Apple Inc. is the largest company in the United States and it is better coupled with the S&P 500. In CI+SF, stocks from a similar field (e.g., similar industrial products) to the COI stock are selected. In fact, two companies in the same industry affect each other dramatically [44]. For example, an event like a \"Samsung recall\" may decrease Samsung's stock value while increasing the stock price of SK Hynix Inc. at the same time because SK Hynix Inc. is the world's second-largest memory chip-maker (after Samsung). In contrast, CI+CS uses stocks that have the most similar direction in closing price movements. In our work, cosine similarity between two vectors including one-day returns of two stocks is calculated over the entire training time period. As shown in Table 5, CI+SF achieves better average accuracy than CI+CS by 1.73%. Additionally, CI+HMC aims at using top stocks that have the highest market capitalization in order to construct model inputs. When the COI is one of the top companies (e.g., Samsung Electronics Co., SK Hynix Inc, Apple Inc., or Amazon.com Inc.), CI+HMC obtains slightly better performance than CI+CS. Unlike these top companies, in the case of JPMorgan Chase & Co., CI+HMC achieves the worst performance at 54.01% average accuracy. Note that JPMorgan Chase & Co. is the largest company in the US financial field.