# Development of a Stock Price Prediction Framework for Intelligent Media and Technical Analysis
## 4 Conclusions  
The scientific novelty of the present research is that it aimed to construct a framework for intelligent media and technical analysis by forecasting stock price movement and closing prices using news headlines, tweets, and technical indicators. The paper first constructed a sentiment analysis classifier using BERT. A South African dataset was constructed by scraping South African-related tweets on Twitter; these were then concatenated with the Stanford Twitter Sentiment Corpus to create the training dataset. Two experiments were constructed: the fundamental analysis experiment to predict Vodacom's closing price movement and the technical analysis experiment to predict Vodacom's closing price. The first experiment was split into three sub-experiments which were run on three datasets. The first dataset was the news headlines dataset, the second was the news headlines and sentiment dataset, and the last was the news headlines, tweets, and sentiment dataset. These datasets were constructed by scraping news headlines related to Vodacom Group Limited on the Moneyweb site and tweets with the hashtag vodacom on Twitter. The BERT-trained sentiment classifier extracted the sentiment feature from the collected text. The experiment included four machine learning models: support vector machine, linear discriminant analysis, decision tree, and random forest. The fundamental analysis experiment returned positive results, with the linear discriminant analysis being the best performing model in the experiment. The model achieved an accuracy of 96% in predicting Vodacom's closing price movement. The second experiment, the technical analysis experiment, presented a different objective than the first. In this experiment, Vodacom's closing price was predicted based on two datasets. The first dataset was constructed using Vodacom's closing price and named the univariate dataset. The second dataset utilized Vodacom's stock data, namely the opening price, high price, low price, volume, closing price, and features extracted from three technical indicators, the three moving average, MACD, and Bollinger Bands. This second dataset was called the multivariate dataset. Three sub-experiments were performed; the first two included five different LSTM architectures, namely ordinary LSTM, stacked (deep) LSTM, convLSTM, and CNNxlSTM. These LSTM architectures were chosen to compare and assess which model would perform the best on the data; as seen in the results, the most complex model was not always the best performing one. The final experiment introduced the encoder-decoder LSTM architecture and yielded different outcomes between the univariate and multivariate datasets. For the first experiment, stacked LSTM archived the best results in predicting the closing price, with an RMSE of 220.83, and ordinary LSTM performed best with an RMSE of 75.54. The last experiment in Experiment 2 introduced encoder-decoder LSTM; this model achieved the best overall performance on the univariate dataset, with an RMSE of 0.023. In Experiment 1, the addition of the sentiment feature returned a significant improvement in predicting Vodacom's stock price movement due to the introduction of a South African-based sentiment classifier. The second experiment yielded mixed results. Though the developed encoder-decoder LSTM achieved the best performance on the univariate dataset, the multivariate dataset achieved the best overall performance in predicting Vodacom's closing price via the LSTM architectures. The presented outcomes of the experiments provide beneficial results in answering the research questions by predicting price movements for fundamental analysis and predicting the closing price using technical indicators. The present research has shown that including \begin{table}
\begin{tabular}{c c} \hline \hline
**Test Data (ZAR)** & **Predictions (ZAR)** \\ \hline
12,901 & 12,959.947 \\
12,700 & 12,939.186 \\
12,376 & 12,875.924 \\
12,051 & 12,830.503 \\
12,112 & 12,801.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experiment 2.3 results.