## 4 Stock Price Prediction Model Methodology and Research Design  
### General Approach to Stock Price Movement Prediction  
The stock price movement prediction task that is the focus of this study can be solved in many different ways. The key questions addressed were:  
* Which input data to use? * How to process the input data as a time series? * Whether to aim to achieve the prediction objective as a classification or regression problem? * Which model archetype could provide the optimal solution to the problem? Below, the alternatives considered for each of the above questions are outlined, and justification for the selected options is provided. Based on the analysis of prior works in the field, as described earlier, the decision was made to focus on market data as the primary source of features for the stock price prediction model, particularly high-frequency market data. These data are characterised by low latency and are particularly crucial for intraday price movement prediction. In contrast, fundamental data are less suitable for intraday trading due to their low-frequency nature. Key business performance metrics, such as sales, EBITDA, and net profit, are sourced from financial statements, which are typically published on a quarterly basis at best. This limited frequency would restrict trading opportunities based on such data to only one day in a quarter. Alternative data for trading, although potentially valuable, are often either not available to the general public or prohibitively expensive. For instance, satellite photos of parking areas near stores could be useful in predicting the sales and, consequently, the stock price of a retail company. However, the cost to obtain this information is unaffordable for the majority of investors. Therefore, the decision was made to use high-frequency market data as the primary source for features in the stock movement prediction model for this work. The abundance of high-frequency market data provides the advantage of enabling effective training, especially with a deep learning architecture. However, it also makes the experiment more computationally intensive. To overcome this computational challenge, the model architecture was designed considering the utilisation of a GPU for training. More details on the market data used in this research are presented in Section 4.2. In the realm of high-frequency trading, especially when dealing with tick-by-tick market data characterised by minimal changes from timestamp to timestamp, it has become a prevalent practice in the research field to address stock price movement prediction as a classification task rather than a regression task. This paradigm shift is substantiated by several key factors. Firstly, the sheer frequency and granularity of high-frequency data, with updates occurring at a sub-second level, present challenges for predicting precise price changes. The negligible variations between consecutive timestamps make it arduous to discern meaningful trends amidst the noise of the market. Empirical evidence from various studies shows that attempting to predict exact price changes in such a high-frequency environment can lead to overfitting and capturing short-term noise rather than genuine trends. Moreover, the inherent noise and volatility present in high-frequency markets contribute to the complexity of predicting precise price movements. Classifying the direction of price changes--whether up, down, or no change--provides a more robust and inter pretable framework for traders seeking actionable signals. This aligns with the practical requirements of decision making in trading scenarios, where clear guidance on buying, selling, or holding positions is essential. The time sensitivity associated with intraday trading further underscores the suitability of the classification approach. Intraday traders require rapid decision making due to short holding periods, and classifying stock price movements offers timely signals that match the fast-paced nature of high-frequency market data.