Machine learning for stock market prediction: applications & technical overview

You need to feed ML-based stock market prediction systems with large volumes of high-quality data to ensure they deliver accurate results. 

• Identify rich and reliable data sources for algorithm training and data analysis. You can choose from a vast selection of financial databases and market intelligence platforms, such as Bloomberg, Reuters, Nasdaq, S&P Global, Polygon.io, MarketWatch, Alpha Vantage, Financial Times, and Marketstack.
• Complement purely economic datasets with information collected from sources like social media posts or financial news articles. This can help you monitor investor perceptions of key stock market trends through sentiment analysis.
• Since data from such sources is typically heterogeneous, you’ll need to integrate and transform it via ETL/ELT pipelines and consolidate it into a suitable repository. This can be, for instance, a data lake to effectively store large volumes of structured and unstructured data, a data warehouse to keep cleansed data ready for analysis, or a database optimized for time series data. ETL/ELT pipeline setup can be facilitated via cloud data integration services.

You need to properly approach ML model training since it greatly impacts the output of your stock market prediction solution.

• Massive training datasets imply an enormous range of variables and extremely long training times. You can mitigate this issue by selecting the most relevant variables (trading volume, open price, etc.) to shorten the training phase and make the resulting data models easier to interpret.
• Overfitting can affect stock market prediction models when algorithms are trained too long on a certain financial dataset and the model they produce performs well on that specific set but can't handle new data samples. To address this problem, divide model training data into training, validation, and test sets and cross-validate their outputs. However, you may still encounter this issue in production. That’s why, in the pilot phase, SMEs should validate every action taken by the algorithm. A post- or pre-check approach can be implemented depending on the algorithm.
• Assess model performance with suitable metrics based on your algorithm’s task. Mean squared error, mean absolute error, root mean square deviation, and mean absolute percentage error are popular metrics for financial predictions. Precision and Recall or F1 can also be useful for certain tasks.
• Model performance can degrade over time due to progressive changes in input variables. This phenomenon, known as model drift, can be addressed by performing multiple post-deployment retraining iterations to adjust model output. Set up proper model drift monitoring to understand when to execute such iterations.