How would you model stock price prediction?
Stock Price Prediction
You are tasked with using machine learning to predict stock prices, or more realistically, future returns or price direction, for a trading application.
Your task involves addressing the following key areas:
- Target Definition: Clearly define what you will predict (e.g., next-day closing price, next-hour return, price direction, volatility) and justify your choice.
- Data Sources: Identify potential data sources such as market data, fundamental data, news, and alternative data. Determine the minimum viable dataset required.
- Feature Engineering: Describe the features you would engineer from the selected data sources.
- Model Selection: Discuss various model families you would consider (e.g., linear models, tree-based models, deep learning, time-series models) and the rationale behind your choices.
- Training and Validation: Explain how you would split time-series data to prevent leakage and how you would tune hyperparameters.
- Evaluation Metrics: Specify the machine learning and trading-specific metrics you would use for evaluation.
- Pitfalls and Mitigation: Address common challenges such as non-stationarity, regime changes, data snooping, survivorship bias, and transaction costs, and outline strategies to mitigate them.
- Production Deployment: Discuss considerations for deploying, monitoring, and retraining the model in a production environment.
AI interview prep
Core dilemma
Balancing predictive accuracy with the inherent noise and non-stationarity of financial markets, while also accounting for practical trading constraints like transaction costs and latency.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
How would you model stock price prediction?
Stock Price Prediction
You are tasked with using machine learning to predict stock prices, or more realistically, future returns or price direction, for a trading application.
Your task involves addressing the following key areas:
- Target Definition: Clearly define what you will predict (e.g., next-day closing price, next-hour return, price direction, volatility) and justify your choice.
- Data Sources: Identify potential data sources such as market data, fundamental data, news, and alternative data. Determine the minimum viable dataset required.
- Feature Engineering: Describe the features you would engineer from the selected data sources.
- Model Selection: Discuss various model families you would consider (e.g., linear models, tree-based models, deep learning, time-series models) and the rationale behind your choices.
- Training and Validation: Explain how you would split time-series data to prevent leakage and how you would tune hyperparameters.
- Evaluation Metrics: Specify the machine learning and trading-specific metrics you would use for evaluation.
- Pitfalls and Mitigation: Address common challenges such as non-stationarity, regime changes, data snooping, survivorship bias, and transaction costs, and outline strategies to mitigate them.
- Production Deployment: Discuss considerations for deploying, monitoring, and retraining the model in a production environment.