Why Use Separate Models for Large-Cap, Mid-Cap, and Small-Cap Stocks?
- Distinct Market Behavior:
• Large-Cap Stocks: Typically more stable, with movements influenced by macroeconomic factors, institutional flows, and global trends.
• Mid-Cap Stocks: Exhibit moderate volatility and are more sensitive to industry trends and company-specific developments.
• Small-Cap Stocks: Highly volatile, with movements driven by liquidity, speculation, and company fundamentals.
- Better Representation of Market Regimes:
• Market regimes (bullish, bearish, sideways) can manifest differently in each segment. For example:
• Large-caps may trend sideways, while small-caps rally during a specific market condition.
• Large-caps may lag in recovery during a bullish regime compared to small-caps.
- Improved Model Accuracy:
• Separate models can focus on patterns unique to each class, leading to better predictions and reduced noise.
- Tailored Strategies:
• By identifying market regimes separately for each segment, you can deploy strategies that are optimized for the behavior of those specific stocks.
How to Approach the Problem
Step 1: Preprocess the Data for Each Segment
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Split your dataset into three subsets: large-cap, mid-cap, and small-cap stocks.
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Calculate segment-specific indicators for each subset:
• Aggregated metrics like average returns, volatility, and momentum for each class.
• Sector performance within each segment, if relevant.
• Breadth indicators (e.g., percentage of advancing stocks in the segment).
Step 2: Build Segment-Specific Models
- Large-Cap Model:
• Focus on macroeconomic indicators, sector performance, and global market indices.
• Likely less volatile, so models like Hidden Markov Models (HMM) or GARCH may work well.
• Use Transformer-based models if capturing complex, long-term dependencies is critical.
- Mid-Cap Model:
• Combine macroeconomic factors with industry-specific trends.
• Mid-caps often serve as a bridge, so clustering or ensemble approaches (e.g., Random Forest + XGBoost) might capture their nuances better.
- Small-Cap Model:
• Focus more on sentiment analysis, liquidity, and speculative indicators.
• Use models like Temporal Fusion Transformer (TFT) or LSTM-CNN hybrids, which excel at handling volatile and non-linear patterns.
Step 3: Train and Validate Models
• Train each model separately on its respective data subset.
• Use features specific to the stock class dynamics:
• Large-Cap: Macro factors, index correlations, earnings trends.
• Mid-Cap: Industry-specific metrics, momentum.
• Small-Cap: Sentiment, liquidity, volatility.
Step 4: Combine Outputs for Holistic Analysis
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After detecting market regimes for each segment, assess whether they align or diverge.
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Combine results to classify the overall market regime:
• Use a weighted approach based on market cap or trading volume.
• Alternatively, treat the dominant regime (e.g., if two segments are bullish, classify the overall market as bullish).
Which Model Should You Choose?
• HMM: If interpretability and simplicity are critical, and you expect clear regime shifts.
• GARCH: If volatility is a key factor for identifying regimes, especially in short-term data.
• TFT: If you want a powerful, state-of-the-art model and have ample data and computational resources.
• LSTM-CNN: If you need to capture both long-term trends and short-term patterns and can handle the complexity.
Next Steps TODOs
- Start Simple:
• Begin with HMMs or GARCH for quick prototyping.
• Use them to establish a baseline for regime detection.
- Experiment with Deep Learning:
• If your baseline models don’t perform well, experiment with TFT or LSTM-CNN hybrids.
• Start with smaller datasets and fewer features to control computational costs.
- Feature Engineering:
• Incorporate key indicators like moving averages, volatility, and volume.
• For advanced models, consider external factors like macroeconomic data.
- Evaluation:
• Validate models on historical data to check regime classification accuracy.
• Use metrics like precision, recall, and F1 score for classification tasks.