Top 10 most commonly used Machine Learning algorithms in Quantitative Finance PART 2
Numbers (not places) 6-10
As a straight up continuation of lase week article: Top 10 most commonly used Machine Learning algorithms in Quantitative Finance PART 1, this article introduces the other half of our top 10 ML models for quantitative finance.
Nowadays Machine Learning becomes a crucial part of more and more data-driven analysis, whether its edge is based over brute computational force, or sharply designed algorithm behind it Artificial Intelligence became significant part of our industry and its usage or at least understanding would be definitely beneficial for your skillset
So without further due, here is final 5 out of 10 ML models in quant finance:
6. K-Nearest Neighbours (KNN)
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For those assuming inspiration and experience gives edge in trading, well, let me introduce you the simple (not so simple) brute force that can definitely compete with years of experience, the KNN. This algorithm is a non-parametric and used for classification and regression analysis. As the name indicates the two dimensional kNN algorithm just investigates the outcome of the past times, given indicators had a similar level. It then looks at the k nearest neighbours, sees their state and thus classifies today point.
Advantages of using K-Nearest Neighbours:
Simple implementation: KNN is relatively easy to implement and does not require a lot of computational resources, making it a good choice for smaller datasets or simpler applications.
Non-parametric modeling: As I stated before, KNN is a non-parametric model that does not assume a specific distribution of the data, making it more flexible than some other models.
Ability to handle complex relationships: Algorithm can capture complex relationships between variables, making it well-suited for applications where non-linear relationships may be present.
Generalizability: In opposite to many popular ML algorithms in example Neural Networks, K-Nearest Neighbours can be less prone to overfitting than some other machine learning models, which can be important for financial applications where generalizability is key.
Disadvantages of using K-Nearest Neighbours:
Sensitivity to distance metric: KNN's performance can be sensitive to the choice of distance metric used to define the nearest neighbours, and choosing an appropriate distance metric can be challenging.
Sensitivity to feature scaling: Algorithm’s performance can be impacted by the scale of the features, and feature scaling may be necessary to ensure optimal performance.
Computationally expensive during inference: While K-Nearest Neighbour is relatively computationally inexpensive during training, it can be more expensive during inference when predicting on new data points, especially for large datasets.
Limited interpretability: Classically, model can be difficult to interpret, making it challenging to understand how the model is making predictions.
Data quality and bias: KNN can be sensitive to biases in the data, and the use of large and complex datasets can increase the risk of data quality issues that can impact the accuracy of the model.
7. Gradient Boosting
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Gradient boosting is a bit different from presented previously ML models as it is build upon already created models usually simple ones such as Decision Trees (that are for example basics for Random Forest) it combines such multiple weak learners to create a stronger learner. It sequentially adds decision trees to the model, adjusting the weights of each observation to prioritize those that were previously poorly predicted.
Advantages of using Gradient Boosting
High accuracy: Gradient Boosting has been shown to be a highly accurate machine learning algorithm, often outperforming other models in terms of prediction accuracy.
Handling complex relationships: To one of its biggest strengths we may also include its ability to capture complex, non-linear relationships between features and target variables, making it well-suited for many quantitative finance applications.
Feature importance: GB can provide information on the importance of each feature in making predictions, which can be helpful in understanding the underlying relationships between variables.
Handling missing data: Gradient Boosting can handle missing data, as it can use the available data to impute missing values and make accurate predictions
Disadvantages of using Gradient Boosting:
Overfitting: GB can be prone to overfitting if the model is too complex or if the hyperparameters are not optimized properly.
Computationally intensive: As the consecutive one on this list, Gradient Boosting can be computationally expensive and can require significant computational resources, especially for large datasets.
Model interpretability: Due to its complexity, Gradient Boosting can be challenging to interpret, as the model is complex and involves the combination of multiple decision trees.
Data quality: Gradient Boosting is very demanding in terms of the quality of the data and may perform poorly if the data is noisy or contains outliers.
8. Extreme Gradient Boosting (XGBoost)
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XGBoost is an improved version of the gradient boosting algorithm, and is particularly useful for tasks such as portfolio optimization and predicting asset prices. Extreme Gradient Boosting (XGBoost) is an advanced version of the Gradient Boosting Machine (GBM) algorithm that is designed to improve upon its weaknesses. XGBoost has become increasingly popular in recent years due to its superior performance and flexibility compared to GBM. However, it is worth noting that GBM is still a powerful algorithm and may be a better choice in certain situations, such as when dealing with small datasets or when interpretability is a priority.
Here are some advantages of using XGBoost with comparison to GB
Speed: XGBoost is faster than GBM due to its parallel processing capability, which can be leveraged to speed up computations.
Regularization: XGBoost has built-in regularization techniques that help to reduce overfitting, whereas GBM requires manual tuning of hyperparameters to prevent overfitting.
Handling missing values: XGBoost can handle missing values in data, whereas GBM cannot. This is because XGBoost uses a default direction for missing values, while GBM requires that missing values be imputed before training the model.
Customization: XGBoost offers a more customizable framework for building models, with a wide range of parameters that can be adjusted to achieve the desired performance.
Scalability: XGBoost is more scalable than GBM, as it can handle large datasets and is optimized for distributed computing
For the balance, here are some reasons number ML Engineers stay at GBM on many occasions:
Complexity: XGBoost can be more complex to understand and implement than GBM, due to its wider range of parameters and features.
Hardware requirements: XGBoost requires more computational resources and memory than GBM, which may limit its use on certain hardware configurations.
Lack of interpretability: XGBoost models can be less interpretable than GBM models, making it more difficult to understand how the model arrived at its predictions.
Higher risk of overfitting: Although XGBoost has built-in regularization techniques, it can still be prone to overfitting if not properly tuned.
9. Principal Component Analysis (PCA)
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PCA is a statistical technique used to identify patterns in data by reducing the dimensionality of a dataset. Data Selectiveness lies at its core and it is natural counter back against overfitting thanks to that selectiveness therefore it can be used in merge with other ML models to improve their performance. In quantitative finance, it is used for tasks such as yield analysis, risk management etc.
Advantages of using Principal Component Analysis:
Dimensionality reduction: PCA can reduce the dimensionality of a dataset by identifying the most important features that capture the most variability in the data. This can be particularly useful for large datasets where it is difficult to visualize or analyse the data.
Improved interpretability: PCA can simplify the data and make it easier to interpret by reducing the number of features that need to be considered.
Improved performance: By reducing the number of features, PCA can help to improve the performance of other machine learning algorithms by reducing overfitting and minimizing the impact of noise in the data.
Visualization: PCA can help to visualize the data by projecting it onto a lower-dimensional space. This can be particularly useful for exploratory data analysis and can help to identify patterns and relationships in the data.
Disadvantages of using Principal Component Analysis:
Loss of information: PCA can result in a loss of information when reducing the dimensionality of the data, as some of the variation in the data may not be captured by the reduced set of features.
Interpretability: Principal component analysis can be difficult to interpret, as the new features are often a combination of the original features and may not have a clear meaning.
Sensitivity to scaling: This model is also sensitive to the scaling of the data, and the results can vary depending on how the data is scaled.
Computationally intensive: PCA can be computationally intensive, particularly for large datasets, as it involves the calculation of eigenvectors and eigenvalues.
10. Long Short-Term Memory (LSTM) Networks
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As last but by far not least we have LSTM, LSTM is natural extension of neural Networks I described before, as one of NN’s shortcomings was its inability to store and use previous outcomes to influence another. To combat that limitation, Recurring Neural Networks (RNNs) include loops in them allowing the information to persist. Finally LSTM is special type of RNN that is capable to learning long-term dependencies.
Advantages of LSTM
Ability to capture long-term dependencies: Long Short-Term Memory Networks can capture long-term dependencies in time-series data, making them well-suited for financial data that is often characterized by complex patterns and non-linear relationships.
Flexible input formats: LSTM Networks can handle a variety of input formats, including sequences, time-series data, and other types of structured and unstructured data.
Prediction accuracy: Model have been shown to achieve high levels of prediction accuracy in financial forecasting tasks.
Model interpretability: LSTM Networks can provide insight into the underlying relationships between variables and help to identify key factors that drive financial outcomes.
Disadvantages of LSTM
Data quality: LSTM Networks can be sensitive to the quality of the data and may perform poorly if the data is noisy or contains outliers.
Computationally intensive: This model as well can be computationally intensive and may require significant computational resources, especially for large datasets.
Overfitting: Finally, Long Short-Term Memory Networks can be prone to overfitting if the model is too complex or if the hyperparameters are not optimized properly.
And that concludes our second part of top 10 most widely used Machine Learning Algorithms in Quantitative Finance. I hope you find this insightful and that your understanding on range of models available for you has improved alongside with this article. If you missed the first part I really recommend you to visit it.
Thank you for your time and if you wish to receive weekly updates about Quant related subjects and deepen your knowledge in this field don’t forget to subscribe!
See you next week,
Tomasz