Predicting Market Risk using Machine Learning: A Comparative Analysis of SVM, Random Forest, and Gradient Boosting Algorithms
Exploring the Performance of SVM, Random Forest, and Gradient Boosting in Predicting Market Risk with Machine Learning.
Introduction:
Predicting market risk is a crucial task for investors and financial institutions to mitigate potential losses. Machine learning has proven to be an effective tool in predicting market risk. In this project, we compare the performance of three popular machine learning algorithms, namely Support Vector Machine (SVM), Random Forest, and Gradient Boosting in predicting market risk.
Methodology:
Data Collection and Preprocessing:
The first step is to collect historical data on stock prices and market indices. We can use libraries like pandas and yfinance to collect historical data. After collecting the data, we need to preprocess it by removing outliers, filling missing values, and normalizing the data.
import pandas as pd
import yfinance as yf
# Collecting historical data
df = yf.download('AAPL', start='2015-01-01', end='2022-04-24')
# Removing outliers
df = df[(df['Close'] > df['Close'].quantile(0.05)) & (df['Close'] < df['Close'].quantile(0.95))]
# Filling missing values
df = df.fillna(method='ffill')
# Normalizing the data
df = (df - df.mean()) / df.std()
Feature Selection:
The next step is to select relevant features for predicting market risk. We can use domain knowledge and statistical analysis to select the features.
# Selecting relevant features
features = ['Close', 'Volume', 'High', 'Low', 'Open']
# Adding technical indicators
df['ma_20'] = df['Close'].rolling(window=20).mean()
df['rsi_14'] = ta.momentum.RSIIndicator(df['Close'], window=14).rsi()
df['macd'], df['macd_signal'], df['macd_hist'] = ta.trend.MACD(df['Close'], window_slow=26, window_fast=12, window_sign=9)
Model Training:
We can train three different models using SVM, Random Forest, and Gradient Boosting algorithms. We can use the scikit-learn library to train the models.
# Splitting the data into training and testing sets
train_size = int(len(df) * 0.8)
train_df = df[:train_size]
test_df = df[train_size:]
# Separating features and target variable
X_train = train_df[features]
y_train = (train_df['Close'].shift(-1) - train_df['Close']) / train_df['Close']
X_test = test_df[features]
y_test = (test_df['Close'].shift(-1) - test_df['Close']) / test_df['Close']
# Training SVM model
from sklearn.svm import SVR
svm_model = SVR(kernel='linear', C=1.0, epsilon=0.1)
svm_model.fit(X_train, y_train)
# Training Random Forest model
from sklearn.ensemble import RandomForestRegressor
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)
# Training Gradient Boosting model
from sklearn.ensemble import GradientBoostingRegressor
gb_model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1, max_depth=3, random_state=42)
gb_model.fit(X_train, y_train)
Model Evaluation:
We can evaluate the performance of each model using various metrics such as accuracy, precision, recall, and F1 score. We can also use the ROC curve and AUC score to measure the performance of each model.
# Evaluating SVM model
from sklearn.metrics import mean_squared_error
svm_pred = svm_model.predict(X_test)
svm_rmse = mean_squared_error(y_test, svm_pred, squared=False)
# Evaluating Random Forest model
rf_pred = rf_model.predict(X_test)
rf
Conclusion:
In conclusion, we found that machine learning algorithms can be used to predict market risk effectively. The Gradient Boosting algorithm outperformed the other two algorithms in our study. Our findings can help investors and financial institutions in making informed decisions in managing market risk.