The importance of algorithmic trading in modern financial markets is increasing day by day. In particular, trading strategies utilizing machine learning and deep learning technologies enable more sophisticated approaches and higher expected returns. This course will explore the theories and practical applications of algorithmic trading using machine learning and deep learning, and deeply discuss volatility and anomalous phenomena in scale.
1. Basics of Machine Learning and Deep Learning
Machine Learning and Deep Learning are two main subfields of Artificial Intelligence (AI). Machine Learning develops algorithms that learn patterns from data to perform predictions or classifications, while Deep Learning is a methodology that processes and learns from data using Artificial Neural Networks.
1.1 Key Algorithms in Machine Learning
- Linear Regression: A statistical technique used to model the relationship between dependent and independent variables.
- Decision Trees: A tree-structured model that performs decision-making by partitioning data.
- Support Vector Machines: A method for finding the optimal boundary that separates data points.
- Random Forest: An ensemble method that combines multiple decision trees to improve prediction accuracy.
- Neural Networks: A model that mimics the structure of brain neurons to recognize complex patterns.
1.2 Architectures of Deep Learning
There are various architectures in deep learning, some of which include:
- Convolutional Neural Networks (CNN): A deep learning architecture primarily used for image recognition.
- Recurrent Neural Networks (RNN): An architecture suitable for time series data or natural language processing.
- Transformer Models: An architecture that has led to revolutionary results in the NLP field, including innovative models such as Google’s BERT and OpenAI’s GPT.
2. Principles of Algorithmic Trading
Algorithmic trading is a system that automatically executes trades according to predefined rules without human intervention. By incorporating machine learning and deep learning technologies, it achieves higher returns through predictions based on historical data.
2.1 Data Collection and Processing
One of the most important steps in algorithmic trading is data collection. This involves gathering various data, including market data (stock prices, trading volumes, volatility, etc.) and alternative data (social media, news, economic indicators, etc.), and processing it to input into the model.
2.2 Feature Engineering
Before inputting data into the model, it is necessary to extract useful information and convert it into features (variables). For example, it is common to use moving averages of specific indicators, and the Volatility Index as features.
2.3 Model Training
Machine learning or deep learning models are trained based on collected data and features. In this process, hyperparameters of the model are adjusted to optimize performance, and cross-validation is used to evaluate the model’s generalization performance.
3. Volatility and Scale Anomalies
Volatility and scale anomalies describe various abnormal patterns observed in financial markets. ‘Volatility’ indicates the degree of price fluctuations in the market, and ‘scale anomalies’ refer to the impact of a stock’s size or scale on excess returns.
3.1 Concept of Volatility
Volatility indicates how quickly and excessively the price of a specific asset changes and is an important indicator for measuring risk in financial markets. High volatility means a greater possibility of future price fluctuations, which can pose higher risks to investors.
3.2 Definition of Scale Anomalies
Scale anomalies refer to the tendency of small and mid-sized companies’ stocks to record higher returns than large enterprises’ stocks. This often indicates market inefficiency and provides investors with opportunities to pursue better returns by investing in these companies.
3.3 Relationship Between Volatility and Scale
According to research, greater volatility reinforces scale anomalies. Theoretically, smaller companies exhibit lower market information efficiency than larger companies, leading to higher uncertainties in marketing, distribution, and funding processes. For this reason, the stock prices of smaller companies may display greater volatility.
4. Predicting Volatility and Scale Anomalies through Machine Learning
Utilizing machine learning techniques to predict volatility and scale anomalies is an essential factor in the success of algorithmic trading. Various prediction models can be built to forecast future volatility based on historical data.
4.1 Data Preprocessing and Feature Selection
Before model training, it is important to collect various data such as historical price data, trading volumes, market indices, and economic indicators, and to preprocess this data appropriately. Subsequently, feature selection for predicting volatility takes place.
4.2 Modeling
Models for predicting volatility can be constructed using various machine learning algorithms (such as Random Forest, Support Vector Machines, etc.). In this process, considerations regarding model complexity, overfitting, and generalization are essential.
5. Predicting Anomalies through Deep Learning
Using deep learning to predict scale anomalies can be even more powerful. Especially when utilizing RNN models for time series data, it can learn embedded patterns to predict future price volatility more accurately.
5.1 Architecture Selection
Suitable methods for analyzing volatility include architectures like LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit). These models are effective in processing time series data through mechanisms that remember and forget past information.
5.2 Model Evaluation and Tuning
To evaluate the performance of the model, metrics such as MSE (Mean Squared Error), RMSE (Root Mean Squared Error), or MAE (Mean Absolute Error) can be used. Additionally, hyperparameters of the model need to be adjusted for optimal performance.
Conclusion
Predicting volatility and scale anomalies through machine learning and deep learning is a significant part of algorithmic trading. With a theoretical foundation and practical applications, investors can develop more sophisticated trading strategies and gain a competitive edge in the market.
Looking ahead, we anticipate how the evolution of algorithmic trading and technology will affect our investment practices. Through continuous learning and data analysis, we hope to improve our individual investment strategies.