To successfully trade in the stock market, data is essential.
Automated trading using machine learning and deep learning algorithms learns patterns from this data
to enhance predictive power, providing the potential to maximize profits.
This article will cover the basic concepts of algorithmic trading using machine learning and deep learning,
and how to scrape and parse earnings call transcripts.
In particular, earnings call transcripts are important materials for understanding a company’s financial status and outlook,
and the technology for analyzing this data will greatly assist in future trading.
Overview of Machine Learning and Deep Learning
Machine learning is a branch of artificial intelligence that learns from data to make predictions or decisions.
Machine learning algorithms analyze given data and identify patterns through statistical models.
In contrast, deep learning is an advanced machine learning technique that shows superior performance on complex data based on artificial neural networks.
It has particularly excelled in the fields of speech recognition, image recognition, and natural language processing.
The Necessity of Algorithmic Trading
Algorithmic trading is a method of executing trades based on predefined trading strategies,
which especially eliminates emotional judgment and enables data-driven decision-making.
By applying machine learning, it’s possible to predict market changes based on historical data and
generate efficient trading signals.
This allows for much faster processing of larger data sets and consistent results compared to traditional trading methods.
What is an Earnings Call Transcript?
An earnings call transcript is a record of the conversation held between a company and its investors following quarterly earnings announcements.
It includes the company’s financial performance, future outlook, and management opinions,
and this information can greatly influence the value of the stock.
Through this, investors can assess the company’s health and market position.
Scraping Earnings Call Transcripts
The process of collecting earnings call transcripts is done through web scraping.
Below is a simple scraping example using Python’s BeautifulSoup and requests libraries.
1. Install Required Libraries
!pip install requests beautifulsoup42. Basic Scraping Code
The following code demonstrates how to scrape the earnings call transcript of a specific company.
This example uses Yahoo Finance.
import requests
from bs4 import BeautifulSoup
def scrape_earning_call_transcript(ticker):
url = f'https://finance.yahoo.com/quote/{ticker}/news?p={ticker}'
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
transcripts = []
for item in soup.find_all('li', class_='js-stream-content'):
title = item.find('h3').text
link = item.find('a')['href']
transcripts.append({'title': title, 'link': link})
return transcripts
# Example
transcripts = scrape_earning_call_transcript('AAPL')
print(transcripts)
Parsing Earnings Call Data
The scraped data exists in its raw form,
so a parsing process is necessary to analyze and extract meaningful information.
It extracts important keywords from the earnings call transcript and
transforms them into structured data that can be used as input for machine learning models.
3. Data Preprocessing
The scraped data is in text format, so it needs to be preprocessed.
The typical preprocessing steps are as follows.
- Convert to lowercase
- Remove special characters
- Remove stop words
- Stem or lemmatize
import re
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
def preprocess_text(text):
# Convert to lowercase
text = text.lower()
# Remove special characters
text = re.sub(r'\W', ' ', text)
# Remove stop words
stop_words = set(stopwords.words('english'))
text = ' '.join(word for word in text.split() if word not in stop_words)
return text
# Example
preprocessed = preprocess_text(transcripts[0]['title'])
print(preprocessed)
Building a Machine Learning Model
The data extracted from earnings call transcripts can be used as input for a machine learning model to predict stock price fluctuations.
Commonly used algorithms include:
- Linear Regression
- Random Forest
- Support Vector Machine
- Neural Networks
4. Model Training
Below is a simple example of training a machine learning model.
We will build a random forest model using the Scikit-learn library.
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
# Prepare Data
X = [...] # Features
y = [...] # Target variable (stock price change)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Train Random Forest Model
model = RandomForestRegressor()
model.fit(X_train, y_train)
# Evaluate Model
predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f'Mean Squared Error: {mse}')
Building a Deep Learning Model
Deep learning models have better pattern recognition capabilities over large datasets.
Let’s explore how to build deep learning models using Keras and TensorFlow.
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
import numpy as np
# Prepare Data (dimension transformation needed for LSTM)
X = np.array([...]).reshape((num_samples, num_timesteps, num_features))
y = np.array([...])
# Model Configuration
model = Sequential()
model.add(LSTM(50, activation='relu', input_shape=(num_timesteps, num_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
# Train Model
model.fit(X, y, epochs=200, verbose=0)
Conclusion
Algorithmic trading utilizing machine learning and deep learning provides insights based on unstructured data such as earnings call transcripts.
The series of processes involved in web scraping, text preprocessing, and machine learning modeling will be a significant aid in predicting market changes.
Properly utilizing these technologies in a constantly changing market environment will greatly increase the probability of achieving high returns.
The content covered in this course is only intended to help with basic understanding,
and further in-depth research and practice on each process are necessary.
The methodologies can vary widely depending on the quality and characteristics of the data,
and experiments with different models are required.
Therefore, do not forget that continuous learning and practice are essential.