Integrating Sentiment Analysis in Stock Price Forecasting with Deep Learning Techniques
In the dynamic domain of financial markets, predicting stock prices remains a challenging yet crucial task for investors and analysts alike.
Traditional stock forecasting models primarily rely on historical financial data, overlooking the impact of public sentiment reflected in news, social media, and other textual content. This paper explores a novel approach to enhance stock price forecasting accuracy by integrating sentiment analysis into deep learning models, specifically focusing on Long Short-Term Memory (LSTM) networks.
The core of our methodology lies in harnessing the potential of sentiment data derived from various sources, analyzed using a pre-trained Natural Language Processing (NLP) model, FinBERT, which is adept at deciphering sentiment in financial texts. By incorporating these sentiment scores as additional features in LSTM models, we aim to capture the underlying mood and opinions that might influence stock prices, thus providing a more holistic view of the market dynamics.
Our study delves into the technical aspects of implementing this integrated approach, from preprocessing and normalizing the data to constructing and training LSTM models with and without sentiment analysis. We meticulously compare the performance of these models, scrutinizing their predictive capabilities through various metrics and visualizations. The goal is to assess whether the inclusion of sentiment analysis data contributes to or detracts from the effectiveness of traditional LSTM network models in forecasting stock prices.
Through this exploration, we seek to bridge the gap between quantitative stock data and qualitative sentiment indicators, offering a more comprehensive tool for market analysis and forecasting.
#Import modules needed for math and managing dataframes
import numpy as np
import pandas as pd
from math import pi,sqrt,exp,pow,log
from numpy import newaxis
from scipy.stats import zscore
#Import modules for plotting
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
#Import modules to build and train neural network
import tensorflow as tf
import keras
from keras.layers import Dense, Activation, Dropout, LSTM
from keras.models import Sequential, load_model
from keras.callbacks import EarlyStopping, ModelCheckpoint
from sklearn.linear_model import LinearRegression
from sklearn import preprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
#from keras import metrics
#Import necessary modules for retrieving text data
from pymongo import MongoClient
# A Python library that offers a sensibleapproach to creating,
# manipulating, formatting and converting dates, times and timestamps.
# Used to ensure correct time and gather text data related to trading day.
import arrow
from arrow import Arrow
from datetime import datetime, time
from dateutil import tz, parser
# To utilize pre-trained FinBERT model to retrieve sentiment scores on text data
from finbert.finbert import predict
from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer
from finbert.finbert import *
import finbert.utils as tools
#To monitor the completion of loops
from tqdm import tqdm
The Python code snippet imports libraries and modules necessary for creating a stock forecasting system using deep learning. It is focused on preparing the environment with the tools required for data manipulation, mathematical operations, visualization, deep learning model creation and training, and text data retrieval for sentiment analysis.
— The code begins by importing numerical and data handling libraries such as NumPy and pandas, along with mathematical functions from the Python math module. These are needed for handling arrays and data frames, and performing various mathematical operations which are essential in data preparation and analysis for forecasting.
— Next, it imports plotting libraries like Matplotlib to visualize data and results. This helps in understanding the trends and performance of the stock forecasting system.
— The snippet then imports TensorFlow and Keras libraries, which provide functionality to design, train, and evaluate neural network models. Specific neural network layers such as Dense, LSTM, and functionalties like Dropout, Activation, Sequential model, and callbacks like EarlyStopping and ModelCheckpoint are included for building the deep learning models that will forecast stock prices.
— It incorporates Scikit-Learn for machine learning tasks, such as data preprocessing with MinMaxScaler and splitting datasets into training and testing sets, along with Linear Regression for possibly establishing baselines or comparisons to the neural network approach.
— The code includes libraries for handling time and date such as Arrow, datetime, and dateutil, which are crucial for processing time-series data, in this case stock prices, which are time-dependent.
— Finally, it mentions the use of FinBERT, a pre-trained language model specific to the finance domain, hinting at the use of sentiment analysis from text data like news articles or financial reports that can influence stock prices. The code thereby may retrieve text data, process it, and utilize sentiment scores as features or inputs for the stock price prediction model.
In summary, this snippet is getting the environment ready to handle numeric and textual data, model a neural network to predict stock prices, and possibly incorporate sentiment analysis for a comprehensive forecasting system. It does not show actual data processing, model training, or prediction operations, but provides the foundational tools for such tasks.