Embeddings for Economic Indicators and Text Data

Embeddings for Economic Indicators and Text Data#

From Words to Indicators: Joint Modeling of Text and Financial Data

Introduction#

The analysis and prediction of financial markets have been a topic of interest for economists and investors for decades. Various models, ranging from econometric models to machine learning algorithms, have been developed to predict market movements. However, these models often face several limitations such as overfitting, data sparsity, and high dimensionality. Furthermore, while these models have been useful in predicting market movements to some extent, their performance is often uncertain, and their ability to explain and diagnose the underlying phenomena is limited.

The primary value of a model for analyzing economic and market data is not in its ability to make precise predictions, but in its ability to provide insights and understanding into the underlying patterns and relationships in the data. By providing a more nuanced and detailed understanding of the market trends and economic indicators, a model can help analysts and policymakers make more informed decisions and adjust their strategies accordingly.

Furthermore, by focusing on explanation and diagnosis, a model can help analysts and policymakers identify potential sources of bias, error, or instability in the data, and take appropriate action to mitigate these risks. This can help to reduce the likelihood of market disruptions or economic crises and promote more stable and sustainable economic growth.

Therefore, in this research, we propose to focus on the role of models in explaining and diagnosing financial phenomena, rather than solely on their ability to predict market movements. Specifically, we explore the use of joint modeling approaches to combine text data and economic indicators for improved understanding and diagnosis of financial phenomena. We compare and evaluate three different approaches: the separate modeling approach, the joint modeling approach, and the joint embeddings with economic indicators as tokens approach.

The separate modeling approach involves training separate models for text data and economic indicators and combining their predictions. The joint modeling approach involves training a single model on both text and economic data. The joint embeddings with economic indicators as tokens approach involves embedding the text data and economic indicators in the same vector space, with economic indicators treated as tokens.

Our research is motivated by the need for more effective and interpretable models in financial analysis. By focusing on the interpretability of the models, we can gain a better understanding of the underlying patterns and relationships in the data and inform more effective decision-making in financial markets. We seek to identify the trade-offs between model performance and interpretability for each approach, and explore the potential benefits and drawbacks of incorporating economic indicators as tokens in the joint embedding approach.

To carry out this research, we will collect text data from news articles, official publications, and social media platforms, as well as economic data from sources such as the Federal Reserve Economic Data (FRED) database. We will preprocess the data using standard NLP techniques, such as tokenization, stemming, and stopword removal, and normalize or scale the economic data. We will then train and evaluate each of the three modeling approaches using a range of performance metrics, such as accuracy, precision, recall, and F1 score. We will also conduct a comprehensive interpretation analysis to identify which approach provides the best insights into the underlying patterns and relationships in the data.

Ultimately, our research aims to contribute to a deeper understanding of financial markets and the development of more effective and interpretable models for financial analysis. We anticipate that the results of this research will be of interest to a wide range of stakeholders, including investors, financial analysts, and policymakers.

Objectives:#

  • To evaluate the performance of the separate modeling approach, the joint modeling approach, and the joint embeddings with economic indicators as tokens approach in predicting financial movements.

  • To compare the interpretability of the three different modeling approaches and identify the trade-offs between model performance and interpretability.

  • To explore the potential benefits and drawbacks of incorporating economic indicators as tokens in the joint embedding approach, including the impact on the performance and interpretability of the model.

Literature Review#

The analysis and prediction of financial markets have been a longstanding interest for economists and investors. Despite the development of numerous models aimed at predicting market movements, the ability of these models to explain and diagnose the underlying phenomena remains limited. Recent research has begun to explore the use of joint modeling approaches to combine text data and economic indicators for improved understanding and diagnosis of financial phenomena.

One popular approach is the use of text data, such as news articles and social media posts, as a source of information to predict financial movements. In addition to predicting market movements, text data can provide valuable insights into the underlying drivers of market trends. << need to cite some papers here >>.

Another approach is the use of economic indicators, such as stock prices and interest rates, to forecast market trends. While these indicators can provide valuable information about market movements, their interpretation can be challenging due to the complex interactions and dependencies between different economic factors. Recent research has explored the use of joint modeling approaches to combine economic indicators with text data for improved interpretation of market trends. << need to cite some papers here >>.

More recently, researchers have explored the use of joint embeddings to combine text data and economic indicators. This approach involves training joint embeddings for both text data and economic indicators, treating the movements of economic indicators as similar tokens to words. << need to cite some papers here >>.

Data Collection and Preprocessing:#

To evaluate the effectiveness of different modeling approaches for combining text data and economic indicators, we collected a dataset of financial news articles and economic indicators. We collected news articles from various sources, including major news outlets and financial news websites, using web scraping techniques. We also collected economic indicators from the Federal Reserve Economic Data (FRED) database, including stock prices, interest rates, and other relevant economic indicators.

To preprocess the text data, we performed standard natural language processing techniques, including tokenization, stopword removal, and stemming. We also performed sentiment analysis on the news articles to capture the overall sentiment of each article, using off-the-shelf sentiment analysis libraries.

To preprocess the economic indicators, we performed standard normalization techniques, including z-score normalization and min-max scaling. We also converted the economic indicators into categorical variables, indicating whether the indicator increased or decreased over a given period.

To prepare the data for modeling, we split the dataset into training, validation, and testing sets, ensuring that each set contained a representative sample of both text data and economic indicators. We also balanced the dataset to ensure that each category of economic indicators was represented equally in the dataset.

Overall, our data collection and preprocessing steps were designed to ensure that our dataset was representative and balanced, and that the text data and economic indicators were properly processed and prepared for modeling.

Methodology#

We compare and evaluate three different approaches for combining text data and economic indicators: the separate modeling approach, the joint modeling approach, and the joint embeddings with economic indicators as tokens approach.

The separate modeling approach involves modeling text data and economic indicators separately and combining their predictions using a simple averaging or weighting method. For the text data, we use a pre-trained word embedding model, such as GloVe, to represent the words in the news articles as vectors. We then feed these vectors into a deep learning model, such as a convolutional neural network (CNN) or recurrent neural network (RNN), to model the sentiment and content of the news articles. For the economic indicators, we use a simple classification model, such as logistic regression or decision trees, to predict whether the indicator increased or decreased over a given period.

The joint modeling approach involves modeling text data and economic indicators jointly using a single deep learning model. We use a model similar to the one proposed by Zhang et al. (2018), which combines a CNN and RNN to jointly model the news articles and economic indicators. We represent the words in the news articles as vectors using a pre-trained word embedding model, and the economic indicators as categorical variables. We then feed both types of data into the deep learning model to jointly model their interactions and dependencies.

The joint embeddings with economic indicators as tokens approach involves treating the movements of economic indicators as similar tokens to words and training joint embeddings for both text data and economic indicators. We use a model similar to the one proposed by Li et al. (2020), which trains joint embeddings for text data and economic indicators using a single neural network. We represent the words in the news articles and the movements of the economic indicators as vectors and feed them into the neural network to jointly train their embeddings.

To evaluate the performance of each approach, we use standard evaluation metrics, including accuracy, precision, recall, and F1 score. We also evaluate the interpretability of each approach, using standard techniques, such as feature importance analysis and partial dependence plots, to gain insights into the underlying patterns and relationships in the data.

The Separate Modeling Approach#

To embed the movements of several economic indicators along with texts that explain those movements, we use a combination of two approaches: (1) encoding the texts as word embeddings using techniques such as word2vec or GloVe, and (2) representing the directional movements as one-hot encodings.

  • Encoding the texts: We encode the texts that explain the economic indicator movements as word embeddings using techniques such as word2vec or GloVe. These techniques learn distributed representations of words that capture their semantic meaning, and will be trained on a large corpus of text data.

  • Representing the movements: We represent the directional movements of the economic indicators as one-hot encodings, where each movement is represented as a binary vector with a 1 in the position corresponding to the direction (UP or DOWN) and 0s elsewhere.

  • Combining the embeddings: We then combine the word embeddings of the texts and the one-hot encodings of the movements to obtain a joint embedding of the economic indicator movements and their textual explanations. One way to do this is to concatenate the two embeddings together to form a single vector, which then will be fed into a neural network for further processing.

  • Training the model: Finally, we train a neural network on a dataset of economic indicator movements and their textual explanations, using the joint embeddings as inputs. The network will be trained to predict the direction of the next movement given the current movement and its textual explanation, or to perform other tasks such as anomaly detection or classification.

The Joint Modeling Approach#

We define a joint model that takes in two inputs: the texts and the economic indicators. The text input is passed through an embedding layer and an LSTM layer to capture the patterns and features in the text, while the economic input is passed through a dense layer to extract relevant features. The outputs of these layers are concatenated and passed through a final dense layer with a sigmoid activation to produce a binary classification output.

The primary difference between the joint modeling approach and the previous separate approach is that the joint model combines the embeddings of the texts and economic indicators into a single model, while the separate model treats them as independent features.

In the separate model, we first train a separate model to embed the texts and another separate model to embed the economic indicators, and then combine the embeddings as inputs to a final model. While this approach allows for greater flexibility in the choice of models and architectures for each separate embedding task, it does not capture the potential interactions or dependencies between the two types of data.

In contrast, the joint modeling approach combines the embeddings of the texts and economic indicators into a single model, allowing for the potential interactions and dependencies between the two types of data to be captured in a more unified and integrated way. This can lead to better overall performance and more accurate predictions, especially when the relationship between the texts and economic indicators is complex and multifaceted.

In terms of interpretability, the separate modeling approach may be easier to interpret since the embeddings of the texts and economic indicators are treated as independent features. This makes it easier to isolate and analyze the contributions of each feature to the overall model performance and to understand the relationships between the features and the predicted outcomes.

In contrast, the joint modeling approach may be more difficult to interpret since the embeddings of the texts and economic indicators are combined into a single model. This makes it more challenging to isolate the contributions of each feature to the overall model performance and to understand the relationships between the features and the predicted outcomes.

The choice between the joint modeling approach and the separate approach will depend on the specific requirements and constraints of the analysis and the nature of the data being analyzed. If the relationship between the texts and economic indicators is complex and multifaceted, and there is potential for interactions and dependencies between the two types of data, then a joint modeling approach may be more appropriate. If the relationship is more straightforward or there is less potential for interactions and dependencies, then a separate approach may be sufficient.

The Joint Embeddings with Economic Indicators as Tokens Approach#

Treating the movements of economic indicators as similar tokens to words and training embeddings altogether is an approach that can lead to a more unified and integrated model. This approach is similar to using n-grams to capture the relationships between words, but extends the idea to include the movements of economic indicators as additional tokens.

The advantage of this approach is that it can capture the complex and dynamic relationships between the movements of economic indicators and the corresponding texts that explain those movements. By embedding the movements of economic indicators and the accompanying texts in a unified vector space, the model can capture the interactions and dependencies between the two types of data and provide a more comprehensive understanding of the underlying patterns and relationships.

However, one potential challenge with this approach is that the movements of economic indicators may not have the same semantic properties as words, which could lead to noisy embeddings and reduced model performance. Additionally, the embeddings may not capture the temporal relationships between the movements of economic indicators and the corresponding texts, which could further reduce the effectiveness of the model.

Treating the movements of economic indicators as similar tokens to words and training embeddings altogether is an interesting approach that has the potential to provide a more unified and integrated model. It is worthwhile to explore this approach alongside other methods to determine the best approach for the specific problem at hand.

Expected Outcomes and Applications#

Our research aims to provide a comprehensive evaluation of different approaches for combining text data and economic indicators for financial analysis. We expect to find that each approach has its own strengths and weaknesses, and that the choice of approach should depend on the specific needs and goals of the financial analysis.

Our study may have several implications for the field of financial analysis. First, our findings may help guide the development of more effective and interpretable models for financial analysis, by providing insights into the trade-offs between model performance and interpretability for different modeling approaches. Second, our study may help inform the use of text data and economic indicators in financial analysis, by providing insights into the underlying patterns and relationships in the data.

Our research has several insights for policymakers in the field of finance and economics. Our models can provide valuable insights into the drivers of market trends, which can inform policymaking decisions. Some of the key insights that policymakers can gain from our research are:

  • Identifying the Key Economic Indicators: Our models can identify the key economic indicators that are driving market trends, providing valuable insights for policymakers. Policymakers can use these insights to prioritize policy interventions that address the underlying drivers of market trends.

  • Analyzing Market Sentiment and Content: Our models can analyze the sentiment and content of news articles, providing policymakers with a more comprehensive understanding of the underlying drivers of market trends. Policymakers can use these insights to develop policies that address the underlying economic and social factors driving market trends.

  • Forecasting Market Movements: Our models can predict market movements based on news articles and economic indicators, providing policymakers with a valuable tool for forecasting future market trends. Policymakers can use these insights to develop policies that are better aligned with market trends and to better manage the risks associated with market volatility.

Our research has several potential use cases for central banks. Some of the key use cases for central banks are:

  • Forecasting Inflation: Our models can predict market movements based on news articles and economic indicators, providing central banks with a valuable tool for forecasting inflation trends. This can inform monetary policy decisions, helping central banks to maintain price stability and promote sustainable economic growth.

  • Analyzing Market Sentiment: Our models can analyze the sentiment and content of news articles, providing central banks with a more comprehensive understanding of the underlying drivers of market trends. This can inform monetary policy decisions, helping central banks to better manage the risks associated with market volatility.

  • Evaluating Financial Stability: Our models can identify the key economic indicators that are driving market trends, providing central banks with insights into the underlying factors that may pose risks to financial stability. This can inform regulatory and supervisory decisions, helping central banks to mitigate risks and promote a stable financial system.

  • Communication strategy: Out models can help central banks craft their communication strategy and effectively convey their message to the public. By analyzing the sentiment of news articles or social media posts related to specific economic indicators, our models can help central banks understand how their policies are perceived by the public and adjust their communication strategy accordingly.

Our models that embed the movements of several economic indicators along with texts that explain those movements, and places them in the same vector space of words, has several potential use cases, including:

  • Sentiment analysis: The model can be used to analyze the sentiment of news articles or social media posts related to specific economic indicators, by embedding the texts and predicting the movement direction of the corresponding indicator. This can help investors or analysts to make more informed decisions based on the sentiment of the market.

  • Economic forecasting: The model can be used to predict the future movements of economic indicators based on the textual explanations that accompany them, by training the model on historical data and using it to make predictions. This can help investors or analysts to make more accurate predictions of the market and adjust their investments accordingly.

  • Market analysis: The model can be used to analyze the relationship between different economic indicators and their impact on the market, by clustering the indicators based on their embeddings and analyzing the properties of each cluster. This can help investors or analysts to gain insights into the market trends and make more informed investment decisions.

  • Risk management: The model can be used to identify the risks associated with specific economic indicators or market trends, by analyzing the textual explanations that accompany the movements and identifying potential sources of risk. This can help investors or analysts to manage their risk exposure and avoid potential losses.

Conclusion#

In this research, we compare and evaluate three different approaches for combining text data and economic indicators for financial analysis: the separate modeling approach, the joint modeling approach, and the joint embeddings with economic indicators as tokens approach. Our reseach tries to reveal that each approach has its own strengths and weaknesses, and that the choice of approach should depend on the specific needs and goals of the financial analysis.

Our research has several potential applications in the field of financial analysis, including predicting market movements, analyzing market sentiment and content, and identifying key economic indicators that are driving market trends. Our research also has potential use cases for policymakers, including identifying the key economic indicators, analyzing market sentiment and content, and forecasting inflation. For central banks, our research can inform monetary policy decisions, evaluating financial stability and analyzing market sentiment.

Our research highlights the potential of combining text data and economic indicators for improved understanding and diagnosis of financial phenomena. By providing insights into the strengths and weaknesses of different approaches, our research can inform the development of more effective and interpretable models for financial analysis, providing valuable insights for policymakers and investors alike.

References#

Appendix#

Implementation#

The Separate Modeling Approach#

import numpy as np
import gensim
import tensorflow as tf

# Define the dataset of economic indicator movements and their textual explanations
movements = np.array([[1, 0], [0, 1], [1, 0]])
texts = [['The', 'stock', 'market', 'rose', 'on', 'positive', 'earnings', 'news'], ['The', 'unemployment', 'rate', 'fell', 'to', 'a', 'new', 'low'], ['The', 'housing', 'market', 'showed', 'signs', 'of', 'weakness']]

# Define the vocabulary of the texts
vocab = set(word for text in texts for word in text)

# Define the embedding dimensions
word_embedding_dim = 50
indicator_embedding_dim = 2

# Initialize the embeddings randomly
word_embeddings = np.random.rand(len(vocab), word_embedding_dim)
indicator_embeddings = np.random.rand(2, indicator_embedding_dim)

# Define the model architecture
input_layer = tf.keras.layers.Input(shape=(len(texts[0]),))
embedding_layer = tf.keras.layers.Embedding(input_dim=len(vocab), output_dim=word_embedding_dim)(input_layer)
flatten_layer = tf.keras.layers.Flatten()(embedding_layer)
shared_layer = tf.keras.layers.Dense(units=10, activation='relu')(flatten_layer)
movement_output_layer = tf.keras.layers.Dense(units=2, activation='softmax')(shared_layer)
word_output_layer = tf.keras.layers.Dense(units=len(vocab), activation='softmax')(shared_layer)

# Define the inputs and outputs of the model
input_data = texts
output_data = [movements, np.array([np.argmax(np.dot(indicator_embeddings, movement)) for movement in movements])]

# Define and compile the model
model = tf.keras.models.Model(inputs=input_layer, outputs=[movement_output_layer, word_output_layer])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model
model.fit(input_data, output_data, epochs=100, verbose=0)

An example of how to find opposite words using vector arithmetic:

import numpy as np
import gensim

# Load the pre-trained word embeddings
model = gensim.models.KeyedVectors.load_word2vec_format('path/to/word2vec.bin', binary=True)

# Define the 'UP' and 'DOWN' vectors
up_vector = np.array([1, 0])
down_vector = np.array([0, 1])

# Compute the difference vector
diff_vector = down_vector - up_vector

# Find the most similar words to the difference vector
opposite_words = model.similar_by_vector(diff_vector, topn=10)

# Print the opposite words
for word, similarity in opposite_words:
    print(word)

The Joint Modeling Approach#

from keras.models import Model
from keras.layers import Input, Embedding, LSTM, Dense, concatenate

# define the maximum length of the texts and the number of economic indicators
MAX_TEXT_LEN = 100
NUM_ECON_INDICATORS = 10

# define the vocabulary size and embedding dimension for the texts
VOCAB_SIZE = 10000
EMBED_DIM = 100

# define the number of units for the LSTM layer
LSTM_UNITS = 128

# define the input layers for the texts and economic indicators
text_input = Input(shape=(MAX_TEXT_LEN,))
econ_input = Input(shape=(NUM_ECON_INDICATORS,))

# define the embedding layer for the texts
text_embedding = Embedding(input_dim=VOCAB_SIZE, output_dim=EMBED_DIM)(text_input)

# define the LSTM layer for the texts
text_lstm = LSTM(units=LSTM_UNITS)(text_embedding)

# define the dense layer for the economic indicators
econ_dense = Dense(units=LSTM_UNITS, activation='relu')(econ_input)

# concatenate the outputs of the LSTM and dense layers
concatenated = concatenate([text_lstm, econ_dense])

# define the output layer for the joint model
output = Dense(units=1, activation='sigmoid')(concatenated)

# define the joint model with the text and economic indicator inputs and the output layer
joint_model = Model(inputs=[text_input, econ_input], outputs=output)

# compile the joint model with the binary crossentropy loss and the Adam optimizer
joint_model.compile(loss='binary_crossentropy', optimizer='adam')

The Joint Embeddings with Economic Indicators as Tokens Approach#

import numpy as np
from gensim.models import Word2Vec

# define the texts and movements of economic indicators
texts = ['The stock market went up today.', 'The economy is doing well.']
indicator_movements = ['UP', 'UP', 'DOWN', 'UP', 'DOWN', 'UP']

# define the joint corpus with both texts and movements of economic indicators
joint_corpus = [text.split() + indicator_movements for text in texts]

# define the size of the embedding vector and the window size
EMBED_DIM = 100
WINDOW_SIZE = 5

# train the joint embedding model with the texts and movements of economic indicators
model = Word2Vec(joint_corpus, size=EMBED_DIM, window=WINDOW_SIZE, min_count=1)

# extract the word and indicator embeddings separately
word_embeddings = np.array([model.wv[word] for word in model.wv.vocab if word not in indicator_movements])
indicator_embeddings = np.array([model.wv[indicator] for indicator in model.wv.vocab if indicator in indicator_movements])
Contents