Measuring uncertainty through word vector representations (original) (raw)

Linking words in economic discourse: Implications for macroeconomic forecasts

International Journal of Forecasting, 2020

This paper develops indicators of unstructured press information by exploiting word vector representations. A model is trained using a corpus covering 90 years of Wall Street Journal content. The information content of the indicators is assessed through business cycle forecast exercises. The vector representations can learn meaningful word associations that are exploited to construct indicators of uncertainty. In-sample and out-of-sample forecast exercises show that the indicators contain valuable information regarding future economic activity. The combination of indices associated with different subjective states (e.g., uncertainty, fear, pessimism) results in further gains in information content. The documented performance is unmatched by previous dictionary-based word counting techniques proposed in the literature.

Text as data: a machine learning-based approach to measuring uncertainty

arXiv: Econometrics, 2020

The Economic Policy Uncertainty index had gained considerable traction with both academics and policy practitioners. Here, we analyse news feed data to construct a simple, general measure of uncertainty in the United States using a highly cited machine learning methodology. Over the period January 1996 through May 2020, we show that the series unequivocally Granger-causes the EPU and there is no Granger-causality in the reverse direction

An Empirical Study of Macroeconomic Factors and Stock Returns in the Context of Economic Uncertainty News Sentiment Using Machine Learning

Complexity

Stock markets accurately reflect countries’ economic health, and stock returns are tightly related to economic indices. One popular area of financial research is the factors that influence stock returns. Several investigations have frequently cited macroeconomic factors, among numerous elements. Therefore, this study focuses on the empirical analysis of the relationship between macroeconomic factors and stock market returns. When a stock market becomes increasingly volatile, it becomes susceptible to economic uncertainty news, and information on social media platforms. Thus, we incorporated a new dimension of economic uncertainty news sentiment (EUNS) for stock return predictions. We employed the daily data ofgold index, crude oil price, interest rate, exchange rate, and stock returns for a set of countries from January 2010 to December 2020. Subsequently, to compute coefficients, we conducted a regression analysis using one of the more sophisticated approaches: single-layer neural ...

Predicting Economic Recessions using Natural Language Processing

Does uncertainty help predict recessions? To control for available information, I use a dynamic factor model to derive common factors from a large macroeconomic panel. In addition to studying existing uncertainty measures, I introduce a novel uncertainty measure by using natural language processing to analyse news. To evaluate forecast performance, I modify tests of equal forecast accuracy for nested models. I demonstrate that many uncertainty measures help predict recessions, especially at longer forecast horizons. Furthermore, I show that my novel measure delivers a considerable increase in predicted probabilities of the 2001 recession onset over the benchmark model. 7500 words

Predicting Stock Price Volatility by Analyzing Semantic Content in Media

2014

Current models for predicting volatility do not incorporate information flow and are solely based on historical volatilities. We suggest a method to quantify the semantic content of words in news articles about a company and use this as a predictor of its stock volatility. The results show that future stock volatility is better predicted by our method than the conventional models. We also analyze the functional role of text in media either as a passive documentation of past information flow or as an active source for new information influencing future volatility. Our data suggest that semantic content may take both roles.

Word Vector Models Approach to Text Regression of Financial Risk Prediction

Symmetry

Linking textual information in finance reports to the stock return volatility provides a perspective on exploring useful insights for risk management. We introduce different kinds of word vector representations in the modeling of textual information: bag-of-words, pre-trained word embeddings, and domain-specific word embeddings. We apply linear and non-linear methods to establish a text regression model for volatility prediction. A large number of collected annually-published financial reports in the period from 1996 to 2013 is used in the experiments. We demonstrate that the domain-specific word vector learned from data not only captures lexical semantics, but also has better performance than the pre-trained word embeddings and traditional bag-of-words model. Our approach significantly outperforms with smaller prediction error in the regression task and obtains a 4%–10% improvement in the ranking task compared to state-of-the-art methods. These improvements suggest that the textual...

The Power of Text-based Indicators in Forecasting the Italian Economic Activity

SSRN Electronic Journal, 2021

Can we use newspaper articles to forecast economic activity? Our answer is yes and, to this aim, we propose a brand new economic dictionary in Italian with valence shifters, and we apply it on a corpus of about two million articles from four popular newspapers. We produce a set of high-frequency text-based sentiment and policy uncertainty indicators (TESI and TEPU, respectively), which are timely, not revised and computed both for the whole economy and for specific sectors or economic topics. To test the predictive power of our text-based indicators, we propose two forecasting exercises. First, using Bayesian Model Averaging (BMA) techniques, we show that our monthly text-based indicators greatly shrink the uncertainty surrounding the short-term forecasts of the main macroeconomic aggregates, especially during recessions. Secondly, we employ these indexes in a weekly GDP growth tracker, delivering sizeable gains in forecasting accuracy in both normal and turbulent times.

UsingWords from Daily News Headlines to Predict the Movement of Stock Market Indices

Managing Global Transitions, 2017

Stock market analysis is one of the biggest areas of interest for text mining. Many researchers proposed different approaches that use text information for predicting the movement of stock market indices. Many of these approaches focus either on maximising the predictive accuracy of the model or on devising alternative methods for model evaluation. In this paper, we propose a more descriptive approach focusing on the models themselves, trying to identify the individual words in the text that most affect the movement of stock market indices. We use data from two sources (for the past eight years): the daily data for the Dow Jones Industrial Average index ('open' and 'close' values for each trading day) and the headlines of the most voted 25 news on the Reddit WorldNews Channel for the previous 'trading days. ' By applying machine learning algorithms on these data and analysing individual words that appear in the final predictive models, we find that the words gay, propaganda and massacre are typically associated with a daily increase of the stock index, while the word iran mostly coincide with its decrease. While this work presents a first step towards qualitative analysis of stock market models, there is still plenty of room for improvements.

The weight of words: textual data versus sentiment analysis in stock returns prediction

2020

The focus of this paper is to understand whether the words contained in a text corpus improves the explained variance of stock returns better than the use of the polarity of the same texts, obtained through a sentiment analysis using a generic ontological dictionary. The empirical analysis is based on the content of a weekly column in the most important Italian financial newspaper, which published past information and analysts’ recommendations on listed companies. The use of textual data clearly increases the explained variance of stock returns but, through comparisons between data mining techniques, we observed minor differences in terms of MSE, by adding a selection of specific terms as features. In this context, the text mining approach proved to be very useful to improve the explanatory power of forecasting models, while it emerged the limited explanatory power of an automatic sentiment analysis based on a generic lexicon. Abstract Il focus di questo contributo è capire se le pa...

Predicting Financial Market Trends using Time Series Analysis and Natural Language Processing

Predicting Financial Market Trends using Time Series Analysis and Natural Language Processing, 2023

Forecasting financial market trends through time series analysis and natural language processing poses a complex and demanding undertaking, owing to the numerous variables that can influence stock prices. These variables encompass a spectrum of economic and political occurrences, as well as prevailing public attitudes. Recent research has indicated that the expression of public sentiments on social media platforms such as Twitter may have a noteworthy impact on the determination of stock prices. The objective of this study was to assess the viability of Twitter sentiments as a tool for predicting stock prices of major corporations such as Tesla, Apple. Our study has revealed a robust association between the emotions conveyed in tweets and fluctuations in stock prices. Our findings indicate that positivity, negativity, and subjectivity are the primary determinants of fluctuations in stock prices. The data was analyzed utilizing the Long-Short Term Memory neural network (LSTM) model, which is currently recognized as the leading methodology for predicting stock prices by incorporating Twitter sentiments and historical stock prices data. Our analysis yielded findings indicating that Twitter sentiments possess significant potential as an informative resource for forecasting stock prices. The models utilized in our study demonstrated a high degree of reliability and yielded precise outcomes for the designated corporations. In summary, this research emphasizes the significance of incorporating public opinions into the prediction of stock prices. The application of Time Series Analysis and Natural Language Processing methodologies can yield significant scientific findings regarding financial market patterns, thereby facilitating informed decision-making among investors. The results of our study indicate that the utilization of Twitter sentiments can serve as a potent instrument for forecasting stock prices, and ought to be factored in when formulating investment strategies.