The Power of Text-based Indicators in Forecasting the Italian Economic Activity (original) (raw)

Forecasting with Economic News

ArXiv, 2022

The goal of this paper is to evaluate the informational content of sentiment extracted from news articles about the state of the economy. We propose a fine-grained aspect-based sentiment analysis that has two main characteristics: 1) we consider only the text in the article that is semantically dependent on a term of interest (aspect-based) and, 2) assign a sentiment score to each word based on a dictionary that we develop for applications in economics and finance (fine-grained). Our data set includes six large US newspapers, for a total of over 6.6 million articles and 4.2 billion words. Our findings suggest that several measures of economic sentiment track closely business cycle fluctuations and that they are relevant predictors for four major macroeconomic variables. We find that there are significant improvements in forecasting when sentiment is considered along with macroeconomic factors. In addition, we also find that sentiment matters to explains the tails of the probability distribution across several macroeconomic variables.

Deciphering Professional Forecasters’ Stories - Analyzing a Corpus of Textual Predictions for the German Economy

2018

We analyze a corpus of 564 business cycle forecast reports for the German economy. The dataset covers nine institutions and 27 years. From the entire reports we select the parts that refer exclusively to the forecast of the German economy. Sentiment and frequency analysis confirm that the mode of the textual expressions varies with the business cycle in line with the hypothesis of adaptive expectations. A calculated 'uncertainty index' based on the occurrence of modal words matches with the economic policy uncertainty index by Baker et al. (2016). The latent Dirichlet allocation (LDA) model and the structural topic model (STM) indicate that topics are significantly state- and time-dependent and different across institutions. Positive or negative forecast 'surprises' experienced in the previous year have an impact on the content of topics.

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

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.

Sentiment Analysis of Italian and English Corpora of Internet News: A Comparison with Some Economic Trends

International Journal of Linguistics, Literature and Translation

In this article, the sentiment analysis of several large Internet corpora made of Italian and English news is performed using a software written by the author, showing a possible connection with some economic trends. In this research, the news includes different topics (not necessarily financial news), and they are extrapolated from a large number of Internet newspapers. The software, already used in a previous article by the same author, is lexicon-based and makes use of scale points ranging from 0 to 100 to calculate an index of positivity in a text. The variation of sentiment tendency in the news corpora, calculated for a time period of several years, is later compared with some graphs showing some parameters of some economic trends, including the gross domestic product (GDP). It is found that the sentiment tendency of the news seems to have a relationship with the tendency of some economic trends that span the same time period. Positive growth of the economy per year seems conne...

Forecasting with news sentiment: Evidence with UK newspapers

International Journal of Forecasting, 2020

We investigate the performance of newspapers for forecasting inflation, output and unemployment in the United Kingdom. We concentrate on whether the economic policy content reported in popular printed media can improve on existing point forecasts. We find no evidence supporting improved nowcasts or short-term forecasts for inflation. The sentiment inferred from printed media, can however be useful for forecasting unemployment and output. Considerable improvements are also noted when using individual newspapers and keyword based indices.

Improving Economic Prediction: A New Method for Measuring Economic Confidence and Its Impact on the Evolution of the Us Economy 1

A number of recent contributions have tried to add to the understanding and forecasting of the macro economy by analysing news and narratives. In this contribution we report on a new approach to the content analysis of very large text databases. It draws on a new social-psychological theory of decision-making under uncertainty to focus content analysis on the presence or absence of specific groups of emotional words in texts. The words identified are ordinary English emotional words. The method identifies, very rapidly, a Relative Sentiment Shift series which measures changes through time in the relations between two core emotional groups, excitement (about gain) and anxiety (about loss). Results are reported using text from the Thomson Reuters News Archive for articles published in the US. We find that shifts in the new emotion series Granger cause changes in US GDP and Gross Domestic Fixed Capital Formation. The RSS series also adds significant explanatory power to the Survey of P...

Predicting Economic Indicators from Web Text Using Sentiment Composition

International Journal of Computer and Communication Engineering, 2014

Of late there has been a significant amount of work on using sources of text data from the Web (such as Twitter or Google Trends) to predict financial and economic variables of interest. Much of this work has relied on some form or other of superficial sentiment analysis to represent the text. In this work we present a novel approach to predicting economic variables using sentiment composition over text streams of Web data. We treat each text stream as a separate sentiment source with its own predictive distribution. We then use a Bayesian classifier combination model to combine the separate predictions into a single optimal prediction for the Nonfarm Payroll index, a primary economic indicator. Our results show that we can achieve high predictive accuracy using sentiment over big text streams.

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