Medium-Term Downside Risk: Insights from Textual Analysis of News - - PowerPoint PPT Presentation

Medium-Term Downside Risk: Insights from Textual Analysis of News

Charles W. Calomiris and Harry Mamaysky Columbia Business School

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Introduction

• Automated processing of natural language is opening a previously

unavailable window into market behavior

• It may fundamentally transform finance practice
• Prior work has been very short-term focused
• But isn’t news (in aggregate) important for longer horizon
• utcomes?
• We look at
• Longer term country-level risk and return responses to news
• How to measure news at the country level?

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Our approach and a peak at findings…

• We develop a theory-neutral approach to map country news into market
• utcomes, which measures word flow and examines connections of word

flow to risk and return.

• We apply this (for the first time, we think) outside the U.S., to 52 countries.
• EMs vs. DMs treated separately, given differences in returns processes.

Key Findings:

• 1. Many measures relevant (sentiment, frequency, entropy), EMs/DMs differ.
• 2. Topical context matters.
• 3. Results change over time importantly.
• 4. News generally has opposite implications for return and risk.
• 5. Drawdown is useful as a measure of risk, especially for EMs.
• 6. We capture more than a popular a priori measure, in and out of sample.

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• 1. Theory-neutral vs. a priori word identifiers

What word flow?

• Theory-neutral vs. a priori approaches (Baker Bloom Davis 2016)
• Theory-neutral does not require advance knowledge of what is

important, and avoids data mining risks.

• But is it possible to construct a comprehensive, parsimonious, and

flexible theory-neutral model of word flow?

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• 2. What aspects of news are important?
• Sentiment
• Frequency
• Unusualness (entropy)
• Topical context interacted with above
• How are topics different from EM to DM?
• How does effect of news, and interpretation of news, differ by topic?

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• 3. Regime changes over time?
• Principal components indicate shift point around Global Crisis
• A priori shift point lines up with second principal component
• Out of sample properties of forecasting in light of this change

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• 4. How to identify topical context?
• Identifying topic-relevant words and their characteristics
• Louvain method

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• 5. Is all news relevant for both returns and risk?
• Will we find opposite signs when an effect is statistically

significant for return, if it is also statistically significant for sigma

• r drawdown?

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• 6. How to measure risk?
• Especially in EMs, returns are not normal and there is momentum

in returns.

• In addition to sigma, we use drawdown (which allows longer term

effects from momentum, skew, and kurtosis to be expressed).

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• 7. How to analyze countries, together or not?
• We separate EMs and DMs and analyze each as a panel.

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• 8. What news source?
• Thomson-Reuters provides a common platform, English

language, and large sample of relevant countries, for which there are other data on returns and on various relevant variables.

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Text measures defined

Data

• Thomson-Reuters digital news archive from 1996—2015
• 5mm EM and 12mm DM articles
• 52 countries (list next page)

Text measures:

• artcount – number of articles per country per month
• entropy – “unusualness” of an article j (Glasserman and Mamaysky 2016)

𝐼

𝑘 = − 𝑗 ∈ {4−grams}

𝑞𝑗 log 𝑛𝑗

• Effectively the average log probability of a word conditional on preceding words
• sentiment – the difference of positive and negative words divided by total words in article j:

𝑡

𝑘 = 𝑄𝑃𝑇 𝑘 − 𝑂𝐹𝐻 𝑘

𝑏𝑘

• Word sentiment comes from Loughran – McDonald dictionary

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Topics

Intuition: Find groups of words that co-occur together in articles Details:

• 1240 econ words
• Start w/ 237 words from index of Beim and Calomiris (2001) and find other

words, bigrams and trigrams from EM corpus based on cosine similarity

• E.g.: barriers, currency, parliament, macroeconomist, and World Bank
• We have 2 document term matrixes:
• 5mm x 1,240 for EM and 12mm x 1,240 for DM
• Compute cosine similarity matrix (1,240 x 1,240)
• Then do community detection (using Louvain method for modularity

maximization)

• Out topics are mutually exclusive (not necessary)

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We find 5 topics for each group of countries

• The Louvain algorithm returns ~40 word clusters with the

following numbers of words

• Place words from small clusters into big clusters

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Topics for EMs

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Topics for DMs

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Topic similarity across EM and DM

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Context specific sentiment

• Let 𝑔

𝜐,𝑘 be the fraction of econ

words in article j that are about topic τ

• Topic sentiment is given by:

𝑡𝜐,𝑘 = 𝑔

𝜐,𝑘 × 𝑡 𝑘

• Aggregate the article level

measures into daily measures (weighted by number of overall words in an article) For a given country, we have 12 daily text measures:

• entropy
• article count
• sMkt / fMkt
• sGovt / fGovt
• sCorp / fCorp
• sComms / fComms
• DM/EM specific:
• sMacro / fMacro (EM)
• sCredit / fCredit (DM)

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Principal Components EM

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EM Sentiment

• For 140 EM sentiment series

(28 countries x 5) we look at first 2 principal components

• PC2 – relative sentiment of

Markets to Government

• Some evidence of a regime

shift in PC2 a little before the financial crisis

Principal components EM

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DM Sentiment

• For 120 DM sentiment series

(24 countries x 5) we look at first 2 principal components

• PC2 – relative sentiment of

Markets to Government (again!)

• Some evidence of a regime

shift in PC2 a little before the financial crisis

Event Studies

• High-frequency top and bottom deciles of sentiment
• Middle as placebo
• Returns lead major sentiment indicators at high frequency
• Some post-event drift for positive and negative events

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Event studies – EM

• Cumulative abnormal

return around deciles of daily news events

• Middle column is

control for boring news

• Some topics show post

event drift: Mkt (both), Comms (negative)

• This is very different

from single name results, where there is little evidence of drift post negative news (only post positive)!

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Event studies – DM

• Some topics show post

event drift: Mkt (negative, both?), Corp (positive), Credit (both)

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Regression results

• We run panel regression with dependent variables given by
• return
• return12
• sigma
• drawdown
• We control for many variables that have been shown to have some

forecasting power for future returns (next page)

• The no-text measure regression is our Baseline model
• All text measures (except entropy) are normalized to unit variance
• We run full sample, 1st and 2nd half of the sample

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Control variables

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Summary of regression results

• News matters for EM and DM!
• Results differ across EM and DM (e.g., artcount matters in EMs)
• Baseline R2 lower for EM
• % increase in R2 from text measures larger for EM
• Sign of effects (i.e. good news or bad) almost always is consistent

across return, sigma, and drawdown

• Context matters: positive sentiment in Govt, Corp – bad news;

positive sentiment in Mkt – good news

• Incremental explanatory power largest for return12 and drawdown;

explanatory power lower for return and sigma

• Evidence of state dependence, especially for entropy
• Goes from a “bad” pre-crisis to a “good” post-crisis

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Out-of-sample testing

• Do we have too many explanatory variables?
• What about regime shifts?
• Check out-of-sample forecasting performance
• Run rolling 5-year regressions in t-60,…,t-1 for forecasting month t
• utcomes

Lasso (least absolute shrinkage and selection operator) min

𝛾

1 2𝑂

1,𝑢

𝑧𝑗,𝑢 − 𝑦𝑗,𝑢−1

′

𝛾

2 + 𝜇 𝛾 1

• Lasso does shrinkage and model selection
• Amount of shrinkage given by 𝛾 1/ 𝛾𝑃𝑀𝑇 1

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Rolling lasso for DM drawdown

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Rolling lasso for EM drawdown

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Out-of-sample performance

• Naïve model

forecasts using country fixed effects

• Base model

includes only the non-text variables

• CM includes all

text measures

• All models

estimated using lasso

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Comparison to Baker, Bloom and Davis

• The two types of measures are correlated.
• BBD has incremental value over Baseline for three market

measures only for DMs.

• In the in-sample panel regressions, our measures subsume BBD

for explaining return, sigma and drawdown.

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Out-of-sample comparisons to EPU

• EPU counts articles

from 10 major papers that contain triplets from uncertainty x economic x {policy terms}

• For 5 EM and 11 DM

countries where we have EPU data, compare out-of- sample performance

• f Base vs Base +

alternative text measures

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Conclusions

• Useful information in text for medium-term country-level
• utcomes (returns and cumulative downside risk)
• Different dimensions of text matter
• In particular, context matters for sentiment
• Effects differ across EM and DM, and over time
• Evidence of out-of-sample forecasting ability
• Next:
• Currency effects
• Connect to GDP nowcasting (Jungian subconscious?),

Fed Beige Book

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