Text-Based Insights into Stock Market Behavior - Steven J. Davis University of California, Berkeley

 
Text-Based Insights into Stock Market Behavior - Steven J. Davis University of California, Berkeley
Text-Based Insights into
 Stock Market Behavior
          Steven J. Davis
    University of California, Berkeley
              24 April 2018
Text-Based Insights into Stock Market Behavior - Steven J. Davis University of California, Berkeley
Text-as-data and text-based methods have
emerged as major tools of analysis in
accounting, finance and economics.
Today’s talk draws on two papers underway to
develop new insights into stock market behavior
• “Diagnosing the Stock Market’s Reaction to Trump’s
  Surprise Election Victory,” with Cristhian Seminario
   – Automated readings of 61,000 10-K filings
• “What Triggers Stock Market Jumps?” with Scott
  Baker, Nick Bloom and Marco Sammon
   – Human readings of newspaper articles about a few
     thousand jumps in national equity markets across
     14 countries, plus a few hundred jumps in U.K. and
     U.S. bond markets and US$ currency markets.       2
Text-Based Insights into Stock Market Behavior - Steven J. Davis University of California, Berkeley
Diagnosing the Stock Market
Reaction to Trump’s Surprise
      Election Victory
  Steven J. Davis (Chicago Booth & Hoover)
     Cristhian Seminario (U of Chicago)

                   2018
Text-Based Insights into Stock Market Behavior - Steven J. Davis University of California, Berkeley
Noon on Election Day!

Washington (CNN) Hillary Clinton's odds of
winning the presidency rose from 78% last week
to 91% Monday before Election Day, according
to CNN's Political Prediction Market.

       But Trump won!!
Text-Based Insights into Stock Market Behavior - Steven J. Davis University of California, Berkeley
Initially, stocks fell sharply in after-hours trading

From “Markets Sent a Strong Signal on Trump … Then Changed Their Minds,” Justin
Wolfers, New York Times, 18 November 2016
Text-Based Insights into Stock Market Behavior - Steven J. Davis University of California, Berkeley
But Stocks Boomed on 9 November
Histogram of Daily Market Returns, U.S. Stocks
    Sample Period: 8 November 2016 +/- 360 Days

                                               Trump Election
                              Trump Election
  Using
  Closing
  Prices
Text-Based Insights into Stock Market Behavior - Steven J. Davis University of California, Berkeley
… Confounding Prominent Economists

Justin Wolfers, New York Times, 18 November
2016: “Throughout the campaign, stocks rose
whenever campaign developments made it
less likely that Mr. Trump would be elected.”

This assessment rests on Wolfers’ pre-election
empirical study with Erik Zitzewitz. Their
bottom line: “[W]e estimate that market
participants believe that a Trump victory would
reduce the value of the S&P 500, the UK, and
Asian stock markets by 10-15%.”
Text-Based Insights into Stock Market Behavior - Steven J. Davis University of California, Berkeley
Our Approach: Examine the Cross-
Section of Abnormal Equity Returns
in Reaction to Trump’s Victory
1. Trump and Clinton were far apart on many policy
   issues: regulation, healthcare, trade, etc. Not a
   Tweedledee vs. Tweedledum election!!
2. Firms differ in their exposures to policy risks.
3. Quantify these risks using Part 1a (“Risk
   Factors”) of listed firms’ annual 10-K filings.
4. Trump’s surprise victory abruptly shifted the level
   and structure of important policy risks.
5. We look to the cross-section of firm-level returns
   to assess the effects of that shift and gain insight
   into the market’s reaction to Trump’s win.
Text-Based Insights into Stock Market Behavior - Steven J. Davis University of California, Berkeley
The Cross-Firm Dispersion of Abnormal Returns
 Was Very High in the Wake of Trump’s Victory
Histogram of Cross-Firm St. Dev. of Daily Abnormal Returns
      Sample Period: 8 November 2016 +/- 360 Days
                Abnormal (CAPM)

                                       Trump Election

                                                        Trump Election
                returns based on
                data for 360 days
                before the election.
Text-Based Insights into Stock Market Behavior - Steven J. Davis University of California, Berkeley
Histogram of Interquartile Range of
Abnormal Daily Returns Across Firms
Sample Period: 8 November 2016 +/- 360 Days

                                 Trump Election

                                                  Trump Election
Analysis Sample
• Common equity securities (primary issue) traded on
  AMEX, NYSE and NASDAQ of firms incorporated in the
  United States, with prices quoted in U.S. Dollars.
• Daily closing prices, shares outstanding and shares
  traded from Compustat North America, with adjustments
  for stock splits, reverse splits, dividends, etc. Market
  return data from Ken French’s website.
• Sample period: ±360 calendar days from Nov 8, 2016
• 3,606 firms with closing prices on 8 and 9 November.
• Matched to 3,383 firms with at least one 10-K filing (with
  non-empty Part 1a) from January 2006 to July 2016.
   – Part 1a is not obligatory for all listed firms.
• Drop 102 firms with no NAICS code. Drop 20 with fewer
  than 126 daily return observations in pre-election window.
• 3,261 firms in the final sample.
   – About 1.5 million daily return observations             11
Part 1A of the 10-Ks
• Since 2006 (for FY 2005) the SEC requires most publicly
  held firms to include a separate discussion of “Risk Factors”
  in Part 1a of their annual 10-K filings.
• In explaining “How to Read a 10-K” at
  www.sec.gov/answers/reada10k.htm, the SEC describes
  Part 1a as follows:
   – Item 1A - “Risk Factors” includes information about the
     most significant risks that apply to the company or to its
     securities. Companies generally list the risk factors in
     order of their importance. In practice, this section focuses
     on the risks themselves, not how the company addresses
     those risks. Some risks may be true for the entire
     economy, some may apply only to the company’s industry
     sector or geographic region, and some may be unique to
     the company.                                              12
How We Use the 10-Ks
1. Develop term sets that correspond to various policy risk
   categories. Examples:
   – Healthcare Policy
   – Food and Drug Policy
   – Environmental and Energy Regulation
   – Trade and Exchange Rate Policy
   – Various tax policy categories
   – Government Spending and Fiscal Stabilization Policy
2. For each 10-K filing – and each term set – calculate the
   percentage of sentences in Part 1a with one or more
   terms in the set.
3. Average the percentage over available years for each firm
   and term set to get our risk exposure measures.
                                                        13
A Warm-Up Investigation
1. For each 10-K filing with a non-empty Part 1a:
   – Calculate the percentage of sentences in Part 1a
      that contains “regulation,” “regulate” or “regulatory.”
   – Average this percentage over years for each firm.
2. This average value is our measure of Raw Regulation
   (Risk) Exposure for the firm.
3. Compute the firm’s daily return as 100 X log change in
   the closing price from November 8 to November 9.
4. Obtain the CAPM abnormal return for each firm from
   November 8 to 9.
5. Relate Raw Regulation Exposure to abnormal equity
   returns in reaction to Trump’s surprise election victory.
6. Plot the firm’s daily return against its Raw Regulation
   Exposure.
                                                                14
Firms with greater exposure to regulatory risks
had higher abnormal returns on 9 November

                    The estimated cross-sectional effect is large:
                    Multiplying the slope coefficient by the IQR of
                    the Raw Regulation Exposure measure (7.6
                    percentage points) implies a daily return
                    differential of 1.2 percentage points.

                    Abnormal Returns from 8-9 Nov:
                    Mean Firm-Level Daily Return: 1.1%
                    IQR of Daily Returns: 4.4%
Extending the Measurement Method
                                                          mean (%)     IQR (%)
                   Variable                  N     % >0                           max (%)
                                                          All   >0    All    >0
Raw Regulation                              3281   99.6   8.6   8.6   7.6   7.6    57.7
Food and Drug Policy                        3281   22.0   1.2   5.6   0.0   7.2    43.3
Trade and Exchange Rate Policy              3281   56.2   0.6   1.1   0.8   1.1    17.1
Government Purchases and Fiscal Policy      3281   28.3   0.1   0.4   0.1   0.3    15.9
Entitlement and Welfare Programs            3281   11.5   0.0   0.2   0.0   0.2    2.1
Government-Sponsored Enterprises            3281   10.7   0.2   1.8   0.0   2.1    18.5
Labor Regulation                            3281   37.8   0.2   0.5   0.2   0.5    8.3
Financial Regulation                        3281   47.1   1.2   2.6   0.4   2.6    33.5
Intellectual Property Policy                3281   23.9   0.1   0.4   0.0   0.4    6.5
Monetary Policy                             3281   23.5   0.3   1.5   0.0   2.0    18.5
Taxes on Business Profits                   3281   14.8   0.0   0.3   0.0   0.3    2.9
Business Tax Credits                        3281   5.5    0.0   0.2   0.0   0.2    3.0
Sales and Excise Taxes                      3281   22.9   0.1   0.5   0.0   0.5    22.4
Generic Tax Terms                           3281   92.3   2.2   2.4   2.6   2.5    32.3
Healthcare Policy                           3281   31.8   0.8   2.4   0.2   2.5    42.1
         Healthcare Industries              453    54.1   2.9   5.4   3.3   6.5    27.4
         Other Industries                   2828   28.2   0.4   1.5   0.1   1.9    42.1
Energy and Environmental Regulations        3281   24.7   0.6   2.4   0.0   3.0    16.8
         Green Sectors (BLS Designations)   810    29.8   0.7   2.4   0.2   2.6    16.8
         Brown Sectors                      2471   23.1   0.5   2.4   0.0   3.1    16.8
Example: The Term Set for
 Energy & Environmental Regulation
Energy And Environmental Regulation: {energy policy},
{energy tax, carbon tax}, {cap and trade}, {cap and tax},
{drilling restrictions}, {offshore drilling}, {pollution controls,
environmental restrictions, clean air act, clean water act},
{environmental protection agency, epa}, {wetlands
protection}, {Federal Energy Regulatory Commission,
FERC}, {ethanol subsidy, ethanol tax credit, ethanol credit,
ethanol tax rebate, ethanol mandate, biofuel tax credit,
biofuel producer tax credit}, {corporate average fuel
economy, CAFE standard}, {endangered species},
{Keystone pipeline}, {Alaska oil pipeline, Trans-Alaska
pipeline}, {greenhouse gas regulation, climate change
regulation}, {Nuclear Regulatory Commission}, {Pipeline
and Hazardous Materials Safety Administration}
Our Basic Firm-Level Abnormal Return Regression
Dependent Variable: Daily Abnormal Equity                         Coefficient Times IQR
Return from November 8 to 9                   Coeff. (t-stat)   IQR|All IQR|Exposure>0
Raw Regulation                                   8.0    (4.3)      0.6            0.6
Food and Drug Policy                            25.5    (5.1)      0.0            1.8
Trade and Exchange Rate Policy                 -11.8 (-1.9)       -0.1           -0.1
Government Purchases and Fiscal Policy          79.2    (4.1)      0.0            0.3
Entitlement and Welfare Programs             -156.5 (-1.5)         0.0           -0.3
Government-Sponsored Enterprises               -19.9 (-5.3)        0.0           -0.4
Labor Regulation                                39.5    (3.2)      0.1            0.2
Financial Regulation                             2.7    (1.2)      0.0            0.1
Intellectual Property Policy                    68.4    (2.2)      0.0            0.2
Monetary Policy                                  5.3    (0.7)      0.0            0.1
Taxes on Business Profits                       39.6    (1.0)      0.0            0.1
Business Tax Credits                         -166.2 (-1.7)         0.0           -0.3
Sales and Excise Taxes                         -26.3 (-1.8)        0.0           -0.1
Generic Tax Terms                              -10.8 (-3.3)       -0.3           -0.3
      Healthcare Industries (Dummy)             -1.6 (-5.5)
Healthcare Policy # Healthcare Industries      -17.3 (-2.3)      -0.6         -1.1
Healthcare Policy # Other Industries            -0.7 (-0.1)       0.0          0.0
      Green Sector (Dummy)                      -0.8 (-4.0)
Energy & Environ. Regs # Green Sector          -35.4 (-5.5)      -0.1         -0.9
Energy & Environ Regs # Brown Sector             4.4    (0.9)     0.0          0.1   18
   Observations: 3261        Adjusted R-Squared: 11.2%
Dependent Variable: Daily Abnormal             Dropping Firms Not Russell 3000 +
        Equity Return from November 8 to 9              In Russell 3000    Leverage Control
    Raw Regulation                                         8.5       (4.0)    7.9    (3.8)
    Food and Drug Policy                                  32.6       (5.1)   34.5    (5.2)
    Trade and Exchange Rate Policy                       -14.9      (-2.2)  -16.0    (-2.3)
    Government Purchases and Fiscal Policy               113.5       (5.1)  116.9    (5.3)
    Entitlement and Welfare Programs                    -335.1      (-2.5) -318.2 (-2.7)
    Government-Sponsored Enterprises                     -24.3      (-5.8)  -20.8    (-4.7)
    Labor Regulation                                      40.8       (3.4)   43.2    (3.6)
    Financial Regulation                                   0.7       (0.3)   -0.4    (-0.2)
    Intellectual Property Policy                         136.4       (2.6)  123.8    (2.3)
    Monetary Policy                                       11.8       (1.4)   10.0    (1.2)
    Taxes on Business Profits                             53.8       (1.4)   62.2    (1.6)
    Business Tax Credits                                -183.0      (-2.0) -194.7 (-2.1)
    Sales and Excise Taxes                               -34.0      (-2.3)  -36.2    (-2.5)
    Generic Tax Terms                                    -17.6      (-4.5)  -16.1    (-4.2)
          Healthcare Industries (Dummy)                   -2.0      (-6.9)   -2.0    (-6.9)
    Healthcare Policy # Healthcare Industries            -20.6      (-2.5)  -20.9    (-2.6)
    Healthcare Policy # Other Industries                  -5.1      (-0.4)  -18.9    (-1.3)
          Green Sector (Dummy)                            -0.9      (-4.2)   -1.0    (-4.8)
    Energy & Environ. Regs # Green Sector                -38.5      (-5.9)  -35.5    (-5.6)
    Energy & Environ Regs # Brown Sector                  -6.3      (-0.9)   -4.5    (-0.7)
    Leverage (long-term debt + short-term liabilities)/Assets in FY 2015     -1.2    (-2.9)
                                                                                      19
Observations (Adjusted R-Squared, %)                      2384 (19.5%)       2371 (19.9%)
Next Few Slides: Partial Regression Scatter
Plots for Our Basic Specification and Sample

                      Residualized values, binned on
                      variable on horizontal axis,
                      controlling for other regressors.
Coefficient (t-stat) on Healthcare
Industry Dummy: -1.6 (-5.5)
Coefficient (t-stat) on Green
Industry Dummy: -0.8 (-4.0).
BLS Green designations.
Firm-Level Abnormal Returns Moved Strongly in
 the Same Direction on November 10, 11 and 14
Russell 3000 Sample Shows a Similar Pattern
A Similar Return Structure on Other Days? No
                         Our Basic Abnormal Returns Regression, Dropping Firms Not in Russell 3000
Dependent Variable: Daily                                   Average Results For Average Results for Average Results for
                                             9 Nov 2016
Firm-Level Abnormal Return                                     Prior 360 days      3 Largest Gains   3 Largest Drops
          Abnormal Return: Mean (IQR)          1.3 (4.4)          -0.0 (1.8)           -0.0 (2.5)        -0.3 (2.8)
               Equity Return: Mean (IQR)       3.0 (4.7)           0.0 (1.9)            2.5 (2.7)        -3.6 (3.5)
Raw Regulation                                  8.5 (4.0)          -0.1 (-0.1)          -0.3 (-0.2)       -0.9 (-0.7)
Food and Drug Policy                          32.6 (5.1)           -1.2 (-0.3)          -5.0 (-1.7)       -3.9 (-1.0)
Trade & Exch. Rate Policy                    -14.9 (-2.2)           0.4 (0.1)           -3.5 (-1.0)       -7.2 (-1.2)
Govt. Purch. & Fiscal Policy                 113.5 (5.1)            1.0 (0.1)         -10.8 (-0.8)        -6.9 (-0.4)
Entitlement & Welfare                       -335.1 (-2.5)           2.7 (0.1)           -1.9 (-0.1)      20.1 (0.4)
GSEs and Related                             -24.3 (-5.8)           0.4 (0.1)            5.6 (1.3)         4.3 (0.9)
Labor Regulation                              40.8 (3.4)           -0.7 (-0.0)           1.4 (-0.2)       -1.8 (-0.4)
Financial Regulation                            0.7 (0.3)           0.1 (0.2)           -7.0 (-3.3)       -4.3 (-1.8)
Intellectual Property Policy                 136.4 (2.6)           -2.7 (-0.1)        -16.1 (-0.3)      -17.2 (-0.5)
Monetary Policy                               11.8 (1.4)            0.7 (0.1)           -9.6 (-1.8)       -3.6 (-0.3)
Taxes on Business Profits                     53.8 (1.4)           -1.2 (-0.0)           5.9 (0.3)       39.1 (1.9)
Business Tax Credits                        -183.0 (-2.0)          -2.0 (-0.0)        -21.1 (-0.4)       19.1 (0.4)
Sales and Excise Taxes                       -34.0 (-2.3)           2.3 (0.2)          11.0 (1.6)        18.1 (1.6)
Generic Tax Terms                            -17.6 (-4.5)           0.0 (0.0)            4.0 (1.1)        -0.2 (-0.1)
Healthcare Ind. (Dummy)                        -2.0 (-6.9)          0.0 (0.1)            0.0 (0.2)         0.6 (3.7)
HC Policy # HC Industry                      -20.6 (-2.5)          -0.2 (-0.1)          -0.8 (-0.4)       -0.9 (-0.6)
HC Policy # Other Industries                   -5.1 (-0.4)         -0.4 (-0.1)           2.3 (0.8)         2.1 (0.6)
Green Sector (Dummy)                           -0.9 (-4.2)          0.0 (0.1)            0.2 (1.3)        -0.1 (-1.1)
E&E Regs # Green Sector                      -38.5 (-5.9)           0.9 (0.3)            5.6 (1.6)       19.8 (6.3)
E&E Regs # Brown Sector                        -6.3 (-0.9)          0.4 (-0.1)         13.8 (2.1)          9.3 (1.7)
Observations (Adj-R2)                        2384 (19.5)         2380 (6.3)           2376 (4.7)        2380 (7.5)
Taking Stock (Tentative)
1. The surprise election outcome triggered a large,
   positive stock market response on 9 November 2016,
   strongly contradicting pre-election assessments of the
   likely market response to a Trump victory.
2. The structure of firm-level equity returns on 9 November
   was highly dispersed, highly unusual, and clearly tied to
   firm-level policy risk exposures, as derived from 10Ks.
   – Firms with high exposures to regulatory risks saw
       especially high equity returns on 9 November, except
       for those reliant on “green” subsidies.
   – Healthcare delivery firms and those with high
       exposures to healthcare policy risks fared poorly in
       relative terms and, in some case, in absolute terms.
– High exposure to risks associated with Trade Policy,
      Entitlement and Welfare Programs, and Government-
      Sponsored Enterprises involved lower returns.
   – High exposure to risks associated with Labor
      Regulation, Food and Drug Policy, and IP Policy
      involved higher returns.
3. The stock market did not fully digest the implications of the
   election outcome by market close on 9 November.
   – Instead, (conditional) firm-level abnormal returns over
      the next 2-3 days strongly reinforced the initial market
      response to the election surprise.
   – The shift in the structure of conditional firm-level
      abnormal returns over 3-4 days after the election was
      about 65% greater than the initial shift from market
      close on 8 November to market close on 9 November.
4. These results suggest that equity prices do not immediately
   and fully adjust to surprise events that (a) involve unusual
   shifts in the structure of price-relevant risks and (b) require
   large information processing resources to fully assess.
   – Human collection and processing of available
       information is costly, and it takes time. Thus, the surprise
       realization of events that satisfy (a) and (b) need not be
       fully and immediately incorporated into equity prices.
   – This interpretation calls for study of other major surprise
       events that meet conditions (a) and (b).
      • Do they also involve a slow-working multi-day
           response in the structure of firm-level equity returns?
      • When (a) or (b) fails, do we instead see rapid
           responses in the structure of firm-level equity returns?
5. Asset price responses to prediction-market probability
   changes are unreliable guides to the actual price effects
   of major surprise outcomes, if they materialize, when
   conditions (a) and (b) hold.
   – Put more positively, asset price responses to shifts in
      the probability of future outcomes are more reliable
      when (^a) the prospective event involves a common,
      well-understood shift in the structure of price-relevant
      risks, and/or (^b) modest information processing
      resources are sufficient to assess the pricing
      implications of the prospective event.
Some Next Steps
1. Improve our term sets and firm-level characterizations:
   – Further parse instances of generic Regulation
   – Tax policy: develop better term sets and code that
     allocates “generic” tax terms
   – Trade policy: distinguish among exporters, import-
     competing and supply-chain dependent firms.
   – Financial regulation: We find little here – a deficiency
     in our Financial Regulation term set?
2. Examine firm-level returns on Wolfers-Zitzewitz dates:
   – Initial work suggests that “signal” associated with
     these episodes is too weak to say much.
3. Compare to Wagner, Zeckhauser and Ziegler papers:
   – They take a different approach, consider a smaller
     sample, and focus on tax considerations.
What Triggers Stock Market Mumps?
      Scott R. Baker (Kellogg, Northwestern)
               Nick Bloom (Stanford)
    Steven J. Davis (Chicago Booth and Hoover)
     Marco Sammon (Kellogg, Northwestern)

                      2018
Why does the stock market jump?
• Two broad views on what drives stock market moves:
   – Eugene Fama: driven by discount rates, risk, dividends, etc.
   – Robert Shiller: Hard to explain fully by fundamentals; narratives
     develop and sometimes spread, affecting prices and returns.
Why does the stock market jump?
• We advance this discussion by considering several thousand large
  daily moves in national equity markets.
• We read next-day newspaper accounts and record the journalist’s
  characterization of the jump.
How We Characterize Equity Market Jumps
1. Set daily jump threshold: Aim for about 1% of
   daily moves à |2.5%| for most countries.
2. Pull dates with market moves > threshold
3. Use newspaper articles to characterize jumps
  A. Go to online newspaper archive
  B. Enter newspaper, date range (next day) and search
     criteria (e.g., “stock market”)
  C. Select article
4. One or more humans read the article(s).
5. Record the reason for the jump, its geographic
   source, confidence of reporter in explanation,
   ease of coding for the reader, etc.              39
Jumps by Reason Template

Policy Categories                   Non-Policy Categories
Government spending                 Macroeconomic news & outlook
Taxes                               Corporate earnings & profits
Monetary policy & central banking   Commodities
Trade & exchange rate policy        Unknown/no explanation
Regulation (other than above)       Foreign Stock Markets
Sovereign military & security       Terrorist attacks & large-scale
actions                             violence by non-state actors
Other policy (specify)              Other non-policy (specify)

                                                                 40
Preview of Selected Findings
1. Policy news is a major trigger of stock
   market jumps (according to newspapers)
–   Journalists attribute 40% of U.S. jumps to policy
–   More than Macro Outlook (27%) or Corporate Earnings (11%)
–   Policy share of jump attribution is smaller in most countries
–   Policy share rises with jump size
2. U.S. developments trigger a huge share
   of equity market jumps across the globe.
– Excluding data for the United States, leading local newspapers
  attribute 34% of jumps in their national stock markets to
  developments that originate in the U.S.
– The U.S. role in this regard dwarfs the role of Europe, China and
  other large regions and countries
– U.S. role is especially pronounced during the GFC.
Preview of Selected Findings
3. The persistence of national equity
   return volatility varies by jump reason.
–    Jumps attributed to monetary policy exhibit much less post-
     jump return volatility.
4. The clarity of journalist perceptions as
   to jump reason varies among jumps
–    We quantify perception clarity in several ways.
5. Greater clarity about jump reason à
–    Lower intraday market volatility on the jump day
–    Lower volume on the jump day
–    Greater concentration of jump-day move in a short interval
–    Smaller jump-day gain in the VIX
100-Page Guidebook for Coders
More on the Coding Process
• Coders = authors and RAs
• Coders read article and record the jump
  reason, the geographic source of the jump-
  triggering news, journalist confidence about
  jump reason, and more.
• RA training process
• RA monitoring process
• We randomize the order in which RAs
  review jump days to avoid conflating coder-
  learning effects and calendar-time effects.
Selected Category Definitions
    and Examples from the
   Data Construction Guide
The examples on the next several slides give
an indication of how we provide guidance for
coding newspaper articles.

                                               45
Taxes
News reports, concerns or events related to current,
planned, or potential tax changes (e.g., income tax hikes,
payroll tax cuts, corporate tax reform, sales tax change,
etc.) and their consequences.

                                                             46
Taxes, 2

       This article is coded as Taxes
       because it claims directly that if
       anything could be cited as a reason
       it would be the tax bill that was
       passed. The confidence would be
       medium to high because the article
       spends some time discussing the
       tax bill and claims that the bill was
       almost certainly the reason, saying
       if any one reason could be cited it
       would be that one.

                                        47
Government Spending
News reports, forecasts, or concerns about
government spending and its consequences,
including spending matters related to stimulus
programs, publicly funded pensions, social
security, health care, etc.

                                                 48
Government Spending, 2

This article is coded as government spending because the first reason
listed for the stock market plunge is the rejection of the government’s
bailout plan. The bailout plan itself involves the government spending
money to help the economy, and even though it is a rejection of the plan, it
is still coded as government spending.
                                                                               49
Macroeconomic News and Outlook
News relating to macroeconomic forecasts or reports such
as inflation, housing prices, unemployment or employment,
personal income, industrial production, manufacturing
activity, etc.

Also included in this category:
• News about financial crisis developments that does not
  fall into another category such as Monetary Policy and
  Central Banking.
• Trade and exchange rate news NOT attributed to policy
  (e.g., news about trade deficits or currency movements)

                                                            50
Macroeconomic News and Outlook, 2

This article claims that the reason for the market move was a fear of a double-dip
recession, a change in the Macroeconomic Outlook. Therefore the article would be
coded as Macroeconomic News and Outlook. The confidence would be high because
the article clearly declares that the fear of recession was the cause for the movement.
                                                                                  51
Monetary Policy and Central
               Banking
Actions, possible actions, and concerns related to the
conduct and policies of the central bank or other chief
monetary authority. Such actions and policies pertain to
interest rate changes and monetary policy
announcements, inflation control, liquidity injections by the
monetary authority, changes in reserve requirements or
other bank regulations used by the monetary authority to
exercise control over monetary conditions, lender-of-last
resort actions, and extraordinary actions by the monetary
authority in response to bank runs, systemic financial crisis
and threats to the payments system.

                                                            52
Monetary Policy and Central
       Banking 2

                This article is coded as Monetary
                Policy because it cites the reason for
                the market rally as a statement from
                the Fed that they will pay less
                attention to weekly swings in the
                monetary supply, a change in their
                policy. The confidence and ease of
                coding would also be high because
                the article clearly claims the Fed
                statement is the reason for the jump.

                                                    53
Distinguishing Monetary Policy &
Central Banking from Macroeconomic
          News & Outlook
 Some news articles that discuss market reactions to macro
 developments also discuss the Fed’s normal response to the
 macro development. Generally, we code an article as Macro
 News & Outlook if it attributes the market move to news
 about the macro economy. We code it as Monetary Policy &
 Central Banking if the article attributes the market move to
 (a) news about a change in how the Fed responds to a given
 macro development or (b) news about unexpected
 consequences of Fed actions.

 It is helpful to approach this classification issue from a Taylor
 Rule perspective. Consider the following cases:
                                                               54
Distinguishing Monetary Policy &
Central Banking from Macroeconomic
         News & Outlook, 2
1. Macro news: The market moves because it anticipates or
   speculates (or sees) that the Fed will respond in its usual
   manner to news about the macro economy. That is, the
   market anticipates or speculates that the Fed will respond
   to macro developments according to a well-understood
   description of the Fed's interest-rate setting behavior.
2. Monetary policy: The market moves because of a surprise
   change in the policy interest rate -- i.e., a surprise
   conditional on the state of the macro economy. From a
   Taylor Rule perspective, we can think of this change as a
   new value for the innovation term in the Taylor rule.
                                                             55
Distinguishing Monetary Policy &
Central Banking from Macroeconomic
         News & Outlook, 3
3. Monetary policy: The market moves because of an
   actual or potential change in the Fed’s policy rule. From
   a Taylor Rule perspective, this event corresponds to an
   actual or potential change in the form of the Taylor Rule
   or a change in specific parameter values. A concrete
   example would be a big market response to proposals to
   increase the target interest rate.
4. Monetary policy: The market moves because of news
   that leads to revised views or concerns about the
   consequences of the Fed's actual or anticipated actions.

Articles in category 1 get coded as Macro News & Outlook.
Articles in categories 2, 3 and 4 get coded as Monetary
                                                               56
Policy & Central Banking
Example 1 (2/8/2018, S&P 500 index return -3.75%):

This article would receive a primary category of Macroeconomic News and Outlook (Non-
Policy) because the article links rising inflationary pressures to tighter monetary policy. These
explanations fit well with Taylor Rule-like conduct by central banks, and therefore would not be
classified as monetary policy.
Coding U.S. Jumps
• Jumps: Days when CRSP Value-Weighted
  Index (VWI) has absolute return of at least
  2.5%, measured using closing prices.
• All jumps coded by 2+ persons for quality
  control and analysis of agreement rates.
• For jumps after WWII, we use five papers
  (WSJ, NYT, BG, WP & LAT) to compare
  interpretations and analyze agreement rates.
US Jumps by Year (policy 40%)
                                        Depression Lowest
                                          Growth (1932)
     50
                                                   Second
                                                Downturn (1937)
                                                                                         Global
                                                                                        Financial
            1893 Panic 1901                                                               Crisis
                      Panic                                                  Black
                                 WWI                              Oil Shock Monday
                                                     WWII
     0

                      Banking                                                         Tech
                      Panic of                                                       Boom/
                       1907                                                           Bust

                                       1929
                                       Crash
     -50

                                                     NY Times and WSJ

            1890         1910            1930          1950       1970       1990            2010

                                   Unknown + No Article                   Non-Policy
                                   Policy

Notes: Each bar shows the number of positive or negative jumps in the year. Shadings indicate the
number of jumps triggered by “Policy” and “Non-Policy” news. The residual category reflects jumps
attributed to unknown causes by the newspaper article and instances in which we found no article about
the jump, which never happens after 1925.
Breakdown of US jumps by Category:
      Prewar vs. Postwar and Positive vs. Negative
                                      1926-1945         1946-2016      Share of Jumps Within Each Period
Category                         Negative Positive Negative Positive     1926-1945          1946-2016      Share Negative
Commodities                        27        21       6         4          9.2%                 2.7%           56.5%
CorporateEarningsandProfit         30        19      26        24          9.2%                12.8%           57.1%
ElectionsandPoliticalTransitions    6         5       7         8          2.0%                 3.8%           50.0%
ForeignStockMarkets                 5         2       4         1          1.2%                 1.2%           77.5%
GovernmentSpending                 12        30      11        15          8.1%                 6.6%           33.3%
MacroNews                          57        61      83        45          22.6%               32.8%           56.8%
MonetaryPolicyCentralBanking       10        27      12        35          7.1%                11.9%           26.3%
NonSovMilitaryTerror                0         1       3         2          0.2%                 1.3%           60.4%
OtherNonPolicy                     15         6       7         3          4.0%                 2.5%           70.9%
OtherPolicy                         8        12       3         4          3.8%                 1.8%           39.6%
Regulation                         18        12       5         5          5.6%                 2.4%           57.3%
SovMilitary                        40        19      15         9          11.3%                6.1%           66.1%
Taxes                               7         5       2         4          2.4%                 1.5%           46.6%
TradeandExchangeRatePolicy          4         8       0         4          2.3%                 1.0%           28.6%
Unknown                            37        20      17        28          11.0%               11.6%           53.2%
             Total                 274       247     201       190        100.0%              100.0%
           Sum Total               912

     US jump definition: Days where the CRSP Value-Weighted
     Index (VWI) has an absolute return of at least 2.5%.
Validation Exercises, 1
1. Industry-Level Jump Responsiveness: For many jumps,
   the explanation offered in next-day accounts implies
   an amplified or dampened response of certain
   industries to the news that moved the overall market.
Example 1, Banks: During the GFC, the stock market responded
positively to upward revisions in the likelihood or generosity of
bank bailouts. For this type of jump, we expect an even more
favorable response for Bank stocks. That is, the response of Bank
stocks is AMPLIFIED relative to the overall market response.
Example 2, Guns: When bad news about the likelihood or duration
of the Iraq war generated a negative jump, we expect the response
for Guns (Defense firms) to be DAMPENED relative to the overall
market response. While a longer war may be bad for the overall
U.S. economy, it is less bad (or even good) for the Guns industry.
Validation Exercises, 2
Implementation:
• Obtain daily portfolio returns for 49 industries.
• Review coding detail for U.S. equity market jumps from 1960 to
  2016. If the detailed description for jump-date t implicates
  industry i, then assign values to !"#$% as follows:
!"#$% = 1, if jump description implies AMPLIFIED response of i.
      = -1, if jump description implies DAMPENED response of I;
      = 0, OTHERWISE.
• In making these industry assignments, we take a conservative
  approach, as follows:
   – We typically make industry assignments based on the Primary jump reason
     only, not the Secondary Jump reason (if there is a Secondary reason.
   – We set !"# to 0 when the detailed explanation for the Jump involved an
     overly broad industry group for our purposes. For example,
     “Manufacturing,” maps to at least 15 of the 49 industry groups.
Validation Exercises, 3
• Most jumps do not map readily to a particular industry.
  Sometimes, we assign 2 industries to a given jump. Most, but not
  all, of these dual assignments involve Sovereign Military Jumps,
  which implicate both Guns and Aerospace.
• Among our 339 jumps from 1960 to 2016, we obtain 115 Jump-
  Day X Industry observations with nonzero Tri values, as follows:
   –   Banks: 38 nonzero values
   –   Guns: 19
   –   Aerospace: 16
   –   Others, all with less than 10 nonzero Tri values: Oil, Coal, Building
       Materials, Construction, Autos, Chips, Hardware, Household Goods,
       Software, Electrical Equipment
• !"# = the daily return for industry portfolio i on day t.
• %!# = the daily return on market portfolio on day t.
Validation Exercises, 4
One-industry-at-a-time approach
Consider daily industry-level returns for i :
     89: = < + >?8: + @ABC9: + DABC9: ?8: + E:

Pooled-sample approach
Consider daily industry-level returns for industries
with at least 3 nonzero Tri values:
89: = ∑9 9 ?8: + ∑9 @9 ABC9: + DABC9: ?8: + E:

Under both approaches, the hypothesis of interest is
               LM : D = 0, LP : D > 0
Validation Exercises, 5
                            Banks                      Pooled Sample
             All Days Jump Days All Days Jump Days
Coefficient  0.80*** 0.74*** 0.55*** 0.51***
(St. Error)   (0.23)    (0.24)   (0.13)    (0.13)
Observations 13,469      339    109,760     4720
R-Squared      0.67      0.83     0.56      0.81
A regression for Guns, yields results similar to the Pooled ones, but the standard
error is large and the coefficient estimate is insignificant. When we set Tri=-1 for
the Aerospace industry for jumps attributed to Sovereign Military Conflict, the
Aerospace regression yields a small, marginally significant coefficient of the wrong
sign. That may reflect the ambiguous nature of Aerospace firms’ responses to
military conflict: (relatively) good news for defense-oriented aerospace firms may,
at the same time, be bad for aerospace firms oriented toward civilian customers.
If we set Tri=1 for Aerospace in these cases, the anomalous Aerospace result
disappears, and the Pooled Sample results get stronger.
Validation Exercises, 6
These results confirm that our classification of jumps (based on next-
day newspaper accounts) reflects useful information about actually
triggered the jump.

2. Announcement-Date Responses: We test
   whether jumps attributed to:
   – Monetary Policy & Central Banking are relatively
     likely on FOMC announcement dates (Yes)
   – Macroeconomic News & Outlook are relatively likely
     on release dates for the Employment Situation
     Report and the CPI Report (Yes)
   – Elections & Political Transitions are relatively likely
     the day after national elections (Yes)
Countries, Time Periods, Newspapers and Jump Thresholds
                                                                              Jump
Country                   Start   End            Primary Newspapers
                                                                            Threshold
United States             1885    2016   Wall Street Journal and NY Times    2.50%
United Kingdom            1930    2011   Financial Times (UK Edition)        2.50%
Australia                 1985    2011   Australian Financial Times          2.50%
Canada                    1980    2012   The Globe and Mail                  2.00%
China (Hong Kong)         1988    2011   South China Morning Post            3.80%
China (Shanghai)          1994    2013   Shanghai Securities Journal         4.00%
Germany                   1985    2012   Handelsblat, FAZ                    2.50%
Greece                    1989    2015   Kathimerini, To Vima                4.00%
Ireland                   1987    2012   The Irish Times                     2.50%
Japan                     1981    2013   Yomiuri and Asahi                   3.00%
New Zealand               1996    2011   New Zealand Herald                  2.50%
Saudi Arabia              1994    2013   Al Riyadh                           2.50%
Singapore                 1980    2013   Business Times and Straits Times    2.50%
South Africa              1986    2013   Business Day                        2.50%
South Korea               1980    2013   Chosun Ilbo                         2.50%

   Notes: Jump thresholds vary somewhat across countries to account for
   differences in unconditional volatility. We select a jump threshold such that
   about 1% of daily moves satisfy the jump criterion.
Breakdown of international jumps by category
     Category                         Negative Positive Share Negative
     Commodities                         84       96        46.7%
     CorporateEarningsandProfit         212      143        59.7%
     ElectionsandPoliticalTransitions    36       34        51.4%
     ForeignStockMarkets                280      221        55.9%
     GovernmentSpending                  56       93        37.6%
     MacroNews                          649      356        64.6%
     MonetaryPolicyCentralBanking       120      206        36.8%
     NonSovMilitaryTerror                39       11        78.0%
     OtherNonPolicy                     245      206        54.3%
     OtherPolicy                         67      111        37.6%
     Regulation                          54       47        53.5%
     SovMilitary                         55       44        55.6%
     Taxes                               7        11        38.9%
     TradeandExchangeRatePolicy          13       17        43.3%
     Unknown                            212      243        46.6%
                  Total                2129     1839
                Sum Total              3968

    The sample for this table starts in 1980 and ends in 2013 to
    maximize sample-period overlap across countries.
Count by year for the UK (policy 33%)
                                       Recession and

   40
                                       1976 IMF crisis

                                                                         Global
                                                                         Financial
                                                                         Crisis
   20

                                                                Tech
          Great                                                 crash
          Depression               Sterling
                                   Crisis
   0

                WWII      Suez
                          Crisis                     Black
                                                     Monday
   -20

         1930          1950           1970               1990             2010

                          Unknown + No Article              Non-Policy
                          Policy
Yearly Count - All Countries (policy 26%)
                                                                     Global

        20
                                                                 Financial Crisis

                        Early
                       1990’s
        10

                      Recession
        0
        -10

                                                                             Eurozone
                                            Asian and                         Crises
                                             Russian
                                            Financial
        -20

                                              Crises
        -30

              1985     1990          1995         2000       2005         2010          2015

                                  Unknown + No Article              Non-Policy
                                  Policy

Notes: Each bar is the average number of jumps per year across the following countries: Australia,
Canada, China (HK), China (Shanghai), Germany, Greece, Ireland, Japan, New Zealand, Saudi Arabia,
Singapore, South Africa, South Korea and UK
Policy share rises with jump threshold

   Increase Initial
    Threshold By      # Jumps Policy Share
        0.0%           4340      26.4%
       0.50%           2933      28.4%
       1.00%           2031      29.0%
       1.50%           1479      30.9%
       2.00%           1134      32.5%
       2.50%            812      31.8%
  Using our sample of 14 countries
Share of Jumps Attributed to U.S. Developments
   .8

         Sample excludes Jumps in U.S. stock market

                                            .8
                                                                               Global
                                                                Tech Boom/     Financial
           Early 1980’s                                         Bust           Crisis
   .6

                                            .6
           recession

                                                                                                  US Share of Global GDP
                        US Share of Jumps
                      1987 Crash

                                            .4
                                                        Asian/LTCM
   .4

                                                        crises

                                                                             Eurozone
                                            .2

                                                                             Crisis
   .2

                          Gulf War I
                                            0

                                                 1980           1990           2000              2010

                                                          U.S. Jump Share       U.S. GDP Share
    0

        1980                                1990                   2000                 2010

Notes: Average share of jumps attributed to U.S. developments by year in Australia, Canada,
China (HK), China (Shanghai), Germany, Greece, Ireland, Japan, New Zealand, Saudi Arabia,
Singapore, South Africa, South Korea and UK. Dot size is proportional to the average number of
jumps by country/year. U.S. Share of PPP-adjusted global GDP using IMF data.
Geographic Source of Jumps by Country
         and Bilateral U.S. Trade Share
          North America: US
      North America: Canada
             Asia: Singapore
         Other: South_Africa
             Other: Australia
             Europe: Ireland
                  Asia: Japan
           Europe: Germany
                  Asia: Korea
            Asia: Hong_Kong
                  Europe: UK
        Other: New_Zealand
             Europe: Greece
        Other: Saudi_Arabia
              Asia: Shanghai

                                0   .2    .4      .6       .8       1

                                          US           Europe
                                          Asia         Other

Notes: Red X shows the average bilateral trade share with the United States from
1999 to 2015, measured as (exports + imports) / (Domestic GDP).
Jump Shares Attributed to U.S. & Europe
          The sample for “US Jump Share” excludes the United States
          The sample for “Europe Jump Share” excludes European countries
   .8
   .6
   .4
   .2
   0

        1980               1990                 2000                   2010

                        Europe Jump Share              US Jump Share
                        Europe GDP Share               US GDP Share

GDP shares computed using contemporaneous exchange rates.
Jumps Attributed to Monetary Policy
  Exhibit Less Post-Jump Volatility
          Using Daily U.S. Data from 1946-2016
          Average Cumulative Squared Returns
   .01
   .008
   .006
   .004
   .002
    0

           0         5           10              15             20
                                 Days After

                         No Jump              Monetary Policy
                         Macro News           All Other Jumps
Monetary Policy Jumps Continue to Show Less Post-Jump
Volatility When We Control for Jump Size and Direction
      U.S. Data
                              (3)
                                 1946-2016
                                            (4)
                                                   1990-2016
                                                        (5)    Daily U.S. Data
        Jump              0.83***       0.43***      0.29**
                           (0.13)        (0.13)      (0.13)
       Return                          10.36***     10.96***
                                                               Note: The dependent variable is the
                                         (1.42)      (2.05)    cumulative realized volatility over the
|Return| x Return < 0                  25.70***     30.82***   following 22 trading days. The category
                                         (2.06)      (3.07)
    Commodities           -0.77***      -0.64***      -0.34
                                                               variables are dummies equal to one for
                           (0.24)        (0.23)      (0.71)    a jump of that type. The omitted
  Corporate Profits         -0.13         -0.08       -0.01    category is Macroeconomic News, the
                           (0.24)        (0.23)      (0.25)
       Military           -0.73***      -0.72***    -0.73***
                                                               most common jump category. In
                           (0.17)        (0.16)      (0.20)    addition to the reported controls, we
   Monetary Policy        -0.57***      -0.48***    -0.47***   include an NBER recession dummy and
                           (0.16)        (0.15)      (0.17)
  Other Non-Policy           0.78          0.61     -0.94***   a dummy for return less than zero on
                           (0.79)        (0.82)      (0.22)    the jump day. Robust standard errors
Fiscal and other policy      0.33          0.32     0.90***
                                                               in parenthesis. *** p
Are jump codings reliable and consistent?
Two potential concerns about the method:
  1. Two persons reading same article may code jumps
     differently
  2. Results may depend on newspaper consulted

To evaluate these concerns we:
  1. Use multiple RAs for same paper - calculate
     agreement
  2. Use multiple papers - Boston Globe, LA Times, NY
     Times, WSJ, Washington Post - calculate agreement
Average Pairwise Agreement Rates
            1
     Across Coders, Same Paper
            1 .8

                                                                                   average 89%
            .8 .6
 P(Agree)

                                                                                          average 75%
            .6 .4
 P(Agree)
            .4 .2
            .2 0

                    1980             1990                        2000                        2010

                            Policy Agreement            16 Category Agreement
            0

Notes: Agreement is the share of codings (at the coder-level) that agree, averages are shown for each year.
                    1980             1990                        2000                        2010
Average Pairwise Agreement Rates Across
     Newspapers, Different Coders
           1

                                                                                        average 79%
           .8
           .6

                                                                                        average 49%
P(Agree)
           .4
           .2
           0

                1980                1990                        2000                        2010

                                         16 Categories          Policy
Notes: There are 9 coders, across 5 newspapers each day. Agreement is the share of pairs that agree, out of
36 possible pairs. Dots represent an annual average of the share of pairs which agree.
Measuring Perceived Jump Clarity
We measure Jump Clarity by averaging five
measures (all on 0-1 scale) for each jump:
1. Pairwise agreement rate among coders as to
    jump reason (according to the journalist)
2. Pairwise agreement rate among coders for
    same newspaper
3. Journal confidence about jump reason
4. Ease of coding the jump
5. Whether the article said the jump reason was
    “Unknown” or offered No Explanation.
We reverse the sign on item 5 before combining
it with the items 1 to 4.
Greater Jump Clarity Involves
• Lower intraday volatility on jump day (sum of
  squared 5-minute returns)
• Lower market volume on jump day (trades in
  SPY, largest and most liquid S&P 500 ETF)
• Greater concentration of market move on jump
  day (share of day’s total return in 5-minute
  window with largest absolute return)
• Smaller VIX gain on jump day (change from
  previous day’s close)
Jump-Day Volatility, Volume, Concentration and VIX Change
        Regressed on Clarity Index and Controls
 U.S. data from 1996 to 2011. All specifications include controls for day of the week,
 month of the year and year.
                        Volatility          Volume Concentration Change in VIX
  Clarity [Alternative] -0.11***           -0.05***   0.04**        -0.03*
                          (0.04)             (0.01)    (0.02)        (0.02)
         Return         95.02***           33.75***     10.7         -5.19
                         (21.36)             (7.97)    (6.85)        (5.98)
       Return < 0          1.23               0.34     -0.16       1.76***
                          (0.85)             (0.39)    (0.46)        (0.49)
 |Return| x Return < 0 160.44***           65.30***     9.61       43.36***
                         (28.20)            (12.11)   (12.65)       (14.34)
        Constant        -3.33***           -2.24***     0.42         -0.82
                          (0.80)             (0.49)    (0.55)        (0.54)

      Observations              235           224             235                223
       R-squared               0.587         0.864           0.356              0.753
Government Bonds and Exchange Rates
We extend our method to U.S. and U.K
government bond yields and U.S. exchange rates.
1. Macroeconomic News & Outlook is the main
   (65%) trigger for jumps in U.S. bond yields.
   Monetary Policy & Central Banking accounts for
   another 28%.
2. U.K. bond yields show a muted version of the
   same pattern: Macro = 43%, Monetary = 23%.
3. Macroeconomic News & Outlook is also the
   most important trigger for jumps in U.S.
   exchange rates, with Monetary Policy next.
10-Year U.S. Government Bonds, Jumps Per Year, 1970-2013,
Jump threshold: |relative yield change| > 0.04 OR |yield change| > 0.2

30

20

10

 0

-10

                        Policy-Triggered Increases    Other Increases
-20
                        No Article Found, Increases   Policy-Triggered Decreases
                        Other Decreases               No Article Found, Decreases
-30
   70

   72

   74

   76

   78

   80

   82

   84

   86

   88

   90

   92

   94

   96

   98

   00

   02

   04

   06

   08

   10

   12
 19

 19

 19

 19

 19

 19

 19

 19

 19

 19

 19

 19

 19

 19

 19

 20

 20

 20

 20

 20

 20

 20
                                                                                    86
10-Year U.K. Government Bonds, Jumps Per Years, 1979-2013,
Jump threshold: |relative yield change| > 0.04 OR |yield change| > 0.2
 20

 15

 10

 5

 0

 -5

-10

                                      Policy-Triggered Increases                Other Increases
-15
                                      No Article Found, Increases               Policy-Triggered Decreases
                                      Other Decreases                           No Article Found, Decreases
-20
   79

          81

                 83

                        85

                               87

                                      89

                                             91

                                                    93

                                                           95

                                                                  97

                                                                         99

                                                                                01

                                                                                       03

                                                                                              05

                                                                                                     07

                                                                                                            09

                                                                                                                   11

                                                                                                                          13
 19

        19

               19

                      19

                             19

                                    19

                                           19

                                                  19

                                                         19

                                                                19

                                                                       19

                                                                              20

                                                                                     20

                                                                                            20

                                                                                                   20

                                                                                                          20

                                                                                                                 20

                                                                                                                        20
                                                                                                                    87
U.S. Trade-Weighted Exchange Rate, Jumps per Year, 1973-2013,
                 Jump threshold: |relative change| > 0.015
15

10

 5

 0

 -5
                                                  Policy-Triggered Appreciation                    Other Appreciation
      Dollar                                      No Article Found, Appreciation                   Policy-Triggered Depreciation
      Depreciation                                Other Depreciation                               No Article Found, Depreciation
-10
   73

          75

                 77

                        79

                               81

                                      83

                                             85

                                                    87

                                                           89

                                                                  91

                                                                         93

                                                                                95

                                                                                       97

                                                                                              99

                                                                                                     01

                                                                                                            03

                                                                                                                   05

                                                                                                                          07

                                                                                                                                 09

                                                                                                                                        11

                                                                                                                                               13
 19

        19

               19

                      19

                             19

                                    19

                                           19

                                                  19

                                                         19

                                                                19

                                                                       19

                                                                              19

                                                                                     19

                                                                                            19

                                                                                                   20

                                                                                                          20

                                                                                                                 20

                                                                                                                        20

                                                                                                                               20

                                                                                                                                      20

                                                                                                                                             20
                                                                                                                                         88
USD-GBP Exchange Rate, Jumps per Year, 1972-2013,
                     Jump threshold: |relative change| > 0.015
20

15

10

 5

 0

 -5

-10

-15
                                                          Policy-Triggered Appreciation                   Other Appreciation
-20                                                       No Article Found, Appreciation                  Policy-Triggered Depreciation
      Dollar
-25
      Depreciation                                        Other Depreciation                              No Article Found, Depreciation
   72

          74

                 76

                        78

                               80

                                      82

                                             84

                                                    86

                                                           88

                                                                  90

                                                                         92

                                                                                94

                                                                                       96

                                                                                              98

                                                                                                     00

                                                                                                            02

                                                                                                                   04

                                                                                                                          06

                                                                                                                                 08

                                                                                                                                        10
 19

        19

               19

                      19

                             19

                                    19

                                           19

                                                  19

                                                         19

                                                                19

                                                                       19

                                                                              19

                                                                                     19

                                                                                            19

                                                                                                   20

                                                                                                          20

                                                                                                                 20

                                                                                                                        20

                                                                                                                               20

                                                                                                                                      20
                                                                                                                                       89
Some Next Steps
1. Code additional jumps and extend sample.
2. Expand analysis of how post-jump market
   behavior relates to jump reason.
3. Some (policy-induced) jumps mitigate market
   volatility and uncertainty, others amplify it.
  – Operationalize and apply this distinction.
4. Improve the Jump Clarity Index
  – Relate Clarity to pre-, post- and jump-day outcomes.
  – Is there more scope for “narratives” to emerge,
    spread and affect asset prices when clarity is low?
5. Automate classifications and extend to smaller
   market moves.
References
Baker, Scott R., Nicholas Bloom and Steven J. Davis, 2016. “Measuring Economic Policy
    Uncertainty,” Quarterly Journal of Economics, 131, no. 4 (November), 1593-1636.
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