1

000

001

002

003

004

005

006

007

008

009

010

011

012

013

014

015

016

017

018

019

020

021

022

023

024

025

026

027

028

029

030

031

032

033

034

035

036

037

038

039

040

041

042

043

044

045

046

047

048

049

050

051

052

053

054

055

056

057

058

059

060

061

062

063

064

065

066

067

068

069

070

071

072

073

074

075

076

077

078

079

080

081

082

083

084

085

086

087

088

089

090

091

092

093

094

095

096

097

098

099

ACL 2018 Submission ***. Confidential Review Copy. DO NOT DISTRIBUTE.

Economics Terminologies in FOMC MinutesCSCI699 Introduction to Information Extraction: project final report

Akira MatsuiStudent ID:5421284665amatsui@usc.edu

Abstract

Professional knowledge is widely used tosolve complex problems in our society.In social science, the vast amount of la-bor has been devoted to public policy is-sues. Many policymakers use their pro-fessional knowledge in social sciences tosolve social problems. However, it isunknown whether these professionals usetheir knowledge explicitly in policy mak-ing situations. Here, we show that pro-fessional knowledge plays a pivotal rolewhen policymakers explain their policiesto the public. We find that the importantterminology semantic change when cen-tral banks change their behavior. We se-lect the technical terminology in the Fed-eral Reserves policy goal and compute theword embedding of these terminologies inthe Federal Open Market Committee min-utes. The changes in the embedding vec-tors affect the federal funds rate and mon-etary base. Our results demonstrate thatthe Federal Reserve Bank (FRB) placesconsiderable value on the concepts in itsmonetary policy goal and the technical ter-minologies convey good indicators of theFRBs behavior. We anticipate this studyto be a starting point to explore how weshould use professional knowledge whenexplaining our findings to the public.

1 Introduction

Many research findings are used to solve real-world problems. Economics is a typical example.Tons of economics research is devoted to solv-ing a wide range of economic problems, from sta-ble marriage problems to optimal monetary pol-icy problems. Even though professionals in eco-

nomics use their economics knowledge in theirminds when they tackle problems in the economy,do they use this knowledge explicitly in discus-sions? If so how these are used?

We hypothesize that policymakers utilize con-cepts from economics when they explain difficultdecisions such as policy rule changes. In otherwords, economics terminology plays a pivotal roleto explain their policy. To study this hypothesis,we use the data from Federal Reserve Bank (FRB),which is the central bank in the United States.

We utilize the dataset made from Federal OpenMarket Committee (FOMC) minutes, and an in-formation extraction technique, to extract relation-ships between economics terminologies and FRBbehavior. We focus on the data from FRB datafor the following reason. First, we can expect thatFOMC members use principal economics con-cepts because all of the FOMC members are eco-nomic experts both from academia and practicalbusiness. Second, the FRB is required to keeptheir transparency by the federal law, which meansthat the FRB always have responsibility to explaintheir policy to the public. The minutes of the com-mittee are open to the public and they have specificgoals of monetary policy that enable us to under-stand what they value. Lastly, we can clearly ob-serve the the FRB’s behavior by studying their pol-icy tools to stimulate the economy such as FederalReserve Banks.

After FOMC meeting, the monetary policy de-cision becomes open to the public. At this point,central banks need to announce their decisionproperly. This communication by the centralbanks is considered one of the important factors toimplement a successful monetary policy [7, 32].Therefore, a central bank deeply values their com-munication not only with the market participantsbut also with the general public. Beside, cen-tral banks have to remain transparent by reporting

2

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

ACL 2018 Submission ***. Confidential Review Copy. DO NOT DISTRIBUTE.

how these decisions are being made because ”bet-ter public understanding makes the policy morecredible and effective1.” The importance of Trans-parency is well known in both theoretical and em-pirical economic studies [10].

Although the specific goals slightly vary amongcentral banks, they aim at keeping economic con-ditions healthy and reducing uncertainty. 2 Theaim of monetary policy, more specifically a centralbank’s goal is considered to promote maximumemployment, stable prices, and economic growth.In recent years, monetary policy is usually a setof policy operations using several tools, such aspurchasing government bonds, assets and settingreserve bank rate [6].

To test this hypothesis, we use FOMC min-utes data from 1993Q1 to 2018Q1, and map theFRB mandate to the terminologies in economicsdictionary to select the important terminologies.In addition, we use the historical data of FederalFunds Rate (FFR) and Monetary Base (MB) to seechanges in the semantics of the terminologies re-flect the policy changes. To extract the informa-tion of the semantic of the terminologies, we useWord2Vec to obtain the word embedding of theterminologies. Then, we study how the changes inthe word embedding affect FRB policy changes.

2 Related Research

In this section, we survey the related research thatuse the documents published by the central banks.Most works use NLP techniques to extract infor-mation from the minutes and study some impor-tant concepts in economics such as communica-tion, uncertainty and transparency. Others are in-terested in extracting features from the minutes toforecast economic outcomes. As discussed in theIntroduction, we are interested more generally inhow professionals utilize the professional knowl-edge to explain their behavior in a policy makingsituation.

2.1 Communication

Most studies about central banks’ communicationsuse NLP to extract features from the documents bythe central bank and find the relationships between

1ECB website, Transparency https://www.ecb.europa.eu/ecb/orga/transparency/html/index.en.html

2To understand why uncertainty matters in monetary pol-icy see Bekaert et al.(2013). [4]

these features and economic indicators such as in-terest rates or monetary policy goals.

Lucca and Trebbi (2009) use sentiment analy-sis with the FOMC minutes and show how thesentiments in the minutes affect Treasury rates.To find the effect of sentiment on Treasury rates,they use Vector Auto Regression (VAR) model,which is the mainstream econometric method inMacroeconomics3. They find that communica-tion4has an impact on Treasury rates and also theminutes tell us the critical implication in mone-tary policy such as rule-based interest rate and theTaylor-rule 5. Tang (2017) studies whether thecentral bank communications affect interest rates[30]. The study presents the methods with theNaive Bayes model and find a strong relationshipthe labor-related contents in the minutes and mon-etary policy response to labor news. Hendry andMadeley (2010) study more specific aspect of therelationship between a central bank communica-tion and Treasury rates by using Latent SemanticAnalysis(LSA) [12]. They study how the commu-nication of the Bank of Canada (BOC) affects re-turns and volatility in short-term as well as long-term interest rate markets6. They find that strongeffects appear in interest rates.

There are studies on the communication dif-ferences in the central banks. Eyup and Odabas(2016) classify the communication strategies [13].They classify the policy statements into severalcategories from the FRB, the European CentralBank (ECB) and Central Bank of the Republic ofTurkey (CBRT). Keida and Takeda (2017) showthe changes in monetary policy communicationamong the two different governors [17]. As ananecdotal evidence, Mr. Kuroda, the BOJ gover-nor, uses the completely different style of speechand interviews than the former governor. Keidaand Takeda (2017) use tf-idf and similarity evalu-ation using vector space model (SVM), and thencompare the differences among two BOJ gover-nors speeches.

3For example, Kuttner (2001) studies the impact of mon-etary policy actions using VAR with a brief explanation ofVAR [14].

4They argues that their semantic analysis extracts thechanges in the monetary policy.

5To understand these rules, see 9.4.2 Monetary policyrules in Kuttner (2018) [15]

6In monetary policy, an attenuating volatility of a interestrate is considered as significant since high volatility meanshigh uncertainty in future economic conditions. For example,see 9.4.1 Monetary policy implementation in Kuttner (2018)[15].

3

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

ACL 2018 Submission ***. Confidential Review Copy. DO NOT DISTRIBUTE.

2.2 Uncertainty and Transparency

Some researchers use NLP to create uncertaintyindex. Andres (2017) use LDA to compose eco-nomic policy uncertainty index from news text [3].Saltzman and Yung (2018) construct uncertaintymeasurements from the FOMC documents cover-ing the long period (from 1970 to 2018) [24]. Theyalso use VAR to find the relation between their un-certainty measurements and economic condition.

Acosta (2015) use LSA to make a transparencymeasurement over 32 years period from FOMCminutes. By using their measurements, this studyshows that the Act’s requirement increased thecentral bank’s transparency [1].

2.2.1 Attitude of committee membersSuda et al. (2018) extract disagreements amongFOMC members and study its effect on asset price[28]. They classify the topics in FOMC mem-bers speeches and define disagreement as the dis-persion of the quantified sentiments. The studyshows that the disagreement among FOMC mem-bers dilutes the announcement of the future mone-tary policy direction 7.

Apel and Grimaldi (2012) use semantic analysisto study the attitudes of the committee members[2]. They use the minutes of the Swedish cen-tral bank and find the attitudes of the committeemembers are useful to predict monetary policy de-cisions.

2.3 Economic prediction

There is a lot of NLP studies that predict macroe-conomic outcomes8. Here, I focus on the stud-ies related to monetary policy. Rohlfs et al.(2016) predict monetary policy targets and Erffec-tive Federal Funds Rate (EFFR)9 by extracting thetopics in FOMC statements with Latent DirichletAllocation (LDA) [23].

Moniz and Jong (2014) predict interest rateexpectations using machine learning. They usethe Bank of England Monetary Policy Commit-tee Minutes (MPCM) from 1997 to 2014 and en-semble methods to predict interest rate expecta-tions in the financial market [20]. Ramachandran

7This type of announcement is described Forward guid-ance, an important tool in today’s monetary policy. To under-stand forward guidance, see 12.5.2 Quantitative easing andforward guidance in Kuttner (2018) [15]

8One of the early works is Wuthrich et al. [33]9To understand Target and Federal Funds Rate, see 9.4

An interest rate-centered view of monetary policy in Kuttner(2018) [15]

and DeRose (2018) classify FOMC meetings in2017 [21]. Stajner et al. (2016) focus on the spe-cific classification problem. They classify specu-lative and non-speculative speech in the transcriptsof FOMC monetary policy meetings [25]. Millerand McCoy (2014) study the content changes inFOMC transcripts and detect the changes betweenthe per and the post-financial crisis[19]. They useLDA to extract topic from the transcripts and clas-sify them. Tan and Lee (2018) study the effectof emphasis on the listeners’ reception in FOMCmembers’ speech [21]. To this aim, they examinerhetoric patterns, hedging, in the transcripts of allFOMC meetings from 1977 to 2008.

While the related researches use the conceptsin a specific discipline or features such as senti-ments, we are focusing on the minimum unit oflanguage, words. Therefore, our study is moregeneral than other related research. The implica-tion of our study can be applied to outside of eco-nomics as well: professional terminologies conveyimportant information to understand their behav-ior.

3 Data

In this section, we provide a brief overview of datacollection and describe the data. We crawl theFOMC minutes from 1967 to 2018 and the eco-nomics terminology dictionary.

The FOMC minutes are the minutes of theFOMC meeting10 to announce the monetary pol-icy decision to the public. As discussed in the Re-lated Research section, the minutes by the centralbanks provide the important information about thecentral bank behavior. The FOMC minute playsthe pivotal role in monetary policy. The FOMCminutes include not only the detail of policy deci-sion making but also the perspectives and the out-look of the economic condition at that time.

To have the FOMC minute datasets, we use theexisting datasets 11 containing the minutes from1967 to 2007. In addition, we write a crawlerto obtain the minutes from 2008 to 2018. Wealso collect the economic terminologies from thewebsite that gives brief explanations of basic eco-nomics concepts, The Economics Classroom12.This website is run by the professor in economics

10https://www.federalreserve.gov/monetarypolicy/fomc_historical_year.htm

11https://stanford.edu/˜rezab/useful/fomc_minutes.html

12https://econclassroom.com/

4

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

ACL 2018 Submission ***. Confidential Review Copy. DO NOT DISTRIBUTE.

Figure 1: Number of words in minutes in eachquarter

After preprocessing the FOMC minute, we de-cided to focus on the minutes in 1993Q1-2018Q1for two reasons. Fist, the minutes before 1993 aremuch shorter than those minutes after 1993. Sec-ond, some minutes before 1993 do not have theterminologies that we select in the Methods sec-tion. The Figure 1 shows the the umber of wordsin minutes over the entire periods. This plot de-scribes the length of minute increases over time.The striking point is between 1992 and 1993 peri-ods. The FRB changed the style of the minute andthey started to publish digital version minutes in1993. To prevent this fact from affecting our anal-ysis, we study the minute from 1993Q1 to 2018Q1in the rest of this paper.

To identfy the relation between the economicterminology usages and the monetary policychanges, we use the historical data of the FFR andthe MB. As discussed in the Related Research,FFR and MB are the important monetary policyoperation tools and the FRB change them to con-duct their policy packages. Therefore, we can as-sume that the changes in these policy tools can beinterpreted as the changes in monetary policy. TheFigure 2 shows log difference of FFR and MB.The most clear change of the monetary policy wasmade after the financial crisis in 2008.

4 Methods

In this section, we will explain the methods toidentify the relationships between changes in theterminology usage by the central bank and in thecentral bank behavior. First, we compute the theterminologies semantic among periods. We utilizeWord2Vec to obtain the word embedding for termsand define the semantic change as the cosine dis-

Figure 2: Log difference over time: Federal FundsRate and Monetary Base

tance between embedding vectors. Second, we useVector Autoregression to identify the central bankbehavioral response to the terminology meaningchanges in the minutes.

4.1 Word2Vec

Word2Vec is a well-known word embeddingmethod introduced by Mikolov et al. (2013) [18].This methods use Skip-gram model to learn syn-tactic and semantic word representations. Ourmethods are highly inspired by Hamilton et al.(2016) [11]. They use word embedding methodsto capture word semantic changes from historicalcorpora and they study the statistical law of theword semantic.

To identify the word semantic transitions acrosstime periods, we construct word embedding foreach time period. We construct word embed-ding from each FOMC minute quarterly corporaby gensim [22]. Then, we aligned the vectorsinto the same coordinate axes. As Hamilton etal. (2016) [11] discuss, this is because low dimen-sion embedding vectors can yield arbitrary orthog-onal transformations. To compare word vectors ofthe same word from different periods, we followHamilton et al. (2016) [11] and use orthogonalProcrustes to align the embedding vectors.

We also select the terminologies to be used inthe analysis by the following procedure. First, wefocus on the words in the economic dictionary.The words in the dictionary can be interpreted asimportant terminologies in economics. Second,we select the words on witch the FRB place im-portance from the dictionary. To this aim, I mapconcepts in the Fed’s mandate 13to the words in the

13https://www.federalreserve.gov/faqs

5

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

ACL 2018 Submission ***. Confidential Review Copy. DO NOT DISTRIBUTE.

dictionary. The Fed’s mandate describes what theFederal Reserve seek to achieve through its mone-tary policy and this can be interpreted as what theyplace importance on. The Fed’s mandate says

The Federal Reserve works to promote astrong U.S. economy. The Congress hasdirected the Fed to conduct the nation’s1) monetary policy to support three spe-cific goals: maximum 2) sustainable em-ployment, 3) stable prices, and moder-ate long-term interest rates.14

We map the three terms in the Fed’s mandateto the economic terminologies: monetary policy,sustainable employment, and stable prices. Thefirst term, ”monetary policy”, is straightforwardbecause we have the same word in the dictionary.However, we need to translate the other two, ”sus-tainable employment” and ”stable prices” as theyare not in the dictionary and high level concepts.We translate ”sustainable employment” as ”labor”because ”sustainable employment” is about the la-bor conditions in the economy. What we can learnfrom ”sustainable employment” is the FRB paysclose attention to ”labor.” For ”stable prices”, Wechoose ”inflation” because inflation is a typical in-dicator of price level and we also assume that theFRB pays close attention to ”inflation”.

We compute how the word embedding vectorsof the selected words vary across the time periods.We use cosine distances between the word vectorsfor the same word,s at time period t as

yst = 1−ws

t ·wst+1

‖wst+1‖‖ws

t ‖

where wst is the word embedding for word s at

time period t. Note that yst is non-negative, i.e.)0 ≤ yst ≤ 2 for any word s and 0 ≤ t. By using thecosine distance, we can focus on how much wordembeddings change between time t and t+ 1.

4.2 Vector AutoregressionTo test our hypothesis that the FRB change theirlanguage when they change their policy behav-ior, we use a statistical model. Since the dataused in this paper is timeseries data, we will useVAR, which is a generalized autoregressive(AR)model[26, 27]15. By using VAR, we can consider

14the numbers are assigned by the author15The lecture note by Zivot provides a good introduction.

[34]

biases typically in time series data such as simul-taneity bias. The VAR model to be estimated hasthe following structure,

yt = c+ Φ1yt−1 + · · ·+ Φpyt−p + εt, εt ∼W.N.(Σ) (1)

yt = (yfft , ymbt , yst )ᵀ, (2)

where c is n×1 interception vector , Φ is n×ncoefficient matrix, and Σ is variance-covariancematrix. The vector, yt, has three endogenous vari-ables at time t. yff is a log difference of Fed-eral Fund Rate, ymb is a log difference of Mone-tary Base. In addition, we incorporate yst into themodel, which is a cosine difference of word em-bedding of terminology s.

VAR allows us to identify the central bank be-havior respond to a terminology semantic changein the minute by using impulse response. Impulseresponse function, IRFij(k), is the function oftime k, which shows the response of variable yito the changes in variable yj at time k.

IRFij(k) =∂yi,t+k

∂εjt(3)

By using the impulse response function,IRFij(k), we can study how a change16 in a vari-able, yj , affects other yi over time periods. Inthis analysis, we focus on how a change in ys af-fects the other variables, yff , ymb. In other words,we focus on how the changes in the word embed-ding of the same words affects the FRB policychanges.

5 Results

5.1 Case StudyBefore answering the research question, we con-duct a case study to examine a typical exam-ple of when the FRB change both their behav-ior and terminologies semantic. We study thedata around the financial crisis in 2008, whichcaused serious economic problems. It is clearthat FRB changed their policy during this time pe-riod [8, 31, 5]. Therefore, we give how the wordembedding changes around these time periods tostudy the case of the FRB changing its behavior.Table 1 shows the top three similar words of ”la-bor” by Word2Vec from annual corpora. We can

16In this paper, one standard deviation change

6

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

550

551

552

553

554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

572

573

574

575

576

577

578

579

580

581

582

583

584

585

586

587

588

589

590

591

592

593

594

595

596

597

598

599

ACL 2018 Submission ***. Confidential Review Copy. DO NOT DISTRIBUTE.

see, after the crisis, the most similar word changedfrom ”ebullient” to ”disorderly”. Although thereare some negative words before the crisis, we cansee more negative words after the crisis such as”tip(s)”,”fragile”, ”disorderly” and ”foreclosed”.This case study shows that looking into an impor-tant words provides a clue to understand the FRB’sperspectives and behavior at that time.

5.2 Impulse Response Analysis

I estimate the VAR model and compute the im-pulse responses with a 95% confident interval.Figures 3, 4 and 5 show the impulse response foreach model. The most notable result is for themodel with s = ”labor”, Figure 3. The secondrow in Figure 3 shows a change in the word em-bedding of ”labor” largely decreases FF and thethird row shows that a change in the ”labor” em-bedding increases MB. This means that when theychange the meaning of ”labor”, they also changetheir policy behavior to stimulate the economy.Although the other two columns shows that thesame results, the impulse responses are weak. Thelast column shows the changes in the word embed-ding of ”monetary policy” has a weak effect on thepolicy behavior. These results show that each ter-minology affects the policy behavior to differentdegree.

The other interesting finding is that in all mod-els (Figures 3, 4 and 5) a change in the terminol-ogy word embedding does not have a long effecton itself. The change in a terminology seman-tic not largely affect the semantic of that termi-nology in the following time periods. In the allfirst rows in Figure 3, 4 and 5, the impulse re-sponses converge faster than other rows. This indi-cate that when they change the meaning of a term,they will adhere to that meaning in the followingterms. This is consistent with the previous stud-ies that show that keeping a central bank’s policyconsistent is optimal monetary policy [16, 29, 9].

6 Conclusion

In today’s complex society, more and more peo-ple are obtaining higher educations to solve socialproblems. However, whether their professionalknowledge help them to explain their solution toour society has not been explained well.

Using the dataset from FOMC minute and ainformation extraction technique, we found thatthe policy maker use their knowledge to explain

Figure 3: Impulse responses in the embeddingwords vector distance ”labor” with a 95% confi-dence interval

Figure 4: Impulse responses in the embeddingwords vector distance ”inflation” with a 95% con-fidence interval

7

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630

631

632

633

634

635

636

637

638

639

640

641

642

643

644

645

646

647

648

649

650

651

652

653

654

655

656

657

658

659

660

661

662

663

664

665

666

667

668

669

670

671

672

673

674

675

676

677

678

679

680

681

682

683

684

685

686

687

688

689

690

691

692

693

694

695

696

697

698

699

ACL 2018 Submission ***. Confidential Review Copy. DO NOT DISTRIBUTE.

Year 2005 2006 2007 2008 2009 2010

1st ebullient(0.308) exchange-rate(0.31) disorderly(0.272) occur(0.259) tips(0.293) disorderly(0.333)2nd incremental(0.245) opportunity-cost(0.278) emerge(0.265) resulting(0.246) nonresidential(0.273) attention(0.26)3rd disorderly(0.218) financial(0.25) exchange-rate(0.26) housing(0.244) fragile(0.265) foreclosed(0.258)

Table 1: The top three similar words of ”labor” with cosine similarity around the financial crisis in 2008.

Figure 5: Impulse responses in the embeddingwords vector distance ”monetary policy” with a95% confidence interval

their policy direction. When they change theusage of the terminologies, their policy changesaccordingly. The degree of effect of semanticchanges differ among terminologies, which meansthe importance of the terminologies varies as well.Moreover, once they change the semantic of theterminologies, they tend to follow the new seman-tic.

This study describes how the professionals useeconomic concepts in practice and how we shouldunderstand economics in order to understand mon-etary policy management by bridging the gap be-tween different research domains, economics, andcomputer science.

The role of the professional terminologies inthis analysis raises a number of further openquestions. Although there is a clear relation-ship between the usage changes and the behaviorchanges, how they changes the usage when theywant to change their behavior is still an open ques-tion. The other problem is that we do not discussthe source of the FRB’s behavior changes. The al-teration in the FRB policy may come from changes

in economic conditions or may come from theFRB policy strategy changes.

6.1 Note related to the class projects

The FRB also provides other documents such astranscripts of FOMC. Although they are informa-tive, they are not analyzed in the report. Sincemost these documents are published five years af-ter the meeting, they are not for communicationwith the public. However, we provide the data andthe code for them in the repository. 17.

References[1] Miguel Acosta. Fomc responses to

calls for transparency. Available atSSRN:https://ssrn.com/abstract=2647486, 2015.

[2] Mikael Apel and Marianna Grimaldi. The informa-tion content of central bank minutes. Available atSSRN: https://ssrn.com/abstract=2092575, 2012.

[3] Andres Azqueta-Gavaldon. Developing news-based economic policy uncertainty index with un-supervised machine learning. Economics Letters,158:47–50, 2017.

[4] Geert Bekaert, Marie Hoerova, and Marco Lo Duca.Risk, uncertainty and monetary policy. Journal ofMonetary Economics, 60(7):771–788, 2013.

[5] Ben Bernanke. The courage to act: A memoir ofa crisis and its aftermath. WW Norton New York,2015.

[6] Ben S Bernanke and Vincent R Reinhart. Conduct-ing monetary policy at very low short-term inter-est rates. American Economic Review, 94(2):85–90,2004.

[7] Alan S Blinder, Michael Ehrmann, MarcelFratzscher, Jakob De Haan, and David-Jan Jansen.Central bank communication and monetary policy:A survey of theory and evidence. Journal ofEconomic Literature, 46(4):910–45, 2008.

[8] Stephen G Cecchetti. Crisis and responses: the fed-eral reserve in the early stages of the financial cri-sis. Journal of Economic Perspectives, 23(1):51–75,2009.

17For detail, please read README in the repository.

8

700

701

702

703

704

705

706

707

708

709

710

711

712

713

714

715

716

717

718

719

720

721

722

723

724

725

726

727

728

729

730

731

732

733

734

735

736

737

738

739

740

741

742

743

744

745

746

747

748

749

750

751

752

753

754

755

756

757

758

759

760

761

762

763

764

765

766

767

768

769

770

771

772

773

774

775

776

777

778

779

780

781

782

783

784

785

786

787

788

789

790

791

792

793

794

795

796

797

798

799

ACL 2018 Submission ***. Confidential Review Copy. DO NOT DISTRIBUTE.

[9] Richard Clarida, Jordi Gali, and Mark Gertler. Mon-etary policy rules and macroeconomic stability: ev-idence and some theory. The Quarterly journal ofeconomics, 115(1):147–180, 2000.

[10] Petra M Geraats. Central bank transparency. Theeconomic journal, 112(483):F532–F565, 2002.

[11] William L Hamilton, Jure Leskovec, and Dan Ju-rafsky. Diachronic word embeddings reveal sta-tistical laws of semantic change. arXiv preprintarXiv:1605.09096, 2016.

[12] Scott Hendry and Alison Madeley. Text min-ing and the information content of bank ofcanada communications. Available at SSRN:https://ssrn.com/abstract=1722829, 2010.

[13] Eyup Kahveci and Aysun Odabas. Central bankscommunication strategy and content analysis ofmonetary policy statements: The case of fed, ecband cbrt. Procedia-Social and Behavioral Sciences,235:618–629, 2016.

[14] Kenneth N Kuttner. Monetary policy surprises andinterest rates: Evidence from the fed funds futuresmarket. Journal of monetary economics, 47(3):523–544, 2001.

[15] Kenneth N Kuttner. Macroeconomicss.https://econ.williams.edu/faculty-pages/teaching-resources/, 2018.

[16] Susanne Lohmann. Optimal commitment in mone-tary policy: credibility versus flexibility. The Amer-ican Economic Review, 82(1):273–286, 1992.

[17] KEIDA Masayuki, TAKEDA Yosuke, et al. A se-mantic analysis of monetary shamanism: A case ofthe boj’s governor haruhiko kuroda. RIETI Discus-sion Paper Series, 2017.

[18] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg SCorrado, and Jeff Dean. Distributed representationsof words and phrases and their compositionality. InAdvances in neural information processing systems,pages 3111–3119, 2013.

[19] John E Miller and Kathleen F McCoy. Changingfocus of the fomc through the financial crisis. NLPUnshared Task in PoliInformatics. URL: http://www.academia. edu/download/34123959/fomc. pdf,2014.

[20] Andy Moniz and Franciska de Jong. Predicting theimpact of central bank communications on financialmarket investors interest rate expectations. In Eu-ropean Semantic Web Conference, pages 144–155.Springer, 2014.

[21] Harish Gandhi Ramachandran and Dan DeRose Jr.A text analysis of federal reserve meeting minutes.arXiv preprint arXiv:1805.07851, 2018.

[22] Radim Rehurek and Petr Sojka. Software Frame-work for Topic Modelling with Large Corpora. InProceedings of the LREC 2010 Workshop on NewChallenges for NLP Frameworks, pages 45–50, Val-letta, Malta, May 2010. ELRA. http://is.muni.cz/publication/884893/en.

[23] Christopher Rohlfs, Sunandan Chakraborty, andLakshminarayanan Subramanian. The effects ofthe content of fomc communications on us treasuryrates. In Proceedings of the 2016 Conference onEmpirical Methods in Natural Language Process-ing, pages 2096–2102, 2016.

[24] Bennett Saltzman and Julieta Yung. A machinelearning approach to identifying different types ofuncertainty. Economics Letters, 171:58–62, 2018.

[25] Sanja Stajner, Nicole Baerg, Simone PaoloPonzetto, and Heiner Stuckenschmidt. Automaticdetection of speculation in policy statements. Auto-matic detection of speculation in policy statements,2016.

[26] James H Stock and Mark W Watson. Vector au-toregressions. Journal of Economic perspectives,15(4):101–115, 2001.

[27] James H Stock, Mark W Watson, et al. Introduc-tion to econometrics, volume 104. Addison WesleyBoston, 2003.

[28] Shintaro Suda, Ryo Ito, and KiyoshiIzumi. Disagreement among fomc mem-bers and asset prices. Available atSSRN:https://ssrn.com/abstract=3109243, 2018.

[29] Lars EO Svensson. Inflation targeting as a mon-etary policy rule. Journal of monetary economics,43(3):607–654, 1999.

[30] Jenny Tang. Fomc communication and in-terest rate sensitivity to news. Available atSSRN:https://ssrn.com/abstract=3075842, 2017.

[31] John B Taylor. The financial crisis and the pol-icy responses: An empirical analysis of what wentwrong. Technical report, National Bureau of Eco-nomic Research, 2009.

[32] Michael Woodford. Central bank communicationand policy effectiveness. Technical report, NationalBureau of Economic Research, 2005.

[33] Beat Wuthrich, D Permunetilleke, Steven Leung,W Lam, Vincent Cho, and J Zhang. Daily predictionof major stock indices from textual www data. Hkietransactions, 5(3):151–156, 1998.

[34] Eric Zivot. Lecture notes in time serieseconometrics, vector autoregressive mod-els for multivariate time series. https://faculty.washington.edu/ezivot/econ584/notes/varModels.pdf, 2006.