--- title: "Voice of Monetary Policy: Gorodnichenko, Pham & Talavera (2023)" description: >- Distilled: A deep learning model detects emotions in Fed chair voices during FOMC press conference Q&A sessions; a more positive voice tone raises S&P 500 returns by roughly 100 basis points over five days, reduces VIX, lowers inflation expectations, and appreciates the dollar against the euro, after controlling for policy actions and text sentiment. American Economic Review 113(2) 2023, paywalled. Seven core results with source locators, the emotion-detection model, VoiceTone construction, and the local-projections specification. LLM-distilled, not human-verified, not reproduced. sidebar: label: Gorodnichenko-Pham-Talavera 2023 order: 1 tags: [paper-summary, monetary-policy, central-banking, text-as-data, machine-learning, financial-markets, local-projections, peer-reviewed, unreplicated, data:swanson-fomc-shocks, data:wu-xia-shadow-rate, data:nexis-uni] paper: authors: Yuriy Gorodnichenko, Tho Pham, Oleksandr Talavera authorList: - { family: Gorodnichenko, given: Yuriy, orcid: "0000-0002-3639-4254", affiliation: "University of California, Berkeley" } - { family: Pham, given: Tho, orcid: "0000-0003-2311-912X", affiliation: "University of Reading" } - { family: Talavera, given: Oleksandr, orcid: "0000-0002-4799-778X", affiliation: "University of Birmingham" } year: 2023 venue: "The American Economic Review 113(2), February 2023, 548-584" venueShort: AER 2023 doi: 10.1257/aer.20220129 jel: codes: [E52, E44, G14] assignedBy: claude-sonnet-4-6 date: 2026-06-25 topics: ["Computational and Text Analysis Methods"] dataAccess: licensed-commercial outcome: - S&P 500 (SPY ETF) returns around FOMC press conferences - stock market volatility (VIX, VIXY) - inflation expectations (GOVT-TIP spread, gold price) - USD/EUR and USD/JPY exchange rates outcomeClass: [security-returns, expectations, macro-aggregates] license: "AEA copyright; no CC licence found in Crossref record (licence URL points to publisher PDF, no Creative Commons identifier); replication data publicly available at ICPSR DOI 10.3886/E178302V1" licenseShort: paywalled access: paywalled machineAccess: "paywalled (AEA site, 2026-06-25); preprint via SSRN DOI 10.2139/ssrn.3809564; replication data at https://doi.org/10.3886/E178302V1" redistribution: extract-only resultsCount: 7 citedByCount: 0 methods: role: both contributes: voice-tone-policy-index family: reduced-form-causal buildsFrom: [local-projections, text-classification, speech-emotion-recognition] identification: selection-on-observables contributionType: [new-method, new-data, new-fact] mechanisms: [information-asymmetry, learning] introducesData: true scope: region: US assetClass: "US equities, government and corporate bonds, currency" period: 2011-04..2019-06 frequency: daily dataType: [market, text, other] granularity: [aggregate] n: "36 FOMC press conferences, 692 Q&A audio segments, 68 FOMC meetings, April 2011 to June 2019" findings: - { ref: R1, outcome: "S&P 500 (SPY) returns", metric: basis-points, value: "~100 bps at h=5 days for +1 unit VoiceTone; ~75 bps for +1 SD (abstract p. 550)", direction: positive } - { ref: R2, outcome: "stock market volatility (VIX)", metric: basis-points, value: "large negative effect building over h=0-15 days; magnitude roughly equal to reduction from a 1-SD FG shock", direction: negative } - { ref: R3, outcome: "US nominal government bond price (GOVT ETF)", metric: basis-points, value: "point estimates near zero, statistically insignificant at all horizons h=0-15", direction: none } - { ref: R4, outcome: "GOVT-TIP spread (inflation expectations proxy)", metric: basis-points, value: "gradual increase peaking at h~10 days; positive tone signals lower expected inflation; statistically significant at longer horizons", direction: positive } - { ref: R5, outcome: "USD/EUR exchange rate (dollar vs euro)", metric: basis-points, value: "~5-7 bps at h=5-10 days (from Figure 13 scale); dollar appreciates; marginally significant", direction: positive } - { ref: R6, outcome: "S&P 500 (SPY) returns", metric: basis-points, value: "BERT text sentiment: positive point estimates but statistically insignificant in this 36-meeting sample", direction: none } - { ref: R7, outcome: "S&P 500 (SPY) returns (intraday)", metric: basis-points, value: "+1 bp at impact (h=0 minutes, flow spec); statistically significant", direction: positive } resultType: new-finding relatesTo: - { cite: "Swanson (2021)", relation: builds-on, note: "three-factor FOMC policy shock series (FFR, FG, AP) used as controls; voice-tone effect on stocks is comparable in magnitude to the 1-SD FG shock effect" } - { cite: "Jorda (2005)", doi: '10.1257/0002828053828518', relation: builds-on, note: "local projections framework adopted to trace impulse responses across h=0-15 days" } - { cite: "Hansen and McMahon (2016)", doi: '10.1093/qje/qjx045', relation: cites, note: "prior textual analysis of FOMC communication that motivates controlling for text sentiment" } - { cite: "Cieslak, Morse, and Vissing-Jorgensen (2019)", doi: '10.1111/jofi.12818', relation: cites, note: "FOMC cycle stock-return anomaly; voice tone provides a nonverbal channel beyond textual content" } - { cite: "Romer and Romer (2000)", doi: '10.1257/aer.90.3.429', relation: cites, note: "Fed information effect: positive voice tone may signal superior Fed knowledge of economic fundamentals" } - { cite: "Curti and Kazinnik (2021)", doi: '10.2139/ssrn.3782239', relation: cites, note: "concurrent work showing negative facial expressions lower stock returns at press conferences; consistent with nonverbal channel" } openQuestions: - "Whether voice tone effects work through a forward-guidance signaling mechanism or a Fed information effect remains unresolved; the two interpretations predict similar qualitative patterns for stocks and inflation expectations but differ in their bond-market implications (pp. 576-578)" - "The paper cannot determine whether observed voice tone variation was intentional or inadvertent, leaving open how central banks should manage vocal cues as a policy instrument (p. 581)" replicationCode: url: "https://doi.org/10.3886/E178302V1" status: available extraction: - { by: "paper-distiller (claude-sonnet-4-6)", date: 2026-06-25, role: extracted, note: "PDF read in full (pp. 548-584). Seven results extracted with figure and page locators. Not human-verified; not reproduced." } - { by: paper-verifier (claude-sonnet-4-6), date: 2026-06-25, role: verified, note: "Locators and reported magnitudes re-checked against the source PDF; fixed four page-locator errors (R3 p.565->p.567, R4 pp.567-568->p.570 and GLD locator p.570->p.571, R5 p.572->p.573) and corrected correlation values confusing text-text with text-voice correlations (Identification and Method sections)." } licenceVerification: - { source: "Crossref REST API works/10.1257/aer.20220129", checked: 2026-06-25, by: "paper-distiller (claude-sonnet-4-6)", found: "licence entry: content-version=vor, URL=https://pubs.aeaweb.org/doi/pdf/10.1257/aer.20220129; no Creative Commons identifier; AEA standard terms apply" } rightsSignalConflict: false --- **What this is.** The paper's core results, the VoiceTone construction and the deep-learning emotion model, and the local-projections specification: enough to know what it found and how, without reading all 37 pages. To replicate or extend it, read the full source at the [original](https://doi.org/10.1257/aer.20220129) or the [preprint](https://doi.org/10.2139/ssrn.3809564). ## TL;DR Gorodnichenko, Pham, and Talavera (2023) build a deep learning model that classifies emotions from audio features of Fed chair answers during FOMC press conference Q&A sessions (April 2011 to June 2019) into positive, negative, or neutral. They aggregate these into a VoiceTone measure, then run local projections following Jorda (2005) of daily returns on 14 financial outcomes against VoiceTone and TextSentiment, controlling for Swanson (2021) policy shocks (FFR, FG, AP) and the Wu and Xia (2016) shadow rate. A one-unit increase in VoiceTone raises S&P 500 returns by roughly 100 basis points over five days, reduces VIX, lowers inflation expectations, and appreciates the dollar against the euro. The bond market shows no significant response. Text sentiment (BERT-based) does not have a statistically significant effect on stock prices in this 36-meeting sample. The results are robust to Fed chair fixed effects, alternative text sentiment measures (RoBERTa, FinBERT, search-and-count, human classification per Cieslak, Morse, and Vissing-Jorgensen (2019) style controls), and a high-frequency intraday analysis showing a +1 bp immediate impact on impact. A concurrent paper by Curti and Kazinnik (2021) confirms nonverbal effects through facial expressions. ## Core results Estimates from nonparametric accelerated bootstrap with 90% bias-corrected confidence intervals. Locators point into the source PDF. | # | Result | Locator | Magnitude | |---|---|---|---| | R1 | **Positive voice tone raises S&P 500 (SPY) returns**: effect builds over days, significant at 10% | Figure 3 Panel B, p. 562 | +~100 bps at h=5 days for +1 unit VoiceTone; +~75 bps per +1 SD (abstract, p. 550) | | R2 | **Positive voice tone reduces stock market volatility (VIX)**: economically significant negative effect, builds over 15 days | Figure 4 Panel B, p. 563-564 | Reduction roughly equal to increase from a 1-SD FG shock (paper's comparison, p. 563) | | R3 | **Bond market (GOVT ETF) does not respond significantly to voice tone**: statistically insignificant at all horizons | Figure 7 Panel B, p. 567 | Point estimates fluctuate near zero; 90% CI contains zero throughout h=0-15 days | | R4 | **Positive voice tone raises GOVT-TIP spread** (signals lower inflation expectations): near zero on impact, builds and peaks at h~10 days | Figure 10, p. 570 | Gradual increase in spread, statistically significant at longer horizons; GLD (gold) price shows similar pattern (Figure 11, p. 571) | | R5 | **Positive voice tone leads to dollar appreciation vs. euro** (EUR/USD coefficient positive): builds over 5-10 days | Figure 13, p. 573 | ~5-7 bps (from figure scale), marginally significant; JPY/USD shows no significant response | | R6 | **Text sentiment (BERT) does not significantly move SPY**: positive point estimates but inside CI throughout | Figure 3 Panel C, p. 562 | Positive point estimates across all horizons, statistically insignificant in this sample | | R7 | **Intraday (flow spec): positive voice tone raises SPY by +1 bp on impact**, answer by answer | Figure 15 top panels, p. 576-577 | +1 bp at h=0 minutes, statistically significant; cumulative spec shows gradual build during Q&A session | **Overall (paper's conclusion).** Voice tone conveys economically significant information that moves financial markets across multiple asset classes, beyond the content of spoken words and beyond the Fed's explicit policy actions. The effect builds gradually over days, consistent with information frictions and slow incorporation of nonverbal signals into prices via continued media coverage (Figure 16, p. 578-580). ## Theory / model The paper has no formal structural model. The empirical strategy tests whether voice tone carries market-moving information orthogonal to text and policy actions, under two interpretations that the data cannot distinguish. **Interpretation 1 (forward-guidance signaling, pp. 576-577).** A positive voice tone signals that the Fed is unlikely to tighten policy in the near future. Under the zero lower bound, this attenuates rate risk, reduces perceived future volatility (VIX falls), lowers discount uncertainty (equity prices rise), and, since monetary tightening is a lower-probability event, reduces the expected future path of inflation (GOVT-TIP spread rises). Responses to voice tone are qualitatively similar to those for the FG shock from Swanson (2021), consistent with this interpretation. **Interpretation 2 (Fed information effect, Romer and Romer 2000, p. 577).** The Fed chair's positive tone signals superior private information about a stronger economic outlook. A more upbeat assessment reduces uncertainty, may lower the rate-risk component of yields, and raises equity valuations. The paper tests this by checking whether positive-tone press conferences are followed by more dovish Fed tweets on days h=1 to 15; Figure 16 (p. 578-579) confirms this pattern, consistent with information revelation. **Identification.** The design controls for observable channels: VoiceTone enters after partialling out TextSentiment (BERT-based, built from statements, remarks, and Q&A responses), three identified policy shocks from Swanson (2021) (FFR, FG, AP), the Wu and Xia (2016) shadow rate, and an indicator for meetings without press conferences. Voice tone and text sentiment are only weakly correlated ($$\rho = 0.37$$ (Pearson; Spearman = 0.30) for statement text sentiment vs. voice tone; $$\rho = 0.29$$ for Q&A text sentiment vs. voice tone; pp. 557-558), providing independent variation. Hansen and McMahon (2016) document that FOMC textual content carries significant information; controlling for it isolates the vocal dimension. The design is selection-on-observables: no instrument for voice tone is used, and the identifying assumption is that residual VoiceTone variation is uncorrelated with omitted drivers of financial outcomes after the controls listed above. ## Method **Step 1: Emotion detection and VoiceTone construction (pp. 552-555).** Each FOMC press conference audio is manually split into individual answers (692 segments across 36 press conferences). For each audio segment, three audio feature sets are extracted via Librosa: $$ \text{Features}_s = \bigl[\underbrace{128 \text{ mel spectrogram coeff.}}_{\text{loudness at frequency}} ,\; \underbrace{12 \text{ chroma coeff.}}_{\text{melodic/harmonic energy}} ,\; \underbrace{40 \text{ MFCCs}}_{\text{spectral envelope}}\bigr] \quad (180 \text{ features total}) $$ A fully connected neural network with architecture: Input(180) -> Linear(200) -> Linear(200) -> Linear(200) -> Softmax(5) [with dropout rate 0.3 after each hidden layer] is trained on RAVDESS and TESS labeled emotion databases (80% train / 20% test). Accuracy on the test set is 84% overall (equation 1, p. 553): $$ \text{Accuracy}(y,\hat{y}) = \frac{1}{n} \sum_{i=1}^{n} \mathbf{1}\{\hat{y}_i = y_i\} \tag{1} $$ with per-class accuracy: angry 87%, sad 84%, neutral 74%, surprised 87%, happy 80%. Each segment is then collapsed to positive (happy or pleasantly surprised), negative (sad or angry), or neutral. Press-conference-level VoiceTone is (equation 2, p. 554): $$ \text{VoiceTone} = \frac{\text{Positive answers} - \text{Negative answers}}{\text{Positive answers} + \text{Negative answers}}, \qquad \text{VoiceTone} \in [-1, +1] \tag{2} $$ The measure is constructed for three Fed chairs: Bernanke (12 press conferences), Yellen (16), and Powell (8). Mean VoiceTone is +0.64 (Bernanke), -0.13 (Yellen), -0.30 (Powell); within-chair variation is substantial (Table 1, p. 558). **Step 2: TextSentiment via BERT (pp. 556-557).** BERT base model (12 layers, 768 hidden states, 110M parameters) generates word embeddings for each paragraph of FOMC policy texts. A neural network trained on hand-scored statements from 1997-2010 (scored -10 to +10 on dovish-hawkish axis by research assistants; 81% accuracy) classifies each paragraph as dovish (score $$\geq +0.5$$), hawkish (score $$\leq -0.5$$), or neutral. TextSentiment aggregates over statements, opening remarks, and Q&A responses (equation 3, p. 557): $$ \text{TextSentiment} = \frac{\text{Dovish text} - \text{Hawkish text}}{\text{Dovish text} + \text{Hawkish text}}, \qquad \text{TextSentiment} \in [-1, +1] \tag{3} $$ A positive value signals a dovish/expansionary stance. TextSentiment and VoiceTone are weakly correlated ($$\rho \approx 0.29$$ for Q&A text vs. voice tone; $$\rho \approx 0.37$$ (Pearson; Spearman = 0.30) for statement text vs. voice tone), confirming partial independence (pp. 557-558; Figure 1, p. 559). This method builds on `text-classification` for the BERT-based text scoring and `speech-emotion-recognition` (proposed) for the audio emotion classification. ## Empirical specifications **Main specification (local projections, Jorda 2005).** For each horizon $$h = 0, 1, \ldots, 15$$ days, equation (4) from p. 560 is estimated separately by OLS: $$ \text{Outcome}_{t,t+h} = b_0^{(h)} + b_1^{(h)} \text{VoiceTone}_t + b_2^{(h)} \text{TextSentiment}_t + b_3^{(h)} \text{FFRShock}_t + b_4^{(h)} \text{FGShock}_t + b_5^{(h)} \text{APShock}_t + b_6^{(h)} \text{ShadowRate}_t + b_7^{(h)} \mathbf{1}\{\text{NoPressConference}_t\} + \text{error}_t^{(h)} \tag{4} $$ where $$t$$ indexes FOMC meetings; $$\text{Outcome}_{t,t+h}$$ is the log-return from open on FOMC day $$t$$ to close on day $$t+h$$ (e.g. $$\log(\text{SPY}_{t+h}^{\text{close}}) - \log(\text{SPY}_t^{\text{open}})$$); FFRShock, FGShock, APShock are Swanson (2021) policy shocks (normalized to unit variance over the pre-ZLB period); ShadowRate is from Wu and Xia (2016); and $$\mathbf{1}\{\text{NoPressConference}\} = 1$$ when no press conference was held. The coefficient path $$\{b_1^{(h)}\}_{h=0}^{15}$$ traces the impulse response to a unit increase in voice tone. Standard errors are from nonparametric accelerated bootstrap (90% bias-corrected confidence intervals; p. 561). The sample has 68 FOMC meetings, 36 with press conferences. Results (R1)-(R6) in the Core results table are from specification (4). Outcomes studied: SPY (R1, R6), VIX (R2), VIXY, VIXM (Figure 5, p. 564-565), GOVT (R3), GOVT-TIP spread (R4), EUR/USD (R5), and others (LQD, GLD, IVR, JPY). **Robustness variants (pp. 570-575; Figure 14, p. 574).** Adding the Citigroup Economic Surprise Index (Panel A), controlling for volume of corporate earnings announcements (Panel B), adding pre-FOMC media sentiment (Panel C), adding Fed chair fixed effects (Panel D), allowing sign asymmetry in voice tone (Panel E), and replacing BERT with RoBERTa (Panel F), FinBERT (Panel G), search-and-count (Panel H), or human classification (Panel I) leave the voice tone coefficient on SPY stable and statistically significant. **High-frequency (intraday) specifications (pp. 575-577).** Audio segments are timed to the second; SPY prices are matched to each answer. "Flow" specification (eq. 6', p. 575), answer by answer: $$ \text{Outcome}_{t \to t+h,m,s} = b_0^{(h)} + b_1^{(h)} \text{VoiceTone}_{t,m,s} + b_2^{(h)} \text{TextSentiment}_{t,m,s} + \lambda_m + \gamma_s + \text{error}^{(h)} \tag{6'} $$ "Cumulative" specification (eq. 6'', p. 575), cumulating from press conference start $$t_0$$: $$ \text{Outcome}_{t_0 \to t+h,m,s} = b_0^{(h)} + b_1^{(h)} \text{VoiceTone}_{t_0 \to t,m,s} + b_2^{(h)} \text{TextSentiment}_{t_0 \to t,m,s} + \lambda_m + \gamma_s + \text{error}^{(h)} \tag{6''} $$ where $$\lambda_m$$ is a meeting fixed effect and $$\gamma_s$$ is a question-order fixed effect; $$h$$ is in minutes; bootstrap clusters at the meeting level. Result R7 uses specification (6'). ## Datasets used | Dataset | Role in paper | Wiki page | |---|---|---| | FOMC press conference audio (Fed YouTube, 36 conferences, April 2011-June 2019) | Construct VoiceTone: 692 Q&A audio segments, split per answer; author-introduced dataset | No page yet | | FOMC statements, opening remarks, Q&A transcripts (Fed website) | Construct TextSentiment; training corpus from 1997-2010 statements | No page yet | | RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) | Train emotion-detection neural network (80% of combined training set) | No page yet | | TESS (Toronto Emotional Speech Set) | Train emotion-detection neural network (joint with RAVDESS) | No page yet | | ETF prices: SPY, VIX, VIXY, VIXM, GOVT, TIP, LQD, LQDH, IVR, GLD (Thomson Reuters, Yahoo Finance, Tiingo) | Outcome variables: daily log returns from open on FOMC day to close on day t+h | No page yet | | Swanson (2021) policy shocks (FFR, FG, AP) | Controls for monetary policy actions in specification (4) | [No page yet](/wiki/datasets/) | | Wu and Xia (2016) shadow policy rate | Control for ZLB period and monetary policy cycle in specification (4) | No page yet | | Nexis Uni news database (23,275 FOMC-related articles, Jan 2011-Jul 2019) | Robustness: MediaSentiment control for media framing before each FOMC meeting | No page yet | | Federal Reserve Twitter (Board of Governors + regional Fed accounts) | Robustness: post-FOMC tweet sentiment tracks positive voice tone (Figure 16, p. 578-579) | No page yet | Sample: 68 FOMC meetings, 36 press conferences, April 2011 to June 2019. VoiceTone mean = 0.09, SD = 0.75 across all meetings (Table 1, p. 558). ## When to read the full paper Use the [original](https://doi.org/10.1257/aer.20220129) (or [preprint](https://doi.org/10.2139/ssrn.3809564)) if you are: studying nonverbal central bank communication as a channel for market expectations; building or extending an emotion-detection model for audio from policy events; evaluating whether text-only NLP models fully capture FOMC communication; checking exact robustness specifications and the Internet Appendix with additional results by ETF maturity and currency; or replicating via the [ICPSR replication data](https://doi.org/10.3886/E178302V1). The locators above point to the exact figures and tables. ## Attribution and rights Source: peer-reviewed, *The American Economic Review* 113(2), February 2023. Paywalled; preprint available at SSRN. This distillation was extracted by an LLM on 2026-06-25 and is **not human-verified or independently reproduced**. Rights are held by the American Economic Association; no CC licence was found in the Crossref record. Reproduce and adapt only per AEA terms. > Gorodnichenko, Yuriy, Tho Pham, and Oleksandr Talavera. "The Voice of Monetary Policy." *The American Economic Review* 113, no. 2 (February 2023): 548-584. DOI: 10.1257/aer.20220129.