Event studies with text signals, timestamp alignment and topic modeling.

An event study with text

Lesson 2.3.5 built event studies with dates: earnings announcements, index additions, dividends. The mechanics are the same with text (abnormal return, CAR, windows before and after), with two differences that change the results:

• The event has a time. A news item at 7:00 and another at 17:40 on the same day do not enter the same close-to-close return. With daily data, the question is in which session the price can react.
• The signal is a noisy score. The sentiment comes from a dictionary, from FinBERT or from an LLM (lesson 6.1.1), not from an accounting figure. And there are many more events: tens of thousands of news items, with copies and wrap-ups that are not new events.

flowchart LR
  P[Publication] --> S[Reaction session]
  S --> D[Drift]
  C[Pre-event control] --> P

The pre-event control (sessions −5 to −1) has to come out flat: if the CAR moves before publication, either there is a leak, or the alignment is wrong, or the news item reports something that had already happened.

The data are those of level 6: 39,570 synthetic news items with their publication instant to the second and the closes of 120 fictitious stocks over 2,011 NYSE sessions. Most of the news is written in Spanish and some in English; the texts are kept as they are, since the code scores them with the Spanish and English lexicon of lesson 6.1.1. The generator planted a known effect: on the reaction day, each news item moves the price in proportion to its true sentiment and to the materiality of the event, with a decaying drift over the next five sessions. The planted slope is 123 bp per unit of sentiment (level 6 README). The abnormal return is the stock's return minus the mean of the 120 in the panel:

import re

import exchange_calendars as xcals
import numpy as np
import pandas as pd

noticias = pd.read_parquet("noticias6_corpus.parquet")
verdad = pd.read_csv("noticias6_verdad.csv")
df = noticias.merge(verdad, on="id_noticia")
precios = pd.read_parquet("acciones6_precios.parquet")
cierre = precios.pivot(index="fecha", columns="ticker",
                       values="cierre")
r = cierre.pct_change()
anormal = r.sub(r.mean(axis=1), axis=0).to_numpy()
N = len(cierre)
columna = {t: j for j, t in enumerate(cierre.columns)}
df["j"] = df["ticker_actual"].map(columna)
print(cierre.shape, df.shape)
print(df["tipo_noticia"].value_counts().to_dict())

(2011, 120) (39570, 22)
{'original': 22593, 'sindicada': 12266, 'resultados': 3360, 'mercado': 1209, 'correccion': 142}

The news types are original stories, syndicated copies (sindicada), earnings (resultados), market wrap-ups (mercado) and corrections (correccion). ticker_actual is the ticker in force today, the one used by the price table: six companies changed ticker and their old news carries the old one (lesson 6.1.2). Joining by the news ticker would lose those rows.

From the instant to the reaction session

A closing price summarizes what was known at 16:00 New York time. A news item published at instant $t$ can only enter the returns of the sessions whose close is later than $t$. With the exchange calendar (lesson 1.1.2), the reaction session is the first session that closes after publication:

exchange_calendars gives the opening and closing time of each session in UTC, with the half-day sessions (the day after Thanksgiving, Christmas Eve and some days before July 4 close at 13:00) and the daylight saving time changes included. With that table, the reaction session is a binary search:

cal = xcals.get_calendar("XNYS")
horario = cal.schedule.loc[cierre.index[0]:cierre.index[-1]]
print((horario.index.tz_localize(None) == cierre.index).all())
cierres_us = pd.DatetimeIndex(horario["close"]).as_unit("us").asi8
aperturas_us = pd.DatetimeIndex(horario["open"]).as_unit("us").asi8
hora_cierre = horario["close"].dt.tz_convert("America/New_York")
medias = horario[hora_cierre.dt.hour < 16]
print(len(medias), "half-day sessions")
medias.loc["2019-11-29":"2019-12-24"]

True
18 half-day sessions
                                open break_start break_end                     close
2019-11-29 2019-11-29 14:30:00+00:00         NaT       NaT 2019-11-29 18:00:00+00:00
2019-12-24 2019-12-24 14:30:00+00:00         NaT       NaT 2019-12-24 18:00:00+00:00

The 2,011 sessions of the calendar match those of the price table. On November 29, 2019 the market closes at 18:00 UTC (13:00 in New York): a news item at 14:00 that day no longer enters its return.

def sesion_de(ts):
    """First session that closes after `ts` and whether `ts` falls
    inside that session (between its open and its close)."""
    t = pd.DatetimeIndex(ts).as_unit("us").asi8
    k = np.searchsorted(cierres_us, t, side="right")
    dentro = t > aperturas_us[np.minimum(k, N - 1)]
    return k, dentro

df["k"], df["en_sesion"] = sesion_de(df["ts"])
calculada = cierre.index[np.minimum(df["k"], N - 1)]
coincide = (calculada.strftime("%Y-%m-%d")
            == df["sesion_reaccion"])
con_verdad = df["sesion_reaccion"].notna() & (df["k"] < N)
print(f"matches the truth: {coincide[con_verdad].mean():.1%}",
      f"of {con_verdad.sum()} news items")

ny = df["ts"].dt.tz_convert("America/New_York")
hora = ny.dt.hour + ny.dt.minute / 60
dia_con_sesion = ny.dt.tz_localize(None).dt.normalize().isin(
    cierre.index)
df["franja"] = np.select(
    [~dia_con_sesion, hora < 9.5, df["en_sesion"], hora < 20],
    ["no session", "pre-open", "in session", "after close"],
    "overnight")
orig = df[(df["tipo_noticia"] == "original")
          & (df["k"] >= 6) & (df["k"] < N - 12)].copy()
orig["franja"].value_counts(normalize=True).round(3)

matches the truth: 100.0% of 27304 news items
franja
in session     0.407
pre-open       0.280
after close    0.181
no session     0.088
overnight      0.044
Name: proportion, dtype: float64

The computed session matches the one in the truth file for the 27,304 news items that have it (syndicated copies do not carry their own session in that file). 18% of the original news comes out after the close, 4% overnight and 9% on days without a session (weekends and holidays): almost a third of the corpus has its effect in a session different from that of its date. The news with k outside the sample (the first six sessions and the last twelve) is left out so that all event windows are complete.

The error of aligning by date, measured

The reaction day is the reaction session and, if the news came out during the session, also the next one (where the rest of the effect falls). The slope of its abnormal return on the true sentiment measures how much one unit of sentiment moves the price. With the correct alignment and with two naive ones: the New York date of the news item and its UTC date, each with the return of that date:

def pendiente_pb(x, y):
    x, y = np.asarray(x, float), np.asarray(y, float)
    b, a = np.polyfit(x, y, 1)
    resid = y - a - b * x
    se = np.sqrt(resid @ resid / (len(x) - 2)
                 / ((x - x.mean()) ** 2).sum())
    return float(round(1e4 * b, 1)), float(round(1e4 * se, 1))

def dia_reaccion(d):
    k, j = d["k"].to_numpy(), d["j"].to_numpy()
    siguiente = anormal[np.minimum(k + 1, N - 1), j]
    return anormal[k, j] + np.where(d["en_sesion"], siguiente, 0)

def por_fecha(d, fechas):
    i = cierre.index.get_indexer(fechas)
    return np.where(i >= 0, anormal[np.maximum(i, 0), d["j"]], np.nan)

orig["y_reaccion"] = dia_reaccion(orig)
ny_o = orig["ts"].dt.tz_convert("America/New_York")
orig["y_fecha_ny"] = por_fecha(orig, ny_o.dt.tz_localize(None)
                               .dt.normalize())
orig["y_fecha_utc"] = por_fecha(orig, orig["ts"].dt.tz_localize(None)
                                .dt.normalize())
s = orig["sentimiento_verdadero"]
filas = {}
for c in ["y_reaccion", "y_fecha_ny", "y_fecha_utc"]:
    ok = orig[c].notna()
    filas[c] = (int(ok.sum()), *pendiente_pb(s[ok], orig.loc[ok, c]))
ok = orig["y_fecha_ny"].notna()
mismas = pendiente_pb(s[ok], orig.loc[ok, "y_reaccion"])
filas["y_reaccion, mismas"] = (int(ok.sum()), *mismas)
pd.DataFrame(filas, index=["noticias", "pb", "error_tipico"]).T

                    noticias     pb  error_tipico
y_reaccion           22518.0  118.8           3.0
y_fecha_ny           20540.0   61.4           2.7
y_fecha_utc          20341.0   68.5           2.7
y_reaccion, mismas   20540.0  117.4           3.2

With the reaction session, the slope is about 119 bp per unit (column pb), close to the 123 planted (the difference is market noise: the standard error, error_tipico, is 3 bp). With the New York date it falls by half, to 61 bp, even when compared on the same news (mismas; 117 bp with the correct session). The UTC date (68 bp) fixes nothing: at 20:00 in New York it is already the next day in UTC, so it gets the overnight news right and the evening news wrong.

Which time slots does the error come from? The two alignments, slot by slot:

def por_franja(g):
    ok = g["y_fecha_ny"].notna()
    fila = {"noticias": len(g),
            "correcta": pendiente_pb(g["sentimiento_verdadero"],
                                     g["y_reaccion"])[0]}
    fila["fecha_ny"] = (pendiente_pb(g.loc[ok, "sentimiento_verdadero"],
                                     g.loc[ok, "y_fecha_ny"])[0]
                        if ok.sum() > 30 else np.nan)
    return pd.Series(fila)

orig.groupby("franja").apply(por_franja, include_groups=False)

             noticias  correcta  fecha_ny
franja
after close    4067.0     119.2     -10.3
in session     9169.0     115.1      59.0
no session     1978.0     134.0       NaN
overnight      1000.0     120.6      12.4
pre-open       6304.0     119.0     119.0

Before the open the two coincide: the date of the news item is the reaction session. After the close, the date gives a negative, meaningless slope: it compares each news item with the return of a day on which it was not yet known. During the session, the date picks up only the part of the effect that arrives before the close, half of it. News from days without a session has no return for its date, and a date alignment loses it (or, worse, assigns it to the previous session with an ffill).

The header figure summarizes the three alignments. It is a bias toward zero, not upward: whoever aligns by date concludes that text “works less”, discards good signals or underestimates a strategy's capacity. The dangerous (upward) bias appears with documents that describe the price.

Two traps in the corpus: copies and wrap-ups

The 1,209 market wrap-ups in the corpus are written after the close from the day's move (“X se desploma un 8 %”, X plunges 8%). Their tone summarizes the return that has just happened:

cr = df[(df["tipo_noticia"] == "mercado") & (df["k"] < N - 6)].copy()
ny_cr = cr["ts"].dt.tz_convert("America/New_York")
mismo_dia = por_fecha(cr, ny_cr.dt.tz_localize(None).dt.normalize())
siguiente = anormal[cr["k"], cr["j"]]
s_cr = cr["sentimiento_verdadero"]
print("with the wrap-up's date:  ",
      round(np.corrcoef(s_cr, mismo_dia)[0, 1], 3))
print("with the reaction session:",
      round(np.corrcoef(s_cr, siguiente)[0, 1], 3))

with the wrap-up's date:   0.933
with the reaction session: 0.047

With the date, a correlation of 0.93: the perfect “signal”. With the session in which you could trade, nothing. It is reverse causality, and in a real corpus it is common: closing wrap-ups, weekly summaries, analyst notes that explain a move. A filter by news type, or the correct alignment, removes it.

The 12,266 syndicated copies repeat a story at another agency, from 2 minutes to 6 hours later. Counting them as events:

copias = df[df["tipo_noticia"].isin(["original", "sindicada"])
            & (df["k"] >= 6) & (df["k"] < N - 12)].copy()
copias["y_reaccion"] = dia_reaccion(copias)
print("originals only:", len(orig),
      pendiente_pb(orig["sentimiento_verdadero"], orig["y_reaccion"]))
print("with copies:   ", len(copias),
      pendiente_pb(copias["sentimiento_verdadero"],
                   copias["y_reaccion"]))

originals only: 22518 (118.8, 3.0)
with copies:    34743 (118.4, 2.4)

The slope barely moves (118.4 against 118.8: copies inherit the sentiment and almost always the session of the original), but the number of events grows 54% and the standard error falls from 3.0 to 2.4 bp as if there were more information. There is not: the t-statistic is inflated. Worse still in a strategy: three copies of the same news item would be three signals and the position would triple. Group by story before anything else (lesson 6.1.2 teaches how to detect copies without the truth file) and, if two different news items about the same company fall in the same session, aggregate them.

CAR by dictionary tone

With the alignment solved, the text signal is the one from lesson 6.1.1: the tone of the course's financial lexicon, $(p - n)/(p + n + 1)$. The news items with nonzero tone are split into terciles and the abnormal return is accumulated in event time, from session −5 to +10 relative to the reaction session:

lex = pd.read_csv("texto6_lexico.csv").query("lista == 'financiera'")
listas = {i: (set(g.loc[g["categoria"] == "positiva", "palabra"]),
              set(g.loc[g["categoria"] == "negativa", "palabra"]))
          for i, g in lex.groupby("idioma")}

def tono(texto, idioma):
    pos, neg = listas[idioma]
    w = re.findall(r"[a-záéíóúüñ]+", texto.lower())
    p, n = sum(x in pos for x in w), sum(x in neg for x in w)
    return (p - n) / (p + n + 1)

orig["texto"] = orig["titular"] + ". " + orig["cuerpo"]
orig["tono"] = [tono(t, i) for t, i in
                zip(orig["texto"], orig["idioma"])]
con_tono = orig[orig["tono"] != 0]
tercil = pd.qcut(con_tono["tono"], 3,
                 labels=["negative", "middle", "positive"])
dias = np.arange(-5, 11)
k, j = con_tono["k"].to_numpy(), con_tono["j"].to_numpy()
ar = pd.DataFrame(anormal[k[:, None] + dias, j[:, None]],
                  columns=dias)
ar_t = ar.groupby(tercil.to_numpy(), observed=True).mean()
car = 100 * ar_t.cumsum(axis=1)
resumen = pd.DataFrame({
    "noticias": tercil.value_counts(),
    "antes_-5_-1": 100 * ar_t.loc[:, -5:-1].sum(axis=1),
    "dia_0": 100 * ar_t[0],
    "deriva_1_10": 100 * ar_t.loc[:, 1:10].sum(axis=1),
})
resumen.round(2)

          noticias  antes_-5_-1  dia_0  deriva_1_10
middle        3014         0.10   0.20         0.16
negative      6451         0.04  -0.36        -0.19
positive      3913        -0.01   0.44         0.30

The positive tercile rises 0.44% on the reaction day and a further 0.30% over the next ten sessions (column deriva_1_10, drift); the negative one falls 0.36% and a further 0.19%. The middle tercile also rises (0.20% on day 0): the tone takes few values (with a single tone word it is ±0.5) and the ties leave the terciles unequal; the middle one gathers mostly news with tone +0.5, with a mean tone of +0.37 and a mean true sentiment of +0.23. The five previous sessions (antes_-5_-1) are the control: between −0.01% and 0.10% in the three terciles, without the pattern of a leak or of a botched alignment. The drift from day +1 onward includes the rest of the effect of the news published during the session (which is completed on day +1) and the planted drift.

As in 2.3.5, a mean CAR is not a strategy: it remains to check that the drift survives costs (it is tenths of a point over ten sessions), the capacity of small companies, where the effect is larger (exercise 1), and latency: the day 0 reaction to a 7:00 news item is only captured by buying at the open, and that of a news item during the session, in seconds.

Topic modeling: what the news is about

A topic model groups documents by their vocabulary without labels. LDA (latent Dirichlet allocation, Blei, Ng and Jordan, 2003) assumes that each document is a mixture of topics and each topic a distribution over words, and estimates both from the document-word count matrix. In an event study it serves two purposes: knowing what type each news item is when the vendor does not say, and separating the topics that move the price from the routine ones.

With the Spanish news, without stop words or company names (otherwise each company would be a topic), 20 topics and a sample of 8,000 news items to fit (the online mini-batch fit takes a few seconds; then every item is assigned a topic). The stop word list is Spanish because the texts are:

from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import normalized_mutual_info_score

es = orig[orig["idioma"] == "es"].copy()
empresas = pd.read_csv("acciones6_empresas.csv")
nombres = {w for x in empresas["nombre"]
           for w in re.findall(r"[a-záéíóúüñ]+", x.lower())}
vacias = set("""de la el en los las del un una que con por se su sus
al lo y a es para ha como más este esta sobre tras ante sin según
hasta entre dólares millones acciones sesión última cerraron
compañía grupo empresa""".split()) | nombres
vec = CountVectorizer(stop_words=sorted(vacias), min_df=15,
                      max_df=0.3,
                      token_pattern=r"(?u)\b[^\W\d_]{3,}\b")
muestra = es.sample(8000, random_state=0)
lda = LatentDirichletAllocation(n_components=20, random_state=0,
                                learning_method="online",
                                batch_size=1024, max_iter=10)
lda.fit(vec.fit_transform(muestra["texto"]))
es["tema"] = lda.transform(vec.transform(es["texto"])).argmax(axis=1)
vocab = vec.get_feature_names_out()
print("vocabulary:", len(vocab))
print("normalized mutual information with the event:",
      round(normalized_mutual_info_score(es["evento"], es["tema"]), 3))

vocabulary: 341
normalized mutual information with the event: 0.825

Normalized mutual information measures, between 0 and 1, how similar two partitions are: here, the topics LDA finds without labels and the 20 event types with which the generator wrote the news. A value of 0.8 is high agreement: LDA recovers almost all the event types. With template texts it is easier than with real news, in which a topic mixes vocabularies and the number of topics is a debatable choice. The most probable words of some topics, with the most frequent true event:

def resumen_tema(t):
    g = es[es["tema"] == t]
    pal = vocab[np.argsort(lda.components_[t])[::-1][:5]]
    ev = g["evento"].value_counts()
    return pd.Series({"noticias": len(g), "palabras": " ".join(pal),
                      "evento": ev.index[0],
                      "pureza": round(ev.iloc[0] / len(g), 2)})

temas = pd.DataFrame({t: resumen_tema(t) for t in range(20)}).T
temas.sort_values("noticias", ascending=False).head(8)

   noticias                                         palabras         evento pureza
8      1813               contrato próximos años firmado usd       contrato    0.8
15     1606       junta accionistas general dentro celebrará          junta   0.72
13     1453  precio objetivo ficticios valores recomendación  analista_baja   0.58
4      1418    inversores foro presentará conferencia sector    conferencia    1.0
3      1334       plataforma lanza impulso fuentes favorable       producto   0.88
5       951        inversión banco sintético precio objetivo  analista_baja   0.52
6       927         director nuevo nombra responsable jubila   nombramiento    1.0
17      924        mayor pedido reciente adjudicado historia       contrato   0.95

The words and the events are Spanish data: topic 8, for example, is contracts (contrato: “contrato próximos años firmado”, contract, coming years, signed), topic 15 shareholder meetings (junta), topic 13 analyst downgrades (analista_baja: “precio objetivo”, price target), topic 4 investor conferences (conferencia) and topic 6 appointments (nombramiento). The pureza column (purity) is the share of the topic's news that belongs to its most frequent event.

Do all topics move the price equally? A direct measure, which is that of a strategy that buys on positive tone and sells on negative tone: the mean abnormal return of the reaction day in the direction of the tone, $\overline{y \cdot \operatorname{sign}(\text{tone})}$, in bp per news item. Topic by topic, against the mean materiality that the generator gave its news (0 for shareholder meetings, conferences and appointments; up to 2 for a takeover bid):

def por_tema(g):
    signo = np.sign(g["tono"])
    return pd.Series({
        "noticias": len(g),
        "con_tono": (signo != 0).mean(),
        "pb_en_direccion": 1e4 * (g["y_reaccion"] * signo).mean(),
        "materialidad": g["materialidad"].mean(),
    })

pt = es.groupby("tema").apply(por_tema, include_groups=False)
pt["evento"] = temas["evento"]
corr = pt["pb_en_direccion"].corr(pt["materialidad"])
print("correlation:", round(corr, 2))
pt.sort_values("pb_en_direccion").round(2).iloc[[0, 1, 2, -3, -2, -1]]

correlation: 0.86
      noticias  con_tono  pb_en_direccion  materialidad             evento
tema
4       1418.0      0.21            -0.53          0.00        conferencia
15      1606.0      0.21            -0.51          0.00              junta
6        927.0      0.19             2.77          0.00       nombramiento
1        461.0      0.69            53.91          0.80           dimision
0        437.0      0.77            75.06          0.99  retirada_producto
16       661.0      0.89            80.71          1.18        previsiones

The routine topics (conferences, shareholder meetings, appointments) do not move the price in the direction of their tone (between −0.5 and 2.8 bp per news item) even though one in five of their news items has tone: their text carries tone phrases, but the event is not material. The topics of guidance (previsiones), product recalls (retirada_producto) or resignations (dimision) give between 54 and 81 bp per news item. Across the 20 topics, the correlation with true materiality is 0.86. A text signal improves if it is weighted by topic or if the routine ones are filtered out. With a few hundred news items per topic, each figure has a standard error of several bp: the ranking matters more than each value.

BERTopic: topics with embeddings

LDA works with word counts. BERTopic (Grootendorst, 2022) starts from sentence embeddings (those of lesson 6.1.1), reduces their dimension with UMAP, clusters with HDBSCAN and describes each cluster with the most characteristic words of its class (c-TF-IDF)11Note 1 · Grootendorst (2022), “BERTopic: Neural topic modeling with a class-based TF-IDF procedure” (https://arxiv.org/abs/2203.05794); documentation (https://maartengr.github.io/BERTopic/, accessed on October 23, 2026): “the main steps for topic modeling with BERTopic are sentence-transformers, UMAP, HDBSCAN, and c-TF-IDF run in sequence”.:

flowchart LR
  E[Embeddings] --> U[UMAP]
  U --> H[HDBSCAN]
  H --> C[c-TF-IDF]

Unlike LDA, it assigns a single topic per document, decides the number of topics (HDBSCAN leaves noise without a topic) and captures synonyms that share no words. It is installed in the course's ia environment, not in this lesson's; the code, with the seeds that fix the result:

from bertopic import BERTopic
from hdbscan import HDBSCAN
from sentence_transformers import SentenceTransformer
from umap import UMAP

textos = es["texto"].tolist()
st = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2",
                         device="cpu", local_files_only=True)
emb = st.encode(textos, normalize_embeddings=True,
                show_progress_bar=False)
modelo = BERTopic(
    umap_model=UMAP(n_neighbors=15, n_components=5, random_state=0),
    hdbscan_model=HDBSCAN(min_cluster_size=50),
    vectorizer_model=CountVectorizer(stop_words=sorted(vacias)),
)
temas_bt, _ = modelo.fit_transform(textos, emb)
modelo.get_topic_info().head(10)

Run in the ia environment on a sample of 2,000 Spanish news items (with the minimal stop words, without removing the company names), the result is instructive: BERTopic with MiniLM clusters mostly by company and sector (the topics are named after the companies: petroleum, steel, pharmaceuticals) and leaves a third of the news without a topic; its mutual information with the event type is 0.10, against 0.82 for LDA. MiniLM is an English model (lesson 6.1.1): with Spanish text it focuses on what it understands, the proper names. For news in Spanish you need a multilingual embedding model, to remove the company names before clustering and to check the topics against a labeled sample, as here against evento.

A document signal: changes in 10-K risks

Text does not only arrive in news. Cohen, Malloy and Nguyen (2020) showed that companies that change the language of their annual reports a lot subsequently underperform those that do not change it (a portfolio long the non-changers and short the changers earns, according to the article's abstract, up to 188 bp of alpha per month), and that the market does not react on the filing day: the information is absorbed slowly22Note 2 · Cohen, Malloy and Nguyen (2020), “Lazy Prices”, Journal of Finance 75(3), 1371-1415 (https://doi.org/10.1111/jofi.12885): “a portfolio that shorts "changers" and buys "nonchangers" earns up to 188 basis points per month in alpha (over 22% per year) in the future”, with no announcement effect..

The course's synthetic 10-Ks carry an Item 1A. Risk Factors section. In 18% of the 10-Ks from fiscal year 2019 onward, the section changes a lot (impairment risks come in and the sentences become more severe) and the generator plants a −3% drift spread over the next 60 sessions. The signal is the cosine similarity between the bag of words of the section and that of the same company's previous 10-K:

from sklearn.preprocessing import normalize

docs = pd.read_parquet("filings6_documentos.parquet")
k10 = docs[docs["tipo"] == "10-K"].sort_values(["emisor", "ts"]).copy()

def riesgos(html):
    m = re.search(r"<h2>Item 1A\. Risk Factors</h2>(.*?)<h2>", html,
                  re.S)
    return re.sub(r"<[^>]+>", " ", m.group(1)) if m else ""

k10["riesgos"] = k10["texto"].map(riesgos)
X = normalize(CountVectorizer().fit_transform(k10["riesgos"]))
misma = (k10["emisor"].to_numpy()[1:] == k10["emisor"].to_numpy()[:-1])
sim = np.asarray(X[1:].multiply(X[:-1]).sum(axis=1)).ravel()
k10["similitud"] = np.r_[np.nan, np.where(misma, sim, np.nan)]
k10 = k10.dropna(subset=["similitud"])
k10["año"] = k10["ts"].dt.year
k10.groupby("año")["similitud"].describe()[["count", "50%", "min"]]

      count       50%       min
año
2020   60.0  1.000000  0.924000
2021   60.0  0.985860  0.915507
2022   60.0  1.000000  0.946799
2023   60.0  0.999342  0.933138
2024   60.0  1.000000  0.941738
2025   60.0  0.999829  0.952615

The median similarity is almost 1: from one year to the next, most companies copy their risk section with touch-ups. The minimum of each year is the signal. With quintiles by year (so that a change common to all, such as the pandemic risk that enters in 2021, does not take over the quintile), the CAR of the 60 sessions starting at the reaction session. 10-Ks are filed after the close, so that session is the one after the filing:

riesgos_v = pd.read_csv("filings6_riesgos_verdad.csv")
cols = ["id_doc", "ticker_actual", "cambio_riesgos"]
k10 = k10.merge(riesgos_v[cols], on="id_doc")
k10["k"], _ = sesion_de(k10["ts"])
k10["j"] = k10["ticker_actual"].map(columna)
k10 = k10[k10["k"] + 60 <= N].copy()
k10["quintil"] = k10.groupby("año")["similitud"].transform(
    lambda x: pd.qcut(x, 5, labels=False))
k10["cambia"] = k10["quintil"] == 0
ventana = np.arange(60)
ar60 = anormal[k10["k"].to_numpy()[:, None] + ventana,
               k10["j"].to_numpy()[:, None]]
k10["car60"] = 100 * ar60.sum(axis=1)
print(pd.crosstab(k10["cambia"], k10["cambio_riesgos"]))
global_q = pd.qcut(k10["similitud"], 5, labels=False) == 0
print("changes in the quintile without grouping by year:",
      int((global_q & k10["cambio_riesgos"]).sum()))
k10.groupby("cambia")["car60"].agg(["count", "mean", "sem"]).round(2)

cambio_riesgos  False  True
cambia
False             276     12
True               18     54
changes in the quintile without grouping by year: 47
        count  mean   sem
cambia
False     288  0.33  0.87
True       72 -3.88  1.88

The lowest-similarity quintile of each year (cambia, changes) captures 54 of the 66 planted changes (with full-sample quintiles, 47) and its 60-session CAR is −3.88%, against +0.33% for the rest: the method recovers the −3% effect, with a standard error of 1.9 points. With 72 events in the quintile, the interval is wide. The drift is slow, with no jump on the filing day: as in the article, the course's market does not read the changes all at once.

What changes in real life

• The document's time rules. News, 8-Ks and filings with the CNMV (the Spanish securities regulator) carry a time; a daily study assigns them to the first session that closes afterward. If there is only a date, assume the worst: the next session.
• One story, one event. Copies, translations, corrections and wrap-ups are grouped or excluded before measuring; otherwise, the standard errors lie.
• The pre-event control is mandatory. A CAR that moves before the event reveals a leak, bad alignment or a text that describes the past.
• Materiality varies by topic. A text signal aggregated without distinguishing topics dilutes the news that matters among the routine news.
• The course effects are planted. The slopes, CARs and similarities in this lesson are the known answer of a generator. In real data, the size and duration of the effect have to be measured with these same precautions, and the literature gives its own results (Tetlock, 2007, finds that the pessimism of a Wall Street Journal column predicts downward pressure followed by a reversal)33Note 3 · Tetlock (2007), Journal of Finance 62(3), 1139-1168 (https://doi.org/10.1111/j.1540-6261.2007.01232.x): the pessimism of a Wall Street Journal column predicts downward pressure on prices followed by a reversal. Event study methodology: MacKinlay (1997), Journal of Economic Literature 35(1), 13-39.; no figure in this lesson carries over to a real market.

Exercises

1. The level 6 README says that the effect of news is larger in small companies. Compute the reaction-day slope on the true sentiment in the smallest third of companies and in the largest by first-day capitalization (close on 2018-01-02 times shares outstanding).

acciones = empresas.set_index("ticker")["acciones_millones"]
capital = cierre.iloc[0] * acciones.reindex(cierre.columns)
tercio = pd.qcut(capital.rank(), 3, labels=["small", "mid",
                                            "large"])
orig["tamaño"] = orig["ticker_actual"].map(tercio)
orig.groupby("tamaño", observed=True).apply(
    lambda g: pd.Series(pendiente_pb(g["sentimiento_verdadero"],
                                     g["y_reaccion"]),
                        index=["pb", "error_tipico"]),
    include_groups=False)

           pb  error_tipico
tamaño
small   208.5           8.5
mid     132.0           5.1
large    89.2           4.1

2. Measure the drift of sessions +1 to +5 after the reaction day (without counting it) with the true sentiment and with the dictionary tone. What part of the drift does the dictionary recover?

k_d = orig["k"].to_numpy() + orig["en_sesion"].to_numpy()
j_d = orig["j"].to_numpy()
deriva = anormal[k_d[:, None] + np.arange(1, 6),
                 j_d[:, None]].sum(axis=1)
print("truth:      ", pendiente_pb(orig["sentimiento_verdadero"],
                                   deriva))
print("dictionary: ", pendiente_pb(orig["tono"], deriva))

truth:       (34.1, 5.7)
dictionary:  (19.0, 5.2)

3. A vendor delivers news with a fecha (date) column without a time and another hora_recepcion (reception time) column in its server's time zone, without saying which one. How would you check with its own data which alignment is correct?