Same Company, Same Signal: The Role of Identity in Earnings Call Transcripts (2025)

Abstract: Post-earnings volatility prediction is critical for investors, with previous works often leveraging earnings call transcripts under the assumption that their rich semantics contribute significantly. To further investigate how transcripts impact volatility, we introduce DEC, a dataset featuring accurate volatility calculations enabled by the previously overlooked beforeAfterMarket attribute and dense ticker coverage. Unlike established benchmarks, where each ticker has only around two earnings, DEC provides 20 earnings records per ticker. Using DEC, we reveal that post-earnings volatility undergoes significant shifts, with each ticker displaying a distinct volatility distribution. To leverage historical post-earnings volatility and capture ticker-specific patterns, we propose two training-free baselines: Post-earnings Volatility (PEV) and Same-ticker Post-earnings Volatility (STPEV). These baselines surpass all transcripts-based models on DEC as well as on established benchmarks. Additionally, we demonstrate that current transcript representations predominantly capture ticker identity rather than offering financially meaningful insights specific to each earnings. This is evidenced by two key observations: earnings representations from the same ticker exhibit significantly higher similarity compared to those from different tickers, and predictions from transcript-based models show strong correlations with prior post-earnings volatility.

## introduction post-earnings volatility prediction is crucial for investors and an emerging trend in the field of financial natural language processing (finnlp). volatility, defined as the standard deviation of returns over a specific period-post earnings call in this context-is a key financial metric for evaluating a company's performance. traditional finance methods primarily rely on volatility time series and statistical techniques such as garch and its variants @xcite ). however, with the rapid advancements in natural language processing (nlp) and audio processing, numerous studies have focused on utilizing unstructured earnings call data, such as transcripts and audio recordings, to enhance postearnings volatility prediction @xcite @xcite . in this pursuit, researchers have employed a variety of techniques, including heterogeneous graphs @xcite , language model pre-training @xcite and large language models (llms) @xcite , to better address this complex problem. delving deeper into the background of earnings calls, we found that previous benchmarks, ec @xcite and maec @xcite , have overlooked a crucial attribute: beforeaftermarket, which indicates whether earnings are released before the market opens or after it closes. this attribute is indispensable for accurately calculating volatility. we also observed that ec and maec prioritize ticker foot_1 coverage breadth over density, as each ticker appears around twice in these datasets. this limitation prevents tracking a company's earnings over the long term. to address this, we curated a dense earnings call dataset, dec, where each ticker is represented with 20 earnings records. this enables robust long-term trend analysis and detailed quarter-to-quarter comparisons. on dec, we observe that post-earnings absolute returns-and consequently, volatility 3 -are significantly higher than during normal periods, and that each ticker exhibits distinct post-earnings volatility patterns, we thus hypothesize that historical post-earnings volatility plays a dominant role in volatility prediction. to this end, we in-troduce two training-free baselines: pev (postearnings volatility) and stpev (same-ticker postearnings volatility). remarkably, even with a simple mean-based implementation, our approach achieves state-of-the-art (sota) performance on all datasets: ec, maec and dec, compared to transcripts-based models. through further comparisons at both the representation level and the prediction level, we find that transcripts from the same company exhibit high similarity, and the predictions of transcript-based models strongly correlate with those of stpev(mean). this suggests that, under mainstream nlp approaches-which encode transcripts as embeddings to capture financially meaningful semantics-the resulting representations primarily reflect ticker identity and the historical distribution of post-earnings volatility. our contributions include: ## related work considerable research efforts have been dedicated to leveraging earnings call transcripts, often in combination with other modalities such as audio recordings or time-series data, to model financial risk. transcripts-based models. a few models rely exclusively on transcripts for volatility prediction. for instance, the multi-round qa attention model @xcite ) extracts semantic information from each question-answer round, using glove-300 word-embedding, and then integrates features across multiple granularities to predict volatility. transcripts are often combined with audio recordings during earnings calls. the multimodal deep regression model (mdrm) @xcite integrates transcript, embedded using glove-300, and audio information to forecast volatility. building on mdrm, the hierarchical transformer-based model (html) @xcite employs a hierarchical transformer framework to enhance performance. addressing the limitations of language models in processing numerical information, which is critical in transcripts, numerical html (numhtml) @xcite is developed. numhtml enhances bert @xcite model's ability to handle numerical data through tasks such as classifying numeral categories and comparing magnitudes, thereby improving the model's overall effectiveness in representing numerical data. additionally, voltage @xcite ) and echo-gl @xcite demonstrate that correlations between stocks are beneficial for predicting volatility. these models derive stock relationships from the rich semantic content of earnings calls using a heterogeneous graph learning. in the era of llm, risklabs @xcite ) utilizes llms to encode transcripts and news articles, combining these with other modalities to deliver a comprehensive approach for volatility prediction. the ecc analyzer @xcite employs llms to first extract paragraph-level general information by summarizing the text and subsequently identifies fine-grained focus sentences using retrieval-augmented generation (rag). these two studies firstly utilize simcse @xcite for extracting sentence-level representations, followed by openai's foot_3 model for generating paragraph-level embeddings. @xcite highlight that existing pretrained embedding models, including openai's ada and sentencebert foot_4 , often fail to capture subtle shifts in financial narratives for the same company across different periods. to address this limitation, a llm pipeline and a classic triplet network have been deployed, supported by both llmsynthesized and human-annotated datasets, to identify nuanced changes in financial discourses. in this work, we use different llm embeddings to represent both vanilla transcripts and llm finegrained transcripts, which provide deeper insights and a more nuanced understanding. time series-based models the knowledgeenhanced financial volatility prediction (kefvp) model @xcite demonstrates the advantages of integrating time-series data with textual information. pre-earnings volatility series, is processed by the autoformer @xcite . the resulting representations are then conditionally combined with transcript representations, enhancing the model's predictive capabilities. in this work, we introduce a second type of volatility series: the post-earnings volatility series, which comprises the volatility observed following a series of prior earnings announcements from the same company. in contrast, the pre-earnings volatility series refers to the sequence of volatility measured during the period leading up to a specific earnings announcement. further elaboration can be found in section 3. ## post earnings volatility prediction earnings call transcripts earnings call transcripts are written records of the earnings calls held by companies at the end of each quarter or fiscal year. these transcripts capture the detailed discussions about financial results, company performance, and future projections provided by the company's executives, as well as the question and answer session with analysts and investors. volatility in financial terms, volatility @xcite represents the degree of variation of a trading price series over time as measured by the standard deviation of returns. let's assume the focal earnings is released at day t, and mathematically, volatility can be defined over a specific interval, [t, t + τ ], as follows: volatility is a critical measure in finance as it reflects the risk associated with the price movements of a security. in previous work, @xcite utilized various time intervals, τ = {3, 7, 15, 30}, to quantify volatility, capturing both short-term and long-term market behaviors. pre-earnings and post-earnings volatility time series following the volatility definition, we assume the earnings announcement occurs at day t. the pre-earnings volatility series represents volatility from day tw to day t -1, where w foot_5 is a hyperparameter controlling the window size of the pre-earnings series. this series reflects market expectations prior to the earnings release-higher pre-earnings volatility indicates greater market uncertainty about the upcoming announcement. the post-earnings volatility time series captures volatility from the ik-th to the i -1-th earnings announcements, where the focal earnings at time t is the i-th announcement, and k foot_6 is a hyperparameter determining the size of the post-earnings series. this series reflects how the market historically reacts to the company's earnings-higher post-earnings volatility suggests persistent uncertainty regarding the company's financial reports. pre-earnings and post-earnings volatility series are computed using the same approach but differ in their time intervals: the former is measured in trading days, while the latter is measured over quarterly periods. pre-earnings volatility captures shortterm market behavior leading up to individual earnings events, while post-earnings volatility characterizes long-term market behavior across multiple earnings announcements for the same company. notably, when pre-earnings window size, w, exceeds the interval between consecutive earnings, the pre-earnings volatility series encompasses the post-earnings volatility measure(s). task overview figure 1 illustrates the common paradigm for volatility prediction, which leverages both transcripts and volatility time series. these two data sources are processed separately, and their resulting representations are subsequently combined for post-earnings volatility prediction. ## conclusion and future work in this work, we introduce a dense earnings call dataset: dec, in which each ticker is represented by 20 earnings, in contrast to the roughly two per ticker found in established datasets. motivated by two key observations on dec-the significant drift in post-earnings volatility and the presence of ticker-specific volatility regimes-we propose two training-free baselines, pev and stpev. remarkably, these simple statistical mean outperform a range of transcript-based models. we further confirm that, under conventional nlp approaches, which encode transcripts into embeddings intended to capture financially meaningful semantics, the resulting representations primarily reflect ticker identity and thereby the historical distribution of postearnings volatility specific to the same company. this is evidenced by representation-level similarity analyses between earnings from the same ticker versus across tickers, as well as prediction-level comparisons between transcript-based models and stpev(mean). we conclude this work by highlighting two key directions for future research: first, earnings call records, including transcripts and audio, are only part of the broader earnings event. an often-overlooked aspect is the earnings expectation, which is essential for contextualizing and interpreting the content of the call. earnings outcomes should be understood in relation to these expectations, which serve as 'baselines,' rather than treating the transcripts as standalone inputs. moreover, temporal comparisons with a company's historical earnings, combined with horizontal comparisons to peer companies, also offer meaningful context for interpreting the earnings event. the second direction concerns the importance of financial numbers, such as earnings per share (eps) and revenue. in this work, we observe that text-driven embeddings tend to prioritize verbal structures or ticker identity, while the true marketmoving factors-often represented by quantitative metrics-are overshadowed. although efforts have been made to address this issue, for example, numerical html (numhtml) @xcite has attempted to enhance language models' ability to represent numbers , we believe that a more fundamental solution lies in performing information extraction on transcripts and leveraging the extracted numerical data directly for downstream applications.