> For the complete documentation index, see [llms.txt](https://docs.tickertrends.io/tickertrends/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tickertrends.io/tickertrends/~/revisions/S1f5fytN4zp0rB4IMA1c/documentation-v2/4.-features-and-workflows.md). # 4. Features & Workflows **Overview**\ TickerTrends is designed to give investors a complete view of both alternative data signals and financial context for any ticker. When a user searches a ticker in the main search bar, the workflow moves through a structured series of modules inside the KPI Dashboard, with additional tools like Exploding Trends, Unstructured Data, and Sector Trends available for deeper analysis. **KPI Dashboard** * **Social Summary**: High-level overview of what consumers are discussing across social platforms, with key topics automatically extracted.
* **KPI Predictions**: Forward-looking KPI forecasts with revisions, showing actual, predicted, and range bands.
* **Data Source KPIs**: Manually curated alternative data signals (search trends, app usage, web traffic, TikTok views, etc.) selected for relevance to that ticker. This data is also easy to view in a weekly year-over-year format to help investors easily understand how alternative data values are trends versus last year.
* **Related Symbols**: Peer and competitor tickers surfaced for comparison.
* **Transcripts**: Last six quarters of earnings transcripts with AI-generated conference call summaries.
* **Earnings Whisper Score**: Sentiment-driven score highlighting how investor expectations are shaping up into earnings.
* **Events**: Timeline of all past alerts triggered for that ticker, including large social shifts and meaningful YoY changes.
**Exploding Trends**\ Exploding Trends highlights relevant keywords linked to a ticker that are breaking seasonal norms. These are flagged as outliers, giving investors early visibility into sudden shifts in consumer behavior, product launches, or cultural moments.
Exploding Trends also has a standalone page where users can scroll through hundreds / thousands of pages of trends for idea discovery, for each and every data type. We can for non-seasonal abnormal trends for you as well as summarize the companies that appear most frequently right on top.
**Unstructured Data**\ The Unstructured Data tab lets users view raw time series data for any dataset type covered in Section 2. Each chart includes the option to automatically calculate historical Pearson r correlations between the data and KPI or Consumer Interest Trackers. This helps determine whether a keyword, app usage trend, or social signal has historically tracked company performance.
All data sources listed in the beginning of the documentation are available to view raw terms for.
The user will also notice that when they select a type, our similarity matching system automatically find related terms to the selected data source. If a user finds that something is missing, the "Add" button lets them seemlessly add a new value.
**Visualization Tools**
TickerTrends offers a set of visualization options to help investors analyze alternative data and KPI forecasts in flexible, meaningful ways. These tools allow users to view data from different perspectives, highlight patterns, and combine series for deeper insight. **YOY**\ Displays a year-over-year comparison that highlights annual growth patterns for the selected dataset.
**Stacked YOY**\ Compares individual years of data in a stacked format, making it easier to spot seasonality and year-specific trends.
**Volumetric**\ Plots volumized data on one axis for each type, helping visualize scale differences across metrics.
**Average**\ Normalizes and averages all selected data series into a single line to capture the overall trend.
**Aggregate**\ Aggregates (sums up) multiple data series into a combined view, giving a consolidated picture of activity.
**Outlier Cutoff (Beta)**\ A slider tool that lets users filter data based on outlier strength. Values range from 0 (no filtering) to 1 (aggressive filtering), allowing users to smooth charts or isolate extreme movements. This attempts to smooth out event related massive spikes or drops.
**Social Context Timeline**\ Automatically detects abnormal spikes or drops in data and overlays the related social or news events happening at that time. Helps users quickly connect data movements with real-world drivers like product launches, viral moments, or company news.
**Interval Mode**\ Allows users to set the timeframe for viewing data. It sums up all selected values within the chosen interval (weekly, monthly, quarterly, or fiscal quarter) and displays them in a bar graph format, making it easier to spot seasonality and long-term trends.
**Unified Search**\ A single search bar on top that connects across all datasets, forecasts, and trackers. Users can type in a ticker or term and immediately see all relevant KPIs, data sources, exploding trends, transcripts, and related symbols in one workflow. **Enterprise Dashboard**\ A customizable workspace where users can build dashboards for specific tickers or sectors. Any data type in the platform can be added and displayed side by side, allowing analysts to track everything relevant in one view. This saves hours otherwise spent switching between datasets and platforms. Whether you want to monitor KPIs, web traffic, search trends, TikTok activity, or sector-wide themes, the Enterprise Dashboard keeps it all organized and updated in real time.
**Sector Trends**\ Sector-level aggregation allows users to move beyond a single ticker and view broader industry patterns. This helps identify which subsectors are accelerating or losing momentum.
**Ticker Screener**\ The screener enables cross-ticker comparisons using both forecasts and consumer interest trackers. Users can rank and filter companies by growth signals, KPI forecast revisions, or outlier alerts.
**Social Context**\ Social context overlays events, news, and consumer activity directly on KPI and data charts, making it easy to tie trends back to real-world catalysts.
**Event Feed**\ The Event Feed acts as a central hub for key developments across all tickers. It helps users cut through noise and focus on actionable events by surfacing three main types of content: * **Alerts**: Real-time notifications triggered when our system detects large shifts in social activity, unusual YoY changes, or other significant consumer signals.
* **Revisions**: Significant changes to KPI forecasts, allowing users to track how model predictions evolve with new incoming data.
* **Articles**: Proprietary research pieces written by the TickerTrends team that highlight catalysts, risks, or thematic opportunities.
Each row in the Event Feed provides a structured snapshot: * **Time**: When the event occurred (hours, days, or weeks ago). * **Company**: The ticker and company name tied to the event. * **Event**: A short description of what happened, often tagged with a category like “Catalyst” or “High Alert.” * **Platform**: The source dataset or KPI related to the event (e.g., Social Brand Instagram, Tracker KPI, Research). * **Change**: For data-driven alerts, the measured shift in percentage terms. * **Impact**: When available, a quick indication of whether the signal was historically high, low, or unusual. * **Action**: A direct link to view or research the event in more detail. The Event Feed is designed to be idea-generative. Instead of requiring users to sift through raw feeds or every single keyword, it highlights where the most significant changes are happening and where TickerTrends analysts have already done the heavy lifting. **Trend Alerts**\ Our system automatically flags large social shifts or unusual YoY movements in consumer data, triggering alerts. These appear in the Events module of the KPI Dashboard and can also be configured as email notifications. Users can also check or uncheck any box to filter for their own preference. These alerts are designed to help investors collect an actionable set of tickers they want to further analyze.
The "**Alerts**" section will allow users to input all the tickers they care about and get emailed when a new alert surfaces. Additionally, they can also use the Unstructured Data section to add individual data source alerts.
**Watchlist**
Users will notice the purple star next to tickers on the table as well as searched tickers. By staring a ticker, they are automatically adding it to their active watchlist. This enables more filter features in the "Quick Filters" section. #### KPI Table Fields **Ticker**\ The stock ticker symbol and company name tied to the KPI forecast. **KPI**\ The specific metric being forecast (e.g., Subscription Revenue, Daily Active Users, GMV). **Prediction**\ Our current forecast value for the KPI, expressed in the relevant unit (millions, billions, etc.). **Prediction YoY**\ The one-year percentage change of our KPI prediction versus the actual value from the same quarter last year. **Prediction QoQ**\ The one-quarter percentage change of our KPI prediction versus the actual value from the prior quarter. **Revision**\ The change in our forecast since the last update, shown as an increase or decrease. Predictions are revised weekly as new data comes in. Once a revision is made, it is solidified and stored in the record. **YoY Acceleration**\ The difference between the predicted YoY growth rate for the current quarter and the actual YoY growth rate reported in the same quarter last year.\ \&#xNAN;*Example: If UBER Monthly Active Customers rose 11.3% YoY in Q2 last year and we forecast 13.7% YoY growth in Q2 this year, YoY Acceleration = +2.4%.* **QoQ Acceleration**\ The difference between the predicted QoQ growth rate for the current quarter and the actual QoQ growth rate reported in the same quarter last year.\ \&#xNAN;*Example: If UBER Monthly Active Customers rose 1.4% QoQ in Q2 last year and we forecast 1.1% QoQ growth in Q2 this year, QoQ Acceleration = -0.3%.* **Avg MOE (Margin of Error)**\ The average forecast error based on historical backtesting. This provides transparency into how accurate predictions have been over time. **Confidence Level**\ A rating of how strong the alternative data signals are for forecasting this KPI. Categories include: * **Low**: Weak alignment between alt-data and reported results. * **Medium**: Moderate alignment, reasonable forecast strength. * **High**: Strong alignment, high forecast reliability. * **In-Progress**: Model and data pipeline still being developed for that KPI. **Last Updated**\ The date of the most recent forecast revision. Most KPIs are updated weekly, with higher-frequency updates for popular tickers. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.tickertrends.io/tickertrends/~/revisions/S1f5fytN4zp0rB4IMA1c/documentation-v2/4.-features-and-workflows.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.