How to Prepare for Earnings Season With Artificial Intelligence

When it comes to earnings season preparation, the name of the game is to be efficient, comprehensive, and strategic. But access to company filings is only part of the equation — you also need to have tools and systems set in place that enable you to quickly read between the lines and pick out the hidden meanings and key insights that can ultimately help drive your strategy. 

Read on to discover why artificial intelligence is the number one tool that will elevate your earnings season preparation and analysis and ensure you never miss a critical detail again.

What Is Earnings Season?

Earnings season is the time when a majority of publicly traded corporations report their earnings. Earnings offer a glimpse into a public company’s operations and thus are one of the most important drivers of individual stock performance, and ultimately, the market as a whole — ushering in a higher degree of volatility in stock prices for a period of time. 

Earnings season usually begins a few weeks after the end of the fiscal quarter and lasts about six weeks in total. The general timeline is as follows:

• Q1 Earnings Season – begins in mid-April and ends in May
• Q2 Earnings Season – begins in mid-July and ends in August
• Q3 Earnings Season – begins in mid-October and ends in November
• Q4 Earnings Season – begins in mid-January and ends in February

For earnings insights on top companies across corporate and financial services industries, check out our Earnings Tearsheets.

How AI Leads to Better Earnings Season Analysis

There are many different ways to search all the sources of competitive and market data and information, but sifting through pages of irrelevant documents wastes a significant amount of time. 

AI technology, powered by natural language processing (NLP), machine learning, and, more recently, generative AI, not only helps surface relevant results quickly, but also provides key insights on underlying meanings across the content. Generative AI takes this even further by automating and expediting tedious or manual tasks like summarization and read-throughs of earnings transcripts. This ensures you spend less time parsing through massive amounts of company data and more time transforming key insights into research reports, strategies, and investments. And genAI also introduces a new way to interact with documents, transforming static content into a conversational exchange that accelerates understanding and decision-making.

Summarize Earnings Transcripts With Generative AI

Many of our customers spend countless hours reading and summarizing earnings calls from across their portfolios, peer sets, clients, and competitors. AlphaSense makes this task infinitely easier, faster, and more error-proof with our suite of generative AI tools.

If you are looking for specific summarized items from earnings transcripts, look no further than our Smart Summaries tool. 

With Smart Summaries, every earnings call in the AlphaSense platform contains an AI-generated summary that covers all key topics — organized in concise, bulleted form with all sources cited — ready within a few hours of the call.

Smart Summaries features:

• Bulleted summaries that pull from the entire earnings call transcript, delivering results that are ranked in order of importance and that become even more closely tailored to your company’s specific needs with increased usage
• Clickable citation links that take you to the exact places within the document that the insight was sourced from
• User feedback buttons that allow you to contribute to the continuous improvement and evolution of our models

With Smart Summaries, you can be confident in the accuracy and security of our technology because we guarantee:

• Better accuracy – Benefiting from our 10+ years of investment in AI, Smart Summaries is trained specifically on financial data, which means enhanced accuracy and relevance and reduced risk of hallucination. 
• Verifiable information – With our linked citations, you can validate each of our summaries and gain trust in our model.
• Trustworthy content – All language models rely on curated content sets for the generation of summaries. AlphaSense’s model parses our premium, proprietary content sets to generate its high-accuracy summaries.
• Private data – For companies uploading their own earnings insights through AlphaSense’s Notebook or Enterprise Intelligence, Smart Summaries was built with security and privacy as top priorities. Even when other tools have been banned, our unique approach allows all companies to reap the benefits of generative AI and rest assured that their data is safe.

Automate Earnings Analysis with Generative Grid

AlphaSense’s Generative Grid is a revolutionary generative AI-powered tool that expedites and broadens your earnings analysis by extracting insights from multiple transcripts simultaneously. In seconds, Generative Grid applies multiple customizable questions across recent earnings transcripts from your company or companies of interest. All results are displayed in an easy-to-read grid format, allowing you to quickly parse through specific datapoints and get an instant view of a company or industry’s performance over time.

Rather than reading numerous individual earnings transcripts and trying to collate notes across various companies, you get an at-a-glance look into what specific companies are saying about specific topics. You can also see what specific analysts asked management across many earnings calls, or hone in on analyst questions about a specific topic.

For example, let’s say you need to monitor earnings for the biopharma industry. You are interested in a specific set of companies, and you want to quickly understand their main therapeutic areas, growth drivers, sales performance, top-selling therapies, pricing, etc. Simply enter those company names and the keywords/focuses of relevance to you, and you will get a grid displaying what each company’s earnings transcript has said on those topics over a specific period of time.

By using Generative Grid, you can answer research questions at scale in seconds, giving yourself a fundamental competitive edge and accelerating your earnings analysis process.

Ask Questions Across Earnings Documents

Earnings transcripts contain a wealth of information regarding a company’s past, current, and future performance. When you have access to earnings transcripts from various companies across several years, it’s possible to gain insight into historical quarter-over-quarter trends for each company, as well as to compare one company’s performance to another’s. The difficulty lies in extracting the insights from the vast volume of transcripts, without getting bogged down in extraneous details.

That’s precisely where another AlphaSense generative AI feature comes into play: Generative Search.

Generative Search is a genAI-powered chatbot that sources all its data from the AlphaSense platform, including hundreds of thousands of earnings transcripts. When you ask natural language questions to Generative Search, you get instant citable answers that synthesize insights directly from relevant documents — cutting out the manual effort of parsing through those documents and trying to find critical data points yourself.

For example, you can ask Generative Search to summarize, identify key themes, or answer specific questions about a specific earnings document.

You can also ask Generative Search to compare a specific company’s earnings from a certain quarter with a previous quarter, or compare one company’s earnings with another company’s earnings in the same quarter. The instant answers generated by the tool can dramatically cut down on research time and give you insights you can implement right away — all with minimal effort on your part.

Here’s what AlphaSense’s client ODDO BHF has to say about Generative Search:

AlphaSense’s Generative Search is the next big thing for us in how we use the platform because it allows us to ask the platform questions and quickly get good answers. It saves us a lot of work and time in our research process, especially in the beginning stages of investigating a company.”

– Jonas Eisch, Portfolio Manager, ODDO BHF

Why Are Tone and Messaging Important in Earnings Analysis?

During earnings season, it is critical to have a firm grasp on the deeper meaning behind earnings call messaging. Earnings press releases are intended to put a company’s best foot forward, and though earnings calls are meticulously prepared and rehearsed, the company’s executive team will often make off-the-cuff remarks that can be highly valuable and revealing. Company sentiment conveyed in earnings calls often translates to shifts in the market. 

Having a tool that accurately analyzes tone in financial language can help you make better strategic decisions and answer the following questions: 

• What are consumers, analysts, and management teams saying about your peers?
• What is the sentiment toward your own company?
• What is the sentiment of your peers on earnings calls?
• Are your peers speaking positively or negatively about key topics?

Comparing changes in language to prior earnings call transcripts can help you piece together a more comprehensive narrative, allowing you to spot continuity gaps that could indicate red flags. AI allows you to quickly uncover the subtle messaging and tonal cues that will ultimately allow you to draw conclusions and reveal relationships between key topics or themes that would otherwise be easily missed. 

How to Use AlphaSense to Understand Sentiment

AlphaSense’s sentiment analysis function is a one-of-its-kind AI feature that uses machine learning to analyze the tone and sentiment in financial documents, empowering you to quickly understand the landscape and outlook for any company in your coverage universe. With AlphaSense’s sentiment analysis, you can apply a sentiment lens to earnings call transcripts within moments of them appearing on the platform and screen documents to see:

• Companies with the largest sentiment score shift from the previous quarter
• The most negative or positive documents, on a relative scale of -100 to 100
• Highlighting of phrase-level positive or negative sentiment underlying score shifts, honing in on the specific tonal text that our model has identified with high confidence

AlphaSense sentiment analysis represents  a radical improvement compared with how other products on the market apply sentiment to financial documents. Here’s how:

Our AI model is trained on 10+ years of human-curated financial text to understand nuanced context. Like all of the most successful AI systems, our sentiment model is trained on a massive data set. Our AI model surpasses all past sentiment models in capability, leveraging our proprietary access to the vast human-labeled training data we have meticulously built up. Unlike legacy approaches, our model understands the context of financial language, beyond a narrow limited set of scenarios. 

Our model is transparent. Simple dictionary-based approaches fail with many common scenarios like negations or co-references. Our AI model has learned from vast training data and succeeds where past models have failed. We show phrase-level highlighting of positive/negative sentiment, so you can see exactly how we’re arriving at our insights.

Our AI model is built to recognize variation in language. Traditional legacy platforms can fall short in comprehensive market analysis because they fail to capture speech variance and often do not understand industry parlance. This can result in you missing certain critical insights in your analysis. 

Synonym recognition is a proprietary AlphaSense AI technology that can greatly expand keyword searches and also screen out incorrect results for keywords with multiple meanings. With synonym recognition, you won’t miss any instances of the term you are interested in, and you can be sure your analysis is thorough and complete.

Transform your Earnings Season Analysis with AlphaSense

With AlphaSense, we help users cut through the noise with our proprietary AI search technology. Premium features like Generative Grid, Generative Search, Smart Summaries, Smart Synonyms, and Sentiment Analysis allow you to take the lead in your respective industry and amplify your ability to conduct competitive market landscape analyses — in less time and with less resource drain. 

Start your free trial here.