Daloopa Blog

The ASC 606 standard, "Revenue from Contracts with Customers," transforms the way companies recognize revenue by focusing on satisfying performance obligations (POs). RPOs (Remaining Performance Obligations) and cRPOs (Current RPOs) are key to forecasting growth, particularly in large multi-year contracts with enterprise clients. These metrics do not appear in financial statements but are critical to understanding future revenue. In examples like Oracle, which has seen RPO growth from $65B to $98B, and ServiceNow, benefiting from GenAI-driven demand, these figures illustrate substantial forward revenue and long-term deal trends. Modeling these numbers can help predict future financial performance.

Discover the story behind Daloopa's early 'basement days'—a three-year grind in a Long Island basement where three co-founders built their vision from scratch. From overcoming financial struggles to building a game-changing AI tool for financial analysts, learn how hard work, innovation, and dumplings fueled Daloopa’s journey to success.

Discover key financial concerns within Super Micro Computer Inc (SMCI) for FY2024, including plummeting gross margins, ballooning accounts receivables, and negative free cash flow. Dive into these yellow flags highlighted using Daloopa's comprehensive data, amidst weak earnings, a Hindenburg short report, and filing delays

In 2019, Thomas Li left a secure hedge fund job to start Daloopa and tackle a major data extraction challenge for financial analysts. This blog explores the risks and debates about this bold move and how the journey has unfolded over the past four years.

Canalyst and Daloopa share similarities in our commitment to delivering high-quality financial data. However, Canalyst is more focused on providing ready-to-use financial models, whereas Daloopa emphasizes on automating the data extraction and delivery process and allowing greater flexibility in how that data is used in financial modeling. To make an informed investment decision on your coverage, it is essential to understand the specific needs of your financial analysis and the data infrastructure and platform to achieve your objectives.

In the dynamic landscape of financial data and analytics, Canalyst has established itself as a prominent provider for finance professionals. While Canalyst offers robust solutions, many compelling alternatives exist in the market that can meet or exceed your analytical needs. Whether you are seeking comprehensive data feeds or intuitive platforms, exploring these alternatives is crucial to finding the best fit for your requirements.

Building a financial model in Excel demands precision, a deep understanding of financial principles, and advanced techniques. This guide goes beyond the basics to explore complex aspects of financial modeling, including creating robust financial statements, scenario analysis, and long-term projections. By leveraging Excel's powerful functions, you'll learn to build accurate, adaptable models that reflect current financial conditions and anticipate market changes.

Fundamental analysis forms the bedrock of sound investment strategies, allowing us to assess a company's intrinsic value. The most essential data for fundamental analysis comes from financial statements, such as balance sheets, income statements, and cash flow statements. Public sources, such as the Securities and Exchange Commission (SEC) and financial news platforms, provide valuable data, while financial data APIs offer real-time updates for deeper analysis.

Anyone involved in business planning, investment analysis, and decision-making requires essential Excel financial modeling and forecasting tools. Adhering to best practices, such as clear distinctions between inputs and calculations, logical data structuring, and regular updates, ensures accuracy and reliability.

Effective financial modeling is crucial for making informed decisions, especially when quick adjustments are necessary. Properly structured and formatted models enhance adaptability, allowing for swift changes, reducing errors, and saving time.

Financial statement analysis tools enable us to review and interpret financial data, uncovering valuable information for decision-making. These tools range from simple spreadsheets to sophisticated software applications. They help us examine the balance sheet, income statement, and cash flow statement - the three primary financial statements. By comparing these statements across different periods or companies, we can identify trends, strengths, and potential areas of concern.

Daloopa was born from a phone call I made in 2019. Frustrated with the manual data extraction process as a tech analyst, I reached out to Jeremy Huang and Daniel Chen, who were working in tech and had experience in AI. We were driven by the idea that AI could automate the extraction of valuable financial KPIs, which was previously thought impossible. After building a prototype with solid data, we received feedback to expand our coverage. Confident in the product's potential, I decided to go full-time, marking the official start of Daloopa.

Wall Street analysts often waste hours on manual data entry during earnings season, copying numbers from PDFs into Excel. This time-consuming task detracts from valuable analysis. Daloopa solves this problem by using AI to automate data extraction and model updates, delivering accurate and comprehensive data directly to analysts. This automation saves time and improves the quality of financial analysis.

Every analyst thinks they’ve mastered how to use excel for financial analysis until it’s earning seasons and you’re under the gun for a tight deadline. Knowing these formulas can save you valuable minutes wasted on reinventing the wheel, or hours squandered trying to understand how an inherited model actually works.

NVIDIA’s upcoming earnings report on August 28th is expected to bring heightened volatility, driven by strong AI-related spending from major tech companies like Amazon, Google, Meta, and Microsoft, alongside potential delays in the launch of its next-gen Blackwell platform. While cloud providers continue to increase capital expenditures for AI, early signs of AI monetization are emerging from companies like ServiceNow and Palantir. Despite concerns over the Blackwell delay and potential guidance risks, NVIDIA’s long-term position remains robust, making any stock dips likely short-lived.

We've seen young professionals and those transferring into the industry alike struggle to grasp the fundamental definitions of financial ratios in evaluating company performance. This guide aims to shed light on the most crucial ratios and how to use them effectively.

At the heart of financial models are the actual valuation and analysis techniques that are the ultimate end point for all the data we gather. Six of the most common types are below and should all be familiar.

By now, many of you have heard about or experienced the recent Crowdstrike (CRWD) outage. This event has raised several questions about the potential impact on Crowdstrike’s financials. Here, we delve into the details and consider the implications.

Starbucks (SBUX) reported a disappointing Q2, missing revenue expectations by 6% and seeing a 4% decline in same-store sales. Despite trading at multi-year lows, new initiatives like lower-priced food-drink combinations and energy drinks, along with a bullish Goldman Sachs rating, offer potential for recovery. The company's increased cost efficiency targets highlight efforts to boost performance amidst current challenges.

In today's data-driven financial landscape, the ability to efficiently collect, integrate, and analyze vast amounts of information is crucial for financial analysts. While Excel remains a cornerstone tool in finance, its native capabilities can be significantly enhanced through the use of specialized add-ins. These powerful extensions transform Excel into a more robust platform for data management and analysis, enabling financial analysts to work smarter, faster, and with greater accuracy.

In the fast-paced world of finance, the ability to create accurate, dynamic, and insightful financial models is a critical skill. While Excel remains the go-to tool for financial professionals, its native capabilities can be significantly enhanced through specialized add-ins. These powerful extensions transform Excel into a more sophisticated platform for financial modeling and analysis, enabling analysts to work more efficiently, produce more robust models, and generate deeper insights.

In today's data-rich financial landscape, the ability to effectively communicate complex information through compelling visualizations is a critical skill. While Excel offers basic charting capabilities, specialized add-ins can dramatically enhance its data visualization prowess.

Accessing company historical financial data can be crucial for making well-informed investment decisions and understanding market trends. To access this data, we need to start by reviewing publicly available financial statements from reliable databases or company websites. These financial statements usually include income statements, balance sheets, and cash flow statements, providing comprehensive insights into a company's performance over time.

In the modern financial industry, accessing and using historical financial data APIs has become a game-changer. These APIs allow us to retrieve and analyze vast amounts of market data that were previously discarded at the end of each day. APIs simplify the way we access historical financial data, enabling us to make informed decisions and develop smart strategies.

Financial analysis is essential for making informed business decisions, and Excel is one of the most powerful tools to help us do this efficiently. With a few key techniques, we can use Excel to convert raw data into actionable insights. By understanding Excel's interface and functions, we can streamline our financial analysis process.

In today's fast-paced financial world, staying ahead of the curve is crucial for analysts and decision-makers alike. As the volume and complexity of data continue to grow, financial professionals are turning to advanced tools to streamline their workflows and enhance their analytical capabilities.

At its best software like Excel helps organize and action complex data into a format everyone on a team can comfortably work with, but all too often the basics of data collection and formatting can cause unnecessary headaches. As anyone who has ever spent a late night hunched over mysteriously broken formulas can attest to, having the right tools can make all the difference.

We know that accurate and timely data is crucial to your investment research process. That’s why we’re continuing to enhance our New Data Disclosure feature in Daloopa Plus. This feature enhancement aims to set clear expectations and empower analysts to manage their model-updating process more effectively. Learn more about what this entails below.

Daloopa's newest feature - Live Update Notifications - immediately notifies users when new data is available in the Daloopa database, prompting users to update cells with the latest data. Make every second count and stay one step ahead with Daloopa.

Funding Round to Accelerate Growth for the Leading Provider of AI-powered Data Infrastructure for Financial Institutions

Today we announce our Series B funding round! Daloopa CEO Thomas Li takes over the blog to share his views on this exciting milestone for our company.

Two analysts show how they use Daloopa data in different ways. One reviews the comprehensive data sheets to gain quick insights. The other uses Daloopa data to go super deep and compare to peers.

How one analyst uses Daloopa's retrofitter to add hyperlinks and source files to every data point in his model

Daloopa’s newest feature, the Hyperlink Toggle, is designed to streamline your workflow and minimize distractions. This new feature, available exclusively within the Settings tab of our latest Excel Add-in, offers Daloopa users more flexibility to enhance their modeling efficiency.

It's not too late to Use Daloopa to analyze important metrics and get an operational model on Reddit up and running quickly

A look at how one analyst uses Daloopa datasheets to uncover unique KPIs and spark new ideas

Small cap stocks can be a great resource for generating new investment ideas. In this example, we take a look at Genius Sports Limited (GENI), a data provider of the top professional sports leagues to the sports betting industry, which is an under-followed, under-loved small-cap gem at an inflection point of accelerating revenue growth and significant margin improvement. Their recently released Bet Vision product strengthens their standing with the sportsbooks and can be transformative to in-play betting, where they are highly leveraged.

Investment analyst Josh Burwick, uses Daloopa Plus to update his model for Alphabet's Q4 earnings, getting key metrics in seconds and to insights in minutes.

Flutter, parent of FanDuel, is preparing to list on the NYSE next week. Finally, US investors can own the market leader position in the high-growth online sports betting industry. We provide the background and breakdown of the company's industry-leading hold rate and show how its margins are poised to inflect in the years ahead.

A sportsbook’s goal is to attract even amount of bets and dollars on both sides in a game so that they can lock in a risk-free profit. FALSE. The aforementioned fallacy is repeated endlessly, showing the prevailing misunderstanding of sportsbooks’ motivation and economics.

The juxtaposition of DraftKings and PENN with the former’s strong profitability and the latter’s ESPN Bet early stumble is jarring and unequivocal.

PENN’s rebrand as ESPN BET is set to launch in 17 states on November 14th following PENN’s $2 billion+ licensing partnership deal with ESPN announced in August.

Welcome analysts, investors, and model builders. We are thrilled to announce the official launch of the Daloopa Blog, a place where we will cover topics such as Financial Modeling, Investment Management, AI applications, Data, Technology, and more.

Everyone has a different approach to modeling, but there are some key factors that separate good and great models. In this article, we'll go through the characteristics of great models, and how to improve model-building.