For both public and private firms, comparable companies' analysis is widely used as a method for company valuation. In particular, the method is of great value for valuation of private equity companies. The several approaches to the comparable companies' method usually rely on a qualitative approach to identifying similar peer companies, which tend to use established industry classification schemes and/or analyst intuition and knowledge. However, more quantitative methods have started being used in the literature and in the private equity industry, in particular, machine learning clustering, and natural language processing (NLP). For NLP methods, the process consists of extracting product entities from e.g., the company's website or company descriptions from some financial database system and then to perform similarity analysis. Here, using companies' descriptions/summaries from publicly available companies' Wikipedia websites, we show that using large language models (LLMs), such as GPT from OpenAI, has a much higher precision and success rate than using the standard named entity recognition (NER) methods which use manual annotation. We demonstrate quantitatively a higher precision rate, and show that, qualitatively, it can be used to create appropriate comparable companies peer groups which could then be used for equity valuation.

The business applications of artificial intelligence are boundless. Tony Nash realized AI's potential in an underserved niche. His startup, Complete Intelligence, uses AI to focus on decision support, which looks at the data and behavior of costs and prices within a global ecosystem in a global environment to help top-tier companies make better business decisions. "The problem that were solving is companies don't predict their costs and revenues very well," says Nash, the CEO and founder of Complete Intelligence. "There are really high error rates in company costs and revenue forecasts and so what we've done is built a globally integrated artificial intelligence platform that can help people predict their costs and their revenues with a very low error rate."

The carbon footprint of algorithms must be measured and transparently reported so computer scientists can take an honest and active role in environmental sustainability. In this paper, we take analyses usually applied at the industrial level and make them accessible for individual computer science researchers with an easy-to-use Python package. Localizing to the energy mixture of the electrical power grid, we make the conversion from energy usage to CO2 emissions, in addition to contextualizing these results with more human-understandable benchmarks such as automobile miles driven. We also include comparisons with energy mixtures employed in electrical grids around the world. We propose including these automatically-generated Energy Usage Reports as part of standard algorithmic accountability practices, and demonstrate the use of these reports as part of model-choice in a machine learning context.

Natural gas is an important energy source for much of our industrial, heating and electricity needs. The price of natural gas can fluctuate greatly. I made a time series analysis with external regressors to investigate how well modeling could forecast the price of natural gas. Using data from the US Energy Information Administration, I acquired monthly pricing data for Natural Gas from January of 1990 until present. I also acquired data on a number of related energy features.

I'm not qualified as a Financial Advisor, and there's a non-zero chance that my forecasts are ALL WRONG. I'm gonna do a Chicago Wheat fair value forecast for this first post. There's a ton of data from USDA, and it could get quite overwhelming. Breaking the data into smaller bits could help interpret the data more meaningfully for grain traders. I have no clue, looking at these numbers alone without referencing historical precedence.

Have you ever been running late for work, your hand extended into your shower, cursing its name as the water slowly warms to a temperature that would allow you to enter? Well, you may be being unsympathetic to your hot water heater, because it's likely running all day and all night to keep between 40-80 gallons of water heated, so it can be ready at your command. As you ponder the inefficiency of such a system, imagine the hot water needs of a hotel or a high-rise apartment building, with hundreds of rooms and thousands of inhabitants. The founder in this week's Silicon Valley Insider, Sridhar Deivasigamani, estimates that at any point in time in the US, there could be as much as 6 billion gallons of water being kept hot for our consumption, one-sixth the size of Lake Tahoe. Intellihot, the Galesburg, IL company founded in 2009, designs and manufactures tankless water heaters, as well as monitoring devices and apps, for residential, commercial and industrial applications.

The MIT Technology Review has released a list of technologies it believes will make the most impact over the next 12 months, including smarter cities, genetic fortune telling and "babel fish" earphones.

Every year since 2001 we've picked what we call the 10 Breakthrough Technologies. People often ask, what exactly do you mean by "breakthrough"? It's a reasonable question--some of our picks haven't yet reached widespread use, while others may be on the cusp of becoming commercially available. What we're really looking for is a technology, or perhaps even a collection of technologies, that will have a profound effect on our lives.

During Chevron Corporation's (NYSE:CVX) Security Analyst meeting on March 8, several big pieces of news came out. A day before the meeting, Chevron issued a press release stating that its 54 billion Gorgon LNG facility in Australia had just started producing LNG (liquefied natural gas) and condensate. After originally estimated to be operational by the end of 2014 for under 30 billion USD, the project was delayed as costs skyrocketed. As the operator with a 47.3% stake, Chevron lost a lot of credibility due to the massive cost of its mishaps, as did its partners ExxonMobil (NYSE:XOM) and Royal Dutch Shell (NYSE:RDS.A) (NYSE:RDS.B), who each own 25% of the venture. The first cargo of LNG is expected to be shipped out very soon, potentially marking the beginning of a strong source of growth after all the headaches it took to get here.

This paper proposes the use of the Brownian distance correlation to conduct a lead-lag analysis of financial and economic time series. When this methodology is applied to asset prices, the non-linear relationships identified may improve the price discovery process of these assets. The Brownian distance correlation determines relationships similar to those identified by the linear Granger causality test, and it also uncovers additional non-linear relationships among the log prices of oil, coal, and natural gas.