Fine-grained financial sentiment analysis on news headlines is a challenging task requiring human-annotated datasets to achieve high performance. Limited studies have tried to address the sentiment extraction task in a setting where multiple entities are present in a news headline. In an effort to further research in this area, we make publicly available SEntFiN 1.0, a human-annotated dataset of 10,753 news headlines with entity-sentiment annotations, of which 2,847 headlines contain multiple entities, often with conflicting sentiments. We augment our dataset with a database of over 1,000 financial entities and their various representations in news media amounting to over 5,000 phrases. We propose a framework that enables the extraction of entity-relevant sentiments using a feature-based approach rather than an expression-based approach. For sentiment extraction, we utilize 12 different learning schemes utilizing lexicon-based and pre-trained sentence representations and five classification approaches. Our experiments indicate that lexicon-based n-gram ensembles are above par with pre-trained word embedding schemes such as GloVe. Overall, RoBERTa and finBERT (domain-specific BERT) achieve the highest average accuracy of 94.29% and F1-score of 93.27%. Further, using over 210,000 entity-sentiment predictions, we validate the economic effect of sentiments on aggregate market movements over a long duration.

The importance of unspanned macroeconomic variables for Dynamic Term Structure Models has been intensively discussed in the literature. To our best knowledge the earlier studies considered only linear interactions between the economy and the real-world dynamics of interest rates in DTSMs. We propose a generalized modelling setup for Gaussian DTSMs which allows for unspanned nonlinear associations between the two and we exploit it in forecasting. Specifically, we construct a custom sequential Monte Carlo estimation and forecasting scheme where we introduce Gaussian Process priors to model nonlinearities. Sequential scheme we propose can also be used with dynamic portfolio optimization to assess the potential of generated economic value to investors. The methodology is presented using US Treasury data and selected macroeconomic indices. Namely, we look at core inflation and real economic activity. We contrast the results obtained from the nonlinear model with those stemming from an application of a linear model. Unlike for real economic activity, in case of core inflation we find that, compared to linear models, application of nonlinear models leads to statistically significant gains in economic value across considered maturities.

Sometimes you start a blog with a hypothesis in mind, and then that intention changes as you research and realize that your original idea was wrong. Yep, this is one of those blogs. Learning can be fun if you let go of pre-existing dogma and learn along your life journey. I've always been curious (a good trait) about economics' role in developing an organization's business-driven AI and Data strategies. And that relationship comes to life when we compare economic value and the AI utility function.

OpenAI CEO Sam Altman believes AI can help usher in "unbelievable abundance," but he says he wants to ensure that such abundance is shared. Toward that end, Altman has embraced a theory of 19th century political economist Henry George, who in his own lifetime worried about wealth amassing in the hands of the few following the Industrial Revolution. George posited that greater equality could be enjoyed if the economic value of land belonged equally to all members of society. Altman similarly believes that in a world where jobs may create less economic value, a land tax could make up for income tax and guarantee that all individuals' assets rise as land -- a fixed asset -- grows in value. He's putting his money where his mouth is, too, leading a seed round in a six-month-old startup that represents a step in that same direction.

Humans are good at analyzing things. Machines can analyze a set of data and find patterns in it for a multitude of use cases, whether it's fraud or spam detection, forecasting the ETA of your delivery or predicting which TikTok video to show you next. They are getting smarter at these tasks. This is called "Analytical AI," or traditional AI. But humans are not only good at analyzing things--we are also good at creating.

I am a believer that the pathway to innovation must end with putting a product or service into the market that provides economic value to customers, economics the branch of knowledge concerned with the production, consumption, and transfer of wealth. Innovation is about economics and economics is about value creation. Products or services that never reach the market may have been great ideas, but you can't pay the mortgage or buy your Venti Chai Latte with great ideas. Yes, I believe for something to truly be an innovation, it must achieve market success; it must deliver economic value. I know that probably buts me in conflict with some traditional innovation thinkers, but that's what makes my role as Chief Innovation Office at Hitachi Vantara so…interesting (see Figure 1).

There are several technology and business forces in-play that are going to derive and drive new sources of customer, product and operational value. As a set up for this blog on the Economic Value of Data Science, let's review some of those driving forces. "Due to its ability to substantially improve productivity and boost economic output, Artificial Intelligence (AI) has the potential to increase economic growth rates by a weighted average of 1.7% and profitability rates by 38% across a variety of industries by 2035. Source: NorthBridge Consultants "The Artificial Intelligence Revolution: New Challenges & Opport…" Figure 1: Source: "The Artificial Intelligence Revolution: New Challenges & Opportunities" Data Science (Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning) holds the potential to exploit Big Data and IoT to create new sources of economic value (wealth). But what is the source of this economic value when the AI tools that are driving this economic growth (TensorFlow, Spark ML, Caffee2, Keras) are open source and equally available to all players?

AI for health care is all the rage. Who wouldn't be excited about applications that could help detect cancer, diagnose COVID-19 or even dementia well before they are otherwise noticeable, or predict diabetes before its onset? Machine and deep learning have already been shown to make these outcomes possible. Possible, that is, in the research lab. In health care, there is often a long lag between research findings and implementation at the bedside.

With the start of each year come predictions, plans, and surveys from consulting firms. When it comes to artificial intelligence, multiple recent surveys indicate that companies aren't just planning on spending serious money on AI in 2022 -- they are already making good money from the technology. A bit of context might be helpful. Despite some AI successes, one of the challenges in recent years has been that projects involving the technology have frequently lacked sufficient economic returns. In a 2019 MIT Sloan Management Review and Boston Consulting Group AI survey, for example, 7 out of 10 companies reported minimal or no value from their AI investments.

Mike is a 40-something crop farmer from southern Queensland. With a chestnut tan, crushing handshake and a strong outback accent, he's the third generation of his family to grow sorghum, a cereal mostly used for animal fodder. But, like most farmers, Mike faces more challenges than his forbears. Climate change has eroded Australian farms' profitability by an average of 23% over the past 20 years. It's a constant challenge to improve productivity by producing more with less.