Forecasting the Consumer Price Index (CPI) is an important yet challenging task in economics, where most existing approaches rely on low-frequency, survey-based data. With the recent advances of large language models (LLMs), there is growing potential to leverage high-frequency online text data for improved CPI prediction, an area still largely unexplored. This paper proposes LLM-CPI, an LLM-based approach for CPI prediction inference incorporating online text time series. We collect a large set of high-frequency online texts from a popularly used Chinese social network site and employ LLMs such as ChatGPT and the trained BERT models to construct continuous inflation labels for posts that are related to inflation. Online text embeddings are extracted via LDA and BERT. We develop a joint time series framework that combines monthly CPI data with LLM-generated daily CPI surrogates. The monthly model employs an ARX structure combining observed CPI data with text embeddings and macroeconomic variables, while the daily model uses a VARX structure built on LLM-generated CPI surrogates and text embeddings. We establish the asymptotic properties of the method and provide two forms of constructed prediction intervals. The finite-sample performance and practical advantages of LLM-CPI are demonstrated through both simulation and real data examples.

While financial data presents one of the most challenging and interesting sequence modelling tasks due to high noise, heavy tails, and strategic interactions, progress in this area has been hindered by the lack of consensus on quantitative evaluation paradigms. To address this, we present LOB-Bench, a benchmark, implemented in python, designed to evaluate the quality and realism of generative message-by-order data for limit order books (LOB) in the LOBSTER format. Our framework measures distributional differences in conditional and unconditional statistics between generated and real LOB data, supporting flexible multivariate statistical evaluation. The benchmark also includes features commonly used LOB statistics such as spread, order book volumes, order imbalance, and message inter-arrival times, along with scores from a trained discriminator network. Lastly, LOB-Bench contains "market impact metrics", i.e. the cross-correlations and price response functions for specific events in the data. We benchmark generative autoregressive state-space models, a (C)GAN, as well as a parametric LOB model and find that the autoregressive GenAI approach beats traditional model classes.

The over-the-counter (OTC) government bond markets are characterised by their bilateral trading structures, which pose unique challenges to understanding and ensuring market stability and liquidity. In this paper, we develop a bespoke ABM that simulates market-maker interactions within a stylised government bond market. The model focuses on the dynamics of liquidity and stability in the secondary trading of government bonds, particularly in concentrated markets like those found in Australia and the UK. Through this simulation, we test key hypotheses around improving market stability, focusing on the effects of agent diversity, business costs, and client base size. We demonstrate that greater agent diversity enhances market liquidity and that reducing the costs of market-making can improve overall market stability. The model offers insights into computational finance by simulating trading without price transparency, highlighting how micro-structural elements can affect macro-level market outcomes. This research contributes to the evolving field of computational finance by employing computational intelligence techniques to better understand the fundamental mechanics of government bond markets, providing actionable insights for both academics and practitioners.

Last week, I had the pleasure of interviewing Kevin Novak, founder of Rackhouse Venture Capital and Uber's first head of AI, and Alex Lee, founder and CEO, of Truewind, in front of a crowd of investors and LPs. The panel was titled, "AI and the battle to capture its value chain: base layer accrual vs the fine tuners." Here's a sample of the questions and topics we addressed. How has AI evolved since you started working in the field, and what is different about this current hype cycle compared to previous ones? According to the Economist, over 500 generative AI startups have collectively raised over $11B, not including OpenAI.

Syngenta Group is a $28B leading science-based agtech company, operating in more than 100 countries, with more than 50'000 employees. We are proud to stand at the forefront of the tech revolution in agriculture. Using the latest digital innovations, data, and cutting-edge technologies we want to transform the way that crops are managed and enable farmers and agronomists to enhance efficiency and sustainable food production. Our business success reflects the quality and skill of our people. We recognize that human diversity is as important to our business as biodiversity.

Forecasts from Persistence Market Research indicate that by the end of the forecast period in 2027, the worldwide deep learning market would be worth US$ 261,113.0 This indicates a 49.0% compound annual growth rate that was seen over the anticipated period. This development can be ascribed to the demand for improved processing hardware, an increase in global R&D activity in particular industries, and the quick global adoption of cloud-based technologies. A recent research from Persistence Market Research offers a complete review of the worldwide deep learning market. In-depth analysis of the deep learning concept and the performance of the global deep learning market across significant end-use industry sectors throughout seven significant geographies are provided in this study.

Machine learning enables the creation of algorithms that can learn from data, spot any unusual user behaviour, predict risks, and automatically notify financial companies of a threat. Employing data science within finance helps companies manage and store customers' data far more efficiently. Firms can boost profits using AI-driven tools and technologies such as natural language processing (NLP), data mining and text analytics, while machine learning algorithms analyse data, identify valuable insights and suggest better business solutions. Data science can analyse the market landscape and customer data in real time, enabling financial specialists to take action to mitigate risks. The use of data science in the financial sector goes beyond fraud, risk management and customer analysis.

At Fannie Mae, futures are made. The inspiring work we do makes an affordable home a reality and a difference in the lives of Americans. Every day offers compelling opportunities to modernize the nations housing finance system while being part of an inclusive team using new, emerging technologies. Here, you will help lead our industry forward, enhance your technical expertise, and make your career. As a valued colleague on our team, you will work with your team to apply fundamental techniques to support production of insights, new product or change recommendations, process improvement or automation, and predictive modeling.

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.

GENERAL BOILERPLATE At Fannie Mae, futures are made. The inspiring work we do makes an affordable home a reality and a difference in the lives of Americans. Every day offers compelling opportunities to impact the future of the housing industry while being part of an inclusive team thriving in an energizing environment. Here, you will help lead our industry forward and make your career. CORPORATE PROFESSIONAL At Fannie Mae, futures are made.