This paper presents FinCoT, a structured chain-of-thought (CoT) prompting framework that embeds domain-specific expert financial reasoning blueprints to guide large language models' behaviors. We identify three main prompting styles in financial NLP (FinNLP): (1) standard prompting (zero-shot), (2) unstructured CoT (free-form reasoning), and (3) structured CoT (with explicitly structured reasoning steps). Prior work has mainly focused on the first two, while structured CoT remains underexplored and lacks domain expertise incorporation. Therefore, we evaluate all three prompting approaches across ten CFA-style financial domains and introduce FinCoT as the first structured finance-specific prompting approach incorporating blueprints from domain experts. FinCoT improves the accuracy of a general-purpose model, Qwen3-8B-Base, from 63.2% to 80.5%, and boosts Fin-R1 (7B), a finance-specific model, from 65.7% to 75.7%, while reducing output length by up to 8.9x and 1.16x compared to structured CoT methods, respectively. We find that FinCoT proves most effective for models lacking financial post-training. Our findings show that FinCoT does not only improve performance and reduce inference costs but also yields more interpretable and expert-aligned reasoning traces.

With the development of deep learning, Dynamic Portfolio Optimization (DPO) problem has received a lot of attention in recent years, not only in the field of finance but also in the field of deep learning. Some advanced research in recent years has proposed the application of Deep Reinforcement Learning (DRL) to the DPO problem, which demonstrated to be more advantageous than supervised learning in solving the DPO problem. However, there are still certain unsolved issues: 1) DRL algorithms usually have the problems of slow learning speed and high sample complexity, which is especially problematic when dealing with complex financial data. 2) researchers use DRL simply for the purpose of obtaining high returns, but pay little attention to the problem of risk control and trading strategy, which will affect the stability of model returns. In order to address these issues, in this study we revamped the intrinsic structure of the model based on the Deep Deterministic Policy Gradient (DDPG) and proposed the Augmented DDPG model. Besides, we also proposed an innovative risk control strategy based on Quantum Price Levels (QPLs) derived from Quantum Finance Theory (QFT). Our experimental results revealed that our model has better profitability as well as risk control ability with less sample complexity in the DPO problem compared to the baseline models.

In this post, we examine the state of 5G in 2020. And while the United States is seeing good growth with this technology, it lags in average download speeds. Last year, we presented a post asking and answering: Do YOU Know What 5G Is? In March 2020, we noted that a BI Intelligence study found that "39% of respondents to our survey saying they plan to support 5G in IoT products and services before 2021." All the things we hope will make our lives easier, safer, and healthier will require high-speed, always-on internet connections.

Obviously data-driven investment managers are not going to divulge the secret signals that form the basis of their alpha strategies. But when something is not part of your main business it can help to open source the code, which can then be improved. These days open sourcing software is a trend that even large hedge funds such as AHL and AQR in the US taking part in. Saeed Amen, CEO and founder of Cuemacro, is enthusiastic about open source within the big data arena. He has spent over a decade developing algorithmic trading strategies places like Lehman Brothers and Nomura, and a number of large hedge funds. Amen said: "From my perspective, working for a small business as opposed to a big bank, I have found it quite enlightening because you don't need to own infrastructure any more, you can just log onto Amazon Web Services; you can easily just get a server and it's something that's available very quickly to do.

Obviously data-driven investment managers are not going to divulge the secret signals that form the basis of their alpha strategies. But when something is not part of your main business it can help open source the code, which can then be improved. These days open sourcing software is a trend in which even large hedge funds such as AHL and AQR in the U.S. taking part. Saeed Amen, CEO and founder of Cuemacro, is enthusiastic about open source within the big data arena. He has spent over a decade developing algorithmic trading strategies at places like Lehman Brothers and Nomura, and a number of large hedge funds.

Obviously data-driven investment managers are not going to divulge the secret signals that form the basis of their alpha strategies. But when something is not part of your main business it can help to open source the code, which can then be improved. These days open sourcing software is a trend that even large hedge funds such as AHL and AQR in the US taking part in. Saeed Amen, CEO and founder of Cuemacro, is enthusiastic about open source within the big data arena. He has spent over a decade developing algorithmic trading strategies places like Lehman Brothers and Nomura, and a number of large hedge funds.

Just yesterday, for example, Facebook Inc. (FB) rolled out a new feature, VoiceOver, that uses AI technology to give oral descriptions of FB photos for blind and visually-impaired users (iPad and iPhone users only, so far). Perhaps the big Stanley Kubrickian monolith moment for AI happened in March, with the resounding win by the Alphabet Inc. (GOOGL) AI program AlphaGo,over a champion in the complex board game Go--a game previously thought to be far too complex for a computer to play better than a human. If true believers in AI are correct that this long-promised technology is ready for the mainstream, whichever company controls AI could steer the tech industry for years to come. Hence we are witnessing a high-stakes competition to develop the next platform to establish industry dominance in upcoming product cycles. While Amazon.com Inc. (AMZN), FB, GOOGL and Microsoft Corp. (MSFT) are all jockeying furiously, the one company with the most riding on the outcome is International Business Machines Corp. (IBM).