Lote and Partners’ Post

I’ve had several conversations on #DataStrategy with hedge funds and other investment managers in the last few months. It is evident that many do not truly grasp the wide-ranging value of incorporating #alternativedata into the investment workflow. I want to share five use cases for longer-term fund managers. These examples go well beyond the widely known – and written about by the media – short-term use case of counting cars in the parking lot (using satellite images) ahead of a retailer’s quarterly earnings report. 1. Manage risk / generate incremental alpha around quarterly earnings: If the data overwhelmingly suggests a strong or weak quarter, you can add to or trim a longer-term holding or hedge accordingly. 2. On-going monitoring of inflection points / signals: Alternative data can provide early signals to investors that things are changing – good or bad – for sectors, specific names or relevant KPIs that they care about. 3. Investment idea generation: Data is an excellent way to identify good/bad businesses and if they are accelerating or decelerating. 4. Prepare for management meetings and conferences: Alternative data can arm analysts with real-world (and current) data points that are excellent talking points / topics to discuss (and challenge) management teams and industry experts. 5. #GenAI: With the increasing number of finance specific tools and platforms becoming available, sourcing and acquiring the best data will be crucial for fine-tuning proprietary models. Feel free to DM me to discuss these use cases (and many others) and to learn more about alternative data and data strategy.

#hiring *Vice President, GenAI (LLM - Asset Management)*, Boston, *United States*, fulltime #jobs #jobseekers #careers #Bostonjobs #Massachusettsjobs #ConsultingCorporateStrategy *Apply*: https://lnkd.in/gqDpFDS9 Job Description: The RoleYou are a seasoned scientist with a deep understanding of GenAI, and a burning desire to unlock its mysteries in the context of a professional domain such as finance. You are excited about this once-in-a-lifetime opportunity to completely rethink the foundations of your discipline (Computer Science, AI, Finance) around an epoch-defining new technology (in this case, GenAI). You are at home in an applied technical group conceiving of, building and delivering first-of-a-kind knowledge-based applications. You have excellent collaboration skills and revel in working as part of a team to solve deep applied problems.You will have:You have a PhD or Master's in AI/ML, Computer Science or Finance (with a background in Machine learning), with five years plus of industrial experience.An understanding of how state-of-the-art LLMs can be induced to use explicitly supplied structured and unstructured information, their implicit world knowledge and their slow-thinking abilities to solve domain-specific problems (such as extraction, evaluation and comparison or argument structure in investing domains).A grasp of logic- and ML-based AI techniques for knowledge extraction, representation and reasoning. Expertise in combining symbolic, logic-based representations (e.g. knowledge graphs, ontologies) with informal, text-based domain content to solve business problemsAn understanding of how to deliver into production GenAI applications that can reliably process information in open-ended settings, e.g. by playing off state-of-the-art LLMs (from OpenAI, Google, Anthropic) against each other.A track record of managing the "exploration vs exploitation" tradeoff to deliver innovative solutions for first-of-a-kind problemsDeep expertise in Python, data-centric AI (ML) techniques, particularly as applied to text, tables, documents. Excellent knowledge of standard tools of the trade for machine learning engineers.The TeamWe are developing a Large Language Model (LLM)-first knowledge stack for investment professionals in Fidelity Asset Management - analysts and portfolio manager in equities, fixed income, high income, direct lending. The stack will be able to process all the documents of interest to analysts - e.g., analyst reports, earnings notes, spreadsheet models, prospectuses, loan indentures, news reports, regulatory filings. It will support the deployment of personalized assistants that can assist principals in their full range of information consumption/processing/production tasks.These tasks includes summarizing documents (from different points of view), answering questions about passages, extracting terms from documents, comparing extracted information

Daniel Goldberg: excellent insights and observations. Even though #alternativedata has become #mainstreamdata for over a decade now it is still often misunderstood and not optimally used across investment firms. Often by firms who actually spend the most on it. #data is the fuel that powers #decisionmaking. To be utilized optimally it requires a proper data infrastructure (including the ability to store/warehouse it) and processing tools and workflows that filter out noise and channel it to the right destinations: models and #decisionmakers that can take actions to maximize alpha and minimize risk. Spending $$$ on the most expensive fuel, but not taking your car out of the garage, won’t get you to an exciting destination. Spending $$$ on the most expensive fuel, but having a faulty engine, rusty pipes or leaks or blockages and defects, won’t get you to your destination. Spending $$$ on the most expensive fuel but not having a plan, a map, or navigation system won’t get you to your destination. Spending $$$ on the most expensive fuel but not having a driver won’t even get you started.

#hiring *Vice President, GenAI (LLM - Asset Management)*, Boston, *United States*, fulltime #jobs #jobseekers #careers #Bostonjobs #Massachusettsjobs #ConsultingCorporateStrategy *Apply*: https://lnkd.in/gk_-f6az Job Description: The RoleYou are a seasoned scientist with a deep understanding of GenAI, and a burning desire to unlock its mysteries in the context of a professional domain such as finance. You are excited about this once-in-a-lifetime opportunity to completely rethink the foundations of your discipline (Computer Science, AI, Finance) around an epoch-defining new technology (in this case, GenAI). You are at home in an applied technical group conceiving of, building and delivering first-of-a-kind knowledge-based applications. You have excellent collaboration skills and revel in working as part of a team to solve deep applied problems.You will have:You have a PhD or Master's in AI/ML, Computer Science or Finance (with a background in Machine learning), with five years plus of industrial experience.An understanding of how state-of-the-art LLMs can be induced to use explicitly supplied structured and unstructured information, their implicit world knowledge and their slow-thinking abilities to solve domain-specific problems (such as extraction, evaluation and comparison or argument structure in investing domains).A grasp of logic- and ML-based AI techniques for knowledge extraction, representation and reasoning. Expertise in combining symbolic, logic-based representations (e.g. knowledge graphs, ontologies) with informal, text-based domain content to solve business problemsAn understanding of how to deliver into production GenAI applications that can reliably process information in open-ended settings, e.g. by playing off state-of-the-art LLMs (from OpenAI, Google, Anthropic) against each other.A track record of managing the "exploration vs exploitation" tradeoff to deliver innovative solutions for first-of-a-kind problemsDeep expertise in Python, data-centric AI (ML) techniques, particularly as applied to text, tables, documents. Excellent knowledge of standard tools of the trade for machine learning engineers.The TeamWe are developing a Large Language Model (LLM)-first knowledge stack for investment professionals in Fidelity Asset Management - analysts and portfolio manager in equities, fixed income, high income, direct lending. The stack will be able to process all the documents of interest to analysts - e.g., analyst reports, earnings notes, spreadsheet models, prospectuses, loan indentures, news reports, regulatory filings. It will support the deployment of personalized assistants that can assist principals in their full range of information consumption/processing/production tasks.These tasks includes summarizing documents (from different points of view), answering questions about passages, extracting terms from documents, comparing extracted information

Boring Technology Subsector worth a look I read a technology sub-sector report published by HSBC Asset Management, and I liked the investment thesis. Although the report focuses on private equity investment into software and services businesses (SaaS), I feel public market investors can also bet on the sector through Global X Cloud Computing ETF (CLOU). ETF Fact Sheet link (https://lnkd.in/eSCrVYMp). Following are the key points from the report: - Valuations of these companies have come down to a significant discount compared to the recent past (2020-2021). - For high growth companies (growth of 30%+), EV/TTM revenue has dropped down to 16x compared to high of 40x. For the low growth of companies (less than 15%), the same ratio has come down to 4x compared to highs of 10x. - Global software spending has increased at a CAGR of 16% during 2018-2024E and is expected to cross US $ 1 trillion in 2024. Rising labour costs and increased digitization focus will contribute to market growth for software companies in the future. Moreover, revolutionary changes like AI will also augment the growth of the sector. - The sector has transitioned from "growth at all costs" to "profitable growth." - Despite volatility, the technology sector has been the best performing sector since January 01, 2019 (Chart Attached). - Between 2005 and 2020, software and services investments in the US outperformed the average private equity returns by ~5-6% and generated average gross IRR of 26%. Author's Note: This content is for informational and illustration purposes only and reflects personal views. It does not represent the official opinion of Habib Bank AG Zurich. Information Sources: HSBC Asset Management, Global X Mirae Asset

What aspect of the future of work in hedge fund talent development excites you the most? 1. Embracing AI and machine learning for investment strategies 2. Remote work and virtual collaboration opportunities 3. Enhanced focus on diversity and inclusion in hiring practices 4. Investing in continuous learning and professional development 5. Leveraging data analytics for better decision-making 6. Other (please specify) Comment below. #FutureOfWork #HedgeFund #TalentDevelopment

GenAI is Transforming Hedge Fund Operations The hedge fund industry is rapidly embracing Gen AI tools, with 86% of managers allowing their staff to use these technologies to enhance their work. Here's how Gen AI is making waves: 💡 Versatility: Hedge fund managers are leveraging Gen AI for diverse tasks, including improving marketing materials, supporting coding efforts, and conducting research. 💡 Top Tools: ChatGPT leads the pack as the preferred Gen AI tool, followed by Bing and Bard. 💡 Industry Disruption: Key areas like research, IT, legal and compliance, and investor relations are expected to see significant disruption due to Gen AI, according to survey respondents. 💡 Portfolio Management Potential: While current use in portfolio management is limited, 20% of larger hedge fund managers anticipate major disruption in this area within two years. 💡 Challenges: Adoption isn't without hurdles—data security, privacy concerns, inconsistent responses, and the need for comprehensive training are top challenges. 💡 Training Trends: Only 10% of respondents have received Gen AI training so far. However, nearly half of larger hedge fund managers and 26% of smaller ones plan to offer training within the next six months, with many considering third-party training services. 💡 Hiring Trends: Experience with Gen AI tools is becoming increasingly important, with around a third of respondents prioritizing it for new hires, including 10% for front-office and 7% for middle/back-office roles. The landscape is shifting, and Gen AI is at the forefront of this transformation. How is your organization adapting? #zenpulsar #GenAI #HedgeFunds #AI #Innovation #Finance #InvestmentStrategies #TechInFinance #DataScience #FutureOfWork #AITraining #HiringTrends #LLM #ML #Alternativedata #Alphageneration

#hiring *Vice President, GenAI (LLM - Asset Management)*, Boston, *United States*, fulltime #jobs #jobseekers #careers #Bostonjobs #Massachusettsjobs #ConsultingCorporateStrategy *Apply*: https://lnkd.in/dWuZwTJ2 Job Description: The RoleYou are a seasoned scientist with a deep understanding of GenAI, and a burning desire to unlock its mysteries in the context of a professional domain such as finance. You are excited about this once-in-a-lifetime opportunity to completely rethink the foundations of your discipline (Computer Science, AI, Finance) around an epoch-defining new technology (in this case, GenAI). You are at home in an applied technical group conceiving of, building and delivering first-of-a-kind knowledge-based applications. You have excellent collaboration skills and revel in working as part of a team to solve deep applied problems.You will have:You have a PhD or Master's in AI/ML, Computer Science or Finance (with a background in Machine learning), with five years plus of industrial experience.An understanding of how state-of-the-art LLMs can be induced to use explicitly supplied structured and unstructured information, their implicit world knowledge and their slow-thinking abilities to solve domain-specific problems (such as extraction, evaluation and comparison or argument structure in investing domains).A grasp of logic- and ML-based AI techniques for knowledge extraction, representation and reasoning. Expertise in combining symbolic, logic-based representations (e.g. knowledge graphs, ontologies) with informal, text-based domain content to solve business problemsAn understanding of how to deliver into production GenAI applications that can reliably process information in open-ended settings, e.g. by playing off state-of-the-art LLMs (from OpenAI, Google, Anthropic) against each other.A track record of managing the "exploration vs exploitation" tradeoff to deliver innovative solutions for first-of-a-kind problemsDeep expertise in Python, data-centric AI (ML) techniques, particularly as applied to text, tables, documents. Excellent knowledge of standard tools of the trade for machine learning engineers.The TeamWe are developing a Large Language Model (LLM)-first knowledge stack for investment professionals in Fidelity Asset Management - analysts and portfolio manager in equities, fixed income, high income, direct lending. The stack will be able to process all the documents of interest to analysts - e.g., analyst reports, earnings notes, spreadsheet models, prospectuses, loan indentures, news reports, regulatory filings. It will support the deployment of personalized assistants that can assist principals in their full range of information consumption/processing/production tasks.These tasks includes summarizing documents (from different points of view), answering questions about passages, extracting terms from documents, comparing extracted information

#hiring *Vice President GenAI Engineer (LLM - Asset Management)*, Boston, *United States*, fulltime #jobs #jobseekers #careers #Bostonjobs #Massachusettsjobs #ConsultingCorporateStrategy *Apply*: https://lnkd.in/gy9ueNXV Job Description: The RoleYou are a seasoned scientist with a deep understanding of GenAI, and a burning desire to unlock its mysteries in the context of a professional domain such as finance. You are excited about this once-in-a-lifetime opportunity to completely rethink the foundations of your discipline (Computer Science, AI, Finance) around an epoch-defining new technology (in this case, GenAI). You are at home in an applied technical group conceiving of, building and delivering first-of-a-kind knowledge-based applications. You have excellent collaboration skills and revel in working as part of a team to solve deep applied problems.You will have:You have a PhD or Master's in AI/ML, Computer Science or Finance (with a background in Machine learning), with five years plus of industrial experience.An understanding of how state-of-the-art LLMs can be induced to use explicitly supplied structured and unstructured information, their implicit world knowledge and their slow-thinking abilities to solve domain-specific problems (such as extraction, evaluation and comparison or argument structure in investing domains).A grasp of logic- and ML-based AI techniques for knowledge extraction, representation and reasoning. Expertise in combining symbolic, logic-based representations (e.g. knowledge graphs, ontologies) with informal, text-based domain content to solve business problemsAn understanding of how to deliver into production GenAI applications that can reliably process information in open-ended settings, e.g. by playing off state-of-the-art LLMs (from OpenAI, Google, Anthropic) against each other.A track record of managing the "exploration vs exploitation" tradeoff to deliver innovative solutions for first-of-a-kind problemsDeep expertise in Python, data-centric AI (ML) techniques, particularly as applied to text, tables, documents. Excellent knowledge of standard tools of the trade for machine learning engineers.The TeamWe are developing a Large Language Model (LLM)-first knowledge stack for investment professionals in Fidelity Asset Management - analysts and portfolio manager in equities, fixed income, high income, direct lending. The stack will be able to process all the documents of interest to analysts - e.g., analyst reports, earnings notes, spreadsheet models, prospectuses, loan indentures, news reports, regulatory filings. It will support the deployment of personalized assistants that can assist principals in their full range of information consumption/processing/production tasks.These tasks includes summarizing documents (from different points of view), answering questions about passages, extracting terms from documents, comparing extra

One thing I’ve learned building Quanted is that the partnership between technology and asset management is often misunderstood and underutilised. While many firms claim to leverage AI and machine learning, the reality is that most are barely scratching the surface. The true game-changer isn't in automating existing processes, but in fundamentally reimagining the relationship we have with technology — a partnership. Just like in any other successful business relationship, we shouldn't let tech micro-manage our decisions, nor should we macro-neglect its potential. I believe that the right technology should: → 𝗘𝗺𝗽𝗼𝘄𝗲𝗿: Provide tools that amplify our skills and insights, enabling asset managers to make better decisions. → 𝗘𝗱𝘂𝗰𝗮𝘁𝗲: Continuously help master roles, offering new ways to analyze and interpret data. → 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗲: Lead the charge in exploring new frontiers, ensuring we stay ahead in a rapidly evolving market. When we say "AI at Work," we're acknowledging both its presence in our daily tasks and its evolving role as a collaborative partner. We're witnessing a profound shift in how value is created and decisions are made because intelligent systems aren't just working for us, they're working with us, learning, adapting, and driving innovation. At Quanted, our goal is to provide tools that work for you in the optimal way, aligning with your team’s needs and enhancing your capabilities because technology works best when it brings people together. The companies that thrive will be those that see AI not as a mere utility, but as a dynamic force—always at work, always evolving, always pushing the boundaries of what's possible. How do you view your relationship with technology in asset management? I’d love to hear your experiences and insights about this in the comments below! 💬