Paul Budd: We believe it’s just a matter of evolution and convergence
1. What do you consider your biggest professional achievement to date?
During my 22 years with the firm, helping my company to become one of the largest multi-vendor, service providers in the world. To achieve this, we have built long-term trust with our customers, our people, our partners, our communities, and our shareholders.
2. What has been your/your firm’s top 3 priorities for the coming year?
Computacenter has four, stated strategic priorities. Namely:
Customer relationships – retain, maximise, and increase the relationships with our customers over the long term.
Customer value - build unrivalled value for our target market customers by combining our service and product capabilities.
Services growth - lead with and grow our services.
Productivity - improve our productivity and enhance our competitiveness by leveraging our scale and building efficiencies.
3. What do you think are the biggest challenges facing data scientists/AI experts/quantitative practitioners for 2023 and beyond?
In terms of data scientists, then I think the biggest challenges they currently face include:
- Effective communication with non-technical stakeholders.
- Data preparation.
- Handling multiple data sources.
- Increased focus on data security.
- Identifying and addressing the ‘business problem’.
- Effective collaboration with data engineers.
- Misunderstandings about the role.
- Undefined KPIs and metrics.
In terms of AI experts, then again, I believe the following challenges currently exist:
- Lack of transparency.
- Bias and discrimination.
- Privacy concerns.
- Ethical dilemmas.
- Security risks.
- Concentration of power.
- Dependence on AI.
- Job displacement.
The future of quant trading is likely to be shaped by a range of emerging technological, environmental, economic, and regulatory challenges. As the sector grows, the demand for the skills will only increase. Increasingly, it will compete with traditional and out of sector demands for such skills, as data science and AI becomes prevalent across the wider economy.
4. Market and political uncertainty over the last year has seen unpredictable outcomes for some quant firms – how do you think quant firms can prepare for increased uncertainty to come and manage the 40-year inflation high that was seen in 2022?
Undoubtedly, a combination of subdued stocks and high-interest (low growth) economies (with rising prices) around the world, is presenting quant trading firms with openings (and constraints) in equal measure, in terms of securing investment and leveraging funding. Consequently, liquidity will become central to many. Perhaps predictably, the current economic headwinds, will also see an increase M&A activity in the sector.
However, uncertainty brings opportunity, and for many it will be time to take and make share. But to do this, they will need to have the right leadership, skills, and technology.
5. What is your advice to funds hoping to get new systematic strategies into production quickly and more often?
Working in collaboration with our partners, Dell Technologies and NVIDIA, Computacenter brings its supply chain, logistical, professional and consultancy services to bear, to provide the rapid deployment of new technology, where and when it is needed. To do this consistently and competitively, requires best-in-class scale, knowhow, facilities, and processes.
Computacenter has over 40 years’ experience of delivering solutions to some of the world’s leading financial services organisations.
Some specific ways in which Computacenter, Dell Technologies and NVIDIA are helping quant trading firms of different sizes and growth stages include adopting technologies that can scale and flex as they grow; optimising and managing cloud infrastructures to achieve cost savings and efficiencies; and securing and harnessing data to drive actionable insight and decision-making.
6. ChatGPT is everywhere and being used everywhere. How do you see quant funds using this new technology and what advice can you give people using it?
a) What are your predictions for generative AI in the coming years?
In the coming months and years, the role of AI chatbot ChatGPT will evolve to help quant traders improve their trading results and investment returns.
Increasingly, quant traders will integrate ChatGPT into the existing trading systems and allow them to make their transactions such as placing orders, executing trades, etc on the basis of previously set parameters.
This will help traders increase efficiency and allocate more time towards other trading-related activities.
In terms of generative AI, then two of our prime technology partners, Dell Technologies and NVIDIA, have recently announced a unique collaboration to deliver ‘Project Helix’, an integrated approach to enable faster, full-stack GenAI deployments so organisations of all sizes can accelerate their business transformation, scale their AI consistently and deliver trusted AI outcomes. Project Helix delivers a full-stack GenAI solution and is designed to help customers to:
- Accelerate their time to results from Generative AI strategies, infrastructure and expertise that will increase value for the business direction and support their continued transformation.
- Secure their data, reduce data risk and compromise and enable trusted AI outcomes that will impact their business. This requires a two-prong strategy, to first ensure data operations remain on-premises for GenAI treatment (and mitigate risks of company IP sent to the cloud) and secondly, to activate trusted methods that refine responses with guardrails and tuning procedures.
- Restore agility, become leaders in their own market and leverage a more skilled AI workforce that readily converts company-specific or proprietary data into faster recommendations for the enterprise and competitive outcomes.
- Achieve more automation, which drives faster, higher-value outcomes from their operations and leverage repeatable processes and scalable growth of their business.
- Control and manage their infrastructure and operations, so they can drive higher ROI with on-premises, owned approaches.
7. Are you seeing quant investing being used in new geographies/where are you expecting some interesting quant stories to be emerging from?
Not currently. The current centres of excellence remain predominant. But of course, China, will increasingly gain scale.
8. NLP continues to be a big area of interest during our research – is the industry really using it to its full potential? Where else can we go with NLP and have you seen examples in other industries that we can learn from?
Advancements in such fields as speech recognition, automated machine translation, sentiment analysis, and chatbots, can be expected in the next years. As a result, we believe that NLP will become further integrated with other innovative technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and blockchain. These integrations will enable even more automation and optimisation of numerous processes, as well as safer and more efficient communication between devices and systems.
9. At Quant Strats, we always discuss the challenges and opportunities of blending quant and fundamental strategies, and this is always a popular topic – why do you think this is? What do you think is the most important questions for quants when considering this strategy?
Our clients are telling us that fund managers today have both fundamental and quantitative investment teams. Historically, these two groups have sat in separate silos, and for good reason: they have different approaches to the investment process and speak a different day-to-day language.
It seems the root of this divide is their respective educational foundations. Fundamental investors study economics and learn a bottom-up investment process that seeks to identify the future value of a single stock. Quants learn mathematics and engineering and take a top-down approach to investment decision making that starts with a vast quantity of market data.
Yet clearly, fundamental investors have begun to incorporate more quantitative screens and models into their fundamental research as relevant data becomes ever more accessible and data science tools more user-friendly. The indicators are that most fundamental investors today have several spreadsheet-based quant screens - aimed at flagging valuation mismatches, environmental, social, and governance (ESG) scores, and the like, that influences their investment process.
We believe it’s just a matter of evolution and convergence.