Tatjana Puhan: NLP is an exciting field or use case for artificial intelligence
Tatjana will be set to speak at Quant Strats at 5.00 p.m. - closing keynote fireside chat. The panel discussion will take place on the 24th of October at The Park Plaza Victoria, London and she will be joined by Richard Jones, Head of Investment Risk Analysis at AXA Investment Managers and James Munro, Head of ArcticDB at Man AHL. You can find out more information about it by downloading our agenda!
1. What do you consider your biggest professional achievement to date?
I do not like so much to advertise my personal professional achievements because I believe that whatever we achieve, we cannot do alone. We need a strong team. So possibly, I could say that in the past I have been successful in creating the environment for a team to grow together and to become a strong team. This kind of achievement was the basis for other successes that I could then reach together with my team in the different roles I had over the past years.
2. What has been your /your firm’s top 3 priorities for the coming year?
1) Implementing the growth strategy
2) Reorganization of the Asset Management
3) Tackling new upcoming regulation
3. 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?
We need to have a solid framework for how we create our strategies and understand how a changing environment with central bank QE parties and other effects that we have not seen before will feedback onto the ability of our models to do what we think they should do. For this, it is necessary that we have more than pure quantitative knowledge but also a profound understanding of economic fundamentals.
4. Quant investing in other asset classes such as corporate bonds is increasing – what do you think has caused this shift to look at quant investing outside of equities and where do you see this going?
1) The need for scalability and efficiency in times of decreasing margins
2) Decreased alpha potential
3) Investors demanding more transparency and better risk management
4) Better data availability and more computing power
5) Quant investing in the equity space has become quite crowded and it is difficult to have a really differentiating offering
5. 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?
NLP is an exciting field or use case for artificial intelligence. The asset management industry is far from using its full potential, but the reason mainly is that it is also not so straightforward and requires a lot of rigorous research work and computing power to actually make it employable. So this is not possible for many asset managers. Of course, there are also more and more providers that specialize in certain topics and provide their data or signals to the industry. However, buying all of this data from different providers is still quite expensive. Probably in the future, it will become easier and more standard to employ NLP on different use cases with user-friendly tools and easy and free to access data.
6. 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 are the most important questions for quants when considering this strategy?
I think a quantamental approach can work extremely well if you have people on board who are both: really solid quants but also strong economists. This combination is, however, rare and hence the quants will neglect the importance of economic intuition and knowledge of the market and the fundamentally driven investors will feel intimidated by formulas, computer programs and machines taking over decision-making for them. So ideally, as a company you invest in people that have already one side and at least great potential to also develop the other side. Like this, you can have a team on board that is very capable of exploiting all the opportunities that data and computing power as well as a systematic approach provide and at the same time, they are also aware of the limitations of different models and hypotheses and can also evaluate them in the context of a changing environment.
7. To what extent do you see the use of blockchain/crypto integrating into capital markets? As crypto is becoming more mainstream, how have hedge funds responded and what could be the potential impact on capital markets?
I see blockchain technology as a technology to increase efficiency and transparency as the very first use case. Today, the financial industry employs huge back offices that hopefully can be downsized a lot in the future once we can use smart contracts instead.
8. What are your predictions for quant investing in crypto?
I do not think that it will be feasible to reach sufficient capacity on such strategies any time soon so that this can be deployed on serious institutional money.
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