In Conversation with CQF Alumni: Tony Parish

Why did you decide to pursue your current career path? 

I switched from the financial publishing industry to financial services industry about 25 years ago. The capital markets had always intrigued me while I was a journalist, but I came into financial services with very little formal technical training. To address that issue, I did an MBA and then became a Chartered Financial Analyst (CFA) charterholder. After that, I completed the CQF program and finally, I took a degree in data science. I didn't have a grand destination in mind when I entered the industry, but I discovered areas of financial markets that were fascinating to me and sought out the education and experience needed to pursue those interests.

You're at a senior level now; can you describe the path from journalism to investment advisory and how you were able to succeed in this transition?

The answer is persistence and perseverance. All along the way I've been driven by curiosity and continued to develop personally and professionally. I've focused on challenging areas of finance and stayed with it for 25 years. Those are the key elements that brought me to my role as Chief Investment Officer of a registered investment advisory firm. 

What kind of work is involved in your current role? 

On a day-to-day basis, I manage many people and many processes. The core tasks involve numerous meetings and speaking engagements and describing what my team does to various stakeholders. The years of the financial journalism play into this because I can speak clearly about a range of topics in finance, and I am also comfortable producing articles and other published material. Those are both general skills; they’re not specific to this industry, but I've put them to good use. 

What do you enjoy the most about the role you're in now? 

I love data analysis and public speaking and I would like to spend more time working with data – modeling it, analyzing it, and developing observations. When I listen to Paul Wilmott’s lectures at the CQF Institute, they show a similar mindset. He has a passion for data analysis and mathematical breakthroughs, as well as teaching other people about the process. 

What are some of the major highlights in your career to date? 

I joined Alphastar about four years ago and we have grown the firm's assets under management four-fold in just 36 months, from about $400 million in 2019 to $1.6 billion in 2022. I've also had a chance to build several investment products, including a machine learning system to help investors save for retirement. I’m very proud of that product, as well as some of the other innovative investment products that I've developed over the years. 

What have been some of the biggest challenges in this field of work over the past few years?

Each firm where I've worked has had a dominant culture. In the case of asset management, it's either a manufacturing-oriented culture, with portfolio managers, analysts, actuaries, accountants, and data architects, or it’s a marketing culture, driven by the salespeople, relationship managers, and marketing teams. Those two cultures tend to have different priorities, speak different languages, and have different values. Professionally, I am firmly on the manufacturing side of the equation. I'm a quant. I put a high value on data integrity and being able to build systems that are reliable and deliver according to predefined objectives. Sometimes I work with folks on the marketing side who don't go very deep into the weeds and may not even understand what's in the weeds. The risk is that they're making corporate decisions that are unrealistic or fail to take technical issues into consideration. While this may sound like an abstract challenge, I have encountered it throughout my career and it’s an interesting challenge for the industry. 

How have quant finance and the financial industry overall changed over the course of your career? 

There has been a proliferation of electronic, algorithmic trading, and high frequency trading. This has major implications for the financial workforce. There was a time when you could develop a very successful career as an investment manager just by analyzing quarterly and annual reports, listening to corporate earnings calls, talking to executives, and making investment decisions based on that information. You didn't need a high level of quantitative acumen. That is increasingly rare in our industry, which is now driven by data and the connectivity of the data. 

I’ve also seen a migration towards passive investing, with greater adoption of ETFs that have risen in popularity at the expense of active management and mutual funds. We're seeing do-it-yourself investors who realize that instead of buying a mutual fund that offers performance like the S&P 500 and costs 1% per year, they can buy an ETF that tracks the S&P 500 very efficiently and costs just three basis points per year. The dramatic shift towards passive investing is another change that I've seen over the past 20 years. 

How has the CQF influenced your career trajectory?

The CQF gave me the confidence to explore the most complex topics in our industry. It provided a set of practical tools including the ability to build probabilistic models, a framework for thinking about solutions, and an understating of how to apply quantitative techniques to investment and risk analysis. The CQF allowed me to do a deep dive into the data science of investing and it laid the foundation for tackling problems from a rigorous quantitative perspective. 

What skill sets are required to be successful in quant finance today? 

Excel spreadsheets are no longer enough. Programming is necessary and skills in languages like Python or R are very useful. I have been very pleased to see that the CQF includes data science and machine learning as part of the core curriculum. Proficiency in these areas is necessary for a successful career in quant finance today.

In the future, I think fluency with machine learning will continue to grow and be applied in new ways. It's not just about using the Black Scholes equation to establish what the price of an option should be. It's about building systems that incorporate all inputs used for option pricing and allowing those systems to improve as they become more experienced. I think the future will involve a lot of machine learning algorithms, artificial intelligence, and the need for quants to be able to not only solve problems but also to build machines that can get better at solving problems themselves.

What advice would you offer to someone who's just starting out their career in the financial industry? 

I would advise newbies to start broad, with an emphasis on finance and accounting. If you look at the job market, maybe 1 in 100 large companies have a Chief Investment Officer, but they all have a Chief Financial Officer. Develop broad skills sets and go into areas that can open many doors. Finance is ubiquitous. Investing is a subset of it, and you can specialize later, as opportunities arise.