[What is insidious about today’s accelerating rate of change for advisors and asset managers is how new technologies, evolving investment processes, and ensuing new competitors can creep up on you. With the greater speed in adoption rates today of new technologies, financial services professionals need to be steadfastly vigilant and proactively curious about how these new approaches can competitively impact their business offerings and performance results. This is especially so in the rapidly changing money management arena where new technologies are increasingly either going to be used by managers or potentially used against them.
Artificial intelligence (A.I.) and machine learning is one key example of an area to be keenly aware about and to actively follow. To better understand where we are and the bigger implications of these technologies, we talked with Institute member David Aferiat, Co-Founder and Managing Partner of Trade-Ideas – an award winning A.I. and machine learning Fintech firm with a SaaS-based revenue model. How can these technologies directly impact the ability to generate performance and garner increased AUM? ]
Hortz: “Making A.I. Accessible” is your motto and rallying cry on your investment professional website. How exactly are you doing that? What do you see as the key for accessibility?
Aferiat: What we learned, over our 16 year history of enabling our clients to make better decisions in the capital markets, was that building accessibility to A.I. comes from a focus on being impactful and relevant – a focus on providing impactful and relevant information to our clients.
As to impact, the analytics generated by our machine learning A.I. comes from long and short term trading simulations. Three years’ worth of A.I. decisions illustrates the A.I.’s ability to react to Black Swan events like Brexit and presidential elections. Those decisions also show an ability to forecast trend continuation and capture alpha as much as 20 days after each opportunity is identified. Each of the past three years has put the performance of the A.I. 25% or more above the SPY. That’s impact. We recently received the Best Machine Learning Development award by Fund Technology and WSL in their annual 2019 Awards Conference due mainly to our substantial 2018 outperformance results for our A.I. system, which we call “Holly”.
As to relevance, Trade Ideas allows for customization of the analytics produced so that results are relevant to each of our client’s preferences and covenants that, in some cases, dictate which areas of the markets and under what conditions A.I. decisions can be made. Some of these conditions include market cap, Long or Short positions, frequency of trading activity, etc. Ultimately each client decides whether to honor specific trade plans associated with each alpha capture opportunity generated by the A.I. system.
Hortz: Can you map out for us some of the strongest reasons and benefits behind actively employing AI and machine learning technology to the money management process?
Aferiat: Whether you are an Individual or advisor or fund manager, everyone affected by decisions in the market desires reward under optimal risk conditions. We’ve known for a long time the days of set-ups based on eyeing charts are gone. Decisions that produce risk-adjusted positive outcomes require an ever-increasing amount of interpreted and processed data. Many market participants are not equipped with the tools to adapt to this new phase of the markets. A.I. enabled idea generation finds previously unseen trends using a screened selection of only those algorithms ascending in the probabilities of capturing alpha each day. Daily interaction with the A.I. system allows our clients to understand useful meta-information.
AI can be looked at as a virtual research analyst that never sleeps and provides fully formed investment opportunities. We are able to run tens of millions of simulated trades across over 40 different scenarios overnight using structured and unstructured data sets and deliver 5-7 recommended strategies each day, designed to produce predictable alpha, and with risk guardrails against the strategies intraday.
This also levels the playing field with large players, decreases cost, delivers efficiencies, enhances portfolio predictive capability and alpha, and mitigates risk. The adoption of AI and machine learning capabilities represents a great case study in its bottom-line impact on the industry of driving out costs and delivering efficiencies in their pursuit of capturing alpha.
The real question is: Why not leverage these tools to consistently capture alpha as the largest players are currently doing every day?
Hortz: With such strong benefits, what are you experiencing as your greatest challenges? Are there misconceptions about AI that are preventing wider usage in professional money management?
Aferiat: The biggest single misconception is that AI replaces the portfolio manager and trader, which cannot be further from the truth. Rather, AI, as we just discussed, is the most advanced information tool that a manager can have. It is has a capability no team could ever execute, yet all the decisions remain with the manager. Think of it this way: why would a person choose to use a slide rule if a calculator is available? We are strategically providing the benefit of information asymmetry for better decisions. So, paradoxically, AI does not replace the manager, but reallocates time and decisions to where he or she contributes the highest value.
Hortz: Where exactly are we in the development and adoption of AI for money management right now?
Aferiat: We are still in the early adoption stage, with some skepticism. Large firms have built these tools for in-house use every day, putting midsized and independent firms at a disadvantage. Firms that have embraced AI find the accessibility to better information to be immediate. Those who adopt AI as an input into their decision making process will benefit over time from first mover status, as those who delay will be in “catch up” mode. Also, I think there is some hesitancy around the term itself. A.I. is in fact intuitive, with an almost zero learning curve once adopted. Next day use is the frequent use experience by those who are on-boarded. That experience is backed by learning curriculums and client support we provide as users move to become “virtuosos”.
Hortz: Having growing adoption from professional traders, institutional managers and family offices which have an affinity for advanced technology and alternative approaches to money management is one thing. What are the particular challenges in engaging RIA’s, asset managers, and wealth management offices with your A.I. machine learning services?
Aferiat: RIAs, FAs, Wealth Managers and Asset managers are faced with the same problem as any investor: does my strategy perform for my clients? So the real challenge is never around technology, but around performance. Technology needs to be put in the service of that performance, and we can demonstrate that performance now over a 3 year period. So I think there is a communicative disconnect. People hear technology and they are already saturated with it. There’s a tech fatigue as in “I can’t take on any more.” Then there is fear around cost and fees. We focus on their concerns, producing results and gaining AUM, with ease of use in respect to visualizations of the data or delivery of a raw feed if desired, at a more than manageable cost.
Hortz: From your practical experience in working with RIAs and asset managers, can you share a few use cases or case studies on how AI has been used most successfully in addressing their money manager’s challenges?
Aferiat: We have seen a variety of use cases leveraging Trade Ideas’ A.I.:
For individual RIAs, major impact is provided for those who directly invest on behalf of their clients. They use A.I. to develop ideas for defined segments of their client portfolios and as a research tool for other actions based on the algorithms the A.I. selects daily from the inventory of those it's developed, optimized, and monitored for consistent performance in capturing alpha. These RIAs are prepared with daily ideas, data, and direction that they can actively monitor and easily report vs. just quarterly fund performance.
We’ve seen wealth management teams use the previous day’s activity of the AI as input into their investment committee meetings. Analysts present their thesis for investment direction and one team member presents what the AI has identified, as well as, what sectors and algorithms have seen the most activity since the last meeting making A.I. part of the overall decision process.
Hortz: Where do you see the next generations of AI efforts and R&D going?
Aferiat: I know for Trade ideas our focus is on creating and optimizing more longer term algorithms that focus on months and quarters. We also are planning to leverage the A.I. analytics against other asset classes like micro mini futures, crypto currencies, and other international exchanges.
Hortz: Any final thoughts or advice you would like to share to the broader investment professional community about how they can best react to AI technology?
Aferiat: Not to generate fear, but every delay is a day lost, as the competition is increasingly using these tools. To make an analogy, none of us go a day without an iPhone or Android, even though we may prefer not to. And 15 years ago, they did not exist. A.I. and machine learning technologies today are in a similar position as smart phones were in their early years.
We highly recommend that advisors and asset managers develop a plan of action to further investigate A.I. and how it can be best used in their investment process. The benefits in performance are substantial and consistent over time. A.I. has been demonstrated to provide real impact for asset managers. Trade-ideas invites you to investigate further by contacting us so we can share the facts and help you create strategies on how to best deploy this advanced investment tool. So the real question is not: Who needs it? But Why Don’t You Have It?
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