Q: What does a working day at MDT look like?
At MDT Advisers1, portfolio construction is a daily, data-driven process powered by proprietary machine learning tools.
Each night, the team downloads data from vendors, recalculates company characteristics, and runs every stock in the domestic equity universe through our ‘decision tree’2 model to generate fresh forecasts. These forecasts feed into our proprietary portfolio optimiser, which rebalances each strategy and produces a trade list for review.
The day begins with the trade review process, where human oversight plays a critical role. While MDT’s models operate mechanically, the team ensures that the data is accurate and that no material news has emerged that the model may not yet reflect. This step is not about subjective overrides, but about validating inputs and understanding the model’s behaviour.
Beyond daily operations, most of our time is spent on research and model enhancement. Idea generation is central to MDT’s process, and the firm’s commitment to building tools in-house – from back-testing engines to risk models – gives us exceptional flexibility and control.
Q: How does the portfolio optimiser work?
Our proprietary optimiser is a key component of MDT’s portfolio construction process. It integrates multiple dimensions, including:
- Alpha forecasts from our ‘decision tree’ model
- Hard risk constraints applied consistently across portfolios
- Statistical risk models to predict volatility and tracking error
- Trading cost models, including market impact and liquidity considerations
The risk constraints – which are consistent across all our portfolios – along with a statistical risk model that makes predictions for volatility and tracking error – help guide us to more consistent outcomes.
The goal of this optimisation process is to ensure that our portfolios are aligned with the model’s current alpha forecasts, while carefully managing the balance between risk and return, and avoiding excessive turnover.
This disciplined blend of automation and oversight allows us to maintain precision, adaptability, and transparency in our daily investment operations.
Q: How has MDT’s process adapted to changes in the market in recent years?
We’ve witnessed notable shifts in market structure over the past few years – the ever-increasing market share of passive investing, the rise of zero commission and social-media-driven retail trading, and the increased role of high frequency and algorithmic traders – all of which have influenced how quantitative strategies operate.
Rather than attempting to pinpoint a single cause for this change, we emphasise the importance of maintaining active strategies designed to capitalise on inefficiencies – regardless of their origin.
This means we don’t need to know what’s in the driving seat for market behaviour. The important thing is having strategies that are active and able to take advantage of inefficiencies when they present themselves.
Despite the challenges and uncertainties, day to day, we remain energised by the dynamic nature of markets. The constant influx of new data, unexpected macroeconomic developments, and evolving investor behaviour make quantitative investing a continuously engaging field.
There’s always new information out there. There are always curve balls coming from a macro perspective, risks that you’d never seen before that all of a sudden manifest themselves. From my perspective, it’s a great place to be and a really exciting place to be applying my technical background.
This is a shortened and edited version of the transcript from Dan Mahr’s recent appearance on the Capital Allocators podcast. Listen to the full interview here.
For more on MDT, see Power in Data.
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1 MDT Advisers is the active quantitative arm of Federated Hermes with a track record of more than 30 years.
2 Simply put, a decision tree is a series of yes/no questions that leads to a specific outcome. The Federated Hermes MDT investment team has made decision trees central to the investment process. They provide a daily estimate of every company’s likely performance versus the universe based on the most recent market prices, financial reports and sell-side analyst estimates.
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