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Estay is investment technology that thinks in terms of the outcome and dynamics of complex systems, not in predictions.
We develop and license automation software. Our first strategy, Smart Balanced Hedging, is licensed as software: an API that each client connects to the account they open in their own name, choosing the amount and switching it off whenever they want. Estay never holds the money and has no access to deposits or withdrawals.
People and institutions with significant liquid wealth who seek a stable income at very low risk.
We do not want you to put everything in: you decide the amount the software works with and adjust it whenever you wish, to diversify. Wealth decisions are rarely made alone, so we invite you to share this information with whoever you consider necessary. If you have any doubts or questions, please do not hesitate to let us know.
Our philosophy
Win without losing your bearings: control comes before return.
Markets are complex systems that change every second. Chasing the maximum return at any price is the fastest way to lose control, and we see it every week in the financial press.
At Estay we start with the opposite: we first fix the risk we are not willing to exceed and, within that limit, we find the best steady return. It is the philosophy of someone who wants to preserve their wealth and enjoy a stable income, without surprises.
We believe a system is only viable when it keeps balance with its environment. The same holds for a portfolio: it is not the one who risks most who survives, but the one who adapts and regulates itself best.
An objective taken to the extreme ends up breaking the system.
The limit first; then the best return possible inside it.
A world that no longer works as before
The internet is transforming our relationships, our education, our work and also how we invest. Everything changes, and ever faster.
Yesterday's tools
We live in an age of great technological and cultural transitions, and in such periods confusion and disorientation appear. Much of our collective anxiety comes from trying to solve today's problems with yesterday's tools and concepts.
That is what happens in investing. Traditional models are ever more sophisticated, yet ever less able to adapt in time to the volume of data and the speed of change that data brings.
Complicate the environment, not the regulator
Many models grow complexity on the wrong side of the equation: they add complication to the analysis of the environment instead of strengthening the regulating capacity of the investment system itself. We do the opposite.
Every good regulator of a system must be a model of that system.Conant & Ashby, 1970 (the good regulator theorem)
How we think
Four principles guide every decision, from the design of a strategy to the day-to-day operation of the software.
No objective, however desirable, can be pursued to its maximum or minimum without the system collapsing. So we set the risk limits first and only then optimise the return inside them.
Fear and greed move most investors, and each person reacts in their own way. Our decisions rest on probabilities: if they do not line up, we simply do not trade.
A system only learns if it gets things wrong, and only improves if it remembers. Every decision feeds a memory that is used, among other things, not to repeat the same mistake.
Markets evolve and so do strategies. Parameters are readjusted permanently rather than leaning on a single fixed model that will one day stop working.
What sets us apart
Compared with traditional automatic systems, our approach differs in two essential ways.
1. We do not depend on a single strategy
If it goes well, it goes well; if it goes badly, it goes badly, even up to the margin call.
It identifies patterns and acts accordingly. If the odds line up, it invests; if not, it does not.
2. We tell the complicated from the complex
Reductionist approaches simplify the financial system to reach a decision and, on the way, confuse the complicated with the complex. We use external complexity to identify patterns that reduce it, and we widen the variety of our own model. That translates into richer control and more precise decisions.
From action to reaction
We have moved the focus from action guided by data to reaction guided by the relationships between the elements of the financial system and its environment.
To succeed you have to know the consequences of each strategy beforehand, because their effects are felt without delay. That is not achieved with data alone, not even with very large volumes of data.
Beyond the purely mathematical
To the traditional quantitative approach, based on data-driven algorithms and a linear framework, we add a systems view: concepts, themes, analogies and coherences from the economic, political, social and technological environments, grouped into networks of relationships that translate into patterns.
It is not only about retrieving the structure or content of a database, but about rebuilding knowledge from the mechanics of cause and effect.
A control that regulates risk from within
One of our greatest differences is to orient the model towards viability in the short, medium and long term through the scientific management of risk factors.
No objective can be pursued to its maximum or minimum value, because in both cases the system collapses. To avoid it we analyse four dimensions and build controls from them that improve how information is organised and how decisions are made.
How the system regulates itself.
How it interacts with the market.
What makes it durable and adaptable.
What happens and how it is corrected.
A master control system, with enough capacity to adapt, calibrates and regulates all the strategies at once and adjusts their parameters in the face of change, shocks and news. The greatest return potential without added risk is wasted more for not understanding intrinsic control than for any other reason.
