Three Types of Money Machine

Ownership, Risk Capacity, and Information

When thinking about building a money machine, it is natural to start with implementation: assets, signals, portfolios, strategies, or to follow a particular framework for investing, trading, or betting. The large number of different approaches not only makes it difficult to select the right one, but also raises a more general question of how money machines can be classified. More importantly, it raises the question of whether different approaches rely on fundamentally different economic mechanisms, and whether statements about “investing” or “trading” are often implicitly tied to a particular class of such mechanisms.

Arguably, it is useful to start with an even more fundamental question: why should any money machine produce returns at all?

In particular, if we are interested in “real” returns; that is, returns consistently above monetary inflation, we are asking where real gains in purchasing power can originate. These gains do not arise automatically. They must be grounded in identifiable economic mechanisms. Participating in financial markets, whether through investment, trading, or other activities, must connect to one or more of these mechanisms.

While many of these ideas are familiar in different contexts, like investment, factor exposure, alpha generation, there is no single, widely adopted way in which they are typically organised. Different frameworks emphasise different aspects, often tied to particular methods, markets, or objectives. The perspective taken here is deliberately more abstract. Rather than starting from specific models or empirical decompositions, it focuses on the underlying economic mechanisms and the roles that money and financial markets play. The aim is not to replace existing approaches, but to provide a simple, consistent way of relating them to one another.

To approach this question from first principles, it is useful to consider both what money represents and what roles financial markets play.

Money can be understood in at least three fundamental ways. It serves as a store of wealth, allowing value to be preserved and transferred over time. It represents capacity to bear risk, enabling uncertainty to be absorbed and redistributed between participants. And it provides the ability to act in response to opportunities as they arise, particularly as new information becomes available.

Financial markets, in turn, provide the infrastructure through which these capabilities are exercised. They facilitate the allocation of capital to productive uses, the transfer of risk between agents with different preferences or constraints, and the incorporation of information into prices through ongoing price discovery. They also provide liquidity, allowing these processes to take place continuously.

Participation in financial markets by deploying capital connects these two perspectives. Returns arise when capital is deployed in ways that contribute to these underlying functions: through ownership of productive assets, through the bearing and management of risk, or through acting on information not yet reflected in prices.

We can now distinguish three types of money machine, each emphasising one of these dimensions of money and financial market function as its primary source of return or as core abstraction with which the money machine frames it's decision problem: ownership, risk capacity, and the ability to act on information. While any given system may draw on all three, this distinction provides a useful framework for understanding their design and behaviour, and can also be used as ex-post attribution of a money machine's performance to these different sources of return.

Type I - Ownership

The first type of money machine is based on money as a store of wealth.

In this view, capital is deployed as ownership of financial assets, which represent claims on future economic output. Returns arise first as direct pay-outs on these claims of ownership, such as dividends or coupon payments in the case of equities and bonds. In addition, value can be accumulated within the asset itself through retained earnings and reinvestment, leading to an increase in its value over time.

More generally, asset values increase as their capacity to generate economic output improves. This may occur through technological innovation, productivity gains, demographic developments, or structural changes in the economy. It may also reflect changes in scarcity, in the relative valuation of the goods and services produced, or in the distribution of economic value across different parts of the economy.

In this type of money machine, the objective is not to take directional views on price movements, but to hold claims on productive assets over time. In this setting, long-only exposure is a design choice directly implied by the underlying mechanism: shorting does not correspond to participation in economic production in the same sense as ownership.

The central problem is therefore one of allocation: how to distribute wealth across a set of assets in order to achieve a desired balance of return and risk over long horizons.

The relevant processes of return generation we are aiming at are slow-moving. Technological progress, demographic change, and structural economic developments unfold over extended periods, while shorter-term price movements introduce noise around these trends. As a result, systems built on this mechanism tend to operate at relatively low frequency.

Diversification enters as a refinement rather than a source of return. Individual assets are subject to significant idiosyncratic risk, which can obscure the underlying economic mechanism. By holding a diversified portfolio, this noise can be reduced, allowing for a cleaner exposure to the long-term drivers of return.

In practice, this leads to broadly diversified, long-only portfolios that are adjusted infrequently. Natural framings used for this type of money machine include: Strategic and tactical asset allocation, value investing, or (static) portfolio optimisation.

This type of money machine is widely accessible and forms the basis of most conventional investment approaches. Within the MXM framework, it corresponds to MXM-v0: a simple ETF-based tactical asset allocation, updated at low frequency and implementable in a basic spreadsheet.

Type II - Risk Capacity

The second type of money machine is based on money as capacity to bear risk.

In this view, capital is not primarily deployed to own productive assets, but to absorb uncertainty. Financial markets facilitate the transfer of risk between participants with different preferences and constraints. Some participants seek to reduce exposure to particular risks, while others are willing to take them on. The natural position of money machine builders would be that of a holder of capital, who wants to profit from exposing that capital to calculated risks.

Returns arise as compensation for bearing such risks. This compensation is often embedded into asset prices as a risk premium. It reflects the aggregate willingness of market participants to pay to transfer uncertainty, and the corresponding willingness of others to absorb it.

The existence and magnitude of any given risk premium are not fixed. They depend on the structure of the market, the distribution of exposures, and prevailing attitudes toward risk. In many cases, however, there is a persistent imbalance, with aggregate demand for risk reduction leading to positive compensation for those who provide risk-bearing capacity.

This domain is often described in the language of factor investing or alternative beta, where returns are attributed to systematic exposures. These frameworks provide useful empirical descriptions and practical implementations. However, they do not in themselves define the underlying economic mechanism, which is the transfer and pricing of risk between market participants.

Many widely used systematic strategies can be formulated in this way. Momentum can be viewed as exposure to risks associated with persistent price trends, which often arise from the gradual build-up and unwinding of positions across market participants. In diversified implementations, such strategies can exhibit convex return profiles, performing well in sustained market moves, including periods of market stress. Mean-reversion strategies frequently involve providing liquidity during short-term dislocations, thereby absorbing liquidity risk. Carry strategies reflect compensation for holding exposures embedded in forward curves, which incorporate hedging demand, funding constraints, and structural imbalances across markets.

These interpretations are not unique, and the precise drivers of any given return pattern may involve a combination of risk transfer, behavioural effects, and market structure. However, they illustrate how familiar strategies can be understood as different ways of deploying risk-bearing capacity.

The central problem in this setting is not the allocation of capital across assets, but the allocation of risk across exposures. Capital serves as balance sheet capacity, and the objective is to deploy this capacity across a set of risk premia in a controlled and diversified manner.

Unlike the ownership case, these exposures are typically expressed through both long and short positions, often implemented via derivatives. This allows for the separation of risk exposure from capital allocation, and for the explicit use of leverage.

Risk premia are not necessarily constant over time. They may vary with market conditions, regimes, or seasonal patterns, and their realisation can be uneven. As a result, systems operating in this domain tend to adjust exposures more dynamically, while maintaining a systematic approach to risk allocation.

In this formulation, the focus is on broad, systematic exposure to identifiable sources of risk, rather than on the precise pricing of individual risks. The latter moves into the domain of information-based strategies.

Within the MXM framework, this type corresponds to MXM-v1: a systematic, risk-managed portfolio of long and short exposures, designed to derive the majority of its return from the allocation of risk capacity across markets.

Type III - Information

The third type of money machine is based on money as the capacity to act on information.

In this view, capital is deployed in response to information as it becomes available. Financial markets can be understood as decentralised systems for price discovery, in which information from a wide range of participants is aggregated and reflected in prices over time.

Returns arise when positions are taken based on information that is not yet fully incorporated into prices, or when information is processed and acted upon more effectively than by the average market participant. In this sense, participants contribute to the process of price formation, and are compensated when their actions improve the alignment between prices and underlying economic realities.

The notion of “better information” should be understood broadly. It may involve access to new data, superior models, or faster processing. It may also reflect differences in preferences or constraints, which influence how information is translated into prices. In this sense, the boundary between information and risk-taking is not always sharp. However, the defining feature of this type is the active role in shaping prices, rather than passively accepting them. It involved building an opinion not just on where prices are right now, but on what prices (or their distribution) should look like, or will look like in the future.

This domain is typically associated with alpha strategies, where returns are attributed to skill or informational advantage rather than to systematic exposure to risk. Such strategies are often capacity-constrained and subject to decay, as information becomes more widely known and incorporated into prices.

In practice, this leads to systems that are explicitly signal-driven, model-based, and sensitive to timing and execution. The emphasis is on identifying, processing, and acting on information in a disciplined and repeatable way.

In aggregate, this activity contributes to the efficiency of financial markets, improving price discovery and facilitating the allocation of capital and risk.

Within the MXM framework, this type corresponds to MXM-v2 and beyond: systems that aim to generate returns through the systematic extraction and exploitation of informational advantages.

Relationship between Types

The three types of money machine described above are analytically distinct, but not mutually exclusive. In practice, any given system may draw on more than one of these mechanisms.

The distinction is one of both emphasis and explicit problem formulation. Each type leads naturally to a different way of expressing the underlying decision problem. In ownership-based approaches, the task is framed as the allocation of capital across assets, with risk arising as a consequence of these holdings. In risk-based systems, the problem is formulated directly in terms of allocating risk exposures, often with the objective of maintaining a controlled level of overall risk while distributing it across different sources of compensated uncertainty. In information-driven systems, positions are determined dynamically based on signals, with risk deployed in proportion to the expected informational content of each signal.

A long-only portfolio, while primarily based on ownership, may incorporate elements of risk management through diversification. A risk-based strategy may rely on systematic rules that embed informational assumptions. Conversely, information-driven strategies necessarily involve both the deployment of capital and the acceptance of risk.

This framework is also useful for attribution. For any realised money machine, returns can be examined through these three lenses, distinguishing between contributions arising from ownership, from risk-bearing, and from information-driven positioning. This provides a structured way to assess whether observed performance is consistent with the intended design of the system.

The distinction also helps to clarify differences in typical system characteristics. Ownership-based approaches tend to operate over longer horizons, reflecting the slow-moving nature of underlying economic processes. Risk-based strategies often adjust more dynamically, as exposures and premia evolve over time. Information-driven systems are typically (but not necessarily) more sensitive to timing and execution, as their effectiveness depends on acting before prices have fully adjusted.

While these tendencies are not strict boundaries, they provide a useful guide for understanding how different money machines are designed and how they behave in practice.

In practice, a range of models exist to analyse and decompose returns, including frameworks such as the Capital Asset Pricing Model (CAPM), multi-factor models such as the Fama-French three-factor model and its extensions (for example the Carhart four-factor model), as well as broader approaches such as Arbitrage Pricing Theory and industry frameworks often described as alternative beta or risk premia. These approaches often capture aspects of the distinctions described above and are useful for measurement and implementation. However, they are typically based on statistical decompositions of observed return patterns, rather than on an explicit identification of the underlying economic mechanisms. The perspective presented here is intended to complement and combine such approaches by providing a first-principles framework for understanding where returns can originate.

Positioning within Money Ex Machina

The distinction between Money Machine types described above can be used to position the different stages of the Money Ex Machina project. Each stage represents one of the three types of money machine, building toward a more complete system over time.

MXM-v0 corresponds to the ownership-based type. It is implemented as a long-only portfolio of broadly diversified exchange-traded funds, with periodic reallocation across an account-size optimised asset universe.

The objective is to participate in the long-term productive capacity of the world economy through a simple and transparent allocation framework. The system operates at low frequency and can be implemented with minimal infrastructure.

MXM-v1 corresponds to the risk-based type and forms the current focus of development. It is designed as a systematic, risk-managed portfolio of long and short exposures across a range of markets, typically implemented using derivatives.

The objective is to allocate risk capacity across a set of identifiable risk premia in a controlled and diversified manner, on an hourly to daily decision frequency. This requires a more explicit treatment of risk, including the use of leverage, volatility management, and dynamic adjustment of exposures.

While the underlying ideas are well established, robust implementation requires careful system design, data handling, and operational discipline.

While v1 already implements all the technical infrastructure for a full, signal-driven portfolio of strategies, it is left to MXM-v2 and subsequent stages, which correspond to the information-based type. These systems aim to generate returns by acting on signals that reflect information not yet fully incorporated into prices.

This domain is more complex and competitive, as informational advantages tend to be transient and capacity-constrained. It requires explicit modelling of signals, careful evaluation of their stability, and attention to execution.

Development in this direction will build on the infrastructure and understanding established in earlier stages.

Together, these stages reflect a progression from simple ownership-based participation toward more complex systems that explicitly manage risk and incorporate information, while remaining grounded in a consistent underlying framework.

Conclusion

The question of how to build a money machine ultimately reduces to a question of where returns can originate. As argued in this note, financial markets provide three primary mechanisms: participation in economic production through ownership, compensation for bearing risk, and the ability to act on information as it is incorporated into prices.

Distinguishing between these mechanisms does not eliminate the complexity of markets, nor does it prescribe a single correct approach. However, it provides a structured way to think about how different systems are designed, what assumptions they rely on, and where their returns are expected to come from.

This perspective can be used both to classify existing approaches and to guide the construction of new ones. It also offers a basis for evaluating realised performance, by relating observed returns back to the underlying mechanisms they are intended to capture.

The Money Ex Machina project is an attempt to build such systems explicitly, starting from simple ownership-based approaches and progressing toward more sophisticated implementations that manage risk and incorporate information. The framework presented here serves as a conceptual foundation for that process.