EC Improving Time Travel In Long-Term Investing

By Ashby Monk and Dane Rook, Stanford Global Projects Center

Time, Portal, Time Machine, Travel, Futuristic, Fantasy

Image Source: Pixabay

To succeed at investing, you must master time travel.[i] You need to be proficient at beaming yourself into a hypothetical future, and then analyzing the best route to get “there” from the actual “now.” And the route itself is crucial: arriving safely at your target destination can matter just as much as reaching it quickly.

This analytical time-hopping isn’t very tricky if the imagined future looks enough like the past, and if that future isn’t too distant (excluding wormholes and vortexes). But that isn’t the sort of time travel required for most long-term investors (LTIs), such as pension funds, endowments, and sovereign wealth funds. Rather, the possible futures to which LTIs must navigate are usually far off, and may bear seemingly little resemblance to the past or present. And the further ahead these possible futures are, the more of them there are to explore.

Short-term investors mostly avoid these extra complications, but also forgo the sizable opportunities that go with them. Navigating to more distant futures offers LTIs a greater diversity of paths to reach them, which can be an enormous advantage with the right tools for wayfinding. Yet, historically, the tools at LTIs’ disposal haven’t been up to the job. Improving LTIs’ time travelling capabilities, and empowering them to reap more of the inherent advantages from being long-term-oriented, will require upgrading their ‘navigational technology’ (nav-tech).  Simulation is one such prime candidate for an upgrade.

This article series investigates how LTIs can use more advanced simulation technologies to succeed over increasingly long horizons. The current article tackles the issue at a high level: it looks at the basics of why existing simulation toolkits fall short in long-term investing, and spells out the main opportunities for overcoming these shortfalls with emerging tech. Subsequent articles will delve into these topics in finer detail. Throughout the series, we’ll be introducing ideas from a new paradigm in long-term investment that we’ve been developing both as software at RCI and research at Stanford: Portfolio Navigation.

From Simulation to Navigation

Computer-aided simulation has featured in financial analysis for many decades, and can now be performed cheaply and easily: for example, simple tools for building and running simulations come built-in (or as convenient plug-ins) to most spreadsheet software. The chief use for these tools is in exploring future investment outcomes – both intermediate and final – in terms of their associated payoffs and probabilities, by tracing feasible paths through time (…time travel!). Any tool that can perform this essential function might be called ‘simulation technology’.[ii] In this sense, most LTIs use simulation technology, whether purchased as off-the-shelf packages (like STATA or Crystal Ball) or built in-house as customized programs (e.g., with help from statistics-friendly programming languages, such as R, Python, or Julia).

Most of the commonplace simulation technologies used by LTIs are what can be termed user-limited, because they are substantially:

  • User-defined: they rely heavily (often exclusively) on data and assumptions that are provided explicitly by the people (whether individuals or teams) who construct and run the simulations; and
  • User-translated: they rely predominantly on users to interpret the outputs of simulations, and to translate these outputs into actions (whether for further analysis or implementation as live strategies).[iii]

These user-limited simulations may be sufficient in some instances, e.g., when: 1) relatively few assumptions are needed (especially when the simulation’s outputs are not particularly sensitive to them, and/or assumptions can be straightforwardly and confidently made); 2) the future can be reasonably approximated from simple datasets; and 3) judging optimal actions from simulation outputs requires uncomplicated calculations or judgment. But These situations don’t regularly apply to LTIs when they’re analyzing long-horizon portfolios. Instead, LTIs are typically (and increasingly) forced to grapple with the need to:

  • Generate complicated sets of assumptions about complex phenomena, and base these assumptions on inadequate datasets or unproven theories
  • Use datasets that imperfectly approximate the future (with poor knowledge of exactly how future data will differ from past data), or that are non-standard in key ways (for example, alternative data, such as social-media or geolocation data)[iv]
  • Craft dynamic portfolio-management strategies that balance multiple competing objectives (e.g., liquidity, costliness, volatility)
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Erikas Ivan 3 weeks ago Member's comment

The tools and simulations are here, if only one was experienced enough to come up with the precise model. Good read!

William K. 3 weeks ago Member's comment

In engineering we know that for simulation results to be useful the model must be a fairly accurate representation of the system being simulated, no matter if the system is a nut and bolt or a streo amplifier or a bridge. And usually the creation of an adequately accurate model requires a good understanding of the system. That understanding must include both the mechanism of operation and knowledge of the variables. In electronics and in mechanical things that can be obvious, while in the prediction of business operation it is quite a bit less obvious and more subject to changing. For businesses it is not so simple to create an accurate model, but it is possible instead to look at past performance. But in almost every prospectus we are cautioned that past performance does not guarantee future performance. Thus creating accurate models of businesses can be very challenging. THUS i AM SKEPTICAL.