Financial Forecasting in Uncertain Conditions
Abstract
Explore the complexities of financial forecasting, emphasizing the dynamic nature of events. Uncover the importance of defining uncertainty, recognizing S-curves, and analyzing outliers. Gain insights into identifying emerging trends for proactive adaptation in an ever-changing business landscape.
Time to read: 5 to 7 minutes.
Level: Fundamental.
Category: Education Note.
Financial forecasting operates within a context where events are not predestined, and present actions can significantly influence future outcomes. The primary goal of forecasting is to identify a full range of possibilities rather than a limited set of illusory certainties. The focus is on illustrating uncertainty in a world where present actions shape the future, turning uncertainty into opportunity.
In contrast to predictions, projections require a logical foundation, offering a set of simple and common-sense rules. Generating projections is a highly complex task, and our aim is not to establish a definitive framework but to provide tools for evaluating any investor's projection.
The main purpose of projections is to provide decision-makers with valuable information for strategic planning, efficient resource allocation, and anticipation of opportunities in potential future scenarios. Expectations of predicting specific prices should be tempered, acknowledging that projections are estimates subject to uncertainty. Despite this, they are valuable for planning and effective investment, helping identify key elements that determine value at a given moment and offering guidance on associated risks and opportunities.
Framing the Situation when Forecasting
Define a cone of uncertainty: Effective projections emphasize the need to define a "Cone of Uncertainty." While intuition and judgment are inevitable in an uncertain environment, effective projections provide essential context enriching intuition by revealing overlooked possibilities. Visualizing the process as creating a cone of uncertainty helps outline possibilities extending from a specific event. Defining this cone broadly initially maximizes hypothesis generation, as a narrow delineation may lead to unpleasant surprises and the omission of crucial opportunities.
The art of defining the edge of the cone of uncertainty lies in distinguishing between the highly improbable and the practically impossible, with outliers defining this edge. The width of the cone, a measure of uncertainty due to a lack of information about future events, offers opportunity, and its subsequent narrowing is part of the iterative process of good projections. A too-narrow delineation can result in unpleasant surprises and the omission of crucial opportunities, highlighting the importance of questioning events at both the edge and center of the cone.
Emphasize the search for the S-shape:
Change rarely follows a straight line; instead, it often takes the form of a power law, starting slowly, then accelerating suddenly before decelerating. The iconic S-curve of the last 50 years is Moore's Law, predicting that the circuit density on a silicon wafer would double every 18 months. This evolving curve illustrates the fractal nature of S-curves and underscores the importance of identifying emerging patterns before the inflection point. Wise forecasters focus not only on the dramatic takeoff but also on identifying precursors to the left of the curve to anticipate opportunities and significant changes.
The challenge with S-curves is that they tempt us to focus on the inflection point, but a wise forecaster seeks precursors to the left of the curve. The example of Columbus, whose 1492 journey marked an inflection point in Western exploration, highlights that astute forecasters would have observed less successful voyages before Columbus and advised against dismissing his request. Predictions often underestimate the speed of change, as the left side of the S-curve is longer than imagined. Betting on gradual development once the beginnings of the S-curve are identified is advised. When the inflection point arrives, people often underestimate the speed of change, as we are linear thinkers, and S-curves take us by surprise. Anticipating opportunities and changes lies in understanding that even expected futures often arrive unexpectedly.
Analyze things that don't fit, recognizing that the future is already present but unevenly distributed. The leading edge of an emerging S-curve is like a thread hanging from the future, and unusual events could be weak signals of a disruptive S-curve in a nascent industry gaining momentum. The entire left portion of the S-curve before the inflection point is filled with indicators, subtle signals that, when aggregated, offer powerful clues about the future. Identifying an emerging S-curve involves paying attention to what doesn't fit, to things people can't classify. Although we tend to ignore what doesn't fit into familiar boxes, the truly new won't conform to existing categories.
Unusual trends can indicate inflection points in the industry and future opportunities. In this context, identifying trends and being willing to explore what initially seems peculiar are keys to anticipating the future and proactively adapting to changes in the business environment.
It is essential to recognize when not to make a forecast, to be skeptical of apparent changes, and to avoid immediate predictions, remembering that even in times of unprecedented uncertainty, the situation will stabilize. With the appropriate application of intuition, accurate forecasting is possible.
Consider the most probable values, the two extremes, and the median
In the event of occurrence, make your worst-case scenario the best, either by shifting the focus of time or reducing the importance of outcomes in the overall objectives set.
Financial projections involve predicting the future performance of a company or market based on historical data, trends, and assumptions. Financial forecasting can aid in planning, budgeting, decision-making, and risk management. However, the process of generating financial projections has limitations and challenges, such as:
The accuracy of historical data may not reflect current or future business or market conditions. Historical data may be incomplete, inconsistent, or outdated.
The forecast's timeframe can affect its reliability and relevance. The longer the timeframe, the more uncertain and variable the forecast. A shorter timeframe may overlook significant changes or opportunities.
The quality and validity of data and assumptions can impact the projection's outcome. Input data may be inaccurate, biased, or manipulated. Assumptions may be unrealistic, optimistic, or pessimistic.
The forecast method and model can influence results and interpretation. Different methods and models have varying strengths and weaknesses, requiring different levels of expertise and resources. Some methods and models may be more suitable for certain businesses or markets than others.
Therefore, financial projections require careful analysis, assessment, and communication. It is not a precise or definitive tool but rather a guide and scenario builder. To enhance the effectiveness and reliability of financial forecasts, some best practices include:
Start with the end in mind. Define the purpose, scope, and audience of the forecast. Align the forecast with the business or market's strategic goals and objectives.
Prepare multiple facets of the projections. Use different methods, models, and scenarios to capture a range of possibilities and uncertainties. Compare and contrast the results and implications of each perspective.
Be conservative but also plan for the best and worst scenarios. Use realistic and reasonable assumptions and data. Avoid overestimating or underestimating results. Prepare contingency plans and mitigation strategies for unexpected events or risks.
Use a dual approach, from the general to the specific and from the specific to the general. Combine macro and micro perspectives of the business or market. Incorporate internal and external factors and facts that may affect projections. Validate and reconcile the results of both approaches.
Keep assumptions to a minimum and clearly document them. Use the most relevant and reliable information available. Explain the justification and sources of assumptions and how they impact the forecast. Update and review assumptions as new data and information become available.
Make projections within the most relevant and actionable timeframe. Monitor and track actual outcomes and compare them with the forecast. Adjust and update the forecast as necessary to reflect changes and feedback.
Communicate the forecast effectively and transparently. Use clear and concise language and visuals to present projections. Highlight key findings, insights, and recommendations. Acknowledge limitations and uncertainties and provide a margin of error or confidence interval.
References:
Samonas, Michael. Financial Forecasting, Analysis, and Modelling: A Framework for Long-Term Forecasting. Hoboken, NJ: Wiley, 2015.
Campbell, Philip. A Quick Start Guide to Financial Forecasting: Discover the Secret to Driving Growth, Profitability, and Cash Flow Higher. Houston, TX: CreateSpace Independent Publishing Platform, 2017.
Danielsson, Jon. Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk with Implementation in R and Matlab. Chichester, UK: Wiley, 2011.
Hyndman, Rob J., and George Athanasopoulos. Forecasting: Principles and Practice. 3rd ed. Melbourne, Australia: OTexts, 2021.
Makridakis, Spyros, Steven C. Wheelwright, and Rob J. Hyndman. Forecasting Methods and Applications. 3rd ed. New York, NY: Wiley, 1998.
