A priori principles refer to knowledge or assumptions that are derived logically rather than from empirical evidence. In investing, a priori assumptions can significantly impact decisions on where and how to allocate capital. This article will analyze key ways a priori reasoning manifests in investment analysis and decisions, including cognitive biases, valuation methods, risk assessment, portfolio construction approaches and more. We’ll also look at both the upsides and downsides of relying on a priori assumptions when making investment choices to find the right balance between theoretical and evidence-based investing.
Common a priori biases that distort investment decisions
Many a priori biases exist that can skew investment analysis if not properly accounted for. Overconfidence bias leads investors to have unrealistically high conviction in their valuation abilities. Confirmation bias causes investors to disproportionately seek and favor information that confirms pre-existing views. Representativeness leads investors to make decisions based on superficial similarities rather than meaningful statistical relationships. Anchoring fixation causes investors to place too much emphasis on initial values or estimates when making judgments. Without empirical evidence to provide a reality check, these and other a priori biases can propagate without being corrected.
The role of a priori valuation models and their limitations
Valuation models like discounted cash flow analysis and relative valuation ratios rely extensively on a priori assumptions. Required inputs include future cash flow projections, terminal value estimates, discount rates and more. While these models provide an important starting point for analysis, their output is only as good as the accuracy of their underlying assumptions. Overreliance on their theoretical output without real-world scrutiny can lead to oversimplified or plain inaccurate valuations.
Incorporating both a priori and evidence-based inputs for sound decisions
Truly prudent investment analysis incorporates both a priori and evidence-based elements. For example, a discounted cash flow model can provide a preliminary valuation range based on a priori inputs. But this should then be compared to various empirical data points to gauge plausibility, including precedent transactions, trading multiples and past case studies. Bridging theory and reality in this way leads to more grounded outputs and decisions.
While a priori knowledge brings structure and a starting point, investment analysis should ultimately be evidence-based and empirically grounded. By balancing theoretical models with data scrutiny, investors can mitigate biases and make sounder portfolio decisions.