A CONTINUOUS-TIME REEXAMINATION OF DOLLAR-COST AVERAGING
Bibliographic record
Abstract
The widespread practice of dollar-cost averaging (DCA) amongst the investing public, has puzzled most financial economists, ever since Constantinides [2] demonstrated the dynamic inefficiency of this strategy under very general conditions. This enduring phenomena has forced researchers, such as Statman [12], to suggest behavioral explanations for DCA's popularity, predicated on the prospect theory of Kahneman and Tversky [4]. In this paper we reexamine the payoff structure of DCA via continuous-time financial mathematics and then ask the question: Is it possible to reconcile the theory and practice of dollar-cost averaging? To answer this question, we take a slightly different approach to the issue by using the tools of stochastic calculus and Brownian bridges. We demonstrate that engaging in a dollar-cost averaging strategy is akin to purchasing a zero strike arithmetic Asian option on the underlying security. In other words, people who engage in dollar-cost averaging are implicitly purchasing a path-dependent contingent claim. We then prove that the expected return from this exotic option — i.e. the DCA strategy — conditional on knowing the final value of the security will uniformly exceed the return from the underlying security for all sufficiently large volatilities. This leads us to argue that investors may be dollar-cost averaging because they have "target prices" for the underlying asset price. The strategy of dollar-cost averaging would then exceed the returns from lump-sum investing, based on their subjective conditional expectation. In fact, the more volatile the underlying security, the greater is the benefit to dollar-cost averaging — conditional on knowing the final value — which is consistent with common practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".