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Zeusu Sato

Publications and source records attributed to Zeusu Sato.

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A Clarifying Note on Long-Horizon Investment and Dollar-Cost Averaging: An Effective Investment Exposure Perspective

It is widely claimed in investment education and practice that extending the investment horizon reduces risk, and that diversifying investment timing, for example through dollar-cost averaging (DCA), further mitigates investment risk. Although such claims are intuitively appealing, they are often stated without precise definitions of risk or a clear separation between risk and uncertainty. This paper revisits these two beliefs within a unified probabilistic framework. We define risk at the expectation level as a property of the generating distribution of cumulative investment outcomes, and distinguish it from uncertainty, understood as the dispersion of realized outcomes across possible paths. To enable meaningful comparisons across horizons and investment schedules, we introduce the notion of effective investment exposure, defined as time-integrated invested capital. Under stationary return processes with finite variance, we show that extending the investment horizon does not alter expected risk, expected return, or the risk-return ratio on a per-unit-exposure basis. In contrast, different investment timing strategies can induce distinct exposure profiles over time. As a result, lump-sum investment and dollar-cost averaging may differ not only in uncertainty but also in expected risk when compared at equal return exposure, although the resulting risk differences are of constant order and do not grow with the investment horizon. These results clarify why common narratives surrounding long-horizon investment and dollar-cost averaging are conceptually misleading, while also explaining why adopting such strategies under budgetary or timing constraints need not be regarded as irrational.

q-fin.PM

On the epsilon-delta Structure Underlying Chatterjee's Rank Correlation

We provide an epsilon-delta interpretation of Chatterjee's rank correlation by tracing its origin to a notion of local dependence between random variables. Starting from a primitive epsilon-delta construction, we show that rank-based dependence measures arise naturally as epsilon to zero limits of local averaging procedures. Within this framework, Chatterjee's rank correlation admits a transparent interpretation as an empirical realization of a local L1 residual. We emphasize that the probability integral transform plays no structural role in the underlying epsilon-delta mechanism, and is introduced only as a normalization step that renders the final expression distribution-free. We further consider a moment-based analogue obtained by replacing the absolute deviation with a squared residual. This L2 formulation is independent of rank transformations and, under a Gaussian assumption, recovers Pearson's coefficient of determination.

math.ST