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\begin{document}$$X_1,X_2,\ldots ,X_n,\ldots $$\end{document} be a discrete-time stochastic process. The following optimal stopping problem is considered. We observe X1,X2,…\documentclass[12pt]{minimal}
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\begin{document}$$X_1,X_2,\ldots $$\end{document} on the one-by-one basis getting successively the data x1,x2,…\documentclass[12pt]{minimal}
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\begin{document}$$x_1,x_2,\ldots $$\end{document}. At each stage n\documentclass[12pt]{minimal}
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\begin{document}$$n$$\end{document}, n=1,2,…\documentclass[12pt]{minimal}
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\begin{document}$$n=1,2,\ldots $$\end{document}, after the data x1,…,xn\documentclass[12pt]{minimal}
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\begin{document}$$x_1,\ldots ,x_n$$\end{document} have been observed, we may stop, and if we stop, we gain gn(x1,…,xn)\documentclass[12pt]{minimal}
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\begin{document}$$g_n(x_1,\ldots ,x_n)$$\end{document}. In this article, we characterize the structure of all optimal (randomized) stopping times τ\documentclass[12pt]{minimal}
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\begin{document}$$\tau $$\end{document} that maximize the average gain value G(τ)=Egτ(X1,…,Xτ)\documentclass[12pt]{minimal}
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\begin{document}$$G(\tau )=E g_\tau (X_1,\ldots , X_\tau )$$\end{document} in some natural classes of stopping times τ\documentclass[12pt]{minimal}
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\begin{document}$$\tau $$\end{document} we call truncatable: τ\documentclass[12pt]{minimal}
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\begin{document}$$\tau $$\end{document} is called truncatable if G(τ∧N)→G(τ)\documentclass[12pt]{minimal}
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\begin{document}$$G(\tau \wedge N)\rightarrow G(\tau )$$\end{document} as N→∞\documentclass[12pt]{minimal}
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\begin{document}$$N\rightarrow \infty $$\end{document}. It is shown that under some additional conditions on the structure of gn\documentclass[12pt]{minimal}
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\begin{document}$$g_n$$\end{document} (suitable for statistical applications) every finite (with probability 1) stopping time is truncatable.
