The growth-optimal portfolio is designed to have maximum expected log return over the next rebalancing period. Thus, it can be computed with relative ease by solving a static optimization problem. has sparked fascination among finance professionals and researchers because shown outperform any other probability 1 in long run. In short run, however, notoriously volatile. Moreover, its computation requires precise knowledge of asset distribution, which not directly observable but must inferred...

Abstract The valuation of financial derivatives often assumes risk neutrality with respect to the risk-neutral martingale measure, which prevents arbitrage opportunities. However, casual traders may still incur substantial losses when trading at this price, especially price has be paid now and payoff is only realized in future. This study proposes a new framework that provides risk-sensitive investors an additional safeguard. proposed embraces worst-case perspective while exploiting...

We study a mechanism design problem where seller aims to allocate good multiple bidders, each with private value. The supports or favors specific group, referred as the minority group. Specifically, requires that allocations group are at least predetermined fraction (equity level) of those made rest bidders. Such constraints arise in various settings, including government procurement and corporate supply chain policies prioritize small businesses, environmentally responsible suppliers,...

Plain vanilla $K$-means clustering has proven to be successful in practice, yet it suffers from outlier sensitivity and may produce highly unbalanced clusters. To mitigate both shortcomings, we formulate a joint detection problem, which assigns prescribed number of data points an auxiliary cluster performs cardinality-constrained on the residual set, treating cardinalities as given input. We cast this problem mixed-integer linear program (MILP) that admits tractable semidefinite programming...

We derive sharp probability bounds on the tails of a product symmetric nonnegative random variables using only information about their first two moments. If covariance matrix is known exactly, these can be computed numerically semidefinite programming. an upper bound available, right evaluated analytically. The under precise and imprecise coincide for all left as well corresponding to quantiles that are either sufficiently small or large. also prove reduce trivial 1 if number in exceeds...

We study a robust auction design problem with minimax regret objective, in which seller seeks mechanism for selling multiple items to bidders additive values. The knows that the bidders’ values range over box uncertainty set but has no information on their probability distribution. model we requires distributional except upper bounds each item. This is relevant if there trustworthy or any costly time-consuming acquire. propose sells item separately via second price random reserve and prove...

This paper focuses mainly on the problem of computing γth, γ>0, moment a random variable Yn:=∑i=1nαiXi in which αi’s are positive real numbers and Xi’s independent distributed according to noncentral chi-square distributions. Finding an analytical approach for solving such has remained challenge due lack understanding probability distribution Yn, especially when not all equal. We analytically solve this by showing that γth Yn can be expressed terms generalized hypergeometric functions....

The celebrated Kelly betting strategy guarantees, with probability one, higher long-run logarithmic growth than any other causal investment strategy. However, on the way to its long-term supremacy, this has a notable downfall: it typically displays high variation in time-varying realization of bettor's level wealth. Hence, following question arisen: For finite horizon involving N sequential bets, how might Kelly's scheme be modified so as remain provably appealing some sense and also less...

Inspired by the principle of satisficing (Simon 1955), Long et al. (2021) propose an alternative framework for optimization under uncertainty, which we term as a robust model. Instead sizing uncertainty set in optimization, model is specified target objective with aim delivering solution that least impacted achieving target. At heart this framework, minimize level constraint violation all possible realizations within support set. Our based on function evaluates to optimal value standard...

Multimarket Multireservoir Hydro Scheduling Peak/off-peak spreads on European electricity forward and spot markets are eroding due to the ongoing nuclear phaseout in Germany steady growth photovoltaic capacity. The reduced profitability of peak/off-peak arbitrage forces hydropower producers recover part their original reserve markets. In paper titled “A Planner-Trader Decomposition for Scheduling” Schindler, Rujeerapaiboon, Kuhn, Wiesemann propose a bi-layer stochastic programming framework...

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This repository aims to showcase the effectiveness of our proposed regret-minimizing mechanism against various benchmark mechanisms.

Connected and autonomous vehicle (CAV) technology is expected to increase road capacity reduce fuel consumption, but it may take time for all human-driven vehicles (HVs) be replaced by CAVs. During the transition period, CAVs HVs will continue coexist. This paper aims find an optimal deployment of a CAV platoonable corridor facilitate platooning better manage traffic congestion on networks with mixed flow HVs. Generally, dedicated infrastructure CAVs, such as lanes, zones, or corridors, have...

Digital technology is drastically altering business patterns. Enterprises strive to use these technologies discover the worth of their consumers while understanding rivals required get a decisive advantage in competitive marketplaces. In this situation, existing intelligence practices require more fundamental theories underpin them and awareness apply strategies. This study will propose theory-based indicators as criteria evaluate competitors intelligently through machine learning techniques...