Yuriy Nevmyvaka

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We present the first large-scale empirical application of reinforcement learning to the important problem of optimized trade execution in modern financial markets. Our experiments are based on 1.5 years of millisecond time-scale limit order data from NASDAQ, and demonstrate the promise of reinforcement learning methods to market microstructure problems. Our(More)
Dark pools are a recent type of stock exchange in which information about outstanding orders is deliberately hidden in order to minimize the market impact of large-volume trades. The success and proliferation of dark pools have created challenging and interesting problems in algorithmic trading---in particular, the problem of optimizing the allocation of a(More)
Addressing the ongoing examination of high-frequency trading practices in financial markets, we report the results of an extensive empirical study estimating the maximum possible profitability of the most “aggressive” of such practices, and arrive at figures that are surprisingly modest. By “aggressive” we mean any trading strategy employing market orders(More)
In this paper, we address the importance of efficient execution in electronic markets. Due to intense competition for profit opportunities, trading costs can represent a significant portion of overall return. They must be taken into account both when a specific trade is being executed, and when a general investment strategy is being designed. We empirically(More)
We propose a state-based variant of the classical online learning problem of tracking the best expert. In our setting, the actions of the algorithm and experts correspond to local moves through a continuous and bounded state space. At each step, Nature chooses payoffs as a function of each player’s current position and action. Our model therefore integrates(More)
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