Pairs trading is an effective statistical arbitrage strategy considering the spread of paired stocks in a stable cointegration relationship. Nevertheless, rapid market changes may break the relationship (namely structural break), which further leads to tremendous loss in intraday trading. In this paper, we design a two-phase pairs trading strategy optimization framework, namely structural break-aware pairs trading strategy ( SAPT ), by leveraging machine learning techniques. Phase one is a hybrid model extracting frequency- and time-domain features to detect structural breaks. Phase two optimizes pairs trading strategy by sensing important risks, including structural breaks and market-closing risks, with a novel reinforcement learning model. In addition, the transaction cost is factored in a cost-aware objective to avoid significant reduction of profitability. Through large-scale experiments in real Taiwan stock market datasets, SAPT outperforms the state-of-the-art strategies by at least 456% and 934% in terms of profit and Sortino ratio, respectively.

Sample preparation is an essential processing step in most biochemical applications. Various reactants are mixed together to produce a solution with the target concentration. Since reactants generally take a notable part of the cost in a bioassay, their usage should be minimized whenever possible. In this paper, we propose an algorithm, CoDOS, to prepare the target solution with many reactants using common dilution operation sharing on digital microfluidic biochips (DMFBs). CoDOS first represents the given target concentration as a recipe matrix, and then identifies rectangles in the matrix, where each rectangle indicates an opportunity of dilution operation sharing for reactant minimization. Experimental results demonstrate that CoDOS can achieve up to 27% of reactant saving as compared with the bit-scanning method in single-target sample preparation. Moreover, even if CoDOS is not developed for multi-target sample preparation, it still outperforms the recent state-of-theart algorithm, RSMA. Hence, it is convincing that CoDOS is a better alternative for many-reactant sample preparation.

This paper studies the timings of interactive call and conversion decisions made by bond issuers and holders, respectively, due to the presence of different embedded options. We develop a risk-neutral-valuation-based game option model featuring bondholder-stockholder conflicts of interest. The presence of conversion (call) options possessed by the bondholders (issuers) accelerates the call (conversion) decisions. Granting bondholders additional put options further hastens call decisions and hence conversion decisions in response to call accelerations. Our empirical studies are consistent with past call policy research and support our interactive exercise analyses that explain the rationales behind early call and conversion phenomena.

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A pairs trading strategy (PTS) constructs and monitors a stationary portfolio by shorting (longing) when the portfolio is adequately over-(under-)priced measured by a predetermined open threshold. We close this position to earn the price differences when the portfolio's value reverts back to the mean level. When the portfolio is significantly over-(under-)priced measured by another predetermined stop-loss threshold, we close the position to stop loss. This paper develops a two-stage deep learning method to improve the investment performance of a PTS. Note that the literature executes a PTS by selecting the best trigger threshold (a combination of open and stop-loss thresholds) from a restricted, heuristically-determined set of trigger thresholds. Such a design significantly degrades investment performance. However, selecting the best threshold from all possible thresholds yields a non-converged training problem. To resolve this dilemma, we propose in the first stage of our method a representative label mechanism by which to construct a set of candidate trigger thresholds based on all possible thresholds and then train a deep learning (DL) model to select the best from the set. Experiments demonstrate that the proposed first-stage method avoids the non-converged training problem and outperforms most state-of-the-art methods. To further reduce the trading risk, the second stage trains another DL with the profitability of each trade labeled by executing the PTS with trigger thresholds recommended in the first-stage mechanism to remove unprofitable trades. Compared to models that indirectly judge profitability by price movement similarity without considering the quality of the recommended trigger thresholds, our model produces higher win rates and average profits. Furthermore, we find that training with the PTS portfolio value process exhibiting time invariance clearly outperforms training with only time-varying stock/return processes, even though the latter training set contains more information. This is because unpredictable changes in market trends cause the model to learn time-varying patterns from the training set that may not apply to the testing set. INDEX TERMS pairs trading strategy, representative labeling, time (in)variant data, two-stage deep learning