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DNA methylation is related to aging. Some researchers, such as Horvath or Levine have managed to create epigenetic and biological clocks that predict chronological age using methylation data. These authors used Elastic Net methodology to build age predictors that had a high prediction accuracy. In this article, we propose to improve their performance by incorporating an additional step using neural networks trained with Bayesian learning. We shown that this approach outperforms the results obtained when using Horvath's method, neural networks directly, or when using other training algorithms, such as Levenberg-Marquardt's algorithm. The R-squared value obtained when using our proposed approach in empirical (out-of sample) data was 0.934, compared to 0.914 when using a different training algorithm (Levenberg Marquard), or 0.910 when applying the neural network directly (e.g. without first reducing the dimensionality of the data). The results were also tested in independent datasets that were not used during the training phase. Our method obtained better R-squared values and RMSE than Horvath's and Levine's method in 8 independent datasets. We demonstrate that building an age predictor using a Bayesian based algorithm provides accurate age predictions. This method is implemented in an R function, which is available through a package created for predicting purposes and is applicable to methylation data. This will help to elucidate the role of DNA methylation age in complex diseases or traits related to aging.

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The optimal execution of stock trades is a relevant and interesting problem as it is key in maximizing profits and reducing risks when investing in the stock market. In the case of large orders, the problem becomes even more complex as the impact of the order in the market has to be taken into account. The usual solution is to split large orders into a set of smaller suborders that must be executed within a prescribed time window. This leads to the problem of deciding when in the time window execute each suborder. There are popular ways of executing the trading of these split orders like those which try to track the "Time Weighted Average Price" and the "Volume Weighted Average Price", usually called TWAP and VWAP orders. This paper presents a strategy to optimize the splitting of large trade orders over a given time window. The strategy is based on the solution of an optimization problem that is applied following a receding horizon approach. This approach reduces the impact of prediction errors due to the uncertain market dynamics, by using new values of the price time series as they are available as time goes on. Suborder size constraints are taken into account in both market and limit orders. The strategy relies on price and traded volume forecast but it is independent of the prediction technique used. The performance index weighs not only the financial cost of the suborders, but also the impact on the market and the forecasting accuracy. A tailored optimization algorithm is proposed for efficiently solving the corresponding optimization problem. Most of the computations of the algorithm can be parallelized. Finally, the proposed approach has been tested through a case study composed by stocks of the Chinese A-share market.INDEX TERMS algorithmic trading, receding horizon optimization, large stock orders, limit orders, TWAP, VWAP.

In this paper we present a combinatorial nonlinear technical indicator approach for the identification of appropriate combinations of stock technical indicators as inputs in non-linear models. This approach is illustrated with the example of Chinese stock indexes and 35 different stock technical indicators using neural networks as the chosen non-linear method. Stock market technical indicators can generate contradictory signals regarding the future performance of the stock analyzed. Furthermore, some non-linear methods, such as neural networks, can have poor generalization power when dealing with problems of high dimensionality due to the issue of local minima. Therefore, non-linear approaches that can identify appropriate combinations of input variables are of clear importance. It will be shown that the proposed approach, when using neural networks as classifiers, generates error rates lower than those using directly neural networks without dimensionality reduction. It will also be shown that merely increasing the number of neurons does not increase the accuracy. The approach proposed in this article is illustrated with an application to the stock market using neural networks but it could be applied to other fields and it can also be used with other non-linear techniques such as for instance support vector machines.