Active genes are tri-methylated at K4 of histone H3

Lysine methylation of histones in vivo occurs in three states: mono-, di- and tri-methyl. Histone H3 has been found to be di-methylated at lysine 4 (K4) in active euchromatic regions but not in silent heterochromatic sites. Here we show that the Saccharomyces cerevisiae Set1 protein can catalyse di- and tri-methylation of K4 and stimulate the activity of many genes. Using antibodies that discriminate between the di- and tri-methylated state of K4 we show that di-methylation occurs at both inactive and active euchromatic genes, whereas tri-methylation is present exclusively at active genes. It is therefore the presence of a tri-methylated K4 that defines an active state of gene expression. These findings establish the concept of methyl status as a determinant for gene activity and thus extend considerably the complexity of histone modifications.     

An analysis on the predictability of CAPM beta for momentum returns

This paper demonstrates that the forecasted capital asset pricing model (CAPM) beta of momentum portfolios explains a large portion of the return, ranging from 40% to 60% for stock‐level momentum, and from 30% to 50% for industry‐level momentum. Beta forecasts are from a realized beta estimator using daily returns over the prior year. Periods such as 1969–1989 have been found in earlier studies to contain abnormal profits from momentum trading; however, we show that these were spuriously generated by measurement error in systematic risk. These results cast further doubt on the ability of standard momentum trading strategies to generate abnormal profits.     

Robust trading rule selection and forecasting accuracy

Trading rules performing well on a given data set seldom lead to promising out-of-sample results, a problem which is a consequence of the in-sample data snooping bias. Efforts to justify the selection of trading rules by assessing the out-of-sample performance will not really remedy this predica- ment either, because they are prone to be trapped in what is known as the out-of-sample data-snooping bias. Our approach to curb the data-snooping bias consists of constructing a framework for trading rule selection using a-priori robustness strategies, where robustness is gauged on the basis of time- series bootstrap and multi-objective criteria. This approach focuses thus on building robustness into the process of trading rule selection at an early stage, rather than on an ex-post assessment of trading rule fitness. Intra-day FX market data constitute the empirical basis of the proposed investigations. Trading rules are selected from a wide universe created by evolutionary computation tools. The authors show evidence of the benefit of this approach in terms of indirect forecasting accuracy when investing in FX markets.     

Capacity allocation and coordination issues for the timely processing of outsourced operations

We consider dynamic capacity booking problems faced by multiple manufacturers each outsourcing certain operations to a common third-party firm. Each manufacturer, upon observing the current state of the third-party schedule, books capacity with the objective to jointly minimize holding costs that result from early deliveries, tardiness penalties due to late deliveries, and third-party capacity booking costs. When making a reservation, each manufacturer evaluates two alternative courses of action： （i） reserving capacity not yet utilized by other manufactures who booked earlier; or （ii） forming a coalition with a subset or all of other manufacturers to achieve a schedule minimizing coalition costs, i.e., a centralized schedule for that coalition. The latter practice surely benefits the coalition as a whole; however, some manufacturers may incur higher costs if their operations are either pushed back too much, or delivered too early. For this reason, a cost allocation scheme making each manufacturer no worse than they would be when acting differently （i.e., participating in a smaller coalition or acting on their own behalf,） must accompany centralized scheduling for the coalition. We model this relationship among the manufacturers as a cooperative game with transferable utility, and present optimal and/or heuristic algorithms to attain individually and eoalitionally optimal schedules as well as a linear program formulation to find a core allocation of the manufacturers＇ costs.     

Monthly Beta Forecasting with Low‐, Medium‐ and High‐Frequency Stock Returns

This paper evaluates the accuracy of 1‐month‐ahead systematic (beta) risk forecasts in three return measurement settings; monthly, daily and 30 minutes. It was found that the popular Fama–MacBeth beta from 5 years of monthly returns generates the most accurate beta forecast among estimators based on monthly returns. A realized beta estimator from daily returns over the prior year generates the most accurate beta forecast among estimators based on daily returns. A realized beta estimator from 30‐minute returns over the prior 2 months generates the most accurate beta forecast among estimators based on 30‐minute returns. In environments where low‐, medium‐ and high‐frequency returns are accurately available, beta forecasting with low‐frequency returns are the least accurate and beta forecasting with high‐frequency returns are the most accurate. The improvements in precision of the beta forecasts are demonstrated in portfolio optimization for a targeted beta exposure. Copyright © 2016 John Wiley & Sons, Ltd.