Conservative or Aggressive? ConfidenceAware Dynamic Portfolio Construction Indicatorbased investing is a popular investment strategy driven by technical analysis for the stock market, the key issue of which is to construct portfolios from technical indicators. Due to the high volatility and nonstationary of the stock market, the effectiveness of an indicator, however, varies largely across different periods, which has made it necessary to dynamically adjust indicatorbased investing. In this paper, we propose a confidencebased calibration approach for dynamic portfolio construction. The major intuition behind is to tune a more concentrated portfolio when the indicator yields higher confidence otherwise a relatively equalweighted one. To seek a maximized longterm profit, we further propose to integrate learning the confidence (i.e., future effectiveness) of an indicator into a unified portfolio construction approach powered by a recurrent reinforcement learning framework. Compared with the traditional indicator investing strategies, our confidencebased calibrated indicator of investing can obtain significantly higher returns with lower risks.

Global minimum variance portfolios under uncertainty: a robust optimization approach This paper presents new models which seek to optimize the first and second moments of asset returns without estimating expected returns. Motivated by the stability of optimal solutions computed by optimizing only the second moment and applying the robust optimization methodology which allows to incorporate the uncertainty in the optimization model itself, we extend and combine existing methodologies in order to define a method for computing relativerobust and absoluterobust minimum variance portfolios. For the relative robust strategy, where the maximum regret is minimized, regret is defined as the increase in the investment risk resulting from investing in a given portfolio instead of choosing the optimal portfolio of the realized scenario. The absolute robust strategy which minimizes the maximum risk was applied assuming the worstcase scenario over the whole uncertainty set. Across alternate time windows, results provide new evidence that the proposed robust minimum variance portfolios outperform nonrobust portfolios. Whether portfolio measurement is based on return, risk, regret or modified Sharpe ratio, results suggest that the robust methodologies are able to optimize the first and second moments without the need to estimate expected returns.