With the advances in time-series prediction, several recent developments machine learning have shown that integrating prediction methods into portfolio selection is a great opportunity. In this paper, we propose novel approach to formation strategy based on hybrid model combines convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) with robust input features obtai...

The purpose of this study is two-fold. First, it is to invert an insurance industry index(s) from a linearly independent factor structure derived from the application of the Ross (1976) arbitrage-pricing model (APT) on a sample of insurance industry returns. The second objective is to identify the effect this index has on the performance of the Sharpe multi-index model in the formation of the m...

Robust portfolios reduce the uncertainty in portfolio performance. In particular, the worst-case optimization approach is based on the Markowitz model and form portfolios that are more robust compared to mean–variance portfolios. However, since the robust formulation finds a different portfolio from the optimal mean–variance portfolio, the two portfolios may have dissimilar levels of factor exp...

One of the major issues for Markowitz mean–variance model is the errors in estimations cause ‘‘corner solutions’’ and low diversity in the portfolio. In this paper, we compare the mean–variance efficiency, realized portfolio values, and diversity of the models incorporating different entropy measures by applying multiple criteria method. Differing from previous studies, we evaluate twenty-three...

Socially responsible investing is becoming more popular among people because are concerned about the environment and society. investors screen company by considering ESG factors. The question raced whether socially improves portfolio performance how funds perform during uncertain times like Covid-19 pandemic. Since many critics of say that funds’ highly depends on Software Service stocks, so re...

The Modern Portfolio Theory of Markowitz maximized portfolio expected return subject to holding total portfolio variance below a selected level. Digital Portfolio Theory is an extension of Modern Portfolio Theory, with the added dimension of memory. Digital Portfolio Theory decomposes the portfolio variance into independent components using the signal processing decomposition of variance. The r...

This paper studies a continuous-time market where an agent, having specified an investment horizon and a targeted terminal mean return, seeks to minimize the variance of the return. The optimal portfolio of such a problem is called mean-variance efficient à la Markowitz. It is shown that, when the market coefficients are deterministic functions of time, a mean-variance efficient portfolio reali...

This paper features an analysis of the effectiveness of a range of portfolio diversification strategies as applied to a set of daily arithmetically compounded returns on a set of ten market indices representing the major European markets for a nine year period from the beginning of 2005 to the end of 2013. The sample period, which incorporates the periods of both the Global Financial Crisis (GF...

This paper features an analysis of the effectiveness of a range of portfolio diversification strategies, with a focus on down-side risk metrics, as a portfolio diversification strategy in a European market context. We apply these measures to a set of daily arithmetically-compounded returns, in U.S. dollar terms, on a set of ten market indices representing the major European markets for a nine-y...