Researcher:
Pirayesh Negab, Davood

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PhD Student

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Davood

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Pirayesh Negab

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Pirayesh Negab, Davood

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20222

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Researcher Of Publications: search.filters.isAuthorOfPublication.69452471-8cfe-4381-b450-cbf78a83fd77

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    State-dependent asset allocation using neural networks
    (Taylor & Francis, 2022) Bradrania, Reza; N/A; Pirayesh Negab, Davood; PhD Student; Graduate School of Sciences and Engineering; N/A
    Changes in market conditions present challenges for investors as they cause performance to deviate from the ranges predicted by long-term averages of means and covariances. The aim of conditional asset allocation strategies is to overcome this issue by adjusting portfolio allocations to hedge changes in the investment opportunity set. This paper proposes a new approach to conditional asset allocation that is based on machine learning; it analyzes historical market states and asset returns and identifies the optimal portfolio choice in a new period when new observations become available. In this approach, we directly relate state variables to portfolio weights, rather than firstly modeling the return distribution and subsequently estimating the portfolio choice. The method captures nonlinearity among the state (predicting) variables and portfolio weights without assuming any particular distribution of returns and other data, without fitting a model with a fixed number of predicting variables to data and without estimating any parameters. The empirical results for a portfolio of stock and bond indices show the proposed approach generates a more efficient outcome compared to traditional methods and is robust in using different objective functions across different sample periods.
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    State-dependent stock selection in index tracking: a machine learning approach
    (Springer, 2022) Bradrania, R.; Shafizadeh, M.; N/A; Pirayesh Negab, Davood; PhD Student; Graduate School of Sciences and Engineering; N/A
    We focus on the stock selection step of the index tracking problem in passive investment management and incorporate constant changes in the dynamics of markets into the decision. We propose an approach, using machine learning techniques, which analyses the performance of the selection methods used in previous market states and identifies the one that gives the optimal tracking portfolio in each period. We apply the proposed procedure using the popular cointegration technique in index tracking and show that it tracks the S&P 500 with a very high level of accuracy. The empirical evidence shows that our proposed approach outperforms cointegration techniques that use a single criterion (e.g., stocks with the maximum market capitalization) in the asset selection.