Asset allocation is an investment strategy that aims to balance risk and reward by constantly redistributing the portfolio's assets according to certain goals, risk tolerance, and investment horizon. Unfortunately, there is no simple formula that can find the right allocation for every...

In this study a financial market is conceived as a network where the securities are nodes and the links account for returns' correlations. We theoretically prove the negative relationship between the centrality of assets in this financial market network and their optimal weights under the...

Segmentation, the process of dividing a target market into sub-sections, is widely used in the equity markets to help investors limit or increase exposure to a set of stocks with specific common risk and economic characteristics, which underlies the philosophy of diversification. Traditional...

We examine machine learning and factor-based portfolio optimization. We find that factors based on autoencoder neural networks exhibit a weaker relationship with commonly used characteristic-sorted portfolios than popular dimensionality reduction techniques. Machine learning methods also lead to...

Portfolio optimization has been a central problem in finance, often approached with two steps: calibrating the parameters and then solving an optimization problem. Yet, the two-step procedure sometimes encounter the ``error maximization'' problem where inaccuracy in parameter estimation...

We study dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. Our results show statistically and economically significant benefits from using deep learning to form optimal portfolios through certainty...

We use a deep learning model to extract syntax and context information from mutual fund managers' narrative discussions and measure their risk assessment. We validate the forward-looking nature of the risk measure by showing that more negative (positive) risk assessment in managers' narratives...

Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all the trades with each counterparty under both market and credit risk. We present a multi-Gaussian process regression approach, which is well suited for OTC derivative portfolio...

We show how one can use deep neural networks with macro-economic data in conjunction with price-volume data in a walk-forward setting to do tactical asset allocation. Low cost publicly traded ETFs corresponding to major asset classes (equities, fixed income, real estate) and geographies (US,...

Along with the advance of opinion mining techniques, public mood has been found to be a key element for stock market prediction. However, how market participants' behavior is affected by public mood has been rarely discussed. Consequently, there has been little progress in leveraging public mood...