In this article, we extend the Black-Litterman approach to a continuous time setting. We model analyst views jointly with asset prices to estimate the unobservable factors driving asset returns. The key in our approach is that the filtering problem and the stochastic control problem are...

We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them. Our approach is based on a combination of Inverse Reinforcement Learning (IRL) and RL. First, the IRL...

I jointly treat two critical issues in the application of mean-variance portfolios, i.e., estimation risk and portfolio instability. I find that theory-based portfolio strategies known to outperform naive diversification (1/N) in the absence of transaction costs, heavily underperform it under...

Investors often adopt mean-variance efficient portfolios for achieving superior risk-adjusted returns. However, such portfolios are sensitive to estimation errors, which affect portfolio performance. To understand the impact of estimation errors, I develop simple and intuitive formulas of the...

In this article, the authors propose an order flow simulator for meta orders such as those originating from the trading activity of buy-side firms. The simulator is designed with three key goals in mind. First, it should be simple to use and integrate into different applications. Second, it must...

The phrase long-term investing in triple leveraged ETFs is somewhat of an anathema for investment academicians. As a result of daily rebalancing and so-called beta slippage or “the constant leverage trap” it is highly likely over the long term to significantly deviate from the targeted...

Several Bayesian model combination schemes, including some novel approaches that simultaneously allow for parameter uncertainty, model uncertainty and robust time varying model weights, are compared in terms of forecast accuracy and economic gains using financial and macroeconomic time series....

We introduce a simulation-free method to model and forecast multiple asset returns and employ it to investigate the optimal ensemble of features to include when jointly predicting monthly stock and bond excess returns. Our approach builds on the Bayesian Dynamic Linear Models of West and...

We investigate simply the usage of clustering method by inverse covariance estimation for asset allocation in finance. Allocation across various sectors of the market (i.e. sector ETF) is usually well understood through the usage of mean variance allocation. However, stocks inside a same sector...

We propose a density combination approach featuring combination weights that depend on the past forecast performance of the individual models entering the combination through a utility-based objective function. We apply this model combination scheme to forecast stock returns, both at the...