For optimal asset allocation, mean-variance investors must learn about the joint dynamics of new and existing asset classes, not only their profitability. Bitcoin's digital gold narrative provides a unique laboratory to test this hypothesis. We find that a decrease in investors' estimate on...

The problem of optimal wealth allocation is solved under the assumptions that interest rates are stochastic and stock returns are predictable with observed and unobserved factors. The stock risk premium is taken to be an affine function of the predictive variables and the stock return volatility...

We explore the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically, we find that mixed-frequency models...

This paper investigates how different monetary policy designs alter the effect of carry trades on a host small open economy. Capital inflows are expansionary, leading the central bank to raise the interest rate, increasing carry trades' returns, and generating further capital inflows (carry...

This paper investigates the ability of the adaptive learning approach to replicate the expectations of professional forecasters. For a range of macroeconomic and financial variables, we compare constant and decreasing gain learning models to simple, yet powerful benchmark models. We find that...

We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor's risk preference, but learns it over time by observing her portfolio choices in different market environments. We develop an exploration-exploitation algorithm which trades off...

What predicts returns on assets with "hard-to-value" fundamentals, such as Bitcoin and stocks in new industries? We propose an equilibrium model that shows how rational learning enables return predictability through technical analysis. We document that ratios of prices to their moving averages...

Portfolio optimization often struggles in realistic out-of-sample contexts. We de-constructthis stylized fact, comparing historical forecasts of portfolio optimization inputs withsubsequent out of sample values. We confirm that historical forecasts are imprecise guidesof subsequent values but...

This paper proposes a self-calibrated sparse learning approach for estimating a sparse target vector, which is a product of a precision matrix and a vector, and investigates its application to finance to provide an innovative construction of relative-volatility-managed portfolio (RVMP). The...

We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL). By adopting the recently developed continuous-time exploratory control framework, we formulate the exploratory MV problem in high dimensions. We further show the...