Conferences - Seminars

16NOV
2018
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  Friday 16 November 2018 10:30 - 12:00 Unil, Extranef building, room 126

Machine Learning, Asset Pricing and FinTech

By Alberto ROSSI, University of Maryland

Combination of 2 papers:
Paper 1. Predicting Stock Returns with Machine Learning
Abstract
We employ a semi-parametric method known as Boosted Regression Trees (BRT) to forecast stock returns and volatility at the monthly frequency. BRT is a statistical method that generates forecasts on the basis of large sets of conditioning information without imposing strong parametric assumptions such as linearity or monotonicity. It applies soft weighting functions to the predictor variables and performs a type of model averaging that increases the stability of the forecasts and therefore protects it against overfitting. Our results indicate that expanding the conditioning information set results in greater out-of-sample predictive accuracy compared to the standard models proposed in the literature and that the forecasts generate profitable portfolio allocations even when market frictions are considered. By working directly with the mean-variance investor’s conditional Euler equation we also characterize semi-parametrically the relation between the various covariates constituting the conditioning information set and the investor’s optimal portfolio weights. Our results suggest that the relation between predictor variables and the optimal portfolio allocation to risky assets is highly non-linear. 
 
Paper 2. Who Benefits from Robo-advising? Evidence from Machine Learning
Abstract
We study the effects of a large US robo-adviser on investor performance. Across all clients, the robo-adviser reduces investors holdings in money market mutual funds and increases bond holdings. It reduces the holdings of individual stocks and US active mutual funds, and moves investors towards low-cost indexed mutual funds. Finally, it increases investors’ international diversification and investors’ overall risk-adjusted performance. From sign-up, it takes approximately six months for the robo-adviser to adjust investors’ portfolios to the new allocations. We use a machine learning algorithm, known as Boosted Regression Trees (BRT), to explain the cross- sectional variation in the effects of PAS on investors’ portfolio allocation and performance. The investors that benefit the most from robo-advising are the clients with little investment experience, as well as the ones that have high cash-holdings and high trading volume pre-adoption. Clients with little mutual fund holdings and clients invested in high-fee active mutual funds also display significant performance gains. 
 

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