Robust median reversion strategy for online portfolio selection

Ministry of Education, Singapore under its Academic Research Funding Tier

Custom v. Standardized Risk Models

We discuss when and why custom multi-factor risk models are warranted and give source code for computing some risk factors. Pension/mutual funds do not require customization but standardization. However, using standardized risk models in quant trading with much shorter holding horizons is suboptimal: 1) longer horizon risk factors (value, growth, etc.) increase noise trades and trading costs; 2) arbitrary risk factors can neutralize alpha; 3) "standardized" industries are artificial and insufficiently granular; 4) normalization of style risk factors is lost for the trading universe; 5) diversifying risk models lowers P&L correlations, reduces turnover and market impact, and increases capacity. We discuss various aspects of custom risk model building.Comment: 30 pages; minor improvements, more source code added; to appear in Risk

Pairs trading with the persistence-based decomposition model

Recently, the persistence-based decomposition (PBD) model has been introduced to the scientific community by Rende et al. (2019). It decomposes a spread time series between two securities into three components capturing infinite, finite, and no shock persistence. The authors provide empirical evidence that the model adopts well to noisy high-frequency data in terms of model fitting and prediction. We put the PBD model to test on a large-scale high-frequency pairs trading application, using S&P 500 minute-by-minute data from 1998 to 2016. After accounting for execution limitations (waiting rule, volume constraints, and short-selling fees) the PBD model yields statistically significant and economically meaningful annual returns after transaction costs of 9.16 percent. These returns can only partially be explained by the exposure to common risk. In addition, the model is superior in terms of risk-return metrics. The model performs very well in bear markets. We quantify the impact of execution limitations on risk and return measures by relaxing backtesting restrictions step-by-step. If no restrictions are imposed, we find annual returns after costs of 138.6 percent

Robust median reversion strategy for on-line portfolio selection

Ministry of Education, Singapore under its Academic Research Funding Tier

Industry Effects on Firm and Segment Profitability Forecasting

Academics and practitioners have long recognized the importance of a firm’s industry membership in explaining its financial performance. Yet, contrary to conventional wisdom, recent research shows that industry-specific profitability forecasting models are not better than economy-wide models. The objective of this paper is to further explore this result and to provide insights into when and why industry-specific profitability forecasting models are useful. We show that industry-specific forecasts are significantly more accurate in predicting profitability for single-segment firms and, to some extent, for business segments. For multiple-segment firms, the aggregation of segment-level data for external reporting of firm-level financials obliterates the industry effects of their segments

Fund Flows, Manager Changes, and Performance Persistence

Most empirical studies suggest that mutual funds do not persistently outperform an appropriate benchmark in the long run. We analyze this lack of persistence in terms of two equilibrating mechanisms: fund flows and manager changes. Using data on actively managed US equity mutual funds, we find that if neither mechanism is operating, winner funds (top-decile ranked in previous year) continue to significantly outperform loser funds (bottom-decile ranked in previous year) by 4.08 percentage points per annum. However, the difference between previous winner and loser funds declines to zero within one year if the two mechanisms are acting together. Thus, equity mutual fund out- and underperformance are unlikely to persist in well-functioning financial markets