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Forecasting the future movement of yield curves contains valuable information for both academic and practical issues such as bonding pricing, portfolio management, and government policies. The purpose of this paper is to develop a dynamic factor approach that can provide more precise and consistent forecasting results under various yield curve dynamics.

The paper develops a unified dynamic factor model based on Diebold and Li (2006) and Nelson and Siegel (1987) three-factor model to forecast the future movement yield curves. The authors apply the state-space model and the Kalman filter to estimate parameters and extract factors from the US yield curve data.

The authors compare both in-sample and out-of-sample performance of the dynamic approach with various existing models in the literature, and find that the dynamic factor model produces the best in-sample fit, and it dominates existing models in medium- and long-horizon yield curve forecasting performance.

The authors find that the dynamic factor model and the Kalman filter technique should be used with caution when forecasting short maturity yields on a short time horizon, in which the Kalman filter is prone to trade off out-of-sample robustness to maintain its in-sample efficiency.

Bond analysts and portfolio managers can use the dynamic approach to do a more accurate forecast of yield curve movements.

The enhanced forecasting approach also equips the government with a valuable tool in setting macroeconomic policies.

The dynamic factor approach is original in capturing the level, slope, and curvature of yield curves in that the decay rate is set as a free parameter to be estimated from yield curve data, instead of setting it to be a fixed rate as in the existing literature. The difference range of estimated decay rate provides richer yield curve dynamics and is the key to stronger forecasting performance.

Researchers have proposed characteristics‐based pricing models as an alternative to risk‐based pricing models. While supported empirically, these characteristic‐based models lack theoretical support. This paper seeks to reformulate an asset‐pricing model (RAPM) to demonstrate why firm characteristics help to explain stock returns.

The RAPM is grounded in an economic setting where two groups of agents hold different beliefs about firm fundamental values, and the more sophisticated group (rationals) adopts contrarian strategies against the naïve group (quasis). The model is derived in a static equilibrium within the consumption‐investment framework with heterogeneous agents.

The key theoretical result is a parsimonious equation of cross‐sectional expected returns that not only are specified by the traditional risk‐return relation, but also are determined by contrarian adjustments at both market‐wide and firm‐specific levels. When the model is taken to empirical specifications, it leads to consistent explanations for the behaviors of growth and value stocks, and for size and book‐to‐market effects.

The RAPM is a one‐period model that assumes that “rationals” have perfect knowledge about “quasis” sentiment parameter and their relative market weights. In future research, it is planned to extend this static model to multiple periods to incorporate a learning process by which “rationals” learn these parameters over time.

The RAPM clearly identifies four criteria for implementing arbitrage opportunities in investments. These criteria formalize the common practices in the mutual/hedge fund industry.

The paper develops an original framework that formally supports the characteristics‐based models. It offers insights for researchers in behavioral finance and guidelines for investment practitioners.

The Canadian mutual fund setting is unique in that two governance mechanisms – corporate and trust – coexist. This study empirically examines the impact of each mechanism on fund fees and performance. We find that corporate class funds charge higher fees but deliver superior fee-adjusted returns than trust funds. We then analyze the impact of various board characteristics on fees and performance for corporate class funds. We find that a board with smaller size, CEO duality, and a higher percentage of independent directors is more likely to charge lower fees. In addition, smaller boards are strongly associated with higher fee-adjusted performance. Our study supports agency theory over stewardship theory and provides valuable guidelines for Canadian investors and regulatory agencies.