New strategies:
#227 – Trend Factor within Stocks
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Bactest period: 1926 – 2010
Indicative performance: 12.59%
Estimated volatility: 12.75%
Source paper:
Han, Zhou: Trend Factor: A New Determinant of Cross-Section Stock Returns
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2182667
Abstract:
In this paper, we propose a trend factor to capture cross-section stock price trends. Stronger trends are likely when firms are experiencing some persistent and fundamental changes. Following traders and investors in practice, we use simple moving averages to measure trends. Like the popular size, book-to-market or momentum factor, our trend factor is a spread portfolio of buying stocks with the highest expected returns as forecasted by trends and selling those with the lowest forecasted expected returns. We find that the trend factor earns a risk-adjusted 3% return per month, more than tripling that of the size, book-to-market and momentum factors. The trend factor has more than five times the Sharpe ratio of the market, and, during the recent financial crisis, it earns 4.47% per month while the momentum factor loses 1.34% per month. The trend factor return is robust to a variety of control variables including size, price, book-to-market, idiosyncratic volatility, liquidity, etc, and is much higher under greater information uncertainty. Moreover, the trend factor explains well the cross-section portfolio returns sorted by short-term reversal and various price ratios (e.g. E/P) as well as industry portfolios, and performs much better than the momentum factor.
New research papers related to existing strategies:
#1 – Asset Class Trend Following
#2 – Asset Class Momentum – Rotational System
Antonacci: Risk Premia Harvesting Through Dual Momentum
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2042750
Abstract:
Momentum is the premier market anomaly. It is nearly universal in its applicability. Rather than focus on momentum applied to particular assets or asset classes, this paper explores momentum with respect to what makes it most effective. We find that both absolute and relative momentum are effective in enhancing return, but that absolute momentum does more to lessen volatility and drawdown. Combining the absolute and relative momentum gives the best results. We also explore a factor highly rewarded by momentum – extreme past returns, i.e., price volatility. We identify high volatility through the risk premiums in foreign/U.S. equities, high yield/credit bonds, equity/mortgage REITs, and gold/Treasury bonds. Using modules of asset pairs as building blocks, we are able to isolate volatility related risk factors and benefit from cross-asset diversification by combining relative and absolute momentum to capture risk premium profits.
#13 – Short Term Reversal in Stocks
#14 – Momentum Effect in Stocks
#25 – Small Capitalization Stocks Premium Anomaly
#26 – Value (Book-to-Market) Anomaly
Frazzini, Israel, Moskowitz: Trading Costs of Asset Pricing Anomalies
http://www.gsb.stanford.edu/sites/default/files/documents/fin_12_12_moskowitz.pdf
Abstract:
Using nearly a trillion dollars of live trading data from a large institutional money manager across 19 developed equity markets over the period 1998 to 2011, we measure the real-world transactions costs and price impact function facing an arbitrageur and apply them to size, value, momentum, and short-term reversal strategies. We find that actual trading costs are less than a tenth as large as, and therefore the potential scale of these strategies is more than an order of magnitude larger than, previous studies suggest. Furthermore, strategies designed to reduce transactions costs can increase net returns and capacity substantially, without incurring significant style drift. Results vary across styles, with value and momentum being more scalable than size, and short-term reversals being the most constrained by trading costs. We conclude that the main anomalies to standard asset pricing models are robust, implementable, and sizeable.
