New strategies:
#185 – Categorization Effect in Stocks
Period of rebalancing: Weekly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Bactest period: 1997 – 2009
Indicative performance: 21.36%
Estimated volatility: 12.35%
Source paper:
Krueger, Landier, Thesmar: Categorization Bias in the Stock Market
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2034204
Abstract:
This paper provides evidence of categorization bias in financial markets. Some investors perceive individual firms through the lenses of industries. Such categorical thinking generates mispricing and predictability in stock-returns. We measure the difference in returns between a firm's official SIC industry and its underlying fundamentals by constructing a basket of closely related firms, based on Hoberg and Phillips (2010a,b). We find that stocks comove strongly with their official industry in the short-term and then revert toward their basket of Hoberg and Phillips comparables. Long-short strategies exploiting mispricing due to industry categorization generate statistically significant and economically sizable risk-adjusted excess returns. We also provide evidence that financial analysts are biased by industry categorization: When a firm's official industry is less representative of its fundamentals, analysts sometimes tend to put excessive weight on information related to the official industry and thus make predictable forecast errors.
New research papers related to existing strategies:
#5 – FX Carry Trade
Cenedese, Sarno, Tsiakas: Average Variance, Average Correlation and Currency Returns
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2050106
Abstract:
This paper provides an empirical investigation of the time-series predictive ability of average variance and average correlation on the return to carry trades. Using quantile regressions, we find that higher average variance is significantly related to large future carry trade losses, whereas lower average correlation is significantly related to large gains. This is consistent with the carry trade unwinding in times of high volatility and the good performance of the carry trade when asset correlations are low. Finally, a new version of the carry trade that conditions on average variance and average correlation generates considerable performance gains net of transaction costs.
#26 – Value (Book-to-Market) Anomaly
Asness, Frazzini: The Devil in HML's Details
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2054749
Abstract:
This paper challenges the standard method for measuring “value” used in academic work on factor pricing and behavioral finance. The standard method calculates book-to-price (B/P) at portfolio formation using lagged book data, aligns price data using the same lag (ignoring recent price movements), and hold these values constant until the next rebalance. We propose two simple alternatives that use timely price data while retaining the necessary lag for measuring book. We construct portfolios based on the different measures for a US sample (1950-2011) and an International sample (1983-2011). We show that B/P ratios based on timely prices better forecast true (unobservable) B/P ratios at fiscal yearend. Value portfolios based on the most timely measures earn statistically significant alphas ranging between 305 and 378 basis point per year against a 5-factor model itself containing the standard measure of value, as well as market, size, momentum and a short term reversal factor.
Link to valuable blog post related to existing strategy in a database:
#2 – Asset Class Momentum – Rotational System
Butler, Philbrick: Adaptive Asset Allocation: A True Revolution in Portfolio Management
http://gestaltu.blogspot.ca/2012/05/adaptive-asset-allocation-true.html
Abstract:
Modern Portfolio Theory (MTP) has been derided by practitioners, academics, and the media over the past ten years because the dominant application of the theory, Strategic Asset Allocation, has delivered poor performance and high volatility since the millennial technology crash. Strategic Asset Allocation probably deserves the negative press it receives, but the mathematical identity described by Markowitz in his 1967 paper is axiomatic in the same way Pythagoras' equations describe the properties of right triangles, or Schrodinger's equations describe the positional probabilities of electrons. The problem with Strategic Asset Allocation is not the math of MPT – the problem is with the assumption that the best estimates for returns, volatility and correlations are the long-term averages.
