New strategies:
#199 – ROA Effect within Stocks
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Bactest period: 1972 – 2006
Indicative performance: 12.15%
Estimated volatility: 13.36%
Source paper:
Chen, Zhang: A Better Three-Factor Model That Explains More Anomalies
http://faculty.chicagobooth.edu/john.cochrane/teaching/Empirical_Asset_Pricing/Chen_Zhang_JF.pdf
Abstract:
The market factor, an investment factor, and a return-on-assets factor summarize the cross-sectional variation of expected stock returns. The new three-factor model substantially outperforms traditional asset pricing models in explaining anomalies associated with short-term prior returns, financial distress, net stock issues, asset growth, earnings suprises, and valuation ratios. The model's performance, cobined with its economic intuition based on q-theory, suggests that it can be used to obtain expected return estimation in practice.
#200 – Seasonality of Gold
Period of rebalancing: Monthly
Markets traded: commodities
Instruments used for trading: futures, CFDs, ETFs
Complexity: Simple strategy
Bactest period: 1981 – 2010
Indicative performance: 7.16%
Estimated volatility: not stated
Source paper:
Baur: The Seasonality of Gold – The Autumn Effect
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1989593
Abstract:
This paper studies recurring annual events potentially introducing seasonality into gold prices. We analyze gold returns for each month from 1980 to 2010 and find that September and November are the only months with positive and statistically significant gold price changes. This “autumn effect” holds unconditionally and conditional on several risk factors. We argue that the anomaly can be explained with hedging demand by investors in anticipation of the “Halloween effect” in the stock market, wedding season gold jewelery demand in India and negative investor sentiment due to shorter daylight time. The autumn effect can also be characterized by a higher unconditional and conditional volatility than in other seasons.
New research papers related to existing strategies:
#54 – Momentum and State of Market (Sentiment) Filters
Daniel, Jagannathan, Kim: Tail Risk in Momentum Strategy Returns
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2076622
Abstract:
Price momentum strategies have historically generated high positive returns with little systematic risk. However, these strategies also experience infrequent but severe losses. During 13 of the 978 months in our 1929-2010 sample, losses to a US-equity momentum strategy exceed 20 percent per month. We demonstrate that a hidden Markov model in which the market moves between latent "turbulent'' and "calm'' states in a systematic stochastic manner captures these high-loss episodes. The turbulent state is infrequent in our sample: the probability that the hidden state is turbulent is greater than one-half in only 20% of the months in our sample. Yet in each of the 13 severe loss months, the ex-ante probability that the hidden state is turbulent exceeds 70 percent. This strong forecastability accentuates the price momentum puzzle.
