New strategies:
#242 – Filtered Short-Term Reversal
Period of rebalancing: daily
Markets traded: equities
Instruments used for trading: ETFs, futures, CFDs
Complexity: Simple strategy
Bactest period: 1990 – 2012
Indicative performance: 9.77%
Estimated volatility: 18.60%
Source paper:
Yang: Filtered Market Statistics and Technical Trading Rules
http://www.naaim.org/wp-content/uploads/2013/00Q_Filtered%20Technical%20Rules%20-%20All%20Named.pdf
Abstract:
In statistical sampling, error-prone outliers are usually treated as candidates to be left out. However, for time series of daily returns of broad equity market indexes, we argue that the central crowd around zero mean should be filtered, such that we can better explore market inefficiencies to improve rules-based market timing results. We demonstrate that the daily return data of some of the most widely followed stock indexes (like S&P 500, Russell 3000, FTSE 100, Euro Stoxx 50, TOPIX , Hang Seng Index, all from 1990 to 2012) around zero are noisy or less directionally in formative, and contributing little to the long term return drift. We propose a filtering threshold for daily returns (gains or losses) at around 20% level of the daily return standard deviation. Within the threshold, daily returns are close to having a uniform distribution or white noise, whereas the statistics of the remaining daily returns (20% to 30% smaller in sample size) largely resemble that of the original full set. Such structural separation of index daily returns is important for end-of-day type of technical trading in practice. The nature of the application is to exclude all filtered (nearly flat) market trading days in the day-counting scheme of any relevant technical analysis trading rules.
New research papers related to existing strategies:
#77 – Beta Factor in Stocks
Asness, Frazzini, Pedersen: Low-Risk Investing Without Industry Bets
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2259244
Abstract:
The strategy of buying safe low-beta stocks while shorting (or underweighting) riskier high-beta stocks has been shown to deliver significant risk-adjusted returns. However, it has been suggested that such “low-risk investing” delivers high returns primarily due to its industry bet, favoring a slowly changing set of stodgy, stable industries and disliking their opposites. We refute this. We show that a betting against beta (BAB) strategy has delivered positive returns both as an industry-neutral bet within each industry and as a pure bet across industries. In fact, the industry-neutral BAB strategy has performed stronger than the BAB strategy that only bets across industries and it has delivered positive returns in each of 49 U.S. industries and in 61 of 70 global industries. Our findings are consistent with the leverage aversion theory for why low beta investing is effective.
#13 – FX Volatility Effect
Della Corte, Ramadorai, Sarno: Volatility Risk Premia and Exchange Rate Predictability
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2233367
Abstract:
We investigate the predictive information content in foreign exchange volatility risk premia for exchange rate returns. The volatility risk premium is the difference between realized volatility and a model-free measure of expected volatility that is derived from currency options, and reflects the cost of insurance against volatility ‡fluctuations in the underlying currency. We find that a portfolio that sells currencies with high insurance costs and buys currencies with low insurance costs generates sizeable out-of-sample returns and Sharpe ratios. These returns are almost entirely obtained via predictability of spot exchange rates rather than interest rate differentials, and these predictable spot returns are far stronger than those from carry trade and momentum strategies. Canonical risk factors cannot price the returns from this strategy, which can be understood, however, in terms of a simple mechanism with time-varying limits to arbitrage.
