New strategies:
#214 – Time Series Momentum Combined with Volatility Filters in Futures Markets
Period of rebalancing: Daily
Markets traded: equities, bonds, commodities, currencies
Instruments used for trading: futures, CFDs
Complexity: Moderately complex strategy
Bactest period: 1999 – 2004
Indicative performance: 5.47%
Estimated volatility: 5.51%
Source paper:
Dunis, Miao: Volatility Filters for Asset Management: An Application to Managed Futures
http://www.livjm.ac.uk/afe/afe_docs/art0105.pdf
Abstract:
Technical trading rules are known to perform poorly in periods when volatility is high. The objective of this paper is to study whether addition of volatility filters can improve model performance. Different from previous studies on technical trading rules which base their findings from an academic perspective, this paper tries to relate to the real world business: two portfolios, which are highly correlated with a managed futures index and a currency traders’ benchmark index are formed to replicate the performance of the typical managed futures and managed currency funds. The volatility filters proposed are then applied directly to these two portfolios with the hope that proposed techniques will then have both academic and industrial significance. Two volatility filters are proposed, namely a “no-trade” filter where all market positions are closed in volatile periods, and a “reverse” filter where signals from a simple Moving Average Convergence and Divergence (MACD) are reversed if market volatility is higher than a given threshold. To assess the consistency of model performance, the whole period (04/01/1999 to 31/12/2004) is split into 3 sub-periods. Our results show that the addition of the two volatility filters adds value to the models performance in terms of annualised return, maximum drawdown, risk-adjusted Sharpe ratio and Calmar ratio in all the 3 sub-periods.
#215 – Time Series Momentum Combined with Volatility Filters in FOREX
Period of rebalancing: Daily
Markets traded: currencies
Instruments used for trading: futures, CFDs
Complexity: Moderately complex strategy
Bactest period: 1999 – 2004
Indicative performance: 4.63%
Estimated volatility: 5.00%
Source paper:
Dunis, Miao: Volatility Filters for Asset Management: An Application to Managed Futures
http://www.livjm.ac.uk/afe/afe_docs/art0105.pdf
Abstract:
Technical trading rules are known to perform poorly in periods when volatility is high. The objective of this paper is to study whether addition of volatility filters can improve model performance. Different from previous studies on technical trading rules which base their findings from an academic perspective, this paper tries to relate to the real world business: two portfolios, which are highly correlated with a managed futures index and a currency traders’ benchmark index are formed to replicate the performance of the typical managed futures and managed currency funds. The volatility filters proposed are then applied directly to these two portfolios with the hope that proposed techniques will then have both academic and industrial significance. Two volatility filters are proposed, namely a “no-trade” filter where all market positions are closed in volatile periods, and a “reverse” filter where signals from a simple Moving Average Convergence and Divergence (MACD) are reversed if market volatility is higher than a given threshold. To assess the consistency of model performance, the whole period (04/01/1999 to 31/12/2004) is split into 3 sub-periods. Our results show that the addition of the two volatility filters adds value to the models performance in terms of annualised return, maximum drawdown, risk-adjusted Sharpe ratio and Calmar ratio in all the 3 sub-periods.
New research papers related to existing strategies:
#137 – Trendfollowing in Futures Markets
Fong, Tai: The Application of Trend Following Strategies in Stock Market Trading
http://www.fst.umac.mo/en/staff/documents/fstccf/simonfong_2009_idc_trend_following.pdf
Abstract:
Trend-following (TF) strategies use fixed trading mechanism in order to take advantages from the long-term market moves without regards to the past price performance. In contrast with most prediction tools that stemmed from softcomputing such as neural networks to predict a future trend, TF just rides on the current trend pattern to decide on buying or selling. While TF is widely applied in currency markets with a good track record for major currency pairs [1], it is doubtful that if TF can be applied in stock market. In this paper a new TF model that features both strategies of evaluating the trend by static and adaptive rules, is created from simulations and later verified on Hong Kong Hang Seng future indices. The model assesses trend profitability from the statistical features of the return distribution of the asset under consideration. The results and examples facilitate some insights on the merits of using the trend following model.
Clare, Seaton, Thomas: BREAKING INTO THE BLACKBOX: Trend Following, Stop Losses, and the Frequency of Trading: the case of the S&P500
www.cass.city.ac.uk/__data/assets/pdf_file/0020/124355/Stop-Losses-v8.pdf
Abstract:
In this paper we compare a variety of technical trading rules in the context of investing in the S&P500 index. These rules are increasingly popular both among retail investors and CTAs and similar investment funds. We find that a range of fairly simple rules, including the popular 200-day moving average trading rule, dominate the long only, passive investment in the index. In particular, using the latter rule we find that popular stop loss rules do not add value and that monthly end of month investment decision rules are superior to those which trade more frequently: this adds to the growing view that trading can damage your wealth. Finally we compare the MA rule with a variety of simple fundamental metrics and find the latter far inferior to the technical rules over the last 60 years of investing.
