New strategies:
#188 – Short-term Adaptive Reversal in S&P 500 Index
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: ETFs, CFDs, futures
Complexity: Moderately complex strategy
Bactest period: 2006 – 2012
Indicative performance: 19.90%
Estimated volatility: not stated
Source paper:
Bandy: Developing Robust Trading Systems, with Implications for Position Sizing and System Health
http://www.naaim.org/wp-content/uploads/2012/2012/00H_NAAIM_Assessing_Trading_System_Health_Bandy.pdf
Abstract:
The Goal:
As traders and investors, particularly as active investors, it is important to have a high level of confidence that:
– The trading system being used is robust.
– The trading system is healthy.
– Trades are being taken in the correct size to produce the fastest equity growth while keeping drawdown below an acceptable percentage.
– There is a plan for dealing with drawdowns.
This paper describes unique and practical techniques for gaining that confidence, and illustrates with a fully analyzed trading system.
#189 – Timing VIX ETNs
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: ETFs
Complexity: Moderately complex strategy
Bactest period: 2004 – 2011
Indicative performance: 30.80%
Estimated volatility: 23.49%
Source paper:
Alexander, Korovilas: Understanding ETNs on VIX Futures
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2043061
Abstract:
This paper aims to improve transparency in the market for direct, leveraged and inverse exchange-traded notes (ETNs) on VIX futures. The first VIX futures ETNs were issued in 2009. Now there are about 30 of them, with a market cap of about $3 billion and trading volume on some of these products can reach $5 billion per day. Yet volatility trading is highly complex and regulators are rightly concerned that many market participants lack sufficient understanding of the risks they are taking. We recommend that exchanges, market-makers, issuers and potential investors, as well as regulators, read this paper to improve their understanding of these ETNs.
New research papers related to existing strategies:
#6 – Volatility Effect in Stocks – Long-Short Version
#7 – Volatility Effect in Stocks – Long-Only Version
Baker, Haugen: Low Risk Stocks Outperform within All Observable Markets of the World
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2055431
Abstract:
This article provides global evidence supporting the Low Volatility Anomaly: that low risk stocks consistently provide higher returns than high risk stocks. This study covers 33 different markets during the time period from 1990-2011. (Two previous studies by Haugen & Heins (1972) and Haugen & Baker (1991) show the same negative payoff to risk in time periods 1926-1970 and 1970-1990.) The procedure for our study is intentionally simple, transparent and easily replicable. Our samples include non-survivors. We look at an international universe of stocks beginning with the first month of 1990 until December 2011; we compute the volatility of total return for each company in each country over the previous 24 months. Stocks in each country are ranked by volatility and formed into deciles. In the total universe and in each individual country low risk stocks outperform, the relationship with respect to Sharpe ratios is even more impressive. We believe this anomaly is caused primarily by agency issues, namely the compensation structures and internal stock selection processes at asset management firms which lead institutional investors on average to hold more volatile stocks. The article also addresses the implications for how corporate finance managers make capital investment decision in light of this evidence. The evidence presented here dethrones both CAPM and the Efficient Market Hypothesis.
#119 – Google Search Effect
Herzog: Asset Prices and Google's Search Data
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2045766
Abstract:
This paper investigates the relationship of asset price determination via Google data and trading volume. We use weekly data from 2004 to 2010 for 30 international banks. Our study is the first which differentiate between Google’s search volume and Google’s search clicks. We find that asset prices are positively related to the growth rate of Google’s search, trading volume and the level of Google search clicks. Secondly, we find that the absolute level of Google’s search volume and Google’s search clicks behave differently regarding asset price dynamics. Google’s search volume, which measures long-run searches, is negatively related to asset prices and Google’s search click is positively related. We conclude that Google’s data contain important information for the identification of asset bubbles.
