New strategies:
#311 – Currency Option Delta-Hedging Strategy
Period of rebalancing: intraday
Markets traded: currencies
Instruments used for trading: options
Complexity: Complex strategy
Bactest period: 2011-2015
Indicative performance: 31.48%
Estimated volatility: 10.00%
Source paper:
Sorokin: Design and Back-Testing of a Systematic Delta-Hedging Strategy in FX Options Space
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2782638
Abstract:
This paper describes design and back-testing of an automated delta-hedging strategy applied to short-dated fx options (specifically – weekly and monthly at-the-money EURUSD straddles). The results indicate that systematic sale of options that are delta-hedged according to the suggested algorithm generates financial gain for the seller with an attractive Sharpe ratio exceeding 3.0 on after-cost basis (back-testing accounts for volatility bid-offer as well as spot market bid-offer). For weekly options Sharpe ratio significantly depends on the day of week on which the algorithm systematically sells options: delta-hedging of options sold on Thursdays results in highest Sharpe ratio; delta-hedging of options sold on Fridays results in second-best Sharpe ratio. The performance of the algorithmic strategy is not correlated with linear changes in spot price which is in line with Black-Scholes theory. The proposed algorithmic strategy has just a few parameters which serves as a natural protection against over-fitting bias. Further fine-tuning of the algorithm requires access to historical data over longer period and/or access to live trading environment.
#312 – Low-Price Effect
Period of rebalancing: monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Bactest period: 1952-2014
Indicative performance: 18.30%
Estimated volatility: 19.24%
Source paper:
Geertsema, Lu: Split-adjusted stock price and the cross-section of US stock returns
http://www.nzfc.ac.nz/archives/2016/papers/updated/57.pdf
Abstract:
The price of a security potentially contains incremental information regarding its (unobservable) required rate of return. We construct a simple theoretical model to derive the expected rate of return conditional on an observed split-adjusted stock price using Bayesian updating. The model suggests that expected return should be negatively related to the logarithm of split-adjusted price in the cross-section. This provides a theoretical explanation of the split-adjusted price anomaly first documented by Brown and Pfeiffer (2007). There is strong empirical evidence linking split-adjusted price with subsequent realised US stock returns. Among more than 100 anomaly hedge portfolios constructed using the same US stock return data and portfolio construction methods, the split-adjusted price decile hedge portfolio generates the highest value-weighted mean return (151 bp/month, t-statistic 7.12) and among the highest time-series alphas under several benchmark models. We argue that as one of the largest and seemingly most robust anomalies yet documented the split-adjusted price anomaly deserves more attention than it has received so far.
New research papers related to existing strategies:
#92 – January Effect Filter and Momentum in Stocks
#93 – January Effect Filter and Mean Reversion in Stocks
Zaremba: The January Seasonality and the Performance of Country-Level Value and Momentum Strategies
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2778055
Abstract:
The study examines the turn-of-the-year effect in the country-level value and momentum strategies. We re-examine eight distinct value and momentum strategies within 78 markets in the 1995‑2015 period and we test their performance for the seasonal patterns. We find that during the last 20 years the value strategies performed particularly well in January and poor in December. On the contrary, the momentum strategies had high returns in December and low in January. These observations are consistent with the explanations of the January seasonality related to the tax loss selling and window dressing effects.
#293 – Momentum Effect in Anomalies v2
Zaremba, Szyszka: Is There Momentum in Equity Anomalies? Evidence from an Emerging Market
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2778045
Abstract:
Emerging markets are thought to be a cornucopia of equity anomalies. Yet while markets mature, by learning investors raise the level of market efficiency diminishing the profitability of the existing patterns. Taking the Polish stock market as an example, we offer a viable solution to this tendency ― an active asset allocation strategy based on the momentum effect. First, we identify and replicate 100 anomalies in the cross-section of returns. Then, having documented the momentum in their performance, we translate it into a profitable strategy. Going long (short) on the anomalies which performed best (worst) in the past produces significant raw and risk-adjusted returns outperforming a naive benchmark of equal weights of all profitable anomalies. The results are robust to various considerations.
Three additional related research papers have been included into existing free strategy reviews during last 2 week:
On a lighter note:
Hartley, Olson: Jim Cramer's ‘Mad Money’ Charitable Trust Performance and Factor Attribution
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2778724
Abstract:
This study analyzes the complete historical performance of Jim Cramer’s Action Alerts PLUS portfolio from 2001 to 2016 which includes many of the stock recommendations made on Cramer’s TV show “Mad Money”. Both since inception of the portfolio and since the start of “Mad Money” in 2005 (when it was converted into a charitable trust), Cramer’s portfolio has underperformed the S&P 500 total return index and a basket of S&P 500 stocks that does not reinvest dividends (both on an overall returns basis and in Sharpe ratio). These findings contrast with previous studies which analyzed Cramer’s outperformance in short windows before the 2008 financial crisis. Using factor analysis, we find that Cramer’s portfolio returns are primarily driven by underlevered exposure to market returns and in some specifications tilting toward small cap stocks, growth stocks and stocks with low quality of earnings. These results have broad implications for market efficiency, the usefulness of single name stock recommendations made on television, financial education, and the implementation of academic factors thematic in Cramer’s portfolio.
Related to CTA/trendfollowing strategies:
Bouchaud, Dao, Deremble, Lemperiere, Nguyen, Potters: Tail Protection for Long Investors: Convexity at Work
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2777657
Abstract:
We relate the performance of trend following strategy to the difference between a long-term and a short-term variance. We show that this result is rather general, and holds for various definitions of the trend. We use this result to explain the positive convexity property of CTA performance and show that it is a much stronger effect than initially thought. This result also enable us to highlight interesting connections with Risk Parity portfolio. Finally, we propose a new portfolio of options that gives us a pure exposure to the variance of the underlying, shedding some light on the link between trend and volatility, and also helping us understanding the exact role of hedging.
Related to #118 – Time Series Momentum Effect:
Ferreira, Silva, Yen: Information ratio analysis of momentum strategies
http://arxiv.org/abs/1402.3030
Abstract:
In the past 20 years, momentum or trend following strategies have become an established part of the investor toolbox. We introduce a new way of analyzing momentum strategies by looking at the information ratio (IR, average return divided by standard deviation). We calculate the theoretical IR of a momentum strategy, and show that if momentum is mainly due to the positive autocorrelation in returns, IR as a function of the portfolio formation period (look-back) is very different from momentum due to the drift (average return). The IR shows that for look-back periods of a few months, the investor is more likely to tap into autocorrelation. However, for look-back periods closer to 1 year, the investor is more likely to tap into the drift. We compare the historical data to the theoretical IR by constructing stationary periods. The empirical study finds that there are periods/regimes where the autocorrelation is more important than the drift in explaining the IR (particularly pre-1975) and others where the drift is more important (mostly after 1975). We conclude our study by applying our momentum strategy to 100 plus years of the Dow-Jones Industrial Average. We report damped oscillations on the IR for look-back periods of several years and model such oscilations as a reversal to the mean growth rate.
