New strategies:
#307 – Reversal During Earnings-Announcements
Period of rebalancing: daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Bactest period: 1996-2011
Indicative performance: 63.52%
Estimated volatility: 12.29%
Source paper:
So, Wang: News-Driven Return Reversals: Liquidity Provision Ahead of Earnings Announcements
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2275982
Abstract:
This study documents a six-fold increase in short-term return reversals during earnings announcements relative to non-announcement periods. Following prior research, we use reversals as a proxy for expected returns market makers demand for providing liquidity. Our findings highlight significant time-series variation in the magnitude of short-term return reversals and suggest that market makers demand higher expected returns prior to earnings announcements because of increased inventory risks that stem from holding net positions through the release of anticipated earnings news. Collectively, our findings suggest that uncertainty regarding anticipated information events elicits predictable increases in expected returns to liquidity provision and that these increases significantly affect the dynamics and information content of market prices.
#308 – Short-Term Momentum in Currencies
Period of rebalancing: weekly
Markets traded: currencies
Instruments used for trading: futures, forwards, swaps, CFDs
Complexity: Simple strategy
Bactest period: 2005-2011
Indicative performance: 7.10%
Estimated volatility: 8.10%
Source paper:
Raza, Marshall, Visaltanachoti: Is There Momentum or Revesal in Weekly Currency Returns?
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2258253
Abstract:
We investigate whether momentum or reversal is the dominant phenomenon in short horizon (one- to four-week) foreign exchange rate returns. We find, based on a broad sample of 63 emerging and developed market currencies, evidence of momentum rather than reversal. Momentum returns are as large as 9% p.a. The short-term momentum effect appears to be robust. Returns are larger in the earlier sub-period but still exist in the more recent period. The strategies are also profitable in US recessions and expansions, and in up and down currency markets.
New research papers related to existing strategies:
#65 – Enhanced Value Premium
Krauss, Kruger, Beerstecher: The Piotroski F-Score: A fundamental value strategy revisited from an investor's perspective
https://ideas.repec.org/p/zbw/iwqwdp/132015.html
Abstract:
This paper revisits the Piotroski F-score strategy in the U.S. stock universe from an investor's perspective. Primarily, we aim to answer the question, whether the high abnormal returns of more than 20 percent p.a. previously proclaimed by academics (Piotroski, 2000) and practitioners (AAII, 2015) can be feasibly captured by individual or professional investors. As such, our focal point is a pragmatic approach an average investor could opt for as well. We use the software Stock Investor Pro from the American Association of Individual Investors to obtain screenshots of the U.S. stock universe from 2005-2015 on a weekly basis. Next, we devise a long-only and a long-short variant of the Piotroski strategy with monthly or weekly rebalancing frequencies. At first glance, our findings re-confirm the high returns of this fundamental value strategy. Specifically, the monthly (weekly) long-only strategy generates raw returns of 30.93 (65.41) percent p.a. These returns outperform relevant benchmark indices and can only partially be explained by common systematic risk factors. However, consideration of liquidity constraints and an estimate of trading costs in this low liquidity stock universe render both strategies virtually unprofitable. Nevertheless, there may be potential for further research aiming at implementing such a strategy on more liquid investment universes.
#269 – Intraday Currency Seasonality
Krivan: The Elusive Quest for Preserved Quantities in Financial Time Series: Making a Case for Intraday Trading Strategies
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2768923
Abstract:
In the context of the supervised learning problem for time series forecasting, we focus on financial time series and use the currency pair EURUSD to highlight issues that arise when daily data are utilized for one-day forecasts of currency exchange rate moves. In light of our results for forecast horizons of one day or more, we take a closer look at the EURUSD time series data to get a better understanding of typical intraday moves and their magnitude and how their potential can be harnessed for the development of consistently profitable trading strategies. By combining the results of our own numerical studies with published findings from the literature and illuminating them from a practical perspective, we motivate a simple intraday trading strategy for EURUSD that avoids some of the problems associated with longer-term forecasts.
Two additional related research papers have been included into existing free strategy reviews during last 2 week:
A really good academic paper from guys (and girl) behind Quantopian:
Wiecki, Campbell, Lent, Stauth: All that Glitters Is Not Gold: Comparing Backtest and Out-of-Sample Performance on a Large Cohort of Trading Algorithms
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2745220
Abstract:
When automated trading strategies are developed and evaluated using backtests on historical pricing data, there exists a tendency to overfit to the past. Using a unique dataset of 888 algorithmic trading strategies developed and backtested on the Quantopian platform with at least 6 months of out-of-sample performance, we study the prevalence and impact of backtest overfitting. Specifically, we find that commonly reported backtest evaluation metrics like the Sharpe ratio offer little value in predicting out of sample performance (R² < 0.025). In contrast, higher order moments, like volatility and maximum drawdown, as well as portfolio construction features, like hedging, show significant predictive value of relevance to quantitative finance practitioners. Moreover, in line with prior theoretical considerations, we find empirical evidence of overfitting – the more backtesting a quant has done for a strategy, the larger the discrepancy between backtest and out-of-sample performance. Finally, we show that by training non-linear machine learning classifiers on a variety of features that describe backtest behavior, out-of-sample performance can be predicted at a much higher accuracy (R² = 0.17) on hold-out data compared to using linear, univariate features. A portfolio constructed on predictions on hold-out data performed significantly better out-of-sample than one constructed from algorithms with the highest backtest Sharpe ratios.
Cliff Asness (AQR Capital Management) on Factor Timing:
Asness: The Siren Song of Factor Timing
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2763956
Abstract:
Everyone seems to want to time factors. Often the first question after an initial discussion of factors is “ok, what’s the current outlook?” And the common answer, “the same as usual,” is often unsatisfying. There is powerful incentive to oversell timing ability. Factor investing is often done at fees in between active management and cap-weighted indexing and these fees have been falling over time. Factor timing has the potential of reintroducing a type of skill-based “active management” (as timing is generally thought of this way) back into the equation. I think that siren song should be resisted, even if that verdict is disappointing to some. At least when using the simple “value” of the factors themselves, I find such timing strategies to be very weak historically, and some tests of their long-term power to be exaggerated and/or inapplicable.
