New strategies:
#393 – Oil Surprise Factor in Equities
Period of rebalancing: quarterly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Bactest period: 1974-2015
Indicative performance: 6.80%
Estimated volatility: 13.50%
Source paper:
Moore, Jordan and Velikov, Mihail: Oil Price Exposure, Earnings Announcements, and Stock Return Predictability
https://ssrn.com/abstract=3164353
Abstract:
The profits of individual firms have varying degrees of exposure to oil prices. We provide evidence that investors with limited attention are surprised about how current oil price changes predict future earnings announcements. A trading strategy that exploits this inefficiency is profitable and is not explained by common risk factor exposure. Stock prices respond to lagged oil price changes when firms start to announce earnings in the first month of each quarter. Our measure of oil earnings surprise predicts abnormal returns in the earnings announcement window, especially for firms announcing early in the quarter and for quarters following large oil price changes. The return predictability of the oil earnings surprise strategy is not related to other anomalies and is robust to trading costs.
#394 – Curvature Factor in Currencies
Period of rebalancing: monthly
Markets traded: currencies
Instruments used for trading: futures, forwards, swaps, CFDs
Complexity: Very complex strategy
Bactest period: 1974 – 2015
Indicative performance: 2.64%
Estimated volatility: 6.70%
Source paper:
Ferdinand Dreher, Johannes Gräb and Thomas Kostka: From Carry Trades to Curvy Trades
https://ssrn.com/abstract=3172710
Abstract:
Traditional carry trade strategies are based on differences in short-term interest rates, neglecting any other information embedded in yield curves. We derive return distributions of carry trade portfolios among G10 currencies, where the signals to buy and sell currencies are based on summary measures of the yield curve, the Nelson-Siegel factors. We nd that a strategy based on the relative curvature factor, the curvy trade, yields higher Sharpe ratios and a smaller return skewness than traditional carry trade strategies. Curvy trades build less upon the typical carry currencies, like the Japanese yen and the Swiss franc, and are hence less susceptible to crash risk. In line with that, standard pricing factors of traditional carry trade returns, such as exchange rate volatility, fail to explain curvy trade returns in a linear asset pricing framework. Our fi ndings are in line with recent interpretations of the curvature factor. A relatively high curvature signals a relatively higher path of future short-term rates over the medium-term putting upward pressure on the currency.
New research paper related to existing strategies:
#326 – Volatility Investing Across Asset Classes
Nielsen: Systematic Currency Volatility Risk Premia
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3171795
Abstract:
I show that volatility risk of the dollar factor — an equally weighted basket of developed U.S. dollar exchange rates — carries a significant risk premium and that it is priced in the cross-section of currency volatility excess returns. The dollar factor volatility risk premium is negative on average with an upward sloping and concave term structure. Consistent with this pattern, I find that dollar factor volatility risk is most significantly priced in the cross-section of volatility excess returns at shorter maturities. A trading strategy that sells (buys) volatility insurance on currencies with high (low) exposure to dollar factor volatility risk delivers high mean excess returns and Sharpe ratios. At shorter maturities, the profitability of this strategy cannot be explained by exposure to traditional currency factors, equity factors, or currency volatility carry factors.
And three additional related research papers have been included into existing free strategy reviews during last 2 weeks:
A very interesting research paper we recommend to read to all cryptocurrency traders and investors:
Benedetti, Kostovetsky: Digital Tulips? Returns to Investors in Initial Coin Offerings
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3182169
Abstract:
Initial coin offerings (ICOs), sales of cryptocurrency tokens to the general public, have recently been used as a source of crowdfunding for startups in the technology and blockchain industries. We create a dataset on 4,003 executed and planned ICOs, which raised a total of $12 billion in capital, nearly all since January 2017. We find evidence of significant ICO underpricing, with average returns of 179% from the ICO price to the first day’s opening market price, over a holding period that averages just 16 days. Even after imputing returns of -100% to ICOs that don’t list their tokens within 60 days and adjusting for the returns of the asset class, the representative ICO investor earns 82%. After trading begins, tokens continue to appreciate in price, generating average buy-and-hold abnormal returns of 48% in the first 30 trading days. We also study the determinants of ICO underpricing and relate cryptocurrency prices to Twitter followers and activity. While our results could be an indication of bubbles, they are also consistent with high compensation for risk for investing in unproven pre-revenue platforms through unregulated offerings.
Related to all trading strategies:
de Prado, Lewis: What is the Optimal Significance Level for Investment Strategies?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3193697
Abstract:
Most papers in the financial literature estimate the p-value associated with an investment strategy, without reporting the power of the test used to make that discovery. This is a mistake, because a particularly low false positive rate (Type I error) may be achieved at the expense of missing a large proportion of the investment opportunities (Type II error). In this paper we provide analytic estimates to Type I and Type II errors in the context of investments, and derive the familywise significance level that optimizes the performance of hypothesis tests under general assumptions. Contrary to long-held beliefs, we conclude that a familywise significance level below 15% is suboptimal (excessively conservative) in the context of most investment strategies.
#76 – Selecting Best Equity Fund Managers
#85 – Momentum in Mutual Fund Returns
Friesen, Nguyen: The Economic Impact of Mutual Fund Investor Behaviors
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3160271
Abstract:
This study analyzes how the determinants of mutual fund investor cash flows have changed over time, and the associated impact on investor returns. Using data from 1992-2016 we find that investor return-chasing behavior essentially disappeared starting in 2011. Investor flows have become more sensitive to expenses, past risk and alpha. Investors are paying more attention to fund characteristics that matter (e.g. risk, alpha and expenses), and less attention to characteristics that don’t (e.g. past returns). Nevertheless, the average investor dollar-weighted return is about 1.2% below the average buy-and-hold return in their underlying mutual fund nearly every year in our sample, suggesting consistently poor timing ability over the entire period. We decompose the economic impact of investor behaviors on investor returns and find that investors’ focus on alpha is actually more detrimental than their previous focus on past returns. Investors do benefit from choosing high-alpha funds (smart money), but poorly time their cash flows by investing in those funds after periods with the highest realized alphas (dumb money). The dumb money effect dominates the smart money effect for the simple reason that at the fund level, past alphas are strongly and negatively correlated with future alphas. Although past alphas are positively correlated to future alphas in the pooled cross-section of mutual fund data, this result does not hold at the individual fund level, which is the level where most mutual fund customers invest. Overall, our results suggest that mutual fund investors know that alpha is important, but have not yet learned how to effectively integrate this knowledge into their investment decisions.
