New strategies:
#376 – Combining Fundamental FSCORE and Equity Short-Term Reversals
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Bactest period: 1984 – 2015
Indicative performance: 12.01%
Estimated volatility: 20.61%
Source paper:
Zhu, Zhaobo and Sun, Licheng and Chen, Min: Noise Trading, Slow Diffusion of Information, and Short-Term Reversals: A Fundamental Analysis Approach
https://ssrn.com/abstract=3097420
Abstract:
Contrary to the conventional wisdom that short-term return reversals are rooted in investors’ overreaction to fundamental news or driven by liquidity-based noninformational shocks alone, we find strong evidence that both noise trading and investor underreaction to fundamental information contribute to the reversal. With help from a comprehensive and nonparametric measure of firms’ fundamental strength, we document that an enhanced short-term reversal strategy that buys past losers with strong fundamentals and sells past winners with weak fundamentals significantly outperforms the unconditional reversal strategy. Our findings are consistent with the predictions of theoretical models where investors underreact to slowly diffusing fundamental information.
#377 – Trading Futures Using Basis Indicator
Period of rebalancing: Monthly
Markets traded: equities, commodities, bonds, currencies
Instruments used for trading: futures, CFDs
Complexity: Very complex strategy
Bactest period: 1975 – 2016
Indicative performance: 3.78%
Estimated volatility: 7.67%
Source paper:
Molyboga, Marat: Predicting Out-of-Sample Returns: Using Basis to Beat the Historical Average
https://ssrn.com/abstract=3068321
Abstract:
This paper introduces an adaptive predictor that pools information across securities in four major asset classes (commodities, equities, fixed income and foreign exchange) while imposing restrictions on the sign and magnitude of coefficients in return forecasts. I demonstrate that the basis between spot and futures contracts predicts future returns across the asset classes. The predictor consistently beats the historical average, producing a median monthly out-of-sample R2, measured over the period between January 1986 and December 2016, of approximately 0.36%, a value that is comparable to those of the best equity premium predictors considered in Campbell and Thompson (2008). A simple long-short strategy based on the new predictor delivers an out-of-sample alpha of 2.5%-4.5% per annum with respect to the asset pricing models considered and produces an out-of-sample Sharpe ratio of almost 0.5, which is particularly striking since the strategy is countercyclical. This performance is robust across sub-periods, market environments, portfolio construction methodologies and transaction costs. A cross-sectional structure analysis reveals that two observable common factors, constructed as equally-weighted indices of the bases of financial assets and commodities, are related to the short-term interest rate and the business cycle, respectively.
New research papers related to existing strategies:
#14 – Momentum Effect in Stocks
Ross, Moskowitz, Israel, Serban: Implementing Momentum: What Have We Learned?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3081165
Abstract:
An abundance of academic evidence and theory exists on the efficacy and intuition behind momentum investing, yet a limited number of studies discuss the feasibility of running momentum portfolios in practice. And no study to date has directly analyzed implementation costs for a live momentum portfolio. As a result, many are still quick to dismiss momentum as difficult or costly to implement because of its high turnover. In this paper, we use seven years of live data to evaluate the implementability of momentum investing. We show that live momentum portfolios are capable of capturing the momentum premium, even after accounting for expenses, estimated trading costs, taxes, and other frictions associated with real-life portfolios.
Three additional related research papers have been included into existing free strategy reviews during last 2 weeks:
Arnott, Kalesnik, Kose, Wu: Can Momentum Investing Be Saved?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3099687
Abstract:
On paper, momentum is one of the most compelling factors: simulated portfolios based on momentum add remarkable value, in most time periods and in most asset classes, all over the world. So, our title may seem unduly provocative. However, live results for mutual funds that take on a momentum factor loading are surprisingly weak. No US-benchmarked mutual fund with “momentum” in its name has cumulatively outperformed its benchmark since inception, net of fees and expenses. Worse, because the standard momentum factor gave up so much ground in the last momentum crash of 2008–2009, it remains underwater in the United States, not only compared to its 2007 peak, but even relative to its 1999 performance peak. This means 18 years with no alpha, before subtracting trading costs and fees!
To be sure, most advocates of momentum investing will disavow the standard model, and will claim they use proprietary momentum strategies with better simulated, and perhaps better live, performance. A handful (especially in the hedge fund community) may be able to point to respectable fund performance, net of trading costs and fees. But a careful review of the competitive landscape reveals that most claims of the merits of momentum investing are not supported by data, particularly not live mutual fund results, net of trading costs and fees.
The three traps for momentum investing are 1) high turnover, in crowded trades, which leads to high trading costs; 2) a careless sell discipline, because momentum’s profits accrue for months, not years, and then reverse course; and 3) repeat winners (and losers), which have been soaring (or tumbling) for so very long they enjoy little or no momentum follow-through. Each of these traps can be avoided. By evading these traps, we can narrow the gap between paper and live results. Yes, momentum can probably be saved, even net of fees and trading costs.
This is the fourth and final article in the Alice in Factorland series.
Related mainly to #7 – Volatility Effect in Stocks – Long-Only Version:
Baker, Wurgler: Do Strict Capital Requirements Raise the Cost of Capital? Bank Regulation and the Low Risk Anomaly
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2967265
Abstract:
Traditional capital structure theory in frictionless and efficient markets predicts that reducing banks’ leverage reduces the risk and cost of equity but does not change the overall weighted average cost of capital (and thus the rates for borrowers). We test these two predictions. We confirm that the equity of better-capitalized banks has lower beta and idiosyncratic risk. However, over the last 40 years, lower risk banks have higher stock returns on a risk-adjusted or even a raw basis, consistent with a stock market anomaly previously documented in other samples. The size of the low risk anomaly within banks suggests that the cost of capital effects of capital requirements is large enough to be relevant to policy discussions. A calibration assuming competitive lending markets suggests that a binding ten percentage-point increase in Tier 1 capital to risk-weighted assets more than doubles banks’ average risk premium over Treasury yields, from 40 to between 100 and 130 basis points per year, and presumably raises rates for borrowers to a similar extent.
Once again, is cryptocurrency market efficient? Or can we find simple trading strategies based on price overreactions? :
Caporale, Plastun: Price Overreactions in the Cryptocurrency Market
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3088472
Abstract:
This paper examines price overreactions in the case of the following cryptocurrencies: BitCoin, LiteCoin, Ripple and Dash. A number of parametric (t-test, ANOVA, regression analysis with dummy variables) and non-parametric (Mann–Whitney U test) tests confirm the presence of price patterns after overreactions: the next-day price changes in both directions are bigger than after “normal” days. A trading robot approach is then used to establish whether these statistical anomalies can be exploited to generate profits. The results suggest that a strategy based on counter-movements after overreactions is not profitable, whilst one based on inertia appears to be profitable but produces outcomes not statistically different from the random ones. Therefore the overreactions detected in the cryptocurrency market do not give rise to exploitable profit opportunities (possibly because of transaction costs) and cannot be seen as evidence against the Efficient Market Hypothesis (EMH).
