New strategies:
#481 – Holding Artificial VIX in a Portfolio
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: futures, CFDs, ETFs, options
Complexity: Complex strategy
Backtest period: 1996-2007
Indicative performance: 10.10%
Estimated volatility: 15.20%
Source paper:
James Doran: Volatility as an Asset Class: Holding VIX in a Portfolio
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3514141
Abstract:
Hedging market downturns without sacrificing upside has long been sought by investors. If VIX was directly investable, adding it as a hedge to the S&P 500 would result in significantly improved performance over the equity only portfolio. However, tradable VIX products do not provide the hedge or returns investors seek over long-term horizons. Alternatively, deconstructing VIX to find the key S&P 500 options which drive VIX movements leads to a synthetic VIX portfolio that provides a more effective hedge. Using these options captures correlations and returns similar to VIX, and combined with the S&P 500, outperforms the buy-and-hold index portfolio.
#482 – Inteligent Currency Multistrategy
Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: futures, CFDs
Complexity: Complex strategy
Backtest period: 1999-2019
Indicative performance: 3.96%
Estimated volatility: 3.46%
Source paper:
Middleton, Amy: Accessing Currency Returns Through Intelligence Currency Factors
https://ssrn.com/abstract=3522680
Abstract:
This paper presents a methodology for the construction of three “intelligent” currency beta factors based around the popular trading styles of carry, value, and trend/momentum together with a multi-style factor combining all three. The methodology is termed “intelligent” because we demonstrate how, in the case of the carry factor, applying a binary filter to determine risk environment and adjusting trade sizes in periods of risk aversion can lead to improved drawdown and enhanced performance statistics versus more naïve carry factors. In addition, for all three single-style factors we demonstrate how establishing a relationship between the resulting trade weight per currency and the magnitude of the underlying trade signal’s information coefficient can enhance performance versus other currency beta factors that apply an equal trading weight per currency regardless of the strength of signal.
#483 – Forecasting Index Changes in the German DAX Family
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2010-2019
Indicative performance: 5.61%
Estimated volatility: 6.76%
Source paper:
Friedrich‑Carl Franz: Forecasting index changes in the German DAX family
https://link.springer.com/content/pdf/10.1057/s41260-020-00153-6.pdf
Abstract:
Combining market data with a publicly available monthly snapshot of Deutsche Börse’s index ranking list, I create a model that predicts index changes in the DAX, MDAX, SDAX, and TecDAX from 2010 to 2019 before they are officially announced. Even though I empirically show that index changes are predictable, they still earn sizeable post-announcement 1-day abnormal returns up to 1.42% and − 1.54% for promotions and demotions, respectively. While abnormal returns are larger in smaller stocks, I find no evidence that they are related to funding constraints or additional risk for trading on wrong predictions. A trading strategy that trades according to my model yields an annualized Sharpe ratio of 0.83 while being invested for just 4 days a year.
#484 – The Serial Dependence of the Commodity Futures Returns
Period of rebalancing: Monthly
Markets traded: commodities
Instruments used for trading: futures, CFDs
Complexity: Very complex strategy
Backtest period: 2009-2019
Indicative performance: 15.15%
Estimated volatility: 16.24%
Source paper:
Han, Yufeng and Kong, Lingfei: The Serial Dependence of the Commodity Futures Returns: A Machine Learning Approach
https://ssrn.com/abstract=3536046
Abstract:
This paper uses machine learning tools to study the serial dependence (lead-lag relations) of commodity futures returns during the post financialization period (January 2004 – December 2019). We use LASSO (Least Absolute Shrinkage and Selection Operator) to select the predictors as the number of predictors is large relative to the number of observations. We find significant full-sample and out-of-sample predictability. In the full sample, we find that LASSO can identify a sparse set of predictors that come from economically linked commodities or are likely driven by excessive speculative trading. The out-of-sample forecasts based on the LASSO generate statistically and economically large gains. When we separate the indexed futures from the non-indexed futures and replicate the above analysis, we find that the out-of-sample performance exists in the indexed futures but disappears in the non-indexed futures. The lead-lag relations are also more significant after the advent of ETF or ETNs that track the broad futures indices such as S&P GSCI and BCOM indices, indicating that index trading due to financialization drives the excessive comovement among the commodity futures. Overall, we find that serial dependence generates significant predictability during the sample period when the performance of the long-only commodity index futures is poor.
#485 – Toxical Releases and Stock´s Performance
Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1991-2016
Indicative performance: 4.51%
Estimated volatility: 10.50%
Source paper:
Taehyun Kim: Capitalizing on Environmental Sustainability: The Value of Going Green
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3310643
Abstract:
We investigate how Corporate Environmental Responsibility (CER) actions affect firm value. First, we adopt an event studies approach using two 5-to-4 Supreme Court rulings and show that firms gain value when they are expected to increase their CER activities. Second, we examine the cross-sectional returns by forming portfolios based on firms’ engagement in the CER activities. A portfolio of firms that enhanced their environmental sustainability by adopting cleaner production practices earns alphas (4.43% annually for value-weighted returns). Firms that reduced toxic chemical emissions show positive earnings surprises, higher revenue and profitability, and greater capital inflow from institutional investors with longer horizons. These return patterns we find in the event studies and portfolio approaches are more pronounced among firms in regions with high levels of trust. In sum, we present empirical evidence consistent with the market viewing CER as leading to higher firm value.
New research papers related to existing strategies:
#475 – Offshore Sales Networks
Bai, John (Jianqiu) and Garg, Priya and Wan, Chi: Offshore Sales Networks and Stock Return Predictability
https://ssrn.com/abstract=3455426
Abstract:
Based on 10-K textual analysis, we assemble firm-level offshore sales networks (OSN) and find strong return predictability among industry participants that have overlapping offshore sales activities. This intra-industry return predictability based on offshore sales networks is distinct from that along several previously documented economic linkages (e.g., industry momentum, technological links, and standalone vs conglomerate firms). A long-short strategy that exploits the similarity of offshore sales networks yields a monthly alpha of 1.1 percentage points. Moreover, we find that the effect is stronger for firms that receive low investor attention, issue hard-to-read 10-Ks, and pose high arbitrage costs. Our results highlight important asset pricing implications of the commonality of corporate offshore activities, and are broadly consistent with sluggish price adjustment caused by investors’ inattention to offshore networks.
#13 – Short Term Reversal Effect in Stocks
#14 – Momentum Factor Effect in Stocks
#33 – Post-Earnings Announcement Effect
Correira, Barbosa: Can Post-Earnings Announcement Drift and Momentum Explain Reversal?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3501161
Abstract:
We study the interrelation among the post-earnings announcement drift (PEAD) and momentum short-term anomalies, and the reversal long-term anomaly. Some theories argue that PEAD and momentum are a consequence of underreaction to new information on the market. One theory in particular, suggests that this underreaction occurs because investors are unsure about their interpretations of the impact of new information on the fundamental price, and look for the market consensus interpretation for guidance. This learning process takes time, and until it is complete, prices do not fully incorporate the new information. Furthermore, the more difficult it is to interpret the new information, the more prices underreact to the new information arrival, and the longer it takes for them to fully incorporate this information. On the other hand, there are investors that rely too much on past performance (known as “trend chasers”). These investors, motivated by the short-term performance during the underreaction period, push prices beyond their fundamentals, resulting in an overreaction whose correction generates long-run reversal. And the more difficult it is to interpret new information, the more likely it is that trend chasers create overreaction, leading to stronger reversals in the future. Therefore, uncertainty about the interpretation of new information can potentially link today’s reversal to past momentum and PEAD. Our major goal is to test this hypothesis. For that, we use a multifactor risk-based model and NYSE-AMEX stock prices for the period starting at January 1975 to December 2010. Our main conclusion points for a statistical and economical relation between past PEAD and reversal. The higher the returns of the PEAD zero-investment portfolio two years ago, the higher the returns of the reversal zero-investment portfolio today.
#14 – Momentum Factor Effect in Stocks
Raju, Chandrasekaran: Implementing a Systematic Long-only Momentum Strategy: Evidence From India
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3510433
Abstract:
We show that a monthly-rebalanced, long-only portfolio of top-decile stocks selected from the NIFTY100 using `off-the-shelf’ momentum criteria significantly outperforms the NIFTY100 Index – both in terms of absolute returns (by 10.70% pa) and risk adjusted returns, with a mean turnover of 32.10% per month. We show that momentum persists in the near term but dissipates over time. We demonstrate that our long-only approach has a significant tilt to the momentum factor. We also show that time in the market rather than timing the market is important for momentum investing. The strategy has higher volatility and the occasional momentum crash. The strategy’s out performance survives real-world implementation given the rise of discount brokers in India. In the absence of cheap ETFs to get exposure to momentum, the systematic long-only strategy from the most liquid part of the market using `off-the-shelf’ criteria provides a practical, executable investment methodology that exposes an investor to momentum in the Indian market.
And one interesting free blog post has been published during last 2 weeks:
YTD Performance of Equity Factors
Markets are in turmoil, and there exist very few investors who are unscathed by current global events related to coronavirus pandemic. It’s a good time to revisit how are various groups of algorithmic trading strategies navigating current troubled times. The selected sample for this short article consists of 7 well-known equity factor strategies – size, value, momentum, quality, investment, short-term reversal and low volatility factors.
Our analysis shows that we have two groups of factors: strong winners and bad losers. There is no middle ground. A current bear market is ruthless, equity long-short factor strategies either totally nailed it and had a stellar performance or totally disappointed.
