New strategies:
#447 – Logistic Regression and Momentum-Based Trading Strategy
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: ETFs, futures, CFDs
Complexity: Very complex strategy
Backtest period: 1964 – 2018
Indicative performance: 8.60%
Estimated volatility: 14.00%
Source paper:
Beaudan, Patrick and He, Shuoyuan: Applying Machine Learning to Trading Strategies: Using Logistic Regression to Build Momentum-Based Trading Strategies
https://ssrn.com/abstract=3325656
Abstract:
This paper proposes a machine learning approach to building investment strategies that addresses several drawbacks of a classic approach. To demonstrate our approach, we use a logistic regression algorithm to build a time-series dual momentum trading strategy on the S&P 500 Index. Our algorithm outperforms both buy-and-hold and several base-case dual momentum strategies, significantly increasing returns and reducing risk. Applying the algorithm to other U.S. and international large capitalization equity indices generally yields improvements in risk-adjusted performance.
#448 – Short Selling the Issuer’s Stock in the Convertible Bond Arbitrage
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Backtest period: 1997-2016
Indicative performance: 2.25%
Estimated volatility: 4.47%
Source paper:
Nozari, Milad and Pascutti, Michael and Tookes, Heather: Profitable Price Impact: The Case of Convertible Bond Arbitrage
https://ssrn.com/abstract=3426914
Abstract:
We investigate a potential source of profit to convertible bond arbitrageurs that is new to the literature: anticipatory hedging in advance of convertible bond issues. When the reference stock price in a convertible bond contract is determined after a new issue is announced, anticipatory short selling in the underlying stock can result in a “profitable price impact” (PPI). Downward stock price pressure prior to pricing creates an abnormally cheap embedded call option in the bond. Consistent with a PPI, we document significant declines in the stock prices of issuers on bond pricing days. These declines are more concentrated during the last hour of the pricing day and are followed by partial adjustments in the days following pricing.
New research papers related to existing strategies:
#174 – Institutional Ownership Effect
Bulsiewicz: Institutional Ownership Decomposition and Stock Market Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3439729
Abstract:
Evidence presented in Dasgupta et al. (2011) indicates that financial institutions can be net buyers or sellers of a stock over consecutive quarters, implying the existence of trends in a stock’s institutional ownership. I investigate the relation between institutional ownership and returns after controlling for trends by decomposing a stock’s institutional ownership into level, slope, and residual components. I find that the level, slope, and residual components have, respectively, positive but relatively weak, strongly positive, and strongly negative relations with returns. These relations are strongest within arbitrage-constrained stocks and are the result of financial institutions investing and actively managing their equity positions based on differences and changes in underlying firm fundamentals.
#177 – Term Structure of CDS Predicts Equity Returns
#319 – Combined Stock and CDS Momentum
Kakushadze: Healthy… Distress… Default
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3444620
Abstract:
We discuss a simple, exactly solvable model of stochastic stock dynamics that incorporates regime switching between healthy and distressed regimes. Using this model, which is analytically tractable, we discuss a way of extracting expected returns for stocks from realized CDS spreads, essentially, the CDS market sentiment about future stock returns. This alpha/signal could be useful in a cross-sectional (statistical arbitrage) context for equities trading.
And two short free blog posts about interesting related research papers have been published during last 2 weeks:
The United States has a special place in a global financial system. The U.S. dollar is the world’s reserve currency, and U.S. Treasuries are used as primary safe assets. Therefore, it is no surprise that the U.S. has some benefits from this arrangement. Academic research paper written by Krishnamurthy & Lustig shows that the U.S. derives a “convenience yield” from a demand of foreign investors. They consequently incur lower returns on their holdings of dollar-denominated safe assets. The FED’s conventional and unconventional monetary policy actions directly impact the supply of dollar-denominated safe assets. These decisions also affect the size of convenience yield, which causes moves in global financial markets…
Krishnamurthy, Lustig: Mind the Gap in Sovereign Debt Markets: The U.S. Treasury basis and the Dollar Risk Factor
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3443231
Abstract:
The U.S. dollar exchange rate clears the global market for dollar-denominated safe assets. We find that shifts in the demand and supply of safe dollar assets are important drivers of variation in the dollar exchange rate, bond yields, and other global financial variables. An increase in the convenience yield that foreign investors derive from holding safe dollar assets causes the dollar to appreciate, and incentivizes foreign debtors to tilt their issuance towards dollar-denominated instruments. U.S. monetary policy also affects the dollar exchange rate through its impact on the supply of safe dollar assets and the convenience yield. Interest rate spreads with foreign countries are not sufficient statistics to gauge the impact of the stance of U.S. monetary policy on currency markets. The U.S. Treasury basis, which measures the yield on an actual U.S. Treasury minus the yield on an equivalent synthetic U.S. Treasury constructed from a foreign bond, provides a direct measure of the global scarcity of dollar safe assets.
Sun Tzu once wrote (paraphrasing) “know yourself, know your enemy, and you shall win a hundred battles without loss”. This proverb is true also in financial markets as it is always easier to prepare trading/investment strategy when you know who are other market participants and what their intentions probably are. A new academic research paper written by Robe & Roberts gives a more in-depth insight into the CFTC’s weekly “Commitments of Traders” report. The COT’s report offers a small number of trader groupings; therefore, its usefulness is very limited. However, Robe & Roberts use trader-level data that originate from the CFTC’s Large Trader Reporting System (LTRS), which allows them to create a very detailed look at the composition of agricultural futures market …
Robe, Roberts: Who Holds Positions in Agricultural Futures Markets
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3438627
Abstract:
We use non-public data regarding all trader-level futures positions, reported to the U.S. grain and oilseed derivatives market regulator (the CFTC), in order to describe the nature of market participants, the maturity structure of their holdings, and the aggregate position patterns for nine different categories of traders that we separate based on their main lines of business. We provide novel evidence about the overall extent of calendar spreading and about the contribution of commercial traders to total spreading activity.
Our sample’s 3,854 traders account for 86 to 93 percent of the total futures open interest at the end of an average day in 2015–2018. Well over 90 percent of their positions have maturities of less than a year. Among our nine trader categories, just three (hedge funds and commercial dealers/merchants, plus commodity index traders on the long side) account for about four fifths of all reported trader positions. In fact, fewer than 200 “permanent” large traders (overwhelmingly from these three categories) make up the bulk of the daily open interest in the four largest agricultural futures markets.
In the aggregate, the positions of commercial dealers and hedge funds (including commodity pool operators, commodity trading advisors, managed money traders, and associated persons) are highly negatively correlated. This correlation is strikingly strong for short positions: as a result, the sum total of commercial dealers’ and hedge funds’ respective shares of the short open interest fluctuates relatively little over time.
We show, for the first time, that calendar spreads account for more than a third of all large trader positions; that much of the intra-year variation in the total futures open interest can be tied to changes in the extent of calendar spreading; that about half of all spread positions involve contracts expiring in 4 to 12 months (either spreading with shorter-dated contracts, or involving only maturities of 4 to 12 months); and that commercial traders who are not swap dealers (dealers and merchants, mostly) make up from a quarter to two fifths of all calendar spread positions. Again, commercial dealers’ and hedge funds’ shares of the spread open interest are negatively correlated. None of these patterns can be inferred from public data, as the CFTC’s Commitments of Traders Reports (COT) do not break out spreads for “traditional” commercial traders in general and commercial dealers and merchants in particular.
Are you looking for more strategies to read about? Sign up for our newsletter or visit our Blog or Screener.
Do you want to learn more about Quantpedia Premium service? Check how Quantpedia works, our mission and Premium pricing offer.
Do you want to learn more about Quantpedia Pro service? Check its description, watch videos, review reporting capabilities and visit our pricing offer.
Are you looking for historical data or backtesting platforms? Check our list of Algo Trading Discounts.
Would you like free access to our services? Then, open an account with Lightspeed and enjoy one year of Quantpedia Premium at no cost.
Or follow us on:
Facebook Group, Facebook Page, Twitter, Linkedin, Medium or Youtube
