Quantpedia Premium Update – 5th October

New strategies:

#786 – Option Trading and Returns versus the 52-Week High

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: options, stocks
Complexity: Complex strategy
Backtest period: 1996-2018
Indicative performance: 8.47%
Estimated volatility: 11.05%

Source paper:

Choy, Siu Kai and Wei, Jason Zhanshun: Option Trading and Returns versus the 52-Week High and Low
https://ssrn.com/abstract=4119040
Abstract:
We show that option traders suffer from the anchoring effect induced by the stock price’s 52-week high or low. Specifically: 1) trading of all options decreases as the stock price approaches its 52- week high or low; 2) the buy-sell imbalance for calls decreases and that for puts increases as the stock price approaches its 52-week high, and the opposite occurs as the stock price approaches its 52-week low; and 3) the subsequent delta-hedged option returns for both calls and puts are higher as the stock price approaches its 52-week extreme.

#787 – Option Trading and Returns versus the 52-Week Low

Source paper:

#788 – Quality at Reasonable Price

Period of rebalancing: Yearly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2004-2021
Indicative performance: 18.60%
Estimated volatility: 6.60%

Source paper:

Pulcini, Fabio: Do Stock Prices Reflect Firms Fundamentals? An Empirical Analysis of a Large Global Sample
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4136728
Abstract:
It is shown, through a theoretical model, how firms fundamentals should influence stock (scaled) prices. A sample of about five thousand stocks from thirty developed countries is studied over a period of seventeen years. The empirical evidence is consistent with the theoretical assumptions made, but the ability of firm fundamentals to explain price variability is limited. Perhaps because of the imperfect ability of equity prices to reflect firms fundamentals, a long-short investment strategy based on price-fundamental misalignment generates particularly positive risk-adjusted performances which are difficult to reconcile with modern portfolio theory.

#789 – Salience Theory and Cryptocurrency Returns

Period of rebalancing: Monthly
Markets traded: cryptocurrencies
Instruments used for trading: cryptocurrencies
Complexity: Complex strategy
Backtest period: 2014-2021
Indicative performance: 280.10%
Estimated volatility: 45.30%

Source paper:

Cai, Charlie Xiaowu and Zhao, Ran: Salience Theory and Cryptocurrency Returns
https://ssrn.com/abstract=3983602
Abstract:
We document that cross-sectional cryptocurrency returns predictably behaviour according to the salience theory of choice under risk. Investors overweight salience outcome (standout from the average of the alternatives). This leads to overpricing (underpricing) the cryptocurrencies with upward (downward) salience returns and generating negative (positive) expected returns in the subsequent period. The salience effect in the cryptocurrency market is over 20 times stronger than those observed in the equity markets. It is different from existing return anomalies documented in the cryptocurrency market and is a strong contender of risk factors that can explain other cross-sectional strategy returns in the cryptocurrency market.

#790 – International Trade Momentum

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2008-2019
Indicative performance: 20.67%
Estimated volatility: 20.05%

Hong, Weiting: Trade Momentum for Alpha
https://ssrn.com/abstract=4150390
Abstract:
I provide new evidence on the value of geographic disclosure to portfolio choices and demonstrate the attractiveness of trade-based indicators in predicting exporter asset returns. Estimating firms’ geographic exposures with citation shares, I design the Trade Momentum Index by leveraging publicly available export volume and trade barrier data. Using a sample of 16,229 firm-year combinations between 2008 and 2019, I find that a simple Trade Momentum Index-based strategy generates a statistically significant annualized alpha of 14.72% at an annualized Sharpe ratio of 1.031. This strategy exhibits robustness as the abnormal returns persist under different weighting methods and selection criteria.

New research papers related to existing strategies:

#464 – Brand Value Asset Pricing Factor

Boustanifar, Hamid and Kang, Young Dae, Is Buffett Right? Brand Values and Long-run Stock Returns
https://ssrn.com/abstract=4146667
Abstract:
An equal-weighted portfolio of Best Brands (BBs) in the U.S. earns an excess return of 25 to 35 bps per month during the period 2000-2020. This result is remarkably robust across various factor models and therefore is not driven by exposure to common (risk) factors. The excess returns of the BB portfolio are not due to firm characteristics, industry composition, or small-cap stocks. We provide evidence suggesting that expensing investments in brands (instead of capitalizing them) and the tendency to underestimate the effect of brand name on generating future earnings are two mechanisms contributing to the excess returns.

#582 – Carbon Risk in the Cross Section of Corporate Bond Returns

Palazzolo, Christopher and Pomorski, Lukasz and Zhao, Alice: (Car)Bon Voyage: The Road to Low Carbon Investment Portfolios
https://ssrn.com/abstract=3753023
Abstract:
We discuss how an investment portfolio could dramatically reduce its carbon footprint, potentially even achieving ‘net zero.’ Our central message is that very large carbon reductions are feasible but not as straightforward as some commentators suggest. The usual approach of security selection (e.g., divesting from firms with highest emissions) can lead to a substantial carbon reduction but will not be enough for investors with the most ambitious reduction targets. Such investors will need other techniques to achieve their goals, for example shorting high carbon-footprint companies or trading instruments such as carbon offsets and carbon permits. We discuss the pros and cons of such techniques and their importance to allocators traveling on the pathway toward net zero.

#477 – Generalised Risk-Adjusted Momentum in Commodities
#499 – ETF Momentum

Fan, Minyou and Kearney, Fearghal and Li, Youwei and Liu, Jiadong: Momentum and the Cross-Section of Stock Volatility
https://ssrn.com/abstract=3977553
Abstract:
Recent literature shows that momentum strategies exhibit significant downside risks over certain periods, called “momentum crashes”. We find that high uncertainty of momentum strategy returns is sourced from the cross-sectional volatility of individual stocks. Stocks with high realised volatility over the formation period tend to lose momentum effect. We propose a new approach, generalised risk-adjusted momentum (GRJMOM), to mitigate the negative impact of high momentum-specific risks. GRJMOM is proven to be more profitable and less risky than existing momentum ranking approaches across multiple asset classes, including the UK stock, commodity, global equity index, and fixed income markets.

#358 – Monthly Reversal and/or Momentum Based on Intraday Returns
#774 –Afternoon Reversal Trading Strategy

Xu, Haoyu and Zhu, Xiaoneng: A Tale of One Day: Morning Momentum, Afternoon Reversal
https://ssrn.com/abstract=4192163
Abstract:
We document the opposite predictive signals in the morning and afternoon stock returns. Afternoon returns negatively predict future returns, while morning returns positively predict future returns. The short-term reversal is completely driven by the price movement in the afternoon; momentum is primarily driven by the price movement in the morning. We link this phenomenon to the asymmetric market conditions across the trading hours. The gradual decline in information asymmetry implies that the afternoon market is concentrated with non-informed trading. Price impact in the afternoon is temporary and reverses afterwards. The morning momentum strategy subsumes the alpha of traditional momentum, but not vice versa, and its performance does not exhibit long-term reversal.

#674 – Inflation Volatility Risk and Corporate Bonds

Nissinen, Juuso: Global Fixed Income Prices and Funding Currency
https://ssrn.com/abstract=4168957
Abstract:
I show that the relation between expected return and beta varies dramatically over time and across measurement currencies for global fixed income assets. For example, at the end of 2017, the global bond prices align almost perfectly with betas measured in Euros, while measured in United State Dollars, the pricing is unrelated to risks. The risk-return trade-off in the cross-section of assets varies across currencies, as the assets’ riskiness is a non-linear function of the measurement currency. I argue that the currency, in which the expected returns best align with betas, is driven by the funding decisions of levered investors and define it as the funding currency. The emergence of a single dominating funding currency is associated with tightening global funding constraints. Empirically, low short-term interest rate, active central bank, low economic growth rate, and good funding liquidity increase the currency’s importance in pricing global assets. The deviation from covered interest rate parity increases for the funding currency as investors hedge their funding currency positions through constrained markets. The security market line, measured in a given currency, steepens when that currency becomes more important for global pricing. I present a global currency and bond model consistent with the empirical findings.I show that the relation between expected return and beta varies dramatically over time and across measurement currencies for global fixed income assets. For example, at the end of 2017, the global bond prices align almost perfectly with betas measured in Euros, while measured in United State Dollars, the pricing is unrelated to risks. The risk-return trade-off in the cross-section of assets varies across currencies, as the assets’ riskiness is a non-linear function of the measurement currency. I argue that the currency, in which the expected returns best align with betas, is driven by the funding decisions of levered investors and define it as the funding currency. The emergence of a single dominating funding currency is associated with tightening global funding constraints. Empirically, low short-term interest rate, active central bank, low economic growth rate, and good funding liquidity increase the currency’s importance in pricing global assets. The deviation from covered interest rate parity increases for the funding currency as investors hedge their funding currency positions through constrained markets. The security market line, measured in a given currency, steepens when that currency becomes more important for global pricing. I present a global currency and bond model consistent with the empirical findings.

And several interesting free blog posts have been published during last 2 weeks:

How Common is Insider Trading? Evidence from the Options Market

Trading on non-public information has been very profitable in the past (and probably still is). Prominent insiders use their knowledge and share it with influential, wealthy institutional investors who earn money in an illegal way. And especially, options provide attractive leverage and relatively viable ways to “hide” sources of this illegal advantage. But after several big scandals, the resurgence of some forms of insider trading was stopped in 2009 after a trial with hedge fund superstar Raj Rajaratnam. The question is: What is the situation now?

Overnight Sentiment and the Intraday Return Dynamics

Overnight and seasonality effects or analysis of sentiment are favorite themes in quantitative academic research. Novel and very recent research from Baoqing Gan, Vitali Alexeev, and Danny Yeung (August 2022) presents us with an opportunity to discover new findings related to both these phenomena. The main takeaway is that the accumulated sentiment from the overnight non-trading period can predict the next period’s intraday stock return.

The Hidden Costs of Corporate Bond ETFs

Exchange-traded funds (ETFs) have been recently booming in popularity and enjoy great praise for their flexibility and accessibility in terms of liquidity. They allow investors convenient exposure to less liquid assets such as corporate bonds. But liquid ETF instrument based on illiquid assets is a recipe for a lot of hidden problems (and sometimes disasters), especially in such a turbulent period on fixed income markets as it’s now. There are various certain specifics which come with creation of new ETFs and problems for buying of underling prospects to match the fund’s NAV. Chris Reilly’s paper (2022) revolves around the point that ETF managers encourage Authorized Participants (APs) to more aggressively arbitrage tracking errors to the benefit of ETF investors while simultaneously allowing APs to interact strategically with ETF portfolios at the expense of ETF investors. Underlying asset liquidity is a first-order determinant of optimal security design for ETFs. While these ETFs do underperform their benchmark by greater than their stated net expense ratios (as much as claimed 48 bps p.a.), they still offer a liquid alternative for investors that do not have the resources to manage their own fixed income portfolio. This summary could be taken as a good reminder that investors’ expenses to obtain liquidity in the fixed income space are often quite substantial.

Multi Strategy Management for Your Portfolio

If you follow Quantpedia’s blogs, you probably know that Quantpedia PRO already contains multiple risk management and portfolio construction tools for your quantitative investment strategies. The newest Quantpedia PRO tool (available in a few days) will analyze something completely different, though – how to manage multi-strategy portfolios. The newest Quantpedia PRO tool (available in a few days) will analyze something completely different, though – how to manage multi-strategy portfolios. You can easily apply these multi-strategy overlays to various types of underlying – ETFs, systematic strategies, multi-asset portfolios, or multi-strategy portfolios. This article again serves as a primer for the new report’s methodology.