Quantpedia Premium Update – 20th September

New strategies:

#782 – High-to-Price Factor in Commodities

Period of rebalancing: Monthly
Markets traded: commodities
Instruments used for trading: CFDs, futures
Complexity: Simple strategy
Backtest period: 1986-2021
Indicative performance: 9.69%
Estimated volatility: 17.16%

Source paper:

Yasuhiro Iwanaga and Ryuta Sakemoto: Commodity Momentum Decomposition
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4176234
Abstract:
This study decomposes a momentum factor (MOM) in the commodity futures market. A high-to-price (HTP) factor generates a higher Sharpe ratio than a price-to-high (PTH) factor. We uncover that the profitability mechanisms across three momentum factors are different. The positive returns on MOM and PTH are associated with overconfidence and strong self-attribution. In contrast, HTP is linked to investors’ underreaction and the information diffusion process. Moreover, we find that positive demand shocks raise the return on HTP.

#783 – Cash Hedged Momentum

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1978-2020
Indicative performance: 13.11%
Estimated volatility: 20.17%

Source paper:

Ross, Chase P. and Ross, Landon and Ross, Sharon: Cash-Hedged Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4148710
Abstract:
Corporate cash piles vary across companies and over time. A firm’s cash holding is an implicit position in a low-return asset that is correlated across firms. Cash generates variation in beta estimates. We show how investors can hedge out the cash on firms’ balance sheets when making portfolio choices. We decompose stock betas into components that depend on the firm’s cash holding, return on cash, and cash-hedged return. Common asset pricing premia — size, value, and momentum — have large implicit cash positions. Portfolios of cash-hedged premia often have higher Sharpe ratios because firms’ cash returns are correlated.

#784 – Option Volatility Spread Factor Predicts Option Returns

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: options
Complexity: Very complex strategy
Backtest period: 1996-2019
Indicative performance: 12.82%
Estimated volatility: 6.78%

Source paper:

Liu, Chun and Liu, Chun and Wang, Tianyu and Wang, Yintian and Xiang, Hong: Uncertainty of Put-Call Parity Violation and Option Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4177394
Abstract:
This paper presents a new robust predictor for option returns: the uncertainty of put-call parity violation (VVS). We find that the delta-hedged equity option return decreases monotonically with VVS. Although VVS is highly correlated with the classical uncertainty and limit-to-arbitrage measures, the predictability still cannot be explained by standard factors such as jump and volatility risks, short-sale constraints, and stock lottery characteristics. It is also inconsistent with constrained option market makers, since the results are almost similar after controlling for market makers’ order flows.

#785 – Intraday VIX Betas Predict Stocks Returns

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1993-2020
Indicative performance: 9.77%
Estimated volatility: 12.3%

Source paper:

Tong Wang: Does the Volatility-Hedging Portfolio Underreact to Volatility Innovations?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4083250
Abstract:
This paper proposes a new explanation of the negative correlation between VIX betas and expected stock returns documented by Ang et al. (2006). While the relation has been widely cited as the proof that market volatility risk is priced in the cross-section of stocks, we find this view highly implausible because stocks with high VIX betas perform particularly poorly when the VIX index spikes up during the holding period. Also challenging the risk-based explanation is the finding that VIX betas only negatively predict stock returns if the betas are measured using intraday returns. We argue that the beta-return relation is indicative of market inefficiency and develop four theoretical models revolving around the conjecture that stocks with high VIX betas are overpriced because their prices underreact to innovations in market volatility. Further empirical tests show strong support for the underreaction-based explanations.

New research papers related to existing strategies:

#679 – Carbon Emmision Intensity in Stocks
#707 – Benchmarks Portfolios with Decreasing Carbon Footprints

Cheema-Fox, Alexander and LaPerla, Bridget Realmuto and Serafeim, George and Turkington, David and Wang, Hui: Decarbonizing Everything
https://ssrn.com/abstract=3693941
Abstract:
We analyze how the use of different climate risk measures leads to different portfolio carbon outcomes and risk-adjusted returns. Our findings are synthesized in a rules-based investment framework, which selects a different type of climate metric across industries and weighs industries in the portfolio based on the variability of carbon outcomes among firms within each industry. We conclude that analyzing the merits and applicability of various climate data can help investors manage climate risk while increasing risk-adjusted returns.

#55 – Pairs Trading with Country ETFs

Ekin Tokat, Ahmet Cevdet Hayrullahoğlu: Pairs trading: is it applicable to exchange-traded funds?
https://doi.org/10.1016/j.bir.2021.08.001
Abstract:
Among the various statistical trading strategies, pairs trading has been widely employed as a market neutral strategy owing to its simple approach and ease of application. In this context, we develop a cointegration-based pairs trading framework with a set of pre-conditions for pair eligibility and apply it to different asset classes. The performance analysis of a portfolio of 45 pairs is considered for the period of January 2007 to January 2021, which covers the period of a full market cycle of adjacent bull and bear periods; it is studied and benchmarked against the S&P500 index, which is considered as a proxy for the general market. We find an average annual return of 15% with an average Sharpe ratio of 1.43 after considering the transaction costs; we observe that this performance does not vary significantly with a change in the transaction cost levels and does not pass below the risk-free return levels with changing market conditions. Further, the strategy is observed to perform better during bear market conditions. Considering the highly liquid trading environment of the strategy, our findings raise a call for a discussion on the semi-strong form market efficiency.

#576 – Boosted Regression Trees in Corporate Bonds
#697 – Multifactor Corporate Bond Strategy

Cherief, Amina and Ben Slimane, Mohamed and Dumas, Jean-Marie and Fredj, Hamza: Credit Factor Investing with Machine Learning Techniques
https://ssrn.com/abstract=4155247
Abstract:
The most common models to assess asset returns are a linear combination of risk factors. We have employed tree-based machine learning algorithms to capture nonlinearities and detect interaction effects among risk factors in the EUR and USD credit space. We have built a nonlinear credit pricing model and compared it to our baseline linear credit pricing model using error metrics on training and testing sets and during different periods. In sample error metrics revealed the benefit of tree-based regressions. Then, we analysed the explanatory and predictive power measure by factor category and by period in order to evaluate the contribution of each factor in the explanation and prediction of credit excess returns. We found value in adding alternative factors to a traditional factor model and point out which of them prevail across different time horizons and during market crisis periods. Finally, tree-based regressions methods assisted us in improving our understanding of prices through the interaction between features and between each feature and the output of the model.

#537 – The Positive Similarity of Company Filings and Stock Returns
#662 – How to Use Lexical Density of Company Filings

Han, Henry and Wu, Yi and Zhao, Qianyu and Ren, Jie: Forecasting Stock Excess Returns With SEC 8-K Filings
https://ssrn.com/abstract=4182236
Abstract:
The stock excess return forecast with SEC 8-K filings via machine learning presents a challenge in business and AI. In this study, we model it as an im-balanced learning problem and propose an SVM forecast with tuned Gaussian kernels that demonstrate better performance in comparison with peers. It shows that the TF-IDF vectorization has advantages over the BERT vectorization in the forecast. Unlike general assumptions, we find that dimension reduction generally lowers forecasting effectiveness compared to using the original data. Moreover, inappropriate dimension reduction may increase the overfitting risk in the forecast or cause the machine learning model to lose its learning capabilities. We find that resampling techniques cannot enhance forecasting effectiveness. In addition, we propose a novel dimension reduction stacking method to retrieve both global and local data characteristics for vectorized data that outperforms other peer methods in forecasting and decreases learning complexities. The algorithms and techniques proposed in this work can help stakeholders optimize their investment decisions by exploiting the 8-K filings besides shedding light on AI innovations in accounting and finance.

#530 – Jump Risk in Stocks

Bollerslev, Tim: The Jump Leverage Risk Premium
https://ssrn.com/abstract=4129445
Abstract:
Jumps in asset prices are ubiquitous, yet the apparent high price of jump risk observed empirically is commonly viewed as puzzling. We develop new model-free short-time risk-neutral variance expansions, allowing us to clearly delineate the importance of jumps in generating both price and variance risks. We find that simultaneous jumps in the price and the stochastic volatility and/or jump intensity of the market commands a sizeable risk premium. This empirically large “jump leverage” risk premium may be rationalized in the context of equilibrium-based models by jumps in the conditional moments of the underlying fundamentals and/or changes in investors’ risk aversion.

#628 – Social Media Sentiment Factor

Yang, Ni and Fernandez-Perez, Adrian and Indriawan, Ivan: The Price Impact of Tweets: A High-Frequency Study
https://ssrn.com/abstract=4153783
Abstract:
We examine the mechanism by which social media sentiment affects stock prices. Specifically, we assess the impact of Twitter feeds on stock returns at the intraday level. We find that an increase in buyer-initiated trades has a significantly positive price impact. The impact is stronger with an increase in the number of tweets and sentiment, and persists even after controlling for volatility, liquidity shock, and limit-order activity. Both bullish and bearish tweets amplify the impact of trades on returns. The impact of Twitter sentiment on prices causes a permanent price movement, indicating that Twitter sentiment contains information.

#272 – Overnight Stock Trading

Jones, Christopher S. and Pyun, Sungjune and Wang, Tong: Return Extrapolation and Day/Night Effects
https://ssrn.com/abstract=4181093
Abstract:
We propose that differences between overnight and daytime returns are the result of return extrapolation. After high daytime returns, morning order imbalances are high in the first 15 minutes of regular trading the next day, which is consistent with higher overnight returns. The effect is asymmetric, with positive returns having larger response than negative returns, and it is stronger in more overpriced stocks and stocks with more retail interest. At the portfolio level, extrapolative effects can explain most of the cross-sectional variation in the “tug of war” between overnight and daytime returns. Extrapolative trading is also consistent with the upward sloping relation between market beta and average overnight returns.

#121 – Hedgers’ Effect in FX

Yoon, Jungah and Ruan, Xinfeng and Zhang, Jin E.: The Role of Hedgers and Speculators in the Currency Futures Markets
https://ssrn.com/abstract=4186308
Abstract:
Kang et al. (2020) find cross-sectional evidence that short- and long-term variation of position changes are driven by liquidity demand of noncommercials and hedging demands of commercials in commodity futures markets. In this paper, we find the commercials’ hedging demands drive both short-term and long-term position changes in a time-series of AUD, GBP, CHF, EUR, JPY and NZD futures markets. The predictability of hedging pressure comes from macroeconomic conditions of both domestic/foreign countries and the implied volatility of corresponding currency options. The important implication for multinational corporations and policymakers is that monitoring the level of perceived hedging pressure by traders may help form an effective hedging strategy for future payables and receivables.

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

A Study on How Algorithmic Traders Earn Money

Our mission here at Quantpedia is to provide both retail and institutional investors with ideas for trading strategies that are easily understandable while based on and backed by quantitative academic research. Today, we present you with the results from a study that we came across. Although it’s not quantitative, but qualitative, it has really held our interest. The paper does not provide any images or figures; it is a study made from various types of surveys with answers from professionals concluded with an attention-grabbing summary table.

Investing in Deflation, Inflation, and Stagflation Regimes

Investing has been a reliable way to compound one’s inheritance over ages known throughout human history. But different monetary and fiscal situations, especially during times of uncertainty and extreme stress, force both individuals and institutions to adjust their financial habits. A recent research paper written by Guido Baltussen, Laurens Swinkels, and Pim van Vliet analyzed large samples of data starting from the 19th century and brought unique perspectives on how various asset classes perform during “quiet, good” periods and, on the other side, economic turmoil. Research summarized very actual topics of investing during those different cycles and what inflation does to returns across equities, bonds, and cash.