Quantpedia Premium Update – June 25th

New Strategies:

#1014 – Cross-Market Intraday Time-Series Momentum

Period of rebalancing: Intraday
Markets traded: equities
Instruments used for trading: ETFs
Complexity: Moderately complex strategy
Backtest period: 1993-2013
Indicative performance: 20%
Estimated volatility: –

Source paper:

Xu, Dezhong and Li, Bin and Singh, Tarlok and Li, Jinze: Cross-Market Intraday Time-Series Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4651331
Abstract:
We extend the works of Gao et al. (2018) and Li et al. (2022) by proposing a new cross-market intraday momentum: the US stock market’s last half-hour return predicts the next day’s first half-hour stock returns in international markets. This predictability is statistically significant both in- and out-of-sample. The corresponding cross-market intraday time-series momentum (CITSM) strategy shows economic significance in international stock markets investments. The CITSM strategy’s profit remains positive even with the consideration of appropriate transaction costs. The profitability of the CITSM strategy is driven by some specific market characteristics. The CITSM is strong when international market liquidity is low or information uncertainty is high. The CITSM is also strong when the US market information uncertainty is high, or the liquidity is at extreme levels.

#1015 – Senators’ Disclosure and Stock Returns

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2018-2022
Indicative performance: 7.52%
Estimated volatility: 7.94%

Source paper:

Lazzaretto, Pietro: Mind the Trade: Senators’ Disclosure and Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4816497
Abstract:
The trading behavior of U.S. Senators has faced growing public scrutiny due to potential misuse of insider information. I document a novel empirical fact: stock trades by U.S. Senators predict abnormal returns in the same direction after disclosure — exceeding 90 bps in a month — but not after the actual transaction. I reconcile this evidence by isolating a set of potentially speculative trades (multiple purchases and sales of the same stock), which frequently originate from industries under considerable governmental oversight and public controversy. Realized returns on speculative trades earn more than 9 bps daily. Since the speculative nature becomes apparent only ex-post, there is uncertainty on the trade type at the disclosure time. Consequently, the market may overreact or mistakenly replicate also uninformed trades. In accordance, the price impact after disclosure is only temporary. Nonetheless, a trading strategy timed on disclosure yields substantial financial gains, even while being agnostic about the nature of the underlying trade. Overall, this study suggests a re-evaluation of the effectiveness of public disclosure as a disciplining mechanism for insiders. Indeed, politicians are still able to profit abnormally on many of their trades. At the same time, disclosure may unintentionally act as a catalyst in fostering noise trading.

#1016 – Intraday Momentum Strategy for S&P500 ETF

Period of rebalancing: Intraday
Markets traded: equities
Instruments used for trading: CFDs, ETFs, futures
Complexity: Complex strategy
Backtest period: 2007-2024
Indicative performance: 19.6%
Estimated volatility: 14.3%

Source paper:

Zarattini, Carlo and Aziz, Andrew and Barbon, Andrea, Beat the Market: An Effective Intraday Momentum Strategy for S&P500 ETF (SPY)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4824172
Abstract:
This paper investigates the profitability of a simple, yet effective intraday momentum strategy applied to SPY, one of the most liquid ETFs that tracks the S&P500. Unlike the academic literature that typically limits trading to the last 30 minutes of the trading session, our model initiates trend-following positions as soon as there is an indication of abnormal demand/supply imbalance in the intraday price action. Building on trading techniques commonly used by active day traders, which have been discussed in our previous papers, we introduce the use of dynamic trailing stops to mitigate downside risks while allowing for unlimited upside potential. From 2007 to early 2024, the resulting intraday momentum portfolio achieved a total return of 1,985% (net of costs), an annualized return of 19.6%, and a Sharpe Ratio of 1.33. We conduct extensive statistical tests to examine whether the profitability of the strategy is affected by different market volatility regimes and whether the estimated gamma imbalance of dealers could predict changes in strategy profitability. We analyze the daily profitability of the intraday momentum strategy with respect to day-of-the-week effects. Additionally, we evaluate its performance against well-known technical daily patterns to understand its behavior under various market conditions. Given the short-term nature of the model, we also assess the impact of commissions and slippage on the overall profitability of the strategy.

#1017 – QuantPedia’s Composite Seasonality in MesoSim

Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: options
Complexity: Very complex strategy
Backtest period: 2012-2023
Indicative performance: 12.07%
Estimated volatility: 6.16%

Source paper:

Vojtko, Radovan; Kiss, Tibor; Pauchlyová, Margaréta: QuantPedia Composite Seasonality in MesoSim
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4861464
Abstract:
The article tests for the presence of short-term continuation and long-term reversal in commodity futures prices. While contrarian strategies do not work, the article identifies 13 profitable momentum strategies that generate 9.38% average return a year. A closer analysis of the commodity futures that the momentum strategy recommends trading reveals that we buy backwardated contracts and sell contangoed contracts with high volatilities. The correlation between the momentum returns and the returns of traditional asset classes is also found to be low, making the commoditybased relative-strength portfolios excellent candidates for inclusion in well-diversified portfolios.

#1018 – Cryptocurrency Volume-Weighted Time Series Momentum

Period of rebalancing: Daily
Markets traded: cryptos
Instruments used for trading: cryptos
Complexity: Complex strategy
Backtest period: 2014-2023
Indicative performance: 26.65%
Estimated volatility: 12.22%

Source paper:

Huang, Zih-Chun and Sangiorgi, Ivan and Urquhart, Andrew, Cryptocurrency Volume-Weighted Time Series Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4825389
Abstract:
This paper studies the time series momentum effect (TSMOM) in the cryptocurrency market and finds strong and significant evidence of TSMOM using volume-weighted market return. Based on the TSMOM strategy, we show that volume-weighted winner minus loser portfolios can generate significant returns which outperform other TSMOM strategies. Our findings cannot be fully explained by market, size, and momentum factors and are not subsumed by cross-sectional and MAX momentum returns, and is not the result of wash trading volume. Our findings suggest that trading volume may provide signals within cryptocurrency markets to create profitable strategies.

New research papers related to existing strategies:

#628 – Social Media Sentiment Factor

Huang, Chengcheng and Shum Nolan, Pauline: Social Network Sentiment and Markets: Evidence from the Wallstreetbets Forum
https://ssrn.com/abstract=4384743
Abstract:
WallStreetBets provides the perfect echo chamber to study retail investor attention herding and sentiment, and their impact on stock prices. We train a machine learning model to classify daily forum-wide sentiment and individual stock sentiment. We show that forum-wide sentiment is inversely related to the VIX. Our monthly Attention Herding Portfolio generates sizable alphas. When retail attention herds bullishly on a stock, the network spillover is large enough to impact retail trades, but unlike Barber et al. (2022), there is no reversal in stock returns. We also test the role of influencers and their role in the network.

#118 – Time Series Momentum Effect

Zakamulin, Valeriy and Giner, Javier: Optimal Trend-Following With Transaction Costs
https://ssrn.com/abstract=4282126
Abstract:
Despite trend-following investing’s widespread popularity, optimal trend-following with transaction costs remains poorly understood. Existing studies on the subject are limited and use a theoretical approach that is difficult to solve. In this paper, we propose a new, more practical model that strikes a balance between theoretical simplicity and practical relevance. Our model reduces trading costs and produces a solution that is comparable to the popular simple moving average crossover rule. By using our model, traders can justify using the crossover rule in practice. We also provide historical simulations that demonstrate the effectiveness of our model, supporting our theoretical findings. In short, our paper provides a practical and effective solution to the problem of optimal trend-following with transaction costs.

#460 – ESG Level Factor Investing Strategy

Kolle, Janina and Lohre, Harald and Radatz, Erhard and Rother, Carsten: Factor Investing in Paris: Managing Climate Change Risk in Portfolio Construction
https://ssrn.com/abstract=4071422
Abstract:
The 2015 Paris Agreement is a landmark in limiting emissions and targeting global warming well below 2, preferably 1.5, degrees Celsius compared to pre-industrial levels. In this light, we investigate how to efficiently construct equity portfolios that help mitigating climate change risk but at the same time enable harvesting well-established return drivers such as value, momentum or quality. A pure reduction in greenhouse gas intensity or a divestment from fossil sectors is not necessarily leading to a better temperature alignment of a portfolio. Given the limited set of temperature-aligned assets, keeping the average temperature increase below 2 degrees comes with considerable active risks. To this end, we propose a net zero portfolio construction framework that brings temperature alignment together with a reduction in carbon intensity, while harvesting equity factor premia.

#947 – Abnormal Overnight Earnings Return Factor in China

Lan, Qiujun and Xie, Yuxuan and Mi, Xianhua and Zhang, Chunyu: Post Earnings Announcement Drift: Earnings Surprise Measuring, the Medium Effect of Investor Attention and Investing Strategy
https://ssrn.com/abstract=4589824
Abstract:
Drifting in the direction of earnings surprises for a prolonged period is a decades-puzzling financial anomaly, i.e., the “post-earnings-announcement drift” (PEAD). This paper provided a new simple measure of earnings surprise called ORJ. Based on ORJ, not only is the medium effect of investors’ attention on the relationship between earnings surprises and PEAD analyzed, but a tractable and profitable investing strategy is provided. Through comprehensive empirical analysis of the Chinese stock market, we found that i) both earnings surprises and investor attention can increase the degree of PEAD; ii) “good” (bad) earnings surprises strengthen (weaken) the degree of drift by attracting (decreasing) investor attention; it is asymmetric that the positive effects of “good” earnings surprises are stronger than that of “bad” earnings surprises on PEAD; and iii) the strategy obtains an average 6.78% return per quarter in excess of the market and only longs dozens of stocks . iv) Typical pricing factors such as the Fama-French three factors, illiquidity and company characteristics have little explanatory power for the returns of the strategy. This paper strongly shows the importance of monitoring overnight returns of earnings announcements to digging the unexpected information, reveals one mechanism of earnings surprises on PEAD and demonstrates the potential profitability of PEAD in the Chinese market.

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

Quantpedia Composite Seasonality in MesoSim

In one of our older posts titled ‘Case Study: Quantpedia’s Composite Seasonal / Calendar Strategy,’ we offer insights into seasonal trading strategies such as the Turn of the Month, FOMC Meeting Effect, and Option-Expiration Week Effect. These strategies, freely available in our database, are not only examined one by one, but are also combined and explored as a cohesive composite strategy. In partnership with Deltaray, using MesoSim — an options strategy simulator known for its unique flexibility and performance — we decided to explore and quantify how our Seasonal Strategy performs when applied to options trading. Our motivation is to investigate whether this strategy can be improved in terms of risk and return. We aim to systematically harvest the VRP (volatility risk premium) timing the entries using calendar strategy to avoid historically negative trading days.

Oh My! I Bought A Wrong Stock! – Investigation of Lead-Lag Effect in Easily-Mistyped Tickers

Our new study aims to investigate the lead-lag effect between prominent, widely recognized stocks and smaller, less-known stocks with similar ticker symbols (for example, TSLA / TLSA), a phenomenon that has received limited attention in financial literature. The motivation behind this exploration stems from the hypothesis that investors, especially retail investors, may inadvertently trade on less-known stocks due to ticker symbol confusion, thereby impacting their price movements in a manner that correlates with the leading stocks. By examining this potential misidentification effect, our research seeks to shed some light on this interesting factor.

The Art of Financial Illusion: How to Use Martingale Betting Systems to Fool People

The Internet (and especially the part related to finance, trading, and cryptocurrencies) can be dangerous and full of offers of guaranteed returns, pictures of forever-growing bank accounts, and guys with golden rings swimming in the bathtub filled with cash. The truth is usually less rosy. Lucrative frauds, so-called white color crimes, have always been there, but with new technologies, they can spread faster and hide under a colorful disguise. One of the oldest concepts, from the beginnings of conceptualizing probability and statistics branches of mathematics, is Martingale betting, and this method is very often exploited to lure inexperienced new traders, who are then eaten alive by marketing sharks, selling them seemingly non-losing signals. How? An interesting paper by Carlo Zarattini and Andrew Aziz sheds some some light on these schemes.