Quantpedia Premium Update – June 10th

New Strategies:

Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: CFDs, forwards, futures
Complexity: Complex strategy
Backtest period: 1971-2022
Indicative performance: 4.16%
Estimated volatility: 8.03%

Source paper:

Shahini, A. Hedieh, Blades of Carry: The Big Short
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4752712
There is simply a paradox between the carry trade anatomy and the dollar index (DXY). The paradox signals the importance of the carry middle portfolio (ZERO) to study. The presence of a high correlation between covariates in which a portfolio is priced, significantly impacts portfolio diversification (FF, 2015), while a lower correlation generates a hedging factor that reduces the overall risk in a portfolio. I construct a novel predictor using a mode- rate cross-sectional of currency returns and covariates correlation to forecast the return to the carry trade, a popular investment strategy. This new predictor, referred to as the middle correlation risk ”MCOR” demonstrates robust indications of its predictive capability in relation to carry trade returns up to 27%. I also describe a novel currency investment strategy, MaC strategy (Momentum-at-Carry), which is positively skewed. A strong swing in MCOR can also trigger a carry crash and switching to MaC strategy.

#1009 – Market Timing Using Options on Leveraged ETFs as Predictors

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: ETFs
Complexity: Complex strategy
Backtest period: 2016-2024
Indicative performance: 20.84%
Estimated volatility: 28.65%

Source paper:

Gilstrap, Collin and Petkevich, Alex and Teterin, Pavel and Wang, Kainan: Lever up! An analysis of options trading in leveraged ETFs
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4741193
Abstract:
We examine options trading in leveraged ETFs and their impact on the performance of the underlying funds. Using implied volatility innovations in call and put options, we demonstrate that option signals from leveraged ETFs are robust predictors of the underlying ETFs’ performance. While both levered and unlevered option signals forecast ETF returns, the levered signal is more pronounced in both magnitude and relevance. This predictivity power primarily stems from inverse leveraged ETFs and during economic downturns. Furthermore, we use the leveraged ETF option signals to develop a trading strategy that produces an average abnormal performance of 1.13% per month.

#1010 – Cross-Country Factor Momentum

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1986-2021
Indicative performance: 1.94%
Estimated volatility: 4.13%

Source paper:

Fieberg, Christian and Metko, Daniel and Zaremba, Adam, Cross-Country Factor Momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4756018
Abstract:
We study a new class of the momentum effect: cross-country factor momentum. We document a persistent international pattern: factors in winning countries consistently outperform those in losing countries. The effect holds across most anomalies and is robust to many considerations.

#1011 – Social-Link Momentum

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1987-2020
Indicative performance: 6.24%
Estimated volatility: 17.17%

Source paper:

Nie, Mingjian and Wu, Jing: Social Links and Predictable Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4477466
Abstract:
We extract individual and company profiles from BoardEx to identify the executive board network and investigate the relationship between social links and asset returns. Our study documents a cross-predictability effect on returns for firms linked through executive board members. Specifically, the lagged returns of socially-linked peer firms have predictive power for the focal firm’s return, which we refer to as the social-link momentum (SLM) spillover effect. We demonstrate that a long-short strategy based on this momentum effect generates an equal-weighted 3-factor monthly alpha of 0.79% (t=4.65). Importantly, this effect is distinct from industry momentum and cannot be attributed to other well-known momentum effects, such as customer, geographic, and pseudo-conglomerate momentum effects. Our findings are consistent with limited investor attention, as the SLM spillover effect is more pronounced for focal firms that receive lower investor attention, including small firms, firms with low analyst coverage, and firms with less institutional holding.

#1012 – ChatGPT Using Twitter Data Generates Stock Tickers to Buy and Sell for Day Trading

Period of rebalancing: Intraday
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 2022-2023
Indicative performance: 51.53%
Estimated volatility: 63.4%

Source paper:

Cho, Sangheum: Can ChatGPT Generate Stock Tickers to Buy and Sell for Day Trading?
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4759311
Abstract:
This paper examines the generative feature of ChatGPT for empirical asset pricing. I show that ChatGPT can generate stock tickers that provide a profitable day trading strategy. Using input prompts as multiple Twitter posts, including both macro and firm-specific news by major news providers, I ask ChatGPT to generate lists of stock tickers to buy and sell. The trading strategy based on the buy and sell lists earns significant long-short returns in open-to-close intraday trading. By asking again about the reason for generating those stock tickers, keywords of ChatGPT’s answer suggest that tech stocks are important for generating the buy lists, whereas sector- or industry-level analysis is important for generating the sell lists. In particular, ChatGPT’s buy and sell lists consist of economically linked stocks through the supply chain, resulting in lower industry concentration than those of their matching groups. The performance is attributable to the stock selection within each industry, the short leg of the strategy, and stronger in the difficult-to-arbitrage stocks, implying that ChatGPT signals’ applicability of extracting mispricing signals in text data. As most of the Twitter data consists of non-firm-specific news, this finding sheds light on the literature by showing that ChatGPT can process a bulk of seemingly non-firm-specific news to generate firm-specific mispricing signals.

#1013 – Cointegration-Based Strategies in Forex Pair Trading

Period of rebalancing: Daily
Markets traded: currencies
Instruments used for trading: CFDs, forwards, futures
Complexity: Very complex strategy
Backtest period: 2007-2024
Indicative performance: 0.93%
Estimated volatility: 0.63%

Source paper:

Lemishko, Tetiana and Landi, Alexandre and Caicedo-Llano, Juliana: Cointegration-Based Strategies in Forex Pair Trading
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4771108
Abstract:
Pair trading, a popular strategy in financial markets, leverages pricing differentials among correlated assets. Cointegration, a key concept, identifies long-term equilibrium relationships among non-stationary time series data, offering trading opportunities when deviations occur. This paper reviews pair trading methodologies, focusing on the Cointegration approach’s robustness. We propose applying cointegration-based pair trading to Forex markets, aiming to enhance reliability and objectivity in trading. Through parameter optimization, our study identifies profitable strategies, contributing to algorithmic frameworks in financial markets and empowering traders to mitigate emotional biases while maximizing profitability.

New research papers related to existing strategies:

#233 – Using Straddles to Trade on Earnings Announcements

Khan, Waleed and Khan: Hamzah, 17-Year Backtest of Straddles around SP500 Earnings Announcements
https://ssrn.com/abstract=4832160
Abstract:
This paper investigates the efficacy of employing straddle derivatives strategies around S&P 500 earnings announcements to generate consistent alpha. Utilizing a comprehensive 17-year dataset, both long and short straddle positions are analyzed for their viability and profitability.

Long at-the-money (ATM) straddles initiated 30 trading days prior to earnings announcements displayed a decline in value leading up to one day before the event, primarily due to theta decay outweighing potential gains from rising implied volatility.

Conversely, a strategy involving buying a straddle one day before earnings and selling it one day after showed promising results, with a 108% compound annual growth rate (CAGR) and a Sharpe Ratio of 2.2 on 13,120 trades. However, risks such as single-week losses of up to 83.8% and unaccounted costs like commissions, slippage and taxes warrant caution and conservative trade-sizing.

As a counterparty position, short straddles near expiration significantly raised the risk of early assignment, rendering them unviable as a trading strategy. The paper advocates for a conservative approach to trading, emphasizing risk management and capital preservation by avoiding margin-enabled accounts to safeguard against potential losses.

Ultimately, the study advises against setting up long straddles 30 days prior to earnings or engaging in short straddles around earnings announcements as reliable means of generating alpha. Out of the money strangles and iron condors may be viable alternatives to short straddles but will require additional investigation.

#4 – Overnight Anomaly

Ahn, Yongkil and Fan, Alfred Qi and Noh, Don and Park, Stella Y.: Overnight-Intraday Return Gap and the Retail Ebb and Flow
https://ssrn.com/abstract=4752520
Abstract:
In most stock markets, average overnight (close-to-open) returns are high while intraday (open-to-close) returns are low, even negative. We show that retail trading proportion (RTP) is a major explanatory variable for this “overnight-intraday return gap” by using data from Korea where accurate and exhaustive retail trade flows are available. To establish a causal relationship between the return gap and RTP, we use an instrumental variables approach that exploits retail investors’ tendency to more actively trade stocks with low per-share prices. We attribute this relationship to the recurrent retail ebb and flow stemming from the retail investors’ demand for daytime stock market exposure.

#460 – ESG Level Factor Investing Strategy

Torricelli, Costanza and Bertelli, Beatrice: The Trade-Off between ESG Screening and Portfolio Diversification in the Short and in the Long Run
https://ssrn.com/abstract=4210779
Abstract:
This paper investigates the performance of portfolio screening strategies based on ESG (Environmental, Social, Governance) scores, by testing three main hypotheses motivated by introducing Corporate Social Responsibility (CSR) considerations within portfolio theory: i) ESG screened portfolios with low exclusion thresholds overperform the benchmark in the long term; ii) ESG screened portfolios do not overperform the benchmark in the short term independently of the screening threshold and the phase of the economic cycle; iii) ESG screened portfolios have lower systemic risk compared to the benchmark. To this end, negative and positive screening strategies based on Bloomberg ESG disclosure scores and different screening thresholds are set up from the 559 stocks belonging to the EURO STOXX index in the period 2007-2021 and over four short-term subperiods including two crises (2008 global recession and 2020 Covid-19 pandemic). The risk-adjusted performance of the ESG screened portfolios is compared with the benchmark-passive one based on Sharpe Ratio and alphas (from both a one-factor model and the Carhart four-factor model) so as to test performance over total and systemic risk respectively. Three main results emerge. First, we prove overperformance of screened portfolios in the long run only and in the presence of negative screening strategies. Second, we show no overperformance in the short run even in period of financial crises thus contesting the alleged safe-haven property of ESG portfolios. Finally, comparative performance of ESG screened portfolios with respect to the benchmark passive strategy does not improve when we consider a measure of systemic risk.

Iwata, Tsuyoshi and Weibel, Marc: Enhancing Equity Factor Model with Publicly-reported ESG Data
https://ssrn.com/abstract=4411532
Abstract:
This study examines the alpha-generating power of the public-report-based ESG score, which is based on ESG incident data collected by RepRisk from various public sources, and its relationship with the self-disclosure-based ESG score obtained from Refinitiv. We construct pure ESG factor portfolios to neutralize exposure to common style factors and isolate the pure ESG factor returns. Our results suggest that (i) the source difference is the main cause of the negative correlation between the public report ESG and the self-disclosure ESG score, (ii) the public report ESG score and its subscores produce the mixed results in terms of their adjusted factor returns across regions, (iii) the combination of the public report ESG and the self-disclosure ESG score significantly improves the risk-return profiles of the combined ESG factor returns in the US, EU and Japan.

#25 – Size Factor – Small Capitalization Stocks Premium

Vidal-García, Javier and Vidal-García, Javier and Vidal, Marta: The Size Effect on the London Stock Exchange
https://ssrn.com/abstract=4033932
Abstract:
This paper analyzes the performance of stocks listed on the London Stock Exchanges to determine whether there is a size effect. The hypothesis being examined is whether the smaller stocks obtain higher returns than the large ones even after adjusting for risk. The study period is from 1990 to 2023 and we work with the FTSE All-Share, FTSE 250 and FTSE Small Cap Indices as an approximation to the size segments under study with both daily and monthly returns. We find the following results: a) the returns of the FTSE 250 and FTSE Small Cap Indices were higher than the FTSE All-Share Index but not systematically; b) a measurement problem was found in the risk of small stocks manifested in that with monthly data there is a volatility 6 points higher than that obtained with daily data (for the FTSE Small Cap Index); c) we find a size premium of almost 2% for the FTSE 250 and FTSE Small Cap indices after correcting partially the risk of these indices using monthly data.

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

Quantpedia Awards 2024 – Winners Announcement

This is the moment we all have been waiting for, and today, we would like to acknowledge the accomplishments of the researchers behind innovative studies in quantitative trading. So, what do the top five look like, and what will the authors of the papers receive? Let’s find out …

Trading Arbitrage Portfolios Based on Image Representations

Convolutional neural networks (CNNs), inspired by the human brain’s ability to recognize visual patterns, excel in tasks like object detection, facial recognition, and image classification, making them powerful tools for extracting insights from visual data. However, we are traders, so a natural question arises: Can we use that in trading? A recent paper shows that we can actually do it. Utilizing CNNs, Niklas Paluszkiewicz introduces a novel approach to pairs trading by visually analyzing historical price movements while converting traditional time series data into image representations.

Active vs. Passive Life Cycle Savings Strategies

The main goal of our new article is to explore the efficacy of passive versus active management strategies in the context of savings for long-term financial goals. By analyzing the performance of nine distinct asset classes, including Double Leveraged ETFs and an implementation of the Pragmatic Asset Allocation (PAA) strategy, over an almost-century-long horizon, we simulate and compare the outcomes of three passive and three active strategies. This comparative analysis focuses on their influence on key investment characteristics, including Final Portfolio Size, Maximum Drawdown, and Maximum Loss, to determine their potential in enhancing long-term investment results.