New Strategies:
#949 – The 52-Week High Effect in India
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 2004-2023
Indicative performance: 20.39%
Estimated volatility: 13.68%
Source paper:
Raju, Rajan: The 52-Week High Effect and Momentum Investing: Evidence from India
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4587697
Abstract:
Using a dataset from October 2004 to August 2023, we employ various portfolio construction frameworks to show that the 52-week high effect is a distinct and robust phenomenon in the Indian equity market. This study investigates the predictive power of this phenomenon and contrasts it with academic momentum. We find that stocks near their past 52 weeks tend to offer higher returns and Sharpe ratio, even after controlling for firm size. Our findings are statistically significant and valid across various weighting schemes, reaffirming that the 52-week high effect is a distinct and robust market “anomaly”, offering a more stable alpha than academic momentum in Indian equity markets. Furthermore, we find that 52-week high strategies have weaker long-term reversals relative to academic momentum, offering actionable insights for investment managers looking to capitalise on momentum-based anomalies. These findings suggest different underlying market responses to news for the two effects and provide actionable insights for portfolio managers and investors.
#950 – Skewness Risk Premia and the Cross-Section of Currency Returns
Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: CFDs, forwards, futures, swaps
Complexity: Complex strategy
Backtest period: 1998-2021
Indicative performance: 5.64%
Estimated volatility: 7.03%
Source paper:
Li, Junye and Sarno, Lucio and Zinna, Gabriele: Skewness Risk Premia and the Cross-Section of Currency Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4580189
Abstract:
We find that buying currencies with a high skewness risk premium (SRP) and selling currencies with a low SRP generates high returns and Sharpe ratio. A tradable SRP factor enters the currency pricing kernel and is central to the pricing of risks inherent in many currency strategies. This evidence emerges using broad cross-sections of portfolio excess returns, employing both conventional and more recent asset pricing tests – which control for omitted variables and measurement errors – and also using a nontradable SRP factor. The results taken together show that skewness risk is a strong and priced source of currency risk.
#951 – Hedging Momentum Crashes
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1992-2021
Indicative performance: 16.93%
Estimated volatility: 20.33%
Source paper:
Negrete, Mario Enrique, Constant Leverage Covering Strategy for Equity Momentum Portfolio with Transaction Costs
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4495435
Abstract:
Covering strategies use information stored in past daily returns to improve the performance of momentum portfolios, which use information stored in past monthly returns to outperform the stock market. However, covering strategies for momentum portfolios have three shortcomings: they have forward-looking biases, they ignore transaction costs, and they have compensation mechanisms that increase leverage and allow them to increase profits at a faster rate than they increase losses. I develop a Constant Leverage covering strategy that addresses these three problems. First, it avoids the forward-looking bias by using a ten-year rolling window to determine the cutoff between high and low volatility, which activates the covering strategy. Second, it accounts for transaction costs. Third, it buys a fraction of the momentum portfolio when it is activated but is equal to the momentum portfolio when it is inactive. Since it lacks compensation mechanisms for its shortcomings, its performance can only improve if it forecasts and avoids more losses than profits.I use closing bid and ask prices reported by the Center for Research in Security Prices from 1992 to 2021 to compute transaction costs at the individual stock level. During this period, the stock market presented an average excess return of 9.19% and a Sharpe ratio of 0.61, and 9.74% of its returns were crashes. The momentum portfolio adjusted by transaction costs presents excess returns of 10.99% and a Sharpe ratio of 0.31, and 18.05% of its returns were crashes. The Constant Leverage covering strategy adjusted by transaction costs presents excess returns of 16.93% and a Sharpe ratio of 0.84, and only 8.31% of its returns were crashes.These improvements are possible because of the U-shape relationship between momentum returns and volatility. It is impossible to accurately predict momentum crashes because no one knows when a stock market sell-off will end. However, the period between the beginning and the end of a financial crisis are associated with highly persistent loser portfolio volatility. Avoiding the momentum portfolio during these predictable volatility periods considerably improves its performance.
#952 – Cash Operating Profitability Predicts Earnings Announcement Returns
Period of rebalancing: Daily
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1991-2018
Indicative performance: 68.81%
Estimated volatility: 50.57%
Source paper:
Ahmed, Anwer S. and Neel, Michael J. and Safdar, Irfan: Why Does Operating Profitability Predict Returns? New Evidence on Risk versus Mispricing Explanations
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4567087
Abstract:
This study develops new evidence on risk versus mispricing explanations of the well-known profitability premium. First, we examine whether exposure to expected downside risk is a plausible explanation. We find that high profitability is associated with both lower ex-ante and ex-post probabilities of future price crashes. Thus, less profitable firms exhibit greater downside risk than highly profitable firms, making a downside risk explanation implausible. Although this fact is overlooked by the market in general, it is anticipated by options traders; we find that put options of low profitability firms are relatively more expensive. Simultaneously, these firms do not exhibit greater probability of jumps, indicating that volatility(risk)-based explanations for the profitability premium are unlikely to be descriptive. Second, we find that the sticky-expectations model of Bouchard et al. (2019) only partially explains the profitability premium. While on average, analysts’ forecast revisions correct in the same direction as recent profitability, the profitability premium still exhibits a strong relationship to the non-sticky component of analysts’ forecast revisions. Third, institutional investors trade profitability-based signals but do so with a delay, likely contributing to the premium. Overall, our evidence favors the explanation that the profitability premium is related to investor mispricing of potential downside risk and provides greater clarity on recent findings in the literature.
New research papers related to existing strategies:
#652 – Machine Learning Stock Analyst
Liu, Rui and Liang, Jiayou and Chen, Haolong and Hu, Yujia: Analyst Reports and Stock Performance: Evidence from the Chinese Market
https://ssrn.com/abstract=4563238
Abstract:
This article applies natural language processing (NLP) to extract and quantify textual information to predict stock performance. Leveraging an extensive dataset of Chinese analyst reports and employing a customized BERT deep learning model for Chinese text, this study categorizes the sentiment of the reports as positive, neutral, or negative. The findings underscore the predictive capacity of this sentiment indicator for stock volatility, excess returns, and trading volume. Specifically, analyst reports with strong positive sentiment will increase excess return and intraday volatility, and vice versa, reports with strong negative sentiment also increase volatility and trading volume, but decrease future excess return. The magnitude of this effect is greater for positive sentiment reports than for negative sentiment reports. This article contributes to the empirical literature exploring sentiment analysis and the response of the stock market to news on the Chinese stock market.
#239 – Large Price Changes combined with Analyst Revisions
#652 – Machine Learning Stock Analyst
Campbell, John L. and Ham, Harrison and Lu, Zhongjin and Wood, Katherine: Expectations Matter: When (not) to Use Machine Learning Earnings Forecasts
https://ssrn.com/abstract=4495297
Abstract:
We comprehensively examine if machine learning technology can meaningfully improve earnings forecasts, and if so, whether market expectations appear to reflect those superior forecasts. First, using a sample of forecasts from 1990 to 2020, we find consistent evidence that the best machine learning forecast outperforms analysts’ forecasts. However, the best machine expectation does not beat the analyst forecast by a meaningful amount in most cases, except for two distinct instances: (1) the earnings forecast is for small firms (a “size” effect), and (2) the earnings forecast is for a longer horizon (a “horizon” effect). Second, in cases where there are meaningful differences between analyst and machine expectations, earnings response coefficient (ERC) tests imply that investors’ expectations appear to be mostly aligned with the best machine forecast. The alignment with the machine forecast strengthens over time and is especially strong among firms with more sophisticated investors. Third, our time-series analyses suggest that analyst and machine forecasts are converging over time and that analysts’ information production remains critical. Taken together, our results suggest that machines rely on analysts’ information, analysts appear to rely on machines to reduce their biases, and thus the two are unlikely to diverge significantly even as technology continues to evolve.
#703 – Machine Learning in News Articles Predicts Stock Returns
#826 – News Sentiment and Equity Returns – BERT ML Model
Glasserman, Paul and Lin, Caden: Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis
https://ssrn.com/abstract=4586726
Abstract:
Large language models (LLMs), including ChatGPT, can extract profitable trading signals from the sentiment in news text. However, backtesting such strategies poses a challenge because LLMs are trained on many years of data, and backtesting produces biased results if the training and backtesting periods overlap. This bias can take two forms: a look-ahead bias, in which the LLM may have specific knowledge of the stock returns that followed a news article, and a distraction effect, in which general knowledge of the companies named interferes with the measurement of a text’s sentiment. We investigate these sources of bias through trading strategies driven by the sentiment of financial news headlines. We compare trading performance based on the original headlines with de-biased strategies in which we remove the relevant company’s identifiers from the text. In-sample (within the LLM training window), we find, surprisingly, that the anonymized headlines outperform, indicating that the distraction effect has a greater impact than look-ahead bias. This tendency is particularly strong for larger companies — companies about which we expect an LLM to have greater general knowledge. Out-of-sample, look-ahead bias is not a concern but distraction remains possible. Our proposed anonymization procedure is therefore potentially useful in out-of-sample implementation, as well as for de-biased backtesting.
#334 – Volatility-Adjusted Momentum in Corporate Bonds
#576 – Boosted Regression Trees in Corporate Bonds
#619 – Trend Factor in Corporate Bonds
Dickerson, Alexander and Robotti, Cesare and Rossetti, Giulio: Noisy Prices and Return-based Anomalies in Corporate Bonds
https://ssrn.com/abstract=4575879
Abstract:
We argue that the documented large abnormal returns to investors from corporate bond anomalies such as return reversals and momentum mainly stem from ignoring market microstructure noise in transaction-based bond prices and relying on ad hoc return winsorization. To address these issues, we construct bond data that is largely free of microstructure noise and closely mimics industry-grade quote data. We revisit prior findings in the literature and provide conclusive evidence that return-based anomalies, once properly constructed, generate negligible average returns and alphas. Finally, we show that the considered return-based factors (and their underlying signals) are not related to average bond returns.
#460 – ESG Level Factor Investing Strategy
Ai, Li and Silva Gao, Lucia: Investors’ Perception of Climate Risk: Evidence from Weather Disaster Events
https://ssrn.com/abstract=4127803
Abstract:
While climate change impacts most regions, a company’s physical location and geographical diversification could determine how it is affected by the risks associated with climate change. We explore information from extreme climate events to study if and how they impact firm-level risk. The results indicate a positive association between the level of a firm’s exposure to catastrophic climate events, measured based on the location of its headquarters and affiliations, and both systematic and idiosyncratic volatility, suggesting that this risk is to some extent unpredictable and undiversifiable. Furthermore, geographical dispersion and corporate diversification increase the exposure of firms to the risk of extreme climate events. Our results also indicate that this effect increases with investor awareness and is mitigated by better environmental performance of the firm. Overall, our research contributes to a better understanding of the exposure of businesses to risks associated with climate change.
And several interesting free blog posts that have been published during the last 2 weeks:
Cyber Risk and the Cross-Section of Stock Returns
In today’s fast world, where information flows freely and transactions happen at the speed of light, the significance of cybersecurity cannot be overstated. But it’s no longer just a concern for IT professionals or tech enthusiasts. The specter of well-documented hacks and phishing incidents casts a long shadow over investors, acting as powerful illustrations of how security breaches, vulnerabilities, and cyber threats can reverberate through financial markets. In this blog post, we’ll delve into the intricate relationship between cybersecurity risk and stock performance, uncovering how these digital hazards can influence financial markets.
What’s the FED Perspective on Inflation Surprises and Equity Returns
The period of high inflation in the 1970s prompted researchers to carefully examine the relationship between inflation and stock returns and to look for ways to avoid unexpected inflation. The year 2022 brought back inflationary pressures to the U.S. economy not seen in more than 40 years, and this has spurred new efforts to answer long-standing questions about inflation and asset prices. Authors from the Board of Governors of the Federal Reserve System (2023) bring a fresh perspective on this topic, and their paper allows us to get a FED insider’s view on the ageless question of how inflation affects equity returns.
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
698 – Liquidity Volatility in Stocks
707 – Benchmarks Portfolios with Decreasing Carbon Footprints
944 – Overnight-Intraday Reversal in Futures
945 – Military Expenditures and Performance of the Stock Markets
