New strategies:
#807 – Production Complementarity Peer Momentum
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1978 – 2018
Indicative performance: 9.9%
Estimated volatility: 19.34%
Source paper:
Lee, Shi, Sun, Zhang: Production Complementarity and Momentum Spillover Across Industries
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4190096
Abstract:
Economic theory suggests production complementarity is an important driver of sectoral co-movements and business cycle fluctuations. We operationalize this concept by developing a measure of the production complementarity distance (COMPL) between any two companies. We find firms from different industries that are closely aligned in terms of COMPL exhibit strong co-movement in both fundamentals and stock returns. Further, we find a strong lead-lag effect in returns, such that a long-short strategy based on recent COMPL peer returns yields a monthly alpha of 137 basis points, with no reversals. This inter-industry momentum effect is not explained by common risk factors or other network-based effects such as industry membership, customer-supplier relations, and shared analyst coverage. We conclude cross-industry news transfer occurs along complementarity networks, but stock prices do not update instantaneously.
#808 – Market Timing with Relative Sentiment
Period of rebalancing: Weekly
Markets traded: equities
Instruments used for trading: CFDs, ETFs, futures
Complexity: Complex strategy
Backtest period: 2004-2017
Indicative performance: 12.11%
Estimated volatility: 12.88%
Source paper:
Micaletti, Raymond: Want Smart Beta? Follow the Smart Money: Market and Factor Timing Using Relative Sentiment
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3164081
Abstract:
We present a real-time, cross-asset, positions-based relative sentiment indicator to predict the U.S. equity market. Derived from the Commitments of Traders report, the indicator measures — in a novel way — the aggregate positioning in equities of institutional investors relative to individual investors. Applying a wide range of statistical tests and controlling for data snooping, we find this indicator to be highly significant, exceptionally robust, and substantially more powerful than both value and momentum in predicting equity returns. Beyond the broad market, the indicator also facilitates the timing of several “smart beta” equity factors — many of which were thought difficult or impossible to time. We propose a tactical asset allocation strategy based on the indicator and compare it to several value- and/or momentum-based alternatives — finding the proposed strategy produces higher returns (both absolute and risk-adjusted), while having considerably less time-averaged exposure to equities.
#809 – Value Effect in Unprofitable Firms
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 1972-2020
Indicative performance: 14.84%
Estimated volatility: 36.55%
Source paper:
Mohrschladt, Hannes and Siedhoff, Susanne: The Valuation of Loss Firms: A Stock Market Perspective
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4239613
Abstract:
The proportion of exchange-listed firms with negative earnings has increased to over 40% in recent years. Given that the fundamental value of these loss firms is difficult to determine, we expect particularly strong value effects among these firms. We find that the return predictability associated with book-to-market and revenue-to-price is indeed significantly stronger compared to gain firms. Our further analyses on financial analysts, earnings announcement returns, short selling activities, option trading, and limits to arbitrage support a behavioral mechanism for our main finding.
#810 – Integrating ESG into Fixed Income Portfolios
Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: bonds
Complexity: Complex strategy
Backtest period: 2012-2021
Indicative performance: 4.23%
Estimated volatility: 3.93%
Source paper:
Andrew Clare, Aneel Keswani and Nick Motson: The Case for Integrating ESG into Fixed Income Portfolios
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4150263
Abstract:
In this paper, we investigate the possible benefits of integrating ESG considerations into fixed-income portfolio construction. When we create portfolios ranked by a composite ESG value or by an environmental ranking we find evidence to suggest that higher ESG rankings produce an improvement in risk-adjusted returns. When we use industry-standard tilting methodologies we find that portfolios can be tilted towards a particular ESG characteristic without having a material effect on the risk and return characteristics of the portfolios. Finally, we find some limited evidence to suggest that enhancing the ESG credentials of a portfolio can lead to a reduction in the tail risk of a portfolio, that is, it helps to reduce the frequency of extreme downside outcomes.
#811 – Intraday Closing Momentum in Futures
Period of rebalancing: Intraday
Markets traded: bonds, commodities, currencies, equities
Instruments used for trading: CFDs, futures
Complexity: Complex strategy
Backtest period: 2012-2021
Indicative performance: 5.47%
Estimated volatility: 3.42%
Source paper:
Guido Baltussen, Zhi Da, Sten Lammers, and Martin Martens: Hedging demand and market intraday momentum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3760365
Abstract:
Hedging short gamma exposure requires trading in the direction of price movements, thereby creating price momentum. Using intraday returns on over 60 futures on equities, bonds, commodities, and currencies between 1974 and 2020, we document strong “market intraday momentum” everywhere. The return during the last 30 minutes before the market close is positively predicted by the return during the rest of the day (from previous market close to the last 30 minutes). The predictive power is economically and statistically highly significant, and reverts over the next days. We provide novel evidence that links market intraday momentum to the gamma hedging demand from market participants such as market makers of options and leveraged ETFs.
New research papers related to existing strategies:
#569 – Intraday Time-series Momentum in Chinese Futures
Ma, Gaoping and Bouri, Elie and Xu, Yahua and Zhou, Z. Ivy: Night Trading and Intraday Return Predictability: Evidence from Chinese Metal Futures Market
https://ssrn.com/abstract=4282926
Abstract:
In 2013, the Shanghai Futures Exchange (SHFE) introduced a night session in Chinese metal futures markets. Using high-frequency data of gold, silver, and copper futures, we investigate the impact of night trading on intraday return predictability in Chinese metal futures markets. Firstly, we find the intraday return predictability has changed after introducing night trading: before the launch of night trading, the first half-hour daytime returns show significant predictability, whereas the first half-hour night returns exhibit forecasting power after that. Such changes can be explained by the immediate reactions of domestic investors to international news released in the evening. Secondly, the market timing strategy outperforms the always-long and buy-and-hold benchmark strategies. Thirdly, the predictability of night return is stronger on days with higher volatility and volume. Furthermore, stronger intraday predictability is associated with global news releases and positive news sentiment, suggesting enhanced connectedness of Chinese and international metal futures markets after the launch of night trading.
#121 – Hedgers’ Effect in FX
Bräuer, Leonie and Hau, Harald: Can Time-Varying Currency Risk Hedging Explain Exchange Rates?
https://ssrn.com/abstract=4273439
Abstract:
Over the last decade foreign bond portfolio positions in US dollar assets have risen above the reciprocal US investor positions in foreign currencies. In periods of increased economic uncertainty, institutional investors hedge their international bond positions, which creates a net hedging demand for dollar assets that depreciates USD rates in both the forward and spot markets. We document the time-varying nature of this net hedging demand and show how it relates to eco-nomic uncertainty and the US net foreign bond position in various currencies. Based on a parsimonious VAR model, we find that changes in FX hedging pressure can account for approximately 30% of all monthly variation in the seven most important dollar exchange rates from 2012 to 2022.
#409 – Trading Volume in Cryptocurrency Markets and Reversals
#755 – Mean-reversion and Trend-Following Based on MIN and MAX in BTC
Mofakham, Parisa: Bitcoin Investors’ Style, Skill, and Sentiment
https://ssrn.com/abstract=4244231
Abstract:
I examine the style and skill of small, medium, and large Bitcoin traders using order book data from the largest U.S.-based exchange, Coinbase. I find that all but the smallest traders are contrarian—they buy when prices have fallen and vice versa—and the larger the traders, the more contrarian they are. Additionally, large Bitcoin traders anchor to the 30-, 90-, and 120-day highs. On the other hand, smaller investors mainly trade based on investor sentiment and attention. Decomposing the order flows, I find that contrarian trades positively predict Bitcoin returns, and consistently, larger traders who are contrarian on average show evidence of market timing skills. On the other hand, sentiment- and attention-induced trading, which are mainly implemented by smaller traders, have no predictive power for returns. Overall, the results are consistent with larger traders being compensated for providing liquidity to smaller investors’ sentiment and attention-driven trades.
#130 – Investment Factor
#224 – Profitability Factor Combined with Value Factor
#736 – Expected profitability in UK Stocks
#746 – Combined Value and Profitability in US and Chinese Equities
Conlon, Thomas and Cotter, John and Jin, Chenglu: Horizon-Dependent Profitability and Investment Factors: International Evidence
https://ssrn.com/abstract=4243874
Abstract:
The sensitivity of systematic factors to the return horizon has attracted researchers’ attention, while the horizon effect on profitability and investment has not yet been assessed. In this paper, we investigate the pricing roles of these two factors, using overlapping profitability and investment returns over horizons from 1 month to 5 years. Using different combinations of lefthand-side test portfolios and factors in the US market as well as international data in 6 other regions, we find consistent evidence that profitability appears to be pervasively more important across the majority of horizons. In particular, the profitability factor has significant pricing power in most horizons, while the investment factor is only priced when 2 to 5-year horizons are considered. Japan stands out as an exception, where the profitability or investment factors have no cross-sectional explanatory power for expected returns across the majority of horizons.
#510 – Factor Momentum
#534 – Time Series Factor Momentum
#578 – Combining Smart Factors Momentum and Market Portfolio
#738 – Mean Variance Factor Timing
Geertsema, Paul G. and Lu, Helen: Patient Capital and Long-run Expected Stock Returns
https://ssrn.com/abstract=4212133
Abstract:
Models of expected returns are typically evaluated using monthly stock returns. However, it is not clear that one-month-ahead expected returns are appropriate for longer-horizon “patient capital” investors. We use gradient boosting machines to create predictive models of expected returns at horizons ranging from 1 month to 5 years in panel data. We find that long-horizon expected returns are distinct from 1-month expected returns. More than 80% of the variation in the 5-year expected return is unexplained by variation in the 1-month expected return. The stylized fact that market-based indicators are the most important predictors of expected returns is shown to be specific to one-month ahead predictions and does not generalize to expected returns at longer horizons. Instead, the drivers of long-horizon returns are dominated by size, industry, equity issuance and fundamental predictors. The application of common benchmark models to predicted-return strategies yield alphas that remain economically and statistically significant at all horizons, suggesting that expected returns are not well modelled by the linear factor models prevalent in the literature.
#53 – Sentiment and Style Rotation Effect in Stocks
#54 – Momentum and State of Market (Sentiment) Filters
Ashour, Samar and Hao, Grace Qing and Harper, Adam: Investor Sentiment, Style Investing, and Momentum
https://ssrn.com/abstract=4120550
Abstract:
Investor sentiment is an important condition for style investing in affecting asset price predictability. We find that style returns have predictive power for future stock returns in high sentiment periods, but not low sentiment periods. The correlation between style returns and stock returns explains the variation in momentum profits in high sentiment periods, but not low sentiment periods. Sentiment has an interaction effect with style returns, but not market returns. While positive style returns predict future stock returns under high sentiment, negative style returns do not. The effect of investor sentiment on style investing is independent of prior market returns.
#264 – Dividend Risk Premium Strategy
Wang, Tony: Excess Return Properties of Informationally Insensitive Dynamic Return Predictability Strategies
https://ssrn.com/abstract=4208583
Abstract:
Is return predictability sufficient to generate excess returns? I document excess returns associated with investment strategies whose asset allocation varies proportionally to dividend yields and dynamically on a monthly frequency. These strategies are simple in the sense that they are informationally-insensitive and rebalance infrequently; they are thus accessible and potentially appealing to investors with stable risk tolerance. I find that the strategies generate excess returns compared to the benchmark. Excess returns maintain for holding periods greater than three years, but do not in general increase with holding period length. My finding of excess returns from a low-frequency trading strategy based only on publicly-observable dividend yields suggests strong time series return predictability for the market as a whole.
And several interesting free blog posts have been published during last 2 weeks:
How Much Are Bitcoin Returns Driven by News?
The main theme of these days in the crypto world is unmistakenly clear, it’s the mayhem connected with the collapse of the FTX empire, insolvencies of various lenders, and questions about underlying holdings in GBTC OTC ETF and reserves of exchanges and Tether (or other stablecoins as well). With new information, nothing does paint a bright picture of this industry in the financial world now and in the near future. Calls for finally working regulations are getting stronger and stronger, while politicians (and central bankers) are still active on Central Bank Digital Currencies (CBDCs) proposals. While Bitcoin survived several crypto winters, long-term investors are continuing their DCA-ing and “stashing Satoshis” Are they safe? Do they pay attention to the surrounding news? In our blog entry, we will focus on the question of how news impact Bitcoin returns, being both the most famous cryptocurrency and also the one with the highest market capitalization.
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
#537 – The Positive Similarity of Company Filings and Stock Returns
#562 – Betting On and Against the Right Semibetas
#783 – Cash Hedged Momentum
#804 – Skewness and 52-Week Highs in China
#805 – Turn of the Month Effect in Cryptocurencies
