#802 – Fibonacci Supports and Resistances in Cross-Sectional Stock Trading
Period of rebalancing: Weekly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2012-2021
Indicative performance: 37.35%
Estimated volatility: 41.85%
Source paper:
Savva Shanaev and Ryan Gibson: Can Returns Breed Like Rabbits?, Econometric Tests for Fibonacci Retracements
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4215708
Abstract:
This study develops a novel and intuitive econometric test to investigate the predictive power and abnormal return-generating capacity of Fibonacci retracements. Results suggest Fibonacci retracements are prominent for international stock market indices and foreign exchange rates, with 0.0%, 38.1%, 50.0%, 61.2%, and 100.0% being the most important retracements, while the inclusion of 14.6%, 23.6%, 76.4%, 78.6%, or 85.4% levels reduces the predictive power of the model. The findings cannot be explained by calendar market anomalies or return reversals. On individual stock level, an S&P 500-based strategy that longs (shorts) stocks closer to Fibonacci retracement support (resistance) generates positive and statistically significant alpha in Fama-French multi-factor models as well as demonstrates market-timing properties.
#803 – Momentum and (Un)Informed Flows in FX market
Period of rebalancing: Daily
Markets traded: currencies
Instruments used for trading: CFDs, forwards, futures
Complexity: Very complex strategy
Backtest period: 2015-2019
Indicative performance: 11.74%
Estimated volatility: 11.53%
Source paper:
Barrios, Aldo and Franolic, Robert and Giovanardi, Davide and Melvin, Michael, Trading with the Informed and Against the Uninformed: Flows and Positioning in the Global Currency Market
https://ssrn.com/abstract=4215040
Abstract:
FX trade settlement data from CLS provides the most comprehensive view of the opaque market of OTC currency trades. We use the flows of investment funds and non-financial corporates and develop trading signals where the former reflects speculative strategies, while the latter trade for liquidity needs. The implication is we trade in the direction of the funds flows and trade against large corporate flows, which should be followed by price reversals. Trading with informed flows yields positive risk-adjusted performance. Incorporating the liquidity trades signal improves risk-adjusted performance and greatly lowers the tail risk of the model.
#804 – Skewness and 52-Week Highs in China
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2000-2021
Indicative performance: 13.08%
Estimated volatility: 11.49%
Source paper:
Wang, Zhuo and Wang, Ziyue and Wu, Ke: The Role of Anchoring on Investors’ Gambling Preference: Evidence from China
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4178162
Abstract:
This paper examines the anchoring effect of 52-week high price on the investors’ gambling preference in the Chinese A-share market. We document the gambling preference only exists among stocks valued much lower than their 52-week high prices. Specifically, using return skewness as a proxy for lottery stocks, we find that buying stocks in the lowest skewness quintile and selling the highest earns a significant risk-adjusted return of 0.85% per month for stocks far below their 52-week high prices. In contrast, it earns an insignificant return of 0.04% per month for stocks close to their 52-week high prices. Moreover, high arbitrage risk and investor sentiment strengthen the effect of anchoring on gambling preference. Our findings are robust after considering the effects of capital gains overhang, under-reaction to news, firm’s ownership status, and China’s non-tradable share reform.
#805 – Turn of the Month Effect in Cryptocurencies
Period of rebalancing: Daily
Markets traded: cryptos
Instruments used for trading: cryptos, ETFs
Complexity: Simple strategy
Backtest period: 2017-2021
Indicative performance: 29.83%
Estimated volatility: 24.71%
Source paper:
Vasileiou, Evangelos: Is the Turn of the Month an Anomaly on Which an Investment Strategy Could Be Based? Evidence From BitCoin and Ethereum
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4230561
Abstract:
This study examines the Turn of the Month effect (TOM) in BTC and ETH markets. In contrast to most calendar effect studies, we do not take for granted that the TOM period is the last trading day of the month up to the first three trading days (-1,3) as Lakonishok and Smidt (1988) proposed in their seminal paper, but we employ an optimization algorithm which tests several 4-day intramonth periods. Our findings confirm the existence of the TOM effect because the most profitable trading periods during the month are those during the last days of the month and the first trading days of the next one. The dominant (-1,3) definition belongs to this window, but it is not always the optimal. Based on these findings, we test whether or not a TOM-based strategy (according to which we invest only during the TOM days) outperforms a Buy-and-Hold (BnH) strategy. We reach the conclusion that the existence of a TOM effect does not always lead to higher profits in comparison with a Buy-and-Hold (BnH) strategy, but it presents better returns to risk reward. These findings could be useful for investors who want to invest in extremely risky assets, such as cryptocurrencies. If we use leveraged positions during the TOM days or expand the trading period by 4 to 5 or more days, a TOM-based strategy may lead to better results relative to a BnH. Further research in this direction should be carried out to examine the practical implications of TOM-based leveraged strategies in several asset classes.
#806 – Employee Sentiment and Stock Returns
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 2013-2020
Indicative performance: 7.5%
Estimated volatility: 10%
Source paper:
Chen, Jian and Tang, Guohao and Yao, Jiaquan and Zhou, Guofu: Employee Sentiment and Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4250903
Abstract:
We propose an employee sentiment index, complementing investor sentiment and manager sentiment indices, and find that high employee sentiment predicts low monthly (weekly) market returns significantly both in- and out-of-sample. The predictability can also deliver sizable economic gains for mean-variance investors in asset allocation. The impact of employee sentiment is found stronger among employees who work in the headquarters state and are less experienced. The economic driving force of the predictability is unique: high employee sentiment leads to high contemporaneous wage growth due to immobility, which subsequently results in lower firm cash flow and lower stock returns.
New research papers related to existing strategies:
#225 – Improved Merger Arbitrage
Quariguasi Frota Neto, Joao and Bozos, Konstantinos and Dutordoir, Marie and Nikolopoulos, Konstantinos: Forecasting M&A Shareholder Wealth Effects to Prevent Value-Destroying Deals: Can It Be Done?
https://ssrn.com/abstract=4199363
Abstract:
M&A announcements can result in substantial positive or negative abnormal acquiring-firm stock returns and sizeable associated dollar value gains or losses. Unfortunately for decision makers tasked with evaluating potential deals, the existing M&A literature focuses on the in-sample analysis of cross-sectional determinants of acquirer stock price reactions, thereby providing little guidance as to whether a certain deal will generate or reduce shareholder wealth. This paper instead focuses on the out-of-sample forecasting of acquirer share price reactions to M&A announcements. We employ acquirer-, target- and deal-specific features commonly used in the literature and test the accuracy of linear and nonlinear models using state-of-the-art Machine Learning methodology. Random Forest and k-Nearest Neighbor models perform best in terms of forecasting accuracy, but are closely followed by Ridge and OLS approaches. We further document the forecasting models’ ability to disentangle value-creating from value-destroying deals, and illustrate the substantial incremental monetary gains associated with following the suggested heuristic.
#597 – Idiosyncratic Tail Risk
Barunik, Jozef and Nevrla, Matěj: Common Idiosyncratic Quantile Risk
https://ssrn.com/abstract=4204916
Abstract:
We propose a new model of asset returns with common factors that shift relevant parts of the stock return distributions. We show that shocks to such non-linear common movements in the panel of firm’s idiosyncratic quantiles are priced in the cross-section of the US stock returns. Such risk premium is not subsumed by the common volatility, tail beta, downside beta, as well as other popular risk factors. Stocks with high loadings on past quantile risk in the left tail earn up to an annual five-factor alpha 7.4% higher than stocks with low tail risk loadings. Further, we show that quantile factors have predictive power for aggregate market returns.
#797 – Improved Post-Earnings Announcement Drift with NLP Analysis
Kantos, Christopher and Joldzic, Dan and Mitra, Gautam and Thi Hoang, Kieu: Comparative Analysis of NLP Approaches for Earnings Calls
https://ssrn.com/abstract=4210529
Abstract:
The field of natural language processing (NLP) has evolved significantly in recent years. In this chapter we consider two leading and well-established methodologies, namely, those due to Loughran McDonald, and FinBERT. We then contrast our approach to these two approaches and compare our performance against these methods which are considered to be benchmarks. We use S&P 500 market data for our investigations and describe the results obtained following our strategies. Our main consideration is the Earnings Calls for the S&P 500 stocks. We vindicate our findings and present the performance of our trading and fund management strategy which shows better results.
#239 – Large Price Changes combined with Analyst Revisions
#560 – The Cross-Section of Non-Professional Analyst Skill
#652 – Machine Learning Stock Analyst
Bastianello, Federico: Time-Series and Cross-Section of Risk Premia Expectations: A Bottom-Up Approach
https://ssrn.com/abstract=4204968
Abstract:
I construct a new dataset of subjective total return expectations at the single stock level, which I then aggregate at both market and portfolio level to construct the risk premia expectations of sell-side analysts. Total return expectations of these sophisticated stock market participants represent the natural counterparts to cash flow expectations which are currently widely used in the asset pricing literature. Sell-side analysts’ expectations appear to be countercyclical, contrarian and less persistent than CFOs’ expectations, have strong and consistent correlations with many model-based expected risk premium measures and imply a larger discount rate channel than CFOs’ and economists’ forecasts. In addition, sell-side analysts’ expectations are strongly positively correlated (almost 80%) with the square of the VIX index (VIX²). Sell-side analysts’ expected market risk premia forecasts are also able to predict realised stock market risk premia. Using sell-side analysts’ excess return forecasts, CAPM and Fama-French multi-factor models fit the cross-sectional dynamics of subjective expected excess returns remarkably well.
#207 – Value Factor – CAPE Effect within Countries
Chabi-Yo, Fousseni and Langlois, Hugues: Conditional Leverage and the Term Structure of Option-Implied Equity Risk Premia
https://ssrn.com/abstract=4130268
Abstract:
In a one-period economy, Martin (2017) and Chabi-Yo and Loudis (2020) derive bounds for the equity risk premium that use options of the same maturity as the horizon at which the premium is measured. In contrast, we provide an expression and an empirical methodology to measure the premium at a given horizon in a multi-period economy using options of multiple maturities. The premium depends on risk-neutral leverage effects and the expected future risk-neutral market variance and skewness, which contribute to increasing the premium at short horizons. Our measure outperforms in terms of prediction accuracy and portfolio allocation performance. The term structure of expected excess holding period returns is flatter on average and dramatically more negative during market turmoil than those implied by previous measures.
#297 – Combining Fundamental and Transitory Component of Value Strategy
Tidd, Nathan and Willem, Brian: Calculating Factor Fundamentals Using A Business Factor Model
https://ssrn.com/abstract=4113451
Abstract:
This paper presents the motivations, methodology, and initial empirical work for calculating quantitative “factor fundamentals” using a business factor model.
Unlike factor models that explain only equity returns, the business factoring approach seeks to identify a consistent set of factors that also explain the intrinsic value and market price of the companies underlying equity securities. The approach produces “factor fundamentals” such as factor earnings and factor valuations that may enable investors to 1) analyze investment factors much like individual companies, and 2) improve time-varying forecasts of factor returns, particularly by employing factor valuations which are known at the start of the investment period.
Empirical tests of factor fundamentals calculated using a business factor model of US companies from 1998-2019 showed, for virtually all factors tested, an inverse relationship between start-of-period factor valuations and subsequent factor returns. Further tests showed that a factor strategy varying with starting factor valuations performed favorably against a static “risk premium” strategy. Finally, tests showed factor volatility and skew measures also varied by start-of-period factor valuations, suggesting the possibility to improve upon ex-ante risk forecasts based solely on exponential decay (half-life) algorithms.
And several interesting free blog posts have been published during last 2 weeks:
A Simple Approach to Market-Timing Strategy Replication
In previous articles, we discussed the ideas behind portfolio replication with market factors. However, overall robustness of the results suffers significantly if the model portfolio or trading strategy we attempt to synthetize is driven by a market-timing model. We do not know the rules driving the underlying strategy we could apply ourselves beforehand. Furthermore, there is no simple mechanism of market-timing rule detection we could potentially utilize in our regression model. Hypothetically, we could include a variety of market-timing strategies into the factor universe. But since there are countless market-timing methods, covering everything is simply unrealistic. Particularly in context of historic factor universe construction. In an attempt to capture the effects of underlying timing rules, we came up with a simple approach to address this problem to a somewhat satisfactory extent.
Impact of Dataset Selection on the Performance of Trading Strategies
It would be great if the investment factors and trading strategies worked all around the world without change and under all circumstances. But, unfortunately, it doesn’t work like that. Some of the strategies are market-specific, as shown in this short analysis. The Chinese market has its own specifics, mainly higher representation of retail investors and lower efficiency. And it’s not alone; countless strategies work just in cryptocurrencies, selected futures, or some other derivatives markets. So, what’s the takeaway? Simple, it’s really important to understand that each anomaly is linked to the underlying dataset and market structure, and we need to account for it in our backtesting process.
Reviewing Patent-to-Market Trading Strategies
The following article is a short distillation of the research paper Leveraging the Technical Competence of a Stock for the Purpose of Trading written by Rishabh Gupta. The author spent a summer internship at Quantpedia, investigating the Patent-to-Market (PTM) ratio developed by Jiaping Qiu, Kevin Tseng, and Chao Zhang. The PTM ratio uses public information about the number and dates of patents assigned to publicly listed companies, calculates an expected market value of patents, and tries to predict future stock performance.
How to Paper Trade Quantpedia Backtests
Quantpedia’s mission is simple – we want to analyze and process academic research related to quant/algo trading and simplify it into a more user-friendly form to help everyone who looks for new trading strategy ideas. It also means that we are a highly focused quant-research company, not an asset manager, and we do not manage any clients’ funds or managed accounts. But sometimes, our readers contact us with a request to help them to translate strategy backtests performed in Quantconnect into paper trading or real-trading environment. The following article is a short case study that contains a few useful tips on how to do it.
Plus, the following trading strategies have been backtested in QuantConnect in the previous two weeks:
530 – Jump Risk in Stocks
794 – 24-Hour Reversal in Cryptocurrencies
795 – Lunar Monthly Effect in Chinese Stocks
797 – Improved Post-Earnings Announcement Drift with NLP Analysis
798 – Cash Holdings Effect and Net Operating Assets
