New strategies:
#625 – Global Imbalance Currency Factor
Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: CFDs, forwards, futures
Complexity: Moderately complex strategy
Backtest period: 1983-2014
Indicative performance: 4.4%
Estimated volatility: 6.43%
Source paper:
Pasquale Della Corte, Steven J. Riddiough, Lucio Sarno: Currency Premia and Global Imbalances
https://www.cass.city.ac.uk/__data/assets/pdf_file/0013/302080/FX_NFA_full.pdf
Abstract:
We show that a global imbalance risk factor that captures the spread in countries external imbalances and their propensity to issue external liabilities in foreign currency explains the cross-sectional variation in currency excess returns. The economic intuition is simple: net debtor countries ofer a currency risk premium to compensate investors willing to finance negative external imbalances because their currencies depreciate in bad times. This mechanism is consistent with exchange rate theory based on capital flows in imperfect financial markets. We also find that the global imbalance factor is priced in cross sections of other major asset markets.
#626 – Image Recognition in Stock Price Charts Predicts Stock Returns
Period of rebalancing: Weekly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 2001-2019
Indicative performance: 12.11%
Estimated volatility: 6.96%
Source paper:
Jiang, Jingwen and Kelly, Bryan T. and Xiu, Dacheng: (Re-)Imag(in)ing Price Trends
https://ssrn.com/abstract=3756587
Abstract:
We reconsider the idea of trend-based predictability using methods that flexibly learn price patterns that are most predictive of future returns, rather than testing hy- pothesized or pre-specified patterns (e.g., momentum and reversal). Our raw predictor data are images—stock-level price charts—from which we elicit the price patterns that best predict returns using machine learning image analysis methods. The predictive patterns we identify are largely distinct from trend signals commonly analyzed in the literature, give more accurate return predictions, translate into more profitable invest- ment strategies, and are robust to a battery of specification variations. They also appear context-independent: Predictive patterns estimated at short time scales (e.g., daily data) give similarly strong predictions when applied at longer time scales (e.g., monthly), and patterns learned from US stocks predict equally well in international markets.
#627 – Hedging Portfolio
Period of rebalancing: Intraday
Markets traded: bonds, commodities, currencies, equities
Instruments used for trading: bonds, ETFs, futures, options, stocks
Complexity: Complex strategy
Backtest period: 1973-2020
Indicative performance: 8.03%
Estimated volatility: 13.25%
Source paper:
Bhansali, Vineer and Holdom, Jeremie: Diversifying Diversification: Downside Risk Management with Portfolios of Insurance Securities
https://ssrn.com/abstract=3740222
Abstract:
Investors are always in search of diversifying securities and strategies to assist in downside risk management. We consider six popular diversifying securities, i.e. Gold, Swiss Franc, Japanese Yen, Bond Futures, S&P 500 80% strike Put Options, and Trend Following strategies in this paper. Using fifty years of data, we demonstrate that a portfolio approach to diversification strategies results in more robust outcomes when combined with a portfolio which has large equity exposure. While each of the individual securities can be more or less beneficial in specific periods and environments, we conclude that a simple portfolio approach to diversification, whether optimized or not, allows investors to robustly manage risk while not being overly concentrated.
#628 – Social Media Sentiment Factor
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Complex strategy
Backtest period: 1998-2017
Indicative performance: 3.7%
Estimated volatility: 5.98%
Source paper:
Koeppel, Christian: Does Social Media Sentiment Matter in the Pricing of U.S. Stocks?
https://ssrn.com/abstract=3771788
Abstract:
This paper applies a recently developed social media-based sentiment proxy for the construction of a new risk factor for sentiment-augmented asset pricing models on U.S. equities. Accounting for endogeneity, autocorrelation and heteroskedasticity in a GMM framework, we find that the inclusion of sentiment significantly improves the performance of the five-factor model from Fama and French (2015, 2017) for di ff erent industry and style portfolios like size, value, profitability, investment. The sentiment risk premium provides the missing component in the behavioral asset pricing theory of Shefrin and Belotti (2008) and (partially) resolves the pricing puzzles of small extreme growth, small extreme investment stocks and small stocks that invest heavily despite low profitability.
#624 – Residual Credit Spread Predicts Corporate Bond Returns
Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: bonds
Complexity: Moderately complex strategy
Backtest period: 2003-2018
Indicative performance: 6.04%
Estimated volatility: 4.36%
Source paper:
Cici, Gjergji and Zhang, Pei: On the Valuation Skills of Corporate Bond Mutual Funds
https://ssrn.com/abstract=3803890
Abstract:
We introduce a novel measure to assess the valuation skills of investment-grade corporate bond funds. Our measure recognizes funds ex-ante holding a higher fraction of underpriced bonds as having better valuation skills. The measure predicts future fund performance, is stable over time, and is unrelated to other sources of skill. Fund investors recognize such skill by responding more to the past performance of funds with better valuation skills. Consistent with the equilibrium model of Gârleanu and Pedersen (2018), our evidence suggests that as growing capital gets allocated to skilled bond funds, the investment-grade corporate bond market is becoming more efficient.
New research papers related to existing strategies:
#35 – Insiders Trading Effect in Stocks
Avci, Sureyya Burcu and Schipani, Cindy A. and Seyhun, H. Nejat and Verstein, Andrew: Insider Giving
https://ssrn.com/abstract=3795537
Abstract:
Corporate insiders can avoid losses if they dispose of their stock while in possession of material, non-public information. One means of disposal, selling the stock, is illegal and subject to prompt mandatory reporting. A second strategy is almost as effective and it faces lax reporting requirements and enforcement. That second method is to donate the stock to a charity and take a charitable tax deduction at the inflated stock price. “Insider giving” is a potent substitute for insider trading. We show that insider giving is far more widespread than previously believed. In particular, we show that it is not limited to officers and directors. Large investors appear to regularly receive material non-public information and use it to avoid losses. Using a vast dataset of essentially all transactions in public company stock since 1986, we find consistent and economically significant evidence that these shareholders’ impeccable timing likely reflects information leakage. We also document substantial evidence of backdating – investors falsifying the date of their gift to capture a larger tax break. We show why lax reporting and enforcement encourage insider giving, explain why insider giving represents a policy failure, and highlight the theoretical implications of these findings to broader corporate, securities, and tax debates.
#597 – Idiosyncratic Tail Risk
#517 – Predicting Bond Returns with Equity Return
Rubin, Mirco and Ruzzi, Dario: Equity Tail Risk in the Treasury Bond Market
https://ssrn.com/abstract=3826403
Abstract:
This paper quantifies the effects of equity tail risk on the US government bond market. We estimate equity tail risk as the option-implied stock market volatility that stems from large negative jumps as in Bollerslev, Todorov and Xu (2015), and assess its value in reduced-form predictive regressions for Treasury returns and an affine term structure model for interest rates. We document that the left tail volatility of the stock market significantly predicts one-month-ahead excess returns on Treasuries both in- and out-of-sample. The incremental value of employing equity tail risk as a return forecasting factor can be of economic importance for a mean-variance investor trading bonds. The estimated term structure model shows that equity tail risk is priced in the US government bond market. Consistent with the theory of flight-to-safety, we find that Treasury prices increase and funds flow from equities into bonds when the perception of tail risk is higher. Our results concerning the predictive power and pricing of equity tail risk extend to major government bond markets in Europe.
#5 – FX Carry Trade
Demirer, Riza and Yuksel, Asli and Yuksel, Sadettin Aydin: Time-Varying Risk Aversion and Currency Excess Returns
https://ssrn.com/abstract=3820056
Abstract:
This paper documents an economically significant risk premium associated with a currency’s sensitivity to time-varying risk aversion. Consequently, an investment strategy that takes a long (short) position in currencies with high (low) sensitivity to the aggregate market risk aversion yields significantly positive excess returns. While advanced market currencies including the Euro, Yen and Swiss Francs dominate the short end of these portfolios with low sensitivity to risk aversion, emerging market currencies including the Brazilian Real, Mexican Peso and Turkish Lira are found to be the most sensitive currencies to risk aversion. The excess returns from the proposed strategy are significant even after controlling for systematic equity market risk factors as well as liquidity risk and cannot be explained by measures of economic conditions or uncertainty. Interestingly the excess returns generated by the risk aversion based strategy are found to have significant loadings on global momentum, suggesting possible commonality in the behavioral drivers of anomalies in the global equity and currency markets. The findings highlight the role of behavioral factors as predictor of currency excess returns with significant investment implications.
#586 – Genetic Programming Predicts Stock Returns
Mohamed Anwar Abdelsalam Mohamed Ismail:Using Particle Swarm Optimization for Market Timing Strategies
https://kar.kent.ac.uk/87076/1/138IsmailMohamed2021phd.pdf
Abstract:
Market timing is the issue of deciding when to buy or sell a given asset on the market. As one of the core issues of algorithmic trading systems, designers of such system have turned to computational intelligence methods to aid them in this task. In this thesis, we explore the use of Particle Swarm Optimization (PSO) within the domain of market timing. PSO is a search metaheuristic that was first introduced in 1995 [28] and is based on the behavior of birds in flight. Since its inception, the PSO metaheuristic has seen extensions to adapt it to a variety of problems including single objective optimization, multiobjective optimization, niching and dynamic optimization problems. Although popular in other domains, PSO has seen limited application to the issue of market timing. The current incumbent algorithm within the market timing domain is Genetic Algorithms (GA), based on the volume of publications as noted in [40] and [84]. In this thesis, we use PSO to compose market timing strategies using technical analysis indicators. Our first contribution is to use a formulation that considers both the selection of components and the tuning of their parameters in a simultaneous manner, and approach market timing as a single objective optimization problem. Current approaches only considers one of those aspects at a time: either selecting from a set of components with fixed values for their parameters or tuning the parameters of a preset selection of components. Our second contribution is proposing a novel training and testing methodology that explicitly exposes candidate market timing strategies to numerous price trends to reduce the likelihood of overfitting to a particular trend and give a better approximation of performance under various market conditions. Our final contribution is to consider market timing as a multiobjective optimization problem, optimizing five financial metrics and comparing the performance of our PSO variants against a well established multiobjective optimization algorithm. These algorithms address unexplored research areas in the context of PSO algorithms to the best of our knowledge, and are therefore original contributions. The computational results over a range of datasets shows that the proposed PSO algorithms are competitive to GAs using the same formulation. Additionally, the multiobjective variant of our PSO algorithm achieved statistically significant improvements over NSGA-II.
#591 – The Cross-Section of Non-Professional Analyst Skill
Dim, Chukwuma: Should Retail Investors Listen to Social Media Analysts? Evidence from Text-Implied Beliefs
https://ssrn.com/abstract=3813252
Abstract:
Social media is increasingly affecting financial markets, with important implications for market efficiency. This paper uses machine learning to infer beliefs of nonprofessional social media investment analysts (SMAs) from opinions expressed about individual stocks on social media. On average, SMA beliefs are informative about future abnormal returns and earnings surprise. However, there exists substantial heterogeneity in SMAs’ ability to form beliefs that generate value. A small fraction, 10%, of SMAs form beliefs that yield a sizeable abnormal return of 56 bps over a 5-day window, while the remaining 90% generate only 6 bps over the same horizon. SMA characteristics such as specialization, skin in the game, effort, popularity, and disagreement matter for belief formation skill. When forming beliefs, SMAs herd on the consensus; herding is less pronounced in bad times and when the consensus is optimistic, but more pronounced when the consensus is correct ex-post. SMAs also extrapolate from past returns but are less bullish about lottery-type stocks.
And two interesting free blog posts have been published during last 2 weeks:
Pump and Dump in Cryptocurrencies
It is striking how cryptocurrencies are both similar and dissimilar to the more established asset classes at the same time. On the one hand, many findings from traditional asset classes also apply to this novel class. On the other hand, this “new” world with its own characteristics brings many novel “problems” that attract researchers. This week’s blog presents several research papers connected to the pump and dump schemes in cryptos. These pumps and dumps are nothing new, and we already know them from the stock market. However, there are some notable differences…
Measuring Financial Investors Presence in Commodities
No doubt, the financialization in commodities was a significant breakpoint in markets and research as well. Many commodity strategies in Quantpedia’s screener are linked to financialization. It would be naive to think that the speculation in the commodity futures which has emerged did not influence the dynamics of the market. With the increased speculative trading, the Commodity Futures Trading Commission started collecting the net positions, but this dataset did not include all the data and often was connected with misreporting (and is not published anymore). The novel research paper of Adams, Collot and Rossi (2021) offers a different insight on this topic. It shows how to measure the influence using the term structure of commodity prices, focusing on crude oil. The authors suggest that during normal times, the term structure of crude oil futures should be smooth. They consider the term structure that starts with spot price and includes futures with one to twelve-month maturities, but they omit the one and two-month futures (since those are mostly used for speculation). The key finding is that when they estimate the missing futures based on the other prices using the smooth spline interpolation, this estimated term structure curve can be compared to the realized one. The deviation from the predicted (estimated) curve can be interpreted as the degree of speculation in commodity futures markets. As a result, with the mathematical modelling, the authors offer an interesting insight into speculation without any external datasets.
Plus, the following six trading strategies have been backtested in QuantConnect in the previous two weeks:
#293 – Momentum Effect in Anomalies v2
#445 – Hazard Fear in Commodity Markets
#481 – Holding Artificial VIX in a Portfolio
#511 – Cheap Options Are Expensive
#527 – Employee Satisfaction, ESG and Stock Returns
#616 – Output Gap Predicts FX Returns
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