New strategies:
#630 – Machine Learning in Commodities
Period of rebalancing: Monthly
Markets traded: commodities
Instruments used for trading: futures
Complexity: Very complex strategy
Backtest period: 2010-2019
Indicative performance: 6.81%
Estimated volatility: 8.88%
Source paper:
Rad, H., Low, R. K. Y., Miffre, J., & Faff, R. W: The commodity risk premium and neural networks
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3816170
Abstract:
The paper uses linear and nonlinear predictive models to study the linkage between a set of 128 macroeconomic and financial predictors and subsequent commodity futures returns. The linear models use shrinkage methods based on naive averaging and principal components. The nonlinear models use feedforward deep neural networks either as stand-alone (DNN) or in conjunction with LSTM, a recurrent long short-term memory network. Out of the four specifications considered, the LSTM-DNN architecture is the most successful at transforming the 128 predictive variables into profitable investment strategies. The risk premium then modelled is unrelated to, and exceeds, those earned on previously-published characteristic-sorted portfolios. Our analysis is robust to the presence of transaction costs and illiquidity.
#631 – US Climate Policy News and the Cross-section of Stocks
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 2000-2018
Indicative performance: 12.15%
Estimated volatility: 16.79%
Source paper:
Faccini, Renato and Matin, Rastin and Skiadopoulos, George: Dissecting Climate Risks: Are they Reflected in Stock Prices?
https://ssrn.com/abstract=3795964
Abstract:
We construct novel proxies of aggregate physical and transition climate risks by conducting textual analysis of Reuters climate-change news over 2000-2018. This analysis uncovers four textual risk factors related to the topics of U.S. climate policy, international summits, natural disasters, and global warming, respectively. The first two factors proxy transition risks, whereas the last two proxy physical risks. We find that only the climate policy factor is priced in the U.S. stock market, with the evidence being more pronounced over 2012-2018. The documented positive premium is consistent with the argument that investors hedge short-term transition risks. We validate this explanation using a narrative approach to mark the content of climate news. Our results imply that investors’ attention is an important driver of asset returns.
#632 – Classification of Stocks as Anomaly Longs
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1990-2019
Indicative performance: 23.87%
Estimated volatility: 17.38%
Source paper:
Han, X: Risks versus Mispricing : Decomposing Asset Pricing
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3604970
Abstract:
I use classification-based machine-learning methods to decompose 32 anomaly payoffs into risk exposures and mispricing. The component driven by risks earns statistically insignificant returns, despite its efficacy in explaining the time-series variation in anomaly payoffs. The mispricing component is driven by biased cash flow expectations and earns significant returns that subsume anomaly payoffs. These findings indicate that the unconditional averages of anomaly returns can be fully explained by biased expectations, whereas risk exposures play an important role in explaining the time-series variation in anomaly returns.
#633– Liquidity Growth Factor
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 1968-2018
Indicative performance: 7.18%
Estimated volatility: 11.29%
Source paper:
Robert Snigaroff and David Wroblewski: Earnings and Liquidity Factors
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3825213
Abstract:
A model with factors for earnings, liquidity, their respective growth, and the market can offer a consumption rationale with low pricing error. It also subsumes one-year momentum and momentum net of reversal, the factor commonly known as ‘momentum.’ These earnings and liquidity factors are all significant and combine for a model without factor redundancy. Motivated by investors’ ability to establish positions, we construct portfolios based on volume, and reconcile liquidity into reduced form characteristics-based factor models that compliment firm-based factors.
#634 – Chronological Return Ordering
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Backtest period: 1990 – 2020
Indicative performance: 12.01%
Estimated volatility: 12.69%
Source paper:
Cakici, Zaremba: Chronological Return Ordering and the Cross-Section of International Stock Returns
https://ssrn.com/abstract=3832358
Abstract:
Investors often focus their attention on recent information only, underestimating the relevance of information from the distant past. In consequence, the ordering of historical returns robustly predicts future stock performance in the cross-section. Using data from 49 countries, we comprehensively examine this anomaly within international markets. A value-weighted spread portfolio of global stocks that is formed on chronological return ordering earns 0.91% per month. The effect is distinctly robust and prevails among the biggest and most liquid companies. The mispricing is particularly strong in countries that are characterized by high individualism and shareholder protection. Furthermore, the return predictability is concentrated following down markets and periods of excessive volatility.
New research papers related to existing strategies:
#41 – Turn of the Month in Equity Indexes
Vasileiou, Evangelos: Turn-of-the-Month Effect, FX Influence, and Efficient Market Hypothesis: New Perspectives from the Johannesburg Stock Exchange
https://ssrn.com/abstract=3840993
Abstract:
This paper examines the Turn of the Month (TOM) effect in the highly capitalized emerging South African stock market which presents this calendar not only in the stock market, but in the USDZAR FX market also. These characteristics enable us to gain new perspectives on the study of the TOM effect. Specifically, we show that the inefficiencies in FX market (against a hard currency) influence not only the domestic stock market’s performance but also its Calendar Anomalies (CA). Additionally, we present some practical strategies based on the TOM effect which can prove beneficial for investors and outperform the stock market.
#542 – Committee Portfolio Selection
Bessler, Wolfgang and Taushanov, Georgi and Wolff, Dominik: Optimal Asset Allocation Strategies for International Equity Portfolios
https://ssrn.com/abstract=3839816
Abstract:
With greater economic and financial market integration, it is critical for asset managers to choose the investment universe that provides superior diversification and performance op-portunities. Therefore, it is important to investigate whether international diversification benefits arise from industry rather than country allocations. We employ various asset alloca-tion strategies such as 1/N, ‘Risk-Parity’, Minimum-Variance as well as Mean-Variance, Bayes-Stein and Black-Litterman to analyze whether an industry-based or a country-based approach provides a superior performance. We also investigate time-varying effects for ex-pansionary and recessionary sub-periods, equity-only and equity-bond portfolios as well as portfolios with and without short positions. For the 1986-2020 period, we find that industry-based asset allocation strategies attain higher Sharpe and Omega ratios and higher alphas compared to country-based allocations. The Sharpe ratio differences are economically rele-vant yet statistically insignificant in many analyzed settings. The outperformance of sector allocations are independent of the optimization approach and implemented constraints and whether bonds are included in the investment universe. This is consistent with the observa-tion that countries have become more integrated and higher correlated than industries, re-sulting in lower country and relatively higher industry diversification benefits. Especially for periods with unpredictable shocks, industry allocations have superior performance. Our results have important implications for international asset allocation decisions.
#589 – Order Imbalances at Closing Auctions
Chan, Kalok and Yao, Chen: The Effect of a Closing auction on Market Quality and Market Efficiency in the Stock Exchange of Hong Kong
https://ssrn.com/abstract=3839185
Abstract:
Hong Kong introduced the Closing Auction Session (CAS) to the HKEX securities market in 2016. Under the CAS, trading is extended by an extra 8-10 minutes after the daily trading session ends at 16:00 to determine the closing price for the day. Trading under the CAS is in the form of a single price auction, at which buyers and sellers submit orders so that the trading system determines a market clearing closing price for each security and orders are executed at that price. CAS was introduced in three phases, covering constituent stocks of Hang Seng Composite LargeCap and MidCap Index since July 25, 2016, constituent stocks of Hang Seng Composite SmallCap Index since July 26, 2017, and all equities, funds, and leveraged and inverse products since October 8, 2019. The introduction of CAS is to meet the needs of some investors, such as index fund managers, who need to trade at the closing price. This study investigates the effect of CAS on market liquidity and price efficiency, two major aspects of financial market quality, to see whether it may safeguard against extreme price volatility and improves price efficiency. The analysis is conducted based on Hang Seng LargeCap and Midcap stocks after the introduction of CAS in July 2016. Since its inception, the trading volume for LargeCap and MidCap stocks transacted during the CAS has experienced steady growth, with the percentage of volume accounting for more than 8% by June 2018. The CAS draws volume mostly from the end of the continuous trading session, with the last 15-minute interval (15:45-16:00) contributing the largest volume migration. On the other hand, the decline in volume in the rest of the continuous session is very small. To address our main research question, we examine the effect of CAS on a variety of market liquidity and price efficiency measures. Regarding the impact of CAS on market liquidity, we find that the introduction of the close auction results in a slight decline in liquidity during the continuous trading session, with the reduction of liquidity most pronounced in the last 15-minute interval, as evidenced by a wider bid-ask spread and lower depth. However, the price impact (defined as the absolute return divided by the trading volume) in the last 15-minute decreases significantly. Regarding the impact of CAS on price efficiency, if the uninformed traders migrate to the close auction from the daily session, we expect a temporary price pressure near the close of the daily session to fall and a weaker subsequent price reversal. Using the return reversals observed subsequent to the end of the daily trading session, from the trading price at 16:00 to the trading price of the next day, we find that this is indeed the case. There is a general pattern of return reversal, but the magnitude of the reversal is smaller after the introduction of CAS. This indicates that price is more efficient after the implementation of CAS. The finding is in line with the evidence suggesting that passive funds shift from the continuous trading session to the closing auction session. These passive funds are incentivised to trade near the market close, as they are benchmarked against the index they track based on the closing price. Since these passive fund investors demand liquidity from the market, their migration to the closing auction session will lower liquidity in the last 15-minute period of the continuous trading session but can increase price efficiency at the market close. Overall, the introduction of CAS by HKEX is beneficial to many market participants who use the closing price as a reference price for the day-end settlement of many financial securities, such as mutual funds’ NAV, the asset value of exchange-tr aded funds.
#447 – Logistic Regression and Momentum-Based Trading Strategy
Murray, Scott and Xiao, Houping and Xia, Yusen: Charting By Machines
https://ssrn.com/abstract=3853436
Abstract:
We test the efficient market hypothesis by using machine learning to forecast future stock returns from historical performance. These forecasts strongly predict the cross section of future stock returns. The predictive power holds in most subperiods, is strong among the largest 500 stocks, and is distinct from momentum and reversal. The forecasting function has important nonlinearities and interactions and is remarkably stable through time. Our research design ensures that our findings are not a result of data mining. These findings question the efficient market hypothesis and indicate that investment strategies based on technical analysis and charting may have merit.
#224 – Profitability Factor Combined with Value Factor
Cakici, Nusret and Zaremba, Adam: Size, Value, Profitability, and Investment Effects in International Stock Returns: Are They Really There?
https://ssrn.com/abstract=3849983
Abstract:
Using data on 65,000 stocks from 23 countries, the authors re-evaluate the performance of the Fama-French (2015) factors in global markets. The results provide convincing evidence that the value, profitability, and investment factors are far less reliable than commonly thought. Their performance depends strongly on the geographical region and period examined. Furthermore, most factor returns are driven by the smallest firms. Virtually no value or investment effects are present among the big firms representing most of the total market capitalization worldwide. Given that the smallest firms are typically not investable by major financial institutions, these findings cast doubt on the five-factor model’s applicability in international markets.
And two interesting free blog posts have been published during last 2 weeks:
A Deeper Look into Factor Momentum
Momentum seems to be present everywhere and based on academic studies, it is even hard to find assets where the anomaly does not work. Among the large number of research papers related to momentum, the discovery of factor momentum is still relatively new. It is a truly important finding in the world of systematic strategies – there seems to be a return continuation among factors. The novel research of Fan et al. (2021) builds on the recent academic research and shows that, after all, the factor momentum might be different. To be more precise, the authors show that looking at the universe of 20 factor strategies, the factor momentum seems to work and can span individual equity momentum strategies (standard momentum, industry momentum and intermediate momentum). However, the factor momentum is mostly driven by only six factor strategies, and the return continuation of the remaining factors is weak. Additionally, those sixteen non-return continuation strategies cannot span the momentum effects mentioned above. Therefore, the results show that the factor momentum works on the aggregate but individually works much better. In fact, the factor momentum return of the six return continuation factor is significantly better compared to the rest or buy-and-hold portfolio. Moreover, the authors have also identified that the “best” factor momentum strategy is the Betting against beta and conclude that the reason is the unique weighting scheme utilized by the factor. The beta weighting assigns a higher weight to smaller companies, where the momentum tends to be stronger. Overall, the research paper is an important extension of the factor momentum literature.
We present a short article as an insight into the methodology of the Quantpedia Pro report – this time for the Markowitz Portfolio Optimization. As usually, Quantpedia Pro allows the optimization of model portfolios built from the passive market factors (commodities, equities, fixed income, etc.), systematic trading strategies and uploaded user’s equity curves. The current report helps with the calculation of the efficient frontier portfolios based on the various constraints and during various predefined historical periods. The backtests of the periodically rebalanced Minimum-Variance, Maximum Sharpe Ratio and Tangency portfolios will be available at the beginning of July.
Additionally, there is a Case Study dedicated to this Quantpedia Pro tool.
Plus, the following six trading strategies have been backtested in QuantConnect in the previous two weeks:
#42 – Alpha Cloning – Following 13F Fillings
#104 – Credit Spread Predicts Equity Returns
#233 – Using Straddles to Trade on Earnings Announcements
#620 – Long Term Time-series Momentum in India
#623 – Quality Factor in Stock
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