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How Does Weighting Scheme Impact Systematic Equity Portfolios?

April 27, 2022

How often do you think about the weights of the assets in your portfolio? Do you weigh your assets equally, or do you prefer value-weighting? The researchers behind a recent research paper analyzed various weighting schemes and examined their effect on factor strategy return. They studied five weighting schemes that ignore prices: equal weighting, rank weighting, z-score weighting, inverse volatility weighting, and fundamental weighting, and three price-based weighting schemes: Rank x mcap (rank-times-mcap), Z-score x mcap (z-score-times-mcap), and Integrated core.

They found that schemes that are not based on price can inflate turnover and costs. However, the weighting schemes based on price are the most practical to target multiple premiums, provide robust risk control, and decrease turnover and expenses.

The Price of Transaction Costs

April 22, 2022

Capturing the systematic premia is the main aim of many quantitative traders. However, investors tend to overlook an important factor when backtesting. Trading costs are an essential part of every trade, and yet even when we consider them, we only use an approximation. The recent article from Angana Jacob (SigTech) looks into how heavily trading costs affect the overall return of various strategies and analyzes multiple ways of implementing trading costs into the trading rules themselves.

What’s the Best Factor for High Inflation Periods? – Part II

April 13, 2022

This second article offers a different look at high inflation periods, which we already analyzed in What’s the Best Factor for High Inflation Periods? – Part I. The second part looks at factor performance during two 10-year periods of high inflation. What’s our main takeaway? The best hedge for a high inflation period is the value or momentum factor. Other promising factors (energy sector, small-cap stocks, or long-run reversal) don’t perform as consistently as value and momentum.

What’s the Best Factor for High Inflation Periods? – Part I

April 11, 2022

Another period of long sustained high inflation is probably right around the corner, as the Russia-Ukraine Conflict keeps evolving, and its end is nowhere to be seen. In this article, we analyzed the Consumer Price Index from the Federal Reserve Bank of Minneapolis, which includes the rate of inflation in the USA since 1913. We found multiple years during which the inflation was abnormally high and analyzed the performance of the known equity long-short factors. The factors with the highest average performance are HML (value stocks), long-term reversal, momentum, and energy stocks. On the other hand, tech stocks, bond-like assets, and the SMB factor should be avoided during the high inflation periods.

Nuclear Threats and Factor Performance – Takeaway for Russia-Ukraine Conflict

March 31, 2022

The Russian invasion of Ukraine and its repercussions continue to occupy front pages all around the world. While using nuclear forces in war is probably a red line for all of the mature world, there is still possible to use nuclear weapons for blackmailing. What will be the impact of such an event on financial markets? It’s not easy to determine, but we tried to identify multiple events in the past which were also slightly unexpected and carried an indication of nuclear threat and then analyzed their impact on financial markets.

Factor Performance in Cold War Crises – A Lesson for Russia-Ukraine Conflict

March 8, 2022

The Russia-Ukraine war is a conflict that has not been in Europe since WW2. And it has great implications not only on human lives but also on security prices. It bears numerous characteristics of the cold war crises, where two nuclear powers (Soviet Union and USA/NATO) were often very close to hot war or were waging a proxy war in 3rd countries. We thought it might be wise to look at similar periods from the past to understand what happens in such situations. We selected five events and analyzed the performance of main equity factors (market, HML, SMB, momentum & 2x reversal) and energy and fixed income proxy portfolios.

Should Factor Investors Neutralize the Sector Exposure?

February 8, 2022

Factor investors face numerous choices that do not end even after picking the set of factors. For instance, should they neutralize the factor exposure? If the investor pursues sector neutralization, does the decision depend on a particular factor? Or are the choices different for the long-only investor compared to the long-short investor? The research paper by Ehsani, Harvey, and Li (2021) answers these questions and provides investors with an interesting insight on this topic.

Factor Performance in Bull and Bear Markets

January 27, 2022

Do common equity factors suffer during bear markets? Undoubtedly, the market factor is a rather unpleasant investment during bear markets, but what about the long-short factors? Are they able to deliver performance? The research paper by Geertsema and Lu (2021) provides several answers and interesting insights.

Quality Factor in Sector Investing

January 14, 2022

The critical question of this research is to examine whether the quality factor could be found in the aggregated groups of similar stocks such as industries or sectors. Additionally, instead of constructing a comprehensive quality metric like other papers, we examine the individual ratios aggregated to the whole sector. The aim is to investigate the fundamental ratios on which quality is based rather than the composite quality score of sectors.

Out-of-sample Dataset Before the “Sample”: Pervasive Anomalies Before 1926

November 30, 2021

Data are the key to systematic investing/trading strategies. The hypotheses testing, risk or return evaluations, correlations, and factor loadings rely on past data and backtests. With an increasing speed of publication in finance, critiques of quantitative strategies have emerged. Strategies seem to decay in alpha, post-publication returns tend to be lower, and many strategies become insignificant once rigorously tested (in or out-of-sample). Moreover, some might even appear profitable purely by chance and the repetitive examination of the same dataset, such as CRSP stocks after 1963.

Is there any solution to overcome these limitations? Partially, the design of the novel machine learning strategies consisting of training, validation, and testing sets might help. Perhaps the most crucial part of such a scheme is the usage of the purely out-of-sample dataset. In this regard, the novel research by Baltussen et al. (2021) provides several valuable findings for the most recognized factors. The authors constructed a database of U.S. stocks, including dividends and market caps for 1488 major stocks from 1866 to 1926. The sample can be described as the pre-CRSP period, including independent, pre-publication, and “out-of-sample” data that can be a perfect test for the factors utilized today.