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Momentum, Volatility, and Risk-based Allocation
Title
Momentum, Volatility, and Risk-based Allocation
Creator
Qian, Junkai
Date
2019
Description
This study introduces a coherent framework that links together various momentum measures, market beta, and idiosyncratic risk (IRISK)....
Show moreThis study introduces a coherent framework that links together various momentum measures, market beta, and idiosyncratic risk (IRISK). Momentum is measured as lagged 12-month price momentum (MOM) and volatility adjusted momentum (MOMV). The interaction effect of the three factors is tested. It is found that for 70.64% of the time, a high beta high IRISK stock is more likely to be a top 30% MOM stock than a mid 40% MOM stock. Top MOM exhibits significant bias, 30.81% on average, on high beta high IRISK stocks. Such bias tends to be weaker late in an economy recession. In contrast, top MOMV is less sensitive to high beta and high IRISK. Further, for both MOM and MOMV, it is shown that equally weighted momentum portfolios are driven by high beta high IRISK stocks, especially during a momentum crash. To enhance momentum strategies, risk-based weighting schemes, minimum variance (minVar) and risk parity (ERC), are implemented. In the long run, ERC shows a slight improvement compared to equally weighting, while minVar is able to significantly reduce total risk and tail loss at a cost of sacrifice in performance. A dynamic risk weighting scheme based on changes in market dispersion is proposed to balance the benefit and cost of miVar. Such approach is shown to significantly reduce tail loss and improve Sharpe ratio.
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SHARPEN FACTOR INVESTING WITH A CLOSER LOOK AT PROFITABILITY
Title
SHARPEN FACTOR INVESTING WITH A CLOSER LOOK AT PROFITABILITY
Creator
Li, Shengsi
Date
2019
Description
Stock market anomalies have been long researched by academia and used by practitioners. Factor-based allocation has been shown to provide...
Show moreStock market anomalies have been long researched by academia and used by practitioners. Factor-based allocation has been shown to provide better diversification and risk-adjusted returns than the more traditional portfolio approaches. Numerous studies have shown traditional factors such as value, size, and profitability are effective in a cross-sectional fashion, meaning they are effective to all sections. It is found that the factor-return link is not robust across different sectors. Based on this observation, some stylized factor-based investing strategies are refined to improve the return performance measured by risk-adjusted metrics. Further analysis of the firm age moderation effect on the prediction power of profitability over stock return is explored. It is shown that firm age could have a significant moderation effect on the academically proven profitability factor.
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Socially Responsible Investing and Style Investing
Title
Socially Responsible Investing and Style Investing
Creator
He, Di
Date
2020
Description
This study focuses on two popular investment strategies. The first one is a combination of socially responsible investing and factor investing...
Show moreThis study focuses on two popular investment strategies. The first one is a combination of socially responsible investing and factor investing (SRIF), it is therefore a comparison between factor investing portfolios and their corresponding ESG screened factor investing portfolios, aiming at indicating whether there is an opportunity costs or benefits of being responsible in factor investing. Opportunity cost is regarded if the ESG screened factor investing portfolios have lower raw return, Sharpe ratio, and risk-adjusted return than their respective factor investing portfolios. In addition to simply comparison, I also build an empirical SRI strategy, achieving real outperformance of SRI. For the second strategy, investing in R&D intensity (high technology) stocks results in significant positive alpha over 40 years. However, the alphas decrease significantly after the “Tech Bubble”, because investors nowadays prefer those technology firms who can produce true profits. I provide empirical evidence to investor sentiment, proving both risk bearing and investor sentiment play important roles in the positive association between R&D-intensive and excess return.In the first SRIF strategy, five widely-accepted factors in academic: value, size, profit, investment, and momentum are used to construct original single factor investing portfolio as benchmarks, which can naturally solve the benchmark bias, factor bias in previous literature at some extent. In addition to fulfill empirical industry’s generalities and constraints, this study also covers multi-factor framework and constructs different long-short positions for investment processing. Following considerations of ESG measurement (ESG_net and ESG_Industry, the latter one for calibration of industry bias), sample period (whole period and sub period), portfolio weighting methods (equally weighted and capitalization weighted), and after excluding undiversified portfolio, there are total 192 comparisons between factor investing portfolios and ESG screened factor investing portfolios for each measures of performance. Results suggest that most investors (80% - 90%) have to bear non-statistically significant opportunity costs if they want to be socially responsible in factor investing. In addition, the opportunity costs in sub period (2004-2017) is remarkably less in scale than those in whole period (1992-2017), indicating an obvious “time effect” that investors will have less opportunity costs recently with more and more ESG information is disclosed. For empirical consideration of industry, I build a double sorting factor portfolio on profit and value, and its ESG screened portfolio outperform the single factor portfolio.For the second research, R&D expense is a key component of investment. There is long history literature claim that there is a positive relationship between R&D and stock returns. There are two main explanations of the positive association, which are mispricing and risk bearing. This study separates whole sample into two periods: before “Tech Bubble” and after “Tech Bubble”, indicating that the mispricing is weaker after “Tech Bubble” than that in before “Tech Bubble”, while risk bearing is persistent. In addition, this study finds that the excess returns are relatively high for those highly subjective and difficult to arbitrage technology securities, which are small stocks, high volatility stocks, unprofitable stocks, non-dividend-paying stocks before the “Tech Bubble”, but almost vanish after the “Tech Bubble”. Therefore, investor sentiment does exist. While for those true earning technology securities, their excess returns are persistent, indicating compensation of risk bearing.
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