Robustness of Smart Beta Strategies

In this study by EDHEC, the authors demonstrate that there has been significant evidence that systematic equity investment strategies (so-called smart beta strategies) outperform cap-weighted benchmarks in the long run.

Concerning actual investment decisions, it is relevant to question how robust the outperformance is. The paper makes a distinction between relative robustness and absolute robustness. A strategy is assumed to be ‘relatively robust’ if it is able to deliver similar outperformance under similar market conditions by aligning well with the performance of underlying factor exposure it is seeking and reducing unrewarded risks.

Absolute Robustness is the absence of pronounced state and/or time dependencies and a strategy shown to outperform irrespective of prevailing market conditions can be termed as robust in absolute terms The paper goes on to review the importance of robustness for smart beta strategies, it explains various methods by which smart beta strategies try to improve robustness, and discusses how to measure and assess robustness in the performance analysis of smart beta strategies. 

Read the full study here

Scientific Beta Multi-Strategy Factor Indices

Scientific Beta Multi-Strategy Factor Indices: Combining Factor Tilts and Improved Diversification

This paper by EDHEC argues that current smart beta investment approaches only provide a partial answer to the main shortcomings of cap-weighted indices, and introduces Scientific Beta Multi-Strategy Factor Indices which are constructed using a new approach to equity investing referred to as smart factor investing.

It then provides an assessment of the benefits of addressing the two main problems of cap-weighted indices (their undesirable factor exposures and their heavy concentration) simultaneously by constructing factor indices that explicitly seek exposures to rewarded risk factors, while diversifying away unrewarded risks.

The results suggest that ERI Scientific Beta Multi-Strategy Factor Indices lead to considerable improvements in risk-adjusted performance. For long-term US data, smart factor indices for a range of different factor tilts consistently outperform cap-weighted factor tilted indices, and factor indices from popular commercial index providers. Compared to the broad cap-weighted index, smart factor indices roughly double the risk-adjusted return (Sharpe ratio). Outperformance of such indices persists at levels ranging from 2.92% to 4.46% annually, even when assuming unrealistically high transaction costs. Moreover, by providing explicit tilts to consensual factors, such indices improve upon many current smart beta offerings where, more often than not, factor tilts result as unintended consequences of ad hoc methodologies.

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Calculating tail risk in Fixed Income markets

In this study, the researchers from the Cheung Kong Graduate School of business constructed a model-free measure of tail risk for Fixed income markets using a proprietary dataset of swaptions, denoted as TAIL, which captures the price of insuring against extreme movements in interest rate swap rates.

The researchers show that TAIL closely tracks the variations in tail risk in the economy and has strong predictive power for returns on Treasury bonds, corporate bonds, mortgage-backed securities, Fixed-income hedge funds, and even equities, suggesting that interest rate tail risk is universally priced in all major Finanancial markets.

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Using sentiment data to develop a trading strategy

In this paper, Ronald Hochreiter of the Vienna University computed trading strategies based on market sentiment derived from the postings of stocktwits, one of the largest social media accounts following the developments on financial markets. It's a very good example of how data from social media can be used to implement trading strategies.

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Using short selling and hedge fund positions as predictive powers

In this study, the researchers from the Bejiing School of finance exploit the information contained in the joint analysis of the long and short sides of hedge fund trading. They state that opposite changes in short interest and hedge fund holdings are likely driven by information, whereas simultaneous increases (decreases) in short interest and hedge fund holdings are likely motivated by hedging (unwinding) incentives. This intuition allows to utilize short selling and hedge fund holding information to identify informed long and short demand.
Using this identification strategy, the researchers show that informed demand changes have high predictive power for returns.

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The Fundamental Theorem of Derivative Trading

In this study, the researchers from the University of Copenhagen revisit the calculation to allign delta hedged option portfolio's with implied volatilities and create a new version of the Fundamental Theorem

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Using hedge fund trade data as a signal of future performance

In this Harvard study by Jonathan Rhinesmith, the researcher takes the quarterly transactions from hedge funds and treats them as 'informed traders' to see what happens with the fundamental value of the equities. Read the full study here

Optimizing a investment strategy based on market capacity

Costs in executing an investment strategy are non linear; the larger a strategy is, the more expensive it becomes to execute (due to liquidity). In this study by the Toulouse School of Economics, the authors research how to measure this capacity and find the optimal equilibrium.

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The role of securities lending

There are many advantages to passive investing. One of the lesser studied advantages is the money that can be earned in securties lending. This study, performed by Blocher & Whaley of the Vanderbilt University outlines it advantages and its effects it has on the choices made by passive investors.
Read the full study here