An excellent article that has something new to say about the well-mined aspects of financial bubbles. Mr. Sharma's these is that since bubbles cause prices of one segment of the market to rise against others when bubbles pop the fleeing funds end up in the market segments that have been ignored or undervalued. This article points to an interesting strategy for the ongoing sell-off in the technology sector in the U.S.
https://www.nytimes.com/2018/12/29/opinion/tech-bubble-bursting-stock-market.html?action=click&module=Opinion&pgtype=Homepage
Hedge funds (along with liquid alternative investments) and smart beta funds have been among the fastest growing investment vehicles over the past few years. I believe that the reason for this growth is that they both appeal to the investors’ focus on factors or performance drivers rather than alpha or labels. More and more, investors view investments as a bundle of factors that are evaluated for their impact on the overall portfolio.
Major market factors (i.e., stocks and bonds) are gained through index funds and ETFs. Once these are included in a portfolio, investors seek investments with factors that either add diversification to their portfolio or produce better risk adjusted returns that existing portfolio assets. Because hedge funds and smart beta funds are driven by similar factors and play similar roles in investment portfolios, it is inevitable that they will compete for the same investors.
Factor Investing
Factors in this context are economic or financial variables that act as drivers in the risks and returns of an investment. The term is grounded in regression analysis where independent variables (factors) are correlated to (and presumed to determine) the dependent variable (here the investment return). The general formula for the linear case is as follows: Y = a + b(factor 1) + c(factor 2) +d (factor 3)…… Were Y is the performance of a security, index or fund, a is the alpha and b, c, and d are the betas that measure the degree to which each factor impacts Y. The Fama-French three-factor model was the first popular framework for factor investing. According to the model, stock returns are a function of not only the major market (i.e., the S&P 500), but also of two additional factors: value vs. growth and size (market capitalization) of the stock. Since then, literally dozens of factors have been found to help explain the returns of not only stocks, but also mutual funds, ETFs and hedge funds. Fung and Hsieh for example developed a 9-factor model to explain hedge fund index returns. Fama and French themselves added two additional factors to develop the 5-factor model. Andrew Lo introduced momentum as an important factor in explaining hedge fund and mutual fund returns. In a famous study by Jasmina Hansanhodzik & Andrew Lo, “Can hedge fund returns be replicated: the linear case,” ((Journal of Investment Management, Vol. 5, No. 2, (2007), pp. 5–45) the authors identify five factors that are correlated with hedge fund performance: 1) equity markets; 2) US Dollar; 3) Credit spreads; 4) bonds and 5) Commodities. One of the leading practitioners of the factor approach to investing is AQR, headed by Cliff Assness. Assness identifies a number of factors that drive investment returns including value, momentum, profitability, low volatility, interest rate carry, and defernsive company stock. One of the unique features of the AQR funds is their willingness to take both long and short positions in these factors, effectively doing away with the distinction between hedge funds, liquid alternative investments and smart beta.
Investment Alpha
One of the major fall-outs of factor investing is the dramatic shrinking of investment alpha as returns that were originally attributed to manager skill turn out to be correlated with various factors. Increasingly, what was once described as alpha generation by investors is shown to be a result of a factor which explains the return. With a shrinking alpha, investors are focused on factors in building their portfolios. Factors influence investment return independent of the skill of the investment manager. The idea is to let a statistical model determine the optimal number of factors and their relative size in order to maximize performance. (Of course, there is manager input in the initial factors that are included in the model as well as the manner in which the factors are measured). Any return on an investment in excess of the influence of factors is said to be “alpha,” a measure of investor manager skill.
Types of Factors
There are three types of factors that are used in factor models. although some factors are more than one type: Market Exposure Factors – these factors provide non-correlated exposure to various betas in order to provide diversification in investment portfolios Risk Premium Factors – these factors are riskier than risk-free assets and therefore provide investors with a risk premium Market Anomoly Factors – these factors take advantage of market anomolies, such as those caused by emotional behavior of market participants.
Smart Beta Products
Smart beta strategies, which are frequently referred to as multi-factor products or strategic beta, use one or more factors to construct portfolios that either outperform, have a lower correlation or provide a different risk/return profile than the traditional fund weighted indices. The most common factors and anomalies include carry trades, momentum, volatility, value, size, liquidity and quality. Smart beta has been applied to both equities and fixed income. Smart beta funds now number over 600 and represent almost 20% of ETFs, with $400 billion under management. Whereas hedge fund strategies are presented as being either diversifiers or replacements, common portfolio goals for various smart beta include risk parity, maximum diversification, defensive, high dividend, quality and fundamental. (It is noteworthy that smart beta provides a much wider range of roles than hedge funds.) You will note that there is an overlap in some of the factors listed above, notably credit spread, equity markets, bonds, and momentum. However, smart beta exposure costs under 40 basis points and comes in ’40 Act funds that have complete transparency and liquidity and extensive government supervision, while hedge funds’ baggage includes high management fees and incentive compensation; lock-ups and redemption restrictions; lack of government supervision; little transparency; and legal and administrative hassles.
Active Factor Fund
A group of funds recently launched by Unigestian with seed money from the U.K. pension fund RPMI Railpen is an example of a cross over fund that seeks to incorporate factors typically associated with smart beta funds in a liquid alternative investment. The funds are described as follows: “The funds being launched are a long-only active factor fund combining a number of identified factors and a long/short factor fund which follows a market neutral, pure alpha strategy aiming to profit from both positive and negative factor exposure. The factors included in the funds are value (Price/Book, Price/Earnings, Price/Sales, Price/FFO), Momentum (one year return), quality (profitability and leverage), and size (market cap).” We will undoubtedly see many examples of this type of crossover fund in the future.
Major market factors (i.e., stocks and bonds) are gained through index funds and ETFs. Once these are included in a portfolio, investors seek investments with factors that either add diversification to their portfolio or produce better risk adjusted returns that existing portfolio assets. Because hedge funds and smart beta funds are driven by similar factors and play similar roles in investment portfolios, it is inevitable that they will compete for the same investors.
Factor Investing
Factors in this context are economic or financial variables that act as drivers in the risks and returns of an investment. The term is grounded in regression analysis where independent variables (factors) are correlated to (and presumed to determine) the dependent variable (here the investment return). The general formula for the linear case is as follows: Y = a + b(factor 1) + c(factor 2) +d (factor 3)…… Were Y is the performance of a security, index or fund, a is the alpha and b, c, and d are the betas that measure the degree to which each factor impacts Y. The Fama-French three-factor model was the first popular framework for factor investing. According to the model, stock returns are a function of not only the major market (i.e., the S&P 500), but also of two additional factors: value vs. growth and size (market capitalization) of the stock. Since then, literally dozens of factors have been found to help explain the returns of not only stocks, but also mutual funds, ETFs and hedge funds. Fung and Hsieh for example developed a 9-factor model to explain hedge fund index returns. Fama and French themselves added two additional factors to develop the 5-factor model. Andrew Lo introduced momentum as an important factor in explaining hedge fund and mutual fund returns. In a famous study by Jasmina Hansanhodzik & Andrew Lo, “Can hedge fund returns be replicated: the linear case,” ((Journal of Investment Management, Vol. 5, No. 2, (2007), pp. 5–45) the authors identify five factors that are correlated with hedge fund performance: 1) equity markets; 2) US Dollar; 3) Credit spreads; 4) bonds and 5) Commodities. One of the leading practitioners of the factor approach to investing is AQR, headed by Cliff Assness. Assness identifies a number of factors that drive investment returns including value, momentum, profitability, low volatility, interest rate carry, and defernsive company stock. One of the unique features of the AQR funds is their willingness to take both long and short positions in these factors, effectively doing away with the distinction between hedge funds, liquid alternative investments and smart beta.
Investment Alpha
One of the major fall-outs of factor investing is the dramatic shrinking of investment alpha as returns that were originally attributed to manager skill turn out to be correlated with various factors. Increasingly, what was once described as alpha generation by investors is shown to be a result of a factor which explains the return. With a shrinking alpha, investors are focused on factors in building their portfolios. Factors influence investment return independent of the skill of the investment manager. The idea is to let a statistical model determine the optimal number of factors and their relative size in order to maximize performance. (Of course, there is manager input in the initial factors that are included in the model as well as the manner in which the factors are measured). Any return on an investment in excess of the influence of factors is said to be “alpha,” a measure of investor manager skill.
Types of Factors
There are three types of factors that are used in factor models. although some factors are more than one type: Market Exposure Factors – these factors provide non-correlated exposure to various betas in order to provide diversification in investment portfolios Risk Premium Factors – these factors are riskier than risk-free assets and therefore provide investors with a risk premium Market Anomoly Factors – these factors take advantage of market anomolies, such as those caused by emotional behavior of market participants.
Smart Beta Products
Smart beta strategies, which are frequently referred to as multi-factor products or strategic beta, use one or more factors to construct portfolios that either outperform, have a lower correlation or provide a different risk/return profile than the traditional fund weighted indices. The most common factors and anomalies include carry trades, momentum, volatility, value, size, liquidity and quality. Smart beta has been applied to both equities and fixed income. Smart beta funds now number over 600 and represent almost 20% of ETFs, with $400 billion under management. Whereas hedge fund strategies are presented as being either diversifiers or replacements, common portfolio goals for various smart beta include risk parity, maximum diversification, defensive, high dividend, quality and fundamental. (It is noteworthy that smart beta provides a much wider range of roles than hedge funds.) You will note that there is an overlap in some of the factors listed above, notably credit spread, equity markets, bonds, and momentum. However, smart beta exposure costs under 40 basis points and comes in ’40 Act funds that have complete transparency and liquidity and extensive government supervision, while hedge funds’ baggage includes high management fees and incentive compensation; lock-ups and redemption restrictions; lack of government supervision; little transparency; and legal and administrative hassles.
Active Factor Fund
A group of funds recently launched by Unigestian with seed money from the U.K. pension fund RPMI Railpen is an example of a cross over fund that seeks to incorporate factors typically associated with smart beta funds in a liquid alternative investment. The funds are described as follows: “The funds being launched are a long-only active factor fund combining a number of identified factors and a long/short factor fund which follows a market neutral, pure alpha strategy aiming to profit from both positive and negative factor exposure. The factors included in the funds are value (Price/Book, Price/Earnings, Price/Sales, Price/FFO), Momentum (one year return), quality (profitability and leverage), and size (market cap).” We will undoubtedly see many examples of this type of crossover fund in the future.