Evergreen funds are a relatively new concept in the private equity (PE) world compared to traditional closed-end funds. They were introduced to address the negatives of the traditional way to invest in private equity which had been in the form of partnerships.
To determine the impact of sustainable investment strategies on equity returns, Romulo Alves, Philipp Krueger, and Mathijs van Dijk analyzed the relationship between ESG ratings and global stock returns. They found very little evidence that ESG ratings were related to global stock returns over the two-decade period.
Without question the topic of greatest debate among investors, including investment professionals, and financial economists, is whether or not the market, and the technology sector in particular, is overvalued. There are two very strong conflicting views regarding not only the current valuation of technology stocks, but also the valuation of the entire asset class of large-cap growth stocks. One side, I’ll call the “new paradigm” or “it’s different this time” school. The other side, I’ll call “the been there, done that” school. Its theme is those that don’t learn from the past are doomed to repeat the same mistakes. No two sides could have more different viewpoints. To understand each side, let’s imagine a dialogue between the two schools.
Wallstreetbets has become an increasingly prominent source of investment research. Do their recommendations have value?
The shrinking pool of public companies across which active funds can diversify their holdings, increases the risk of crowding, which the research we reviewed shows negatively impacts performance. That provides yet another reason for investors to choose to avoid playing the loser’s game of active management.
The amortization of volatility should be of concern for private capital asset classes. In order to properly budget for beta risks, it is critical that investors in private assets understand the amount of systemic (beta) risk that will “wash” into their private portfolios.
An efficient way to improve the expected performance of an equity strategy would be to systematically exclude penny stocks, as well with high asset growth and extreme past returns, especially if they have low profitability (and exclude funds that don’t screen out such stocks).
Atilgan, Demirtas, and Gunaydin found that there has been a pollution premium for US stocks and that the premium translated into superior investment performance.
While both the S&P 500 and the Nikkei indices have recently hit all-time highs, the valuation and balance sheet data we have reviewed indicate that the downside risks in Japanese stocks appear to be far less than the risks in U.S. stocks. Evidence such as this helps explain why legendary investor Warren Buffett has been buying Japanese stocks.
Low short positions come from positive public news, while negative news can drive average short or extremely high short positions
Volatility laundering causes the risk-adjusted returns and the diversification benefits of private equity to be significantly overstated. However, the problem of volatility laundering is not a problem for all private investments, specifically not for high-quality, floating rate, private credit.
Both investment motives and investment experience are important determinants for investors’ ability to assess (impact) investment opportunities. While investor preference can justify accepting a lower return as the cost of expressing their values, the halo effect should not play a role in making that assessment—both economic theory and empirical evidence should lead investors to expect lower returns on sustainable investments.
There is strong empirical evidence demonstrating that momentum (both cross-sectional and time-series) provides information on the cross-section of returns of many risk assets and has generated alpha relative to existing asset pricing models. Ma, Yang, and Ye’s findings provide another test of both robustness and pervasiveness, increasing our confidence that the findings of momentum in asset prices are not a result of data mining.
There is strong empirical evidence demonstrating that momentum (both cross-sectional and time-series) provides information on the cross-section of returns of many risk assets and has generated alpha relative to existing asset pricing models.
The finding that the recommendations from SA articles resulted in statistically significant risk-adjusted alphas (returns unexplained by conventional academic models using factors such as the market, size, value, momentum, profitability, and quality for equity portfolios) is surprising given that the empirical evidence shows how difficult it is for institutional investors such as mutual funds to show outperformance beyond the randomly expected (as can be seen in the annual SPIVA Scorecards) because of market efficiency.
To date, the best metric we have for forecasting future equity returns and the ERP is current valuations. An interesting question is whether more complicated methods using newly developed machine learning models can provide superior forecasts.
Momentum continues to receive much attention from researchers because of the strong empirical evidence.
Hibbert, Kang, Kumar and Mishra provided us with yet another explanation: social media is providing analysts with information that reduces their forecasting errors. The result has been an increase in market efficiency, leading to a reduction in the PEAD anomaly. The bottom line is that the ability to generate alpha continues to be under assault—trying to outperform the market by stock selection is becoming even more of a loser’s game.
New research reveals that the performance of the hedge fund industry has not been as bad as the results from studies that relied on hedge fund data providers.
While the empirical research on cross-sectional (long-short) momentum has shown that returns have been high, investors have also experienced huge drawdowns—momentum exhibits both high kurtosis and negative skewness. Since 1926 there have been several momentum crashes that featured short, but persistent, periods of highly negative returns. For example, from June to August 1932, the momentum portfolio lost about 91%, followed by a second drawdown from April to July 1933.
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