Investing in Socially Responsible Mutual Funds

We construct optimal portfolios of mutual funds whose objectives include socially responsible investment (SRI). Comparing portfolios of these funds to those constructed from the broader fund universe reveals the cost of imposing the SRI constraint on investors seeking the highest Sharpe ratio. This SRI cost crucially depends on the investor’s views about asset pricing models and stock-picking skill by fund managers. To an investor who strongly believes in the CAPM and rules out managerial skill, that is, a market index investor, the cost of the SRI constraint is typically just a few basis points per month, measured in certainty-equivalent loss. To an investor who still disallows skill but instead believes to some degree in pricing models that associate higher returns with exposures to size, value, and momentum factors, the SRI constraint is much costlier, typically by at least 30 basis points per month. The SRI constraint imposes large costs on investors whose beliefs allow a substantial amount of fund-manager skill, that is, investors who heavily rely on individual funds’ track records to predict future performance.

ESG Investments: Filtering versus Machine Learning Approaches

We designed a machine learning algorithm that identifies patterns between ESG profiles and financial performances for companies in a large investment universe. The algorithm consists of regularly updated sets of rules that map regions into the high-dimensional space of ESG features to excess return predictions. The final aggregated predictions are transformed into scores which allow us to design simple strategies that screen the investment universe for stocks with positive scores. By linking the ESG features with financial performances in a non-linear way, our strategy based upon our machine learning algorithm turns out to be an efficient stock picking tool, which outperforms classic strategies that screen stocks according to their ESG ratings, as the popular best-in-class approach. Our paper brings new ideas in the growing field of financial literature that investigates the links between ESG behavior and the economy. We show indeed that there is clearly some form of alpha in the ESG profile of a company, but that this alpha can be accessed only with powerful, non-linear techniques such as machine learning.

MANAGERIAL ABILITIES AND FACTOR INVESTMENT STYLE PERFORMANCES OF MALAYSIAN MUTUAL FUND MANAGERS

Background and Purpose: Studies focusing on mutual fund managerial abilities and investment style strategies are still scarce in the literature. Thus, this study aims to provide new evidence and insights into the managerial abilities and investment style performances of Malaysian fund managers. Methodology: A total of 444 Malaysian equity mutual funds (EMFs) were evaluated using Carhart’s model incorporated with Treynor-Mazuy (T-M) and Henriksson-Merton (H-M) market timing models for the study period, from January 1995 to December 2017. Findings: Fund managers displayed superior stock selection skills with 32 percent and 43 percent of funds for T-M and H-M respectively, with perverse market timing ability which accounted for 39 percent and 42 percent of funds for T-M and H-M respectively. Perverse timing ability had reduced the superior stock-picking skills of fund managers. This suggests that the EMFs performance could further improve if respective fund managers perform better in market timing ability. The finding also indicates that size effect (SMB) and value effect (HML) play significant roles in investment style strategies, while results of momentum factor (WML) propose that Malaysian fund managers have followed the contrarian strategy. Contributions: This study contributes in several ways especially in the literature of portfolio management as the evidence is obtained from the largest mutual funds sample size and the longest study period. Moreover, this study also used the highest frequency data to study the effects of market timing which were overlooked in previous studies. Keywords: Adjusted carhart, Malaysian market, market timing, mutual fund, stock selection. Cite as: Ling, P-S., & Abdul-Rahim, R. (2021). Managerial abilities and factor investment style performances of Malaysian mutual funds. Journal of Nusantara Studies, 6(1), 118-135. http://dx.doi.org/10.24200/jonus.vol6iss1pp118-135

AN ARGUMENT AGAINST STOCK-PICKING AND MARKET-TIMING: AN EMPIRICAL APPROACH

This paper’s objective is to demystify the world of investing, first by showing and exposing the results the greatest money managers in the Wall Street have obtained over the last years compared to the performance of their benchmark indexes. Index investing represents a passive investment strategy of holding hundreds of stocks instead of the active management approach used by these experts. After exposing said results, a theoretical framework will be presented that explains why money managers have such a difficult time outperforming their benchmark indexes. Later on, a back-test experiment will be presented and thoroughly explained showing five different hypothetical investment scenarios over several 20-year periods with the attempt to quantify the potential benefit of perfectly timing the market and compare it to the cost of waiting for a better time to invest. The results find shows that the cost of waiting is much greater that the potential benefit of perfectly timing the market and the best alternative would be to invest available cash immediately regardless of market or economic outlook.

Style and Skill: Hedge Funds, Mutual Funds, and Momentum

Classifying mandatory 13F stockholding filings by manager type reveals that hedge fund strategies are mostly contrarian, and mutual fund strategies are largely trend following. The only institutional performers—the two thirds of hedge fund managers that are contrarian—earn alpha of 2.4% per year. Contrarian hedge fund managers tend to trade profitably with all other manager types, especially when purchasing stocks from momentum-oriented hedge and mutual fund managers. Superior contrarian hedge fund performance exhibits persistence and stems from stock-picking ability rather than liquidity provision. Aggregate short sales further support these conclusions about the style and skill of various fund manager types. This paper was accepted by Tyler Shumway, finance.

Discovering optimal weights in weighted-scoring stock-picking models: a mixture design approach

Certain literature that constructs a multifactor stock selection model adopted a weighted-scoring approach despite its three shortcomings. First, it cannot effectively identify the connection between the weights of stock-picking concepts and portfolio performances. Second, it cannot provide stock-picking concepts’ optimal combination of weights. Third, it cannot meet various investor preferences. Thus, this study employs a mixture experimental design to determine the weights of stock-picking concepts, collect portfolio performance data, and construct performance prediction models based on the weights of stock-picking concepts. Furthermore, these performance prediction models and optimization techniques are employed to discover stock-picking concepts’ optimal combination of weights that meet investor preferences. The samples consist of stocks listed on the Taiwan stock market. The modeling and testing periods were 1997–2008 and 2009–2015, respectively. Empirical evidence showed (1) that our methodology is robust in predicting performance accurately, (2) that it can identify significant interactions between stock-picking concepts’ weights, and (3) that which their optimal combination should be. This combination of weights can form stock portfolios with the best performances that can meet investor preferences. Thus, our methodology can fill the three drawbacks of the classical weighted-scoring approach.