The literature shows that where we come from affects both how we perceive other people as well as how we are perceived by others. These perceptions can also affect economic behavior. In this study, the author analyzes the role of cultural biases in analysts’ stock recommendations in Europe.
Compared to mutual funds or separately managed accounts, ANY benefit from redeeming in-kind is a bonus. That being said, not all ETFs and situations are created equal when it comes to tax efficiency, and the "golden rule" always applies - when given the option, the IRS wants to create scenarios where they receive tax dollars now instead of later. Here are some big-ticket items that cause inefficiencies (read as taxes…), many related to the “golden rule” above.
This article examines the extent to which these assumptions hold and the extent to which investors should want them to hold. The authors deliver a clever quote from Mark Twain (or maybe it was Robert Frost) that nails the issue in simple terms: “Diversification behaves like the banker who lends you his umbrella when the sun is shining but wants it back the minute it begins to rain”. Nicely expressed!
Taken together, our results suggest that firms’ personnel expenditures reflect not just the cost of labor in the current period but also the investment in human capital contained within that cost, and that market participants fail to fully understand the opportunity and efficacy of human capital development embedded in the disclosure of the expense.
There are two general types of Kalman filter models: steady-state and adaptive. A steady-state filter assumes that the statistics of the process under consideration are constant over time, resulting in fixed, time-invariant filter gains. The gains of an adaptive filter, on the other hand, are able to adjust to processes that have time-varying dynamics, such as financial time series which typically display volatility and non-stationarity.
The primary idea behind this research is that a more sophisticated statistical technology (in the sense of reducing predictive mean squared error) produces predictions with greater variance than a more primitive technology. These technologies range from a simple logistic regression of default outcomes based on borrowers and default variables to random forest machine learning models. Said differently, improvements in predictive technology act as mean-preserving spreads for predicted outcomes—in this case, predicted default propensities on loans, which also means that there will always be some borrowers considered less risky by the new technology, or “winners”, while other borrowers will be deemed riskier “losers”, relative to their position under the pre-existing technology.
Firm characteristics are often missing, which forces both researchers and practitioners to come up with workarounds when handling missing data. Previous approaches resorted to either dropping observations with missing entries or simply imputing the cross-sectional mean of a given characteristic. As both procedures accompany serious drawbacks (see below), there is a need for more advanced methods. The authors set up an attention-based machine learning model, motivated by recent advances in natural language to find some answers
The question of whether or not the FED considers or responds to the stock market in its policy decisions has been studied fairly extensively, the subject of the existence of the "FED put" continues to pop up in the literature. In this particular revival of the issue, the authors are among the first to study FOMC minutes, transcripts, and other sources of information using textual analysis in order to provide an answer to the question: Does the FED respond to stock market events and if it does, what is the nature of the response?
From 2017 through March 2020, the relative performance of value stocks in the U.S. was so poor, experiencing its largest drawdown in history, that many investors jumped to the conclusion that the value premium was dead. It is certainly possible that what economists call a “regime change” could have caused assumptions to change about why the premium should exist/persist.
Having conducted an inordinate amount of research on the momentum factor, we find it comforting (likely due to confirmation bias!) that independent researchers have identified the same thing we have found -- frog in the pan is a robust way to measure momentum if one is seeking to take advantage of the momentum factor.
To date, there is no large-sample empirical evidence on gender balance and career outcomes in academic finance. Though we have looked into and observed where are the women in finance and women in the C-Suite. This paper specifically looks to proved insight into the statistics of female representation in the academic arena of finance.
Inflation -- what's that? ... It has been quite a while since inflation has been considered a problem. Today, however, the angst surrounding the possibility of a resurgence in inflation is real and “top of mind” for investors. If the current fear becomes a reality, how should investors react? What strategies and asset classes perform well in a rising inflationary environment? If inflation does resurge beyond a temporary phase, how should investors restructure or reposition their portfolios? The purpose of this article is to provide context for those decisions.
Empowering investors through education is a foundational tenet of our firm and a big reason why we write these posts. The article we cover here is a meta-analysis 76 randomized studies on the impacts and design of financial education, a topic we've hit on before. It' almost cliche now to hear parents and educators demand schools take the initiative to make financial education a high priority. However, it's reasonable to ask, does financial education even work?
The reported results we covered have important implications for investors in terms of portfolio construction, risk monitoring, and manager selection. Because these common factors explain almost all the returns of bond portfolios, investors should construct their bond portfolios using low-cost, passively (systematically) managed funds with these factors in mind and then carefully monitor their exposure to these systematic risks.
This article considers a different type of filter called the Kalman filter. The Kalman filter is a statistics-based algorithm used to perform the estimation of random processes. Our research will explain what Kalman Filters are and utilize them with financial time series data for trend following purposes.
Allocations to illiquid assets have become increasingly popular requiring asset managers to consider portfolio-wide liquidity characteristics. Although determining the price of illiquidity is a challenge for investors, the construction of a portfolio that includes liquidity constraints can be even more daunting. How do we optimize asset allocation with liquidity as a significant constraint on the portfolio?
Are individuals just naïve performance chasers, unaware of the financial literature, or are they sophisticated investors? Pretty much.
Despite their popularity and the ease of access to university-based endowments, there is little in the academic literature about the history of endowment investing. In this article, the authors aim at filling this gap.
The main takeaway for investors is that Kelly, Moskowitz, and Pruitt demonstrated that past return characteristics are strongly predictive of a stock’s realized exposures to common risk factors, providing direct evidence that price trend strategies are in part explainable as compensation for common factor exposures—past returns predict betas on factors and those factors have high average returns.
Private investment opportunities seem to have been filling investors' portfolios. These investment vehicles come with a discount to the assets value to pay investors for taking on illiquidity risk. Readers of this article are treated to the development of a theory and a practical model that quantifies the illiquidity discount.
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