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Momentum strategies exhibit quarterly seasonality, earning significantly higher average strategy returns in the third month of the quarter than the first month. The authors evaluate the magnitude of quarterly seasonality in various momentum strategies to examine the relation between quarterly seasonality and risk-adjusted monthly returns.

The authors construct long-short portfolios for various types of momentum strategies and calculate the average returns of these portfolios in the three months of the quarter. They also calculate the average changes in institutional ownership across the different portfolios.

The authors demonstrate that quarterly seasonality is directly associated with quarterly changes in net purchases by institutional investors. Additionally, they show that near-term price momentum exhibits more seasonality than other momentum strategies, consistent with institutional investor incentives.

Researchers studying momentum should understand that quarterly seasonality increases the standard deviation of monthly returns for different types of momentum strategies.

Individual investors and investment managers should consider whether it is early or late in the calendar quarter when implementing momentum strategies.

Quarterly seasonality explains several seemingly independent findings in the momentum literature. In cases where researchers show one momentum strategy outperforms another on a risk-adjusted basis, the authors find that the superior strategy exhibits less quarterly seasonality. This pattern holds across types of momentum strategies, strategy formation periods and asset classes.

The purpose of this study is to develop an investment strategy designed both to enable student‐managed investment fund (SMIF) students to more quickly build out their portfolio at the beginning of the academic year and to give them some exposure to quantitative approaches to investment management.

This study uses data and software that would be readily available to typical SMIF students to develop both an asset‐allocation model and a security‐selection model that can be described as a long‐flat (or synthetic protective put) equity strategy with a momentum‐based style‐rotation overlay.

Over the time period since the requisite style‐based ETFs began trading, the composite strategy would have outperformed the S&P 500 index during both market downturns and market upturns, providing better than market returns at lower than market levels of risk.

The key innovation of this paper is the development of a quantitative investment strategy tailored specifically to meet both the educational and the portfolio management needs of SMIF students; a secondary innovation is the demonstration of the efficacy of a style‐rotation strategy, in contrast to the more typical sector/industry‐rotation type of strategy.

The purpose of this paper is to comprehensively review a large and heterogeneous body of academic literature on investors' feedback trading, one of the most popular trading patterns observed historically in financial markets. Specifically, the authors aim to synthesize the diverse theoretical approaches to feedback trading in order to provide a detailed discussion of its various determinants, and to systematically review the empirical literature across various asset classes to gauge whether their feedback trading entails discernible patterns and the determinants that motivate them.

Given the high degree of heterogeneity of both theoretical and empirical approaches, the authors adopt a semi-systematic type of approach to review the feedback trading literature, inspired by the RAMESES protocol for meta-narrative reviews. The final sample consists of 243 papers covering diverse asset classes, investor types and geographies.

The authors find feedback trading to be very widely observed over time and across markets internationally. Institutional investors engage in feedback trading in a herd-like manner, and most noticeably in small domestic stocks and emerging markets. Regulatory changes and financial crises affect the intensity of their feedback trades. Retail investors are mostly contrarian and underperform their institutional counterparts, while the latter's trades can be often motivated by market sentiment.

The authors provide a detailed overview of various possible theoretical determinants, both behavioural and non-behavioural, of feedback trading, as well as a comprehensive overview and synthesis of the empirical literature. The authors also propose a series of possible directions for future research.

This study investigates whether the skilled minority of active equity managers in emerging markets can be identified using a machine learning (ML) framework that incorporates a large set of performance characteristics.

The study uses a cross-section of South African active equity managers from January 2002 to December 2021. The performance characteristics are analysed using ML models, with a particular focus on gradient boosters, and naïve selection techniques such as momentum and style alpha. The out-of-sample nominal, excess and risk-adjusted returns are evaluated, and precision tests are conducted to assess the accuracy of the performance predictions.

A minority of active managers exhibit skill that results in generating alpha, even after accounting for fees, and show that ML models, particularly gradient boosters, are superior at identifying non-linearities. LightGBM (LG) achieves the highest out-of-sample nominal, excess and risk-adjusted return and proves to be the most accurate predictor of performance in precision tests. Naïve selection techniques, such as momentum and style alpha, outperform most ML models in forecasting emerging market active manager performance.

The authors contribute to the literature by demonstrating that a ML approach that incorporates a large set of performance characteristics can be used to identify skilled active equity managers in emerging markets. The findings suggest that both ML models and naïve selection techniques can be used to predict performance, but the former is more accurate in predicting ex ante performance. This study has practical implications for investment practitioners and academics interested in active asset manager performance in emerging markets.

The concept of style is gaining momentum in organizational research. Focussing on its implications for strategy, this paper presents a conceptual and methodological framework to make the notion of style operational and applicable to both research and practice. Style is defined here as a combinatorial, socially situated and semiotic device that can be organized into typologies – recurrent combinations of stylistic dimensions exerting a normative and semiotic function within and across contexts. The empirical analysis, situated in the field of electronic music, considers the music genres and the colour dimension of artists' appearance as components of their style. Results show how coherent style typologies normatively dominate the field and how non-conformist but coherent typologies correspond to superior creative performance. Operating as unifying device, style can transform varied and potentially confounding traits into distinctiveness and shed light on competitive market dynamics that cannot be fully explained via other theoretical constructs.

The purpose of this paper is to investigate whether mutual fund investors can make effective cash flow timing decisions and examine the sensitivity of these decisions to past fund performance using cash flow data at the individual fund level.

This study examines performance persistence and investor timing ability of 200 domestic equity mutual funds in Taiwan between 1996 and 2009. In particular, a performance gap measuring the difference between dollar‐weighted average monthly returns and geometric average monthly returns is used to evaluate investors' timing ability.

The empirical results show that funds that have performed well (poorly) in the previous year tend to continue performing well (poorly) in the following year, and investors' timing performance is negatively related to fund performance. The results also show that investors' timing performance is significantly and negatively related to fund size, length of fund history, and momentum‐style of funds, but positively related to value‐style funds. These results suggest that mutual fund investors are loss‐averse and demonstrate return‐chasing behavior in well‐performing funds.

The paper contributes to the mutual fund performance literature by proposing an integrated framework that jointly tests fund performance and how it affects investors' cash flow timing decisions. Furthermore, the paper individually measures investors' timing sensitivity for the current best (worst) performance funds and consecutive two‐year best (worst) performance funds, and contributes to a growing body of research on the behavior of mutual fund investors.

This paper investigates whether style migration affects industry evolution. The study documents industry evolution in terms of market weights, returns, and risks over the sample period from 1966 to 2000. The study shows that investment styles migrate in different degrees across different industries over time. In addition, the relation between industry evolution and style migration is neither simple nor static. The paper shows that growth‐value migration has predictability about the industries' returns and changes in volatility. Furthermore, style migration in the industry is mainly driven by existing firms changing their investment styles, not by new entrants to the industry causing style shifts. Both investment theory and its application to investment management critically depend on our understanding of stock return persistence anomalies. The ability to outperform buy‐and‐hold strategies by acquiring past winning stocks and selling past losing stocks, commonly referred to as “individual stock momentum,” remains one of the most puzzling of these anomalies. Moskowitz and Grinblatt (1999) attribute the bulk of the observed momentum in individual stock returns to industry momentum—the tendency for stock return patterns at the industry level to persist. It is well known that there are hot and cold IPO markets, and hot and cold sectors of the economy. Investors may simply herd toward (away from) these hot (cold) industries and sectors, causing price pressure that could create return persistence. The recent attraction to internet stocks is perhaps the latest manifestation of such behavior, which is not unlike a similar pattern biotechnology firms and railroad firms witnessed in 1980s and 1900s, respectively. For the active portfolio manager, rotation among different industries may provide opportunities for portfolio performance enhancement. As a result, understanding both the evolution of industries and the style factors causing cyclical variation in industry returns and risk plays an important role in professional portfolio management. Given the fact that a number of researchers have found consistent differences among the returns of various equity classes, investment styles of size and growth‐value are natural candidates for studying what causes cyclical variation in industry returns and risks. Individual investment styles perform differently during various stages of a cycle of bull market and bear market. For example, small cap stocks outperformed large cap stocks in the 1970s, but large cap stocks outperformed small cap stocks in the 1980s. Growth stocks outperformed value stocks in 1998 while the opposite occurred in 1997. Although it is well documented that the cross‐sectional variation in expected returns can be captured by three factors: market, size, and book‐to‐market, it is not yet clear whether cyclical variations in style attributes, not style returns, influence cross‐sectional variation in expected returns and return variance. In the investment industry, cyclical variation in style attributes is commonly called style migration. Perez‐Quiros and Timmermann (2000) provide a rational suggestion that small firms are most strongly affected by tighter credit market conditions in a recession and thus cyclical variations in style performance result from business cycles. As certain equity classes took off and others fell out of favor, investors overreacted, thereby causing cyclical variations in returns and risks of industries where firms are similarly sensitive to the fundamental shocks. In a recent study of behavioral finance, Barberis and Shleifer (2003) argue that in the presence of switchers who can affect asset prices by moving funds across styles, a style‐level momentum strategy could be successful because good performance by a style attracts switcher flows, which then drive the prices even higher. Analyzing the extent of interaction between style migrations and industry evolution may shed light on understanding the sources of predictable components in industry returns and risk. This paper provides such a contribution to the literature. The rest of the paper is organized as follows. Section I describes the sample data and summarizes industry evolution in terms of market capitalization weights in the entire market over time. Section II analyzes style migration within each industry. Section III examines the effect of style migration on industry evolution. Section IV concludes.

The purpose of this study is to investigate the relationship between predictability in return and investment strategy performance. Two measures that characterize investment strategies within a mean‐variance framework, an activity measure and a style measure, are developed and the performance of alternative strategies (e.g. contrarian, momentum, etc.) is examined when risky asset returns are mean reverting.

Returns are assumed to follow a multivariate Ornstein‐Uhlenbeck process, where reversion to a time‐varying mean is governed by an additional variable set, similar to that proposed by Lo and Wang (1995). Depending on its parameterization, this process is capable of producing an autocorrelation pattern consistent with empirical evidence, that is, positive autocorrelation in short‐horizon returns and negative autocorrelation in long‐horizon returns.

The results, for four uninformed investment strategies and assuming that returns are generated by a simple univariate Ornstein‐Uhlenbeck process, show that the unadjusted returns from the contrarian (momentum) strategy are greater than those from the other strategies when the mean reversion parameter, α, is greater than (less than) one. The results are expected, given the relationship between α and the first‐order autocorrelation in returns. The risk level (measured by either the standard deviation of returns or beta) of the contrarian strategy is the lowest at essentially all levels of mean reversion and the risk‐adjusted returns from the contrarian strategy, measured by the both the Sharpe and Treynor ratios, dominate those from the other strategies.

In future research, a number of issues not considered in this study may be investigated. The style measure developed here can be used to determine whether the results obtained hold when an informed, mean‐variance efficient active strategy is employed. In addition, the performance of both the informed and uninformed strategies may be examined under the assumption that the risky return process follows a multivariate Ornstein‐Uhlenbeck process. This work should provide findings that facilitate the separation of fund risk due to dynamic strategies from that due to time‐varying expected returns.

The methodology used here may be easily extended to consider a number of important issues, such as the frequency of portfolio rebalancing, transactions costs, and multiple asset portfolios, that are encountered in practice.

The approach used here provides insight into how predictability affects the relative performance of tactical investment strategies and, thus, may serve as a basis for determining the magnitude and persistence in autocorrelation required for active investment strategies to yield profits significantly different from those of passive strategies. In this sense, this study may have appeal for both academics and investment professionals.

The authors compare sentiment level with sentiment shock from different angles to determine which measure better captures the relationship between sentiment and stock returns.

This paper examines the relationship between investor sentiment and contemporaneous stock returns. It also proposes a model of systems science to explain the empirical findings.

The authors find that sentiment shock has a higher explanatory power on stock returns than sentiment itself, and sentiment shock beta exhibits a much higher statistical significance than sentiment beta. Compared with sentiment level, sentiment shock has a more robust linkage to the market factors and the sentiment shock is more responsive to stock returns.

This is the first study to compare sentiment level and sentiment shock. It concludes that sentiment shock is a better indicator of the relationship between investor sentiment and contemporary stock returns.

The purpose of this paper is to investigate the dynamics of mutual fund investment flows across the business cycle. To account for the differences in the flow patterns of funds catered for institutional investors and those focusing on retail investors, the author conducts this investigation separately for flows of institutional and retail funds.

The author uses the sample of US equity mutual funds for the period between 1999 and 2012. For the samples of each type of fund, the author performs separate analyses for expansion and recession periods. Following Sirri and Tufano (1998), the author implements the Fama MacBeth (1973) approach.

The author finds that flow patterns of both fund types vary across the business cycle. For example, the results reveal that during bad times, institutional investors demonstrate weaker return-chasing behavior, while paying higher attention to Jensen’s α, than during good times. In addition, the author reports results on the effect of fund exposure to various systematic risk factors. For instance, the author observes that during economic downturns, investors of both fund types tend to punish managers with higher market exposure. During expansions, the fund’s market exposure positively affects flows of institutional funds, while its effect on the flows of retail funds remains negative.

To the best of the author’s knowledge, this is the first study that investigates mutual fund investment flow patterns across the business cycle, while simultaneously accounting for differences in flow patterns between retail and institutional funds. A further contribution of this paper is that it explores the previously overlooked relationships between fund flows and their exposure to various systematic risk factors.