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Did Behavioral Mutual Funds Exploit Market Inefficiencies During or After the Financial Crisis?
... По причине популярности в 2000х годах теорий по веденческих финансов такие фонды присутствовали и в линей ке продуктов крупнейших управляющих фондами J. P. Morgan (UBVLX, JIVAX), AQR (AMOMX), Fidelity (FCNTX) и др. [Philippas, 2014]. Однако все перечисленные фонды в среднем имели пере менный успех и не превышали доходность рынка в целом, что не однократно отмечалось в научной литературе [Goodfellow et al., 2013;Miles, 2016;. ...
The paper presents an adaptation of modern behavioral economic theory to portfolio investment in the Russian stock market. The author analyzes the possibility of optimizing the investment portfolio for a private investor using portfolio correction based on changes in monetary policy indicators available in media sources. The behavioral model of portfolio selection is used, based on the value of shares on Moscow Exchange and taking into account a reference point in the theory of prospects in the form of indicators regulated by the Bank of Russia. The key rate and the standard of required reserves, inflation, the average rate on bank deposits, and the exchange rate of the US dollar to the ruble are used as monetary policy indicators. The behavioral model is a modified theory of average variance, in which the calculation of profitability and risk is carried out according to the main behavioral theories, namely the cumulative prospect and mental accounting theory. The author considers various options for forming a portfolio in accordance with the level of risk aversion. As a result of comparing the models of optimization of the average variance portfolio and the models based on the modified theory of average variance using behavioral factors, higher risk-return ratios of the modified models were revealed in the forecast period, while for the analyzed period all models were located on the line of Markowitz efficient portfolio. As a further development of the portfolio behavioral theory, the possibilities of adapting the model are proposed not only depending on the points of reference, but also depending on changes in risk acceptance coefficients and probability estimates.
... Três fundos da amostra apresentaram retornos anormais positivos. Philippas (2014) ...
This work has analyzed the performance of 31 behavioral mutual funds in the USA, Europe and Japan described in Santoni and Kelshiker (2010). Were observed the performances of the funds and their respective benchmarks in four indicators: the Sharpe index, Sortino Index, Omega Measure and the Behavioral Performance Measure. The horizon of analysis was 10 years (Jan/04 to Dec/14) divided in intervals of 6, 12, 36, 60 and 120 months. Based on the consolidation of indicators the funds were ranked and classified into three bands of performance: top, middle and bottom. In the interval of 120 months there was, in general, no significant (5%) difference in the average performance between the funds and the benchmarks. The analysis by intervals indicated that the performance of the funds in relation to the benchmarks worsen as the investment horizon increases. In shorter intervals (6 and 12 months) there was, in average, a significant difference in performance while in longer maturities (36 and 60 months) the average performance of the funds was significantly lower than the benchmarks. In the mean of all intervals the average performance of the funds was significantly lower than the benchmarks
Zielsetzung des Kapitel 5 ist die Überprüfung der Performance von Behavioral Finance Fonds. Dabei werden die bisherigen Performancestudien zu diesen Fonds ausgewertet, Defizite der Untersuchungen aufgezeigt sowie Vorschläge für eine verbesserte inhaltliche und methodische Performancemessung angeführt. Zum Schluss werden die Implikationen der Ergebnisse thematisiert, ihre Ursachen erforscht und potenzielle Verbesserungsvorschläge erarbeitet, die ggf. zu einem höheren Anlageerfolg der Fonds beitragen könnten.
Dieser Teil der Arbeit über die tatsächliche Anlageumsetzung der theoretischen Erkenntnisse der Behavioral Finance Theorie enthält den Kern der Arbeit, weil hier das Vorgehen der Behavioral Finance Fonds ausführlich analysiert wird. Zunächst wird herausgearbeitet, dass die Anlagestrategie das geeignete Kriterium zur Fondsabgrenzung dieser Fonds darstellt. Nach einer Systematisierung der traditionellen Anlagestrategien und der Beschreibung der jüngeren Entwicklung der Behavioral Finance Fonds wird aufgrund einer feststellbaren Strategieintransparenz versucht, auf Basis der Fondsinformationen eine Eingrenzung der Behavioral Finance Fonds in Form einer Abstufung (i. w. S., i. e. S., ohne explizite Begriffsnennung) vorzunehmen. Innerhalb der Kategorie Behavioral Finance Fonds i. e. S. findet eine Klassifizierung der Strategieansätze im Rahmen einer dezidierten Untersuchung der Finanzanalysen, Entscheidungssysteme und Umsetzungsmethodiken statt. Dabei dienen die gewonnenen Ergebnisse abschließend für einen definitorischen Abgrenzungsversuch des neuen Fondskonzeptes.
This paper surveys several mutual fund performance evaluation models. The models are applied to examine the performance of Greek equity and balanced mutual funds. Specifically, the Henriks-son and Merton (1981), Bhattacharya and Pfleiderer (1983) and Lockwood and Kadiyala (1988) models are applied and compared. Empirical results show that models in which beta is treated as random variable imply superior manager performance in terms of selectivity, contrary to models based on the assumption of binary betas. All models are in agreement that fund managers do not exhibit superior macro-forecasting abilities.
The mutual fund industry has undergone tremendous change in the past 60 years. Total assets, number of funds, and fund costs have increased exponentially, whereas both the duration of the funds' portfolio holdings and the duration of their shareholders' holdings have tumbled. The industry's ownership of corporate stocks is at an all-time high, yet mutual fund managers have been noticeably absent from the corporate governance debate. This article details 10 fundamental changes that have taken place in the mutual fund industry since 1945 and finds that, in the aggregate, they have benefited mutual fund managers to the direct and commensurate detriment of mutual fund investors.
This book provides a comprehensive treatment of behavioural finance. With the use of the latest psychological research, Shefrin helps us to understand the human behaviour that guides stock selection, financial services, and corporate financial strategy. He argues that financial practitioners must acknowledge and understand behavioural finance - the application of psychology to financial behaviour - in order to avoid many of the investment pitfalls caused by human error. Shefrin points out the common but costly mistakes that money managers, security analysts, financial planners, investment bankers, and corporate leaders make, so that readers gain valuable insights into their own financial decisions and those of their employees, asset managers, and advisors.
In this paper, the authors use monthly holdings to study timing ability. These data differ from holdings data used in previous studies in that the authors' data have a higher frequency and include a full range of securities, not just traded equities. Using a one-index model, the authors find, as do two recent studies, that management appears to have positive and statistically significant timing ability. When a multiindex model is used, the authors show that timing decisions do not result in an increase in performance, whether timing is measured using conditional or unconditional sensitivities. The authors show that sector rotation decisions with respect to high-tech stocks are a major contribution to negative timing. © The Authors 2011. Published by Oxford University Press [on behalf of the European Finance Association]. All rights reserved.
The theme of my address is transaction costs and their impact on the pricing of financial assets. In various forms, this has been a theme of my research over the course of the past several years. Oftentimes, in models of the financial markets we abstract from market imperfections in order to keep the model tractable and hope that this abstraction does not seriously impair the realism of the model. Thus, we abstract from bid-asked spreads, brokerage fees, execution costs, illiquidity (that is, lack of market depth), borrowing and short-selling restrictions and fees, and from the absence of certain markets which are needed to insure financial risk and labor income risk. How crucial is this abstraction? This is a very broad question and I do not intend to address it here in its full generality. I will focus on just one form of market imperfection: bid-asked spreads, brokerage fees and execution costs, collectively referred to as transaction costs. Other forms of market imperfections will be considered only indirectly and to the extent that they contribute to transaction costs.
We analyze 16 mutual funds that are self-proclaimed or media-identified disciples of behavioral finance to determine whether: (a) they successfully attract investment dollars and (b) their strategies earn abnormal returns for their investors. We find these funds are successfully attracting investment dollars, outperform S&P 500 index funds, load especially heavily on the HML factor, but fail to earn risk-adjusted abnormal returns. Our results suggest behavioral mutual funds are tantamount to value investing and not much more.
The link between asset valuations and investor sentiment is the subject of con-siderable debate in the profession. We address this question by examining how survey data on investor sentiment relates to i) long-horizon returns, and ii) asset valuations. If excessive optimism drives prices above intrinsic values, periods of high sentiment should be followed by low returns as market prices revert to fundamental values. We find this to be the case for the overall stock market at horizons of two to three years. The relation is strongest for large-capitalization, low book-to-market (growth) portfolios. We also examine the relation between sentiment levels and deviations from intrinsic value. Using errors from an inde-pendent pricing model, we find sentiment is positively related to valuation errors using a variety of tests. All of our results are robust to the inclusion of other factors that have been shown to forecast stock returns, including past returns., seminar participants at the Federal Reserve Board, the University of North Car-olina, Virginia Tech, the 1999 Western Finance Association meeting (especially the discussant, Bhaskaran Swaminathan), the 1999 Financial Management Association meeting, and the 2000 Batten Young Scholars Conference at William & Mary for their comments and suggestions. We also thank Gurdip Bakshi, Zhiwu Chen, Ken French, and Steve Sharpe for providing data.
In this paper I derive a risk-adjusted measure of portfolio performance (now known as Jensen's Alpha) that estimates how much a manager's forecasting ability contributes to the fund's returns. The measure is based on the theory of the pricing of capital assets by Sharpe (1964), Lintner (1965a) and Treynor (Undated). I apply the measure to estimate the predictive ability of 115 mutual fund managers in the period 1945-1964 - that is their ability to earn returns which are higher than those we would expect given the level of risk of each of the portfolios. The foundations of the model and the properties of the performance measure suggested here are discussed in Section II. The evidence on mutual fund performance indicates not only that these 115 mutual funds were on average not able to predict security prices well enough to outperform a buy-the-market-and-hold policy, but also that there is very little evidence that any individual fund was able to do significantly better than that which we expected from mere random chance. It is also important to note that these conclusions hold even when we measure the fund returns gross of management expenses (that is assume their bookkeeping, research, and other expenses except brokerage commissions were obtained free). Thus on average the funds apparently were not quite successful enough in their trading activities to recoup even their brokerage expenses.
The aggregate portfolio of actively managed U.S. equity mutual funds is close to the market portfolio, but the high costs of active management show up intact as lower returns to investors. Bootstrap simulations suggest that few funds produce benchmark-adjusted expected returns sufficient to cover their costs. If we add back the costs in fund expense ratios, there is evidence of inferior and superior performance (nonzero true α) in the extreme tails of the cross-section of mutual fund α estimates.
Asset prices are driven by public news and information that is dispersed among many market participants. Traditional asset pricing theories have assumed that all investors hold symmetric information. Research in the past two decades has shown that the inclusion of asymmetric information drastically alters traditional results. This book provides a detailed up‐to‐date survey that serves as a map for students and other researchers navigating through this literature.
The book starts by introducing the reader to different knowledge, equilibrium, and efficiency concepts. After explaining no‐trade theorems, it highlights the important role of asymmetric information in explaining the existence and anatomy of bubbles. The subsequent overview of market microstructure models shows how information is reflected in prices and how traders can infer it from prices. Insights derived from herding models are used to provide explanations for stock market crashes. If investors have short horizons, price correcting arbitrage activity is limited and investors have a tendency to focus on the same (possible unimportant) news, a phenomena that led Keynes to compare the stock market with a beauty contest. The book concludes with a brief summary of bank runs and their connection to financial crises.
In summary, models with asymmetric information provide a better understanding of bubbles, crashes, and other market inefficiencies and frictions.
We provide a model that links an asset's market liquidity (i.e., the ease with which it is traded) and traders' funding liquidity (i.e., the ease with which they can obtain funding). Traders provide market liquidity, and their ability to do so depends on their availability of funding. Conversely, traders' funding, i.e., their capital and margin requirements, depends on the assets' market liquidity. We show that, under certain conditions, margins are destabilizing and market liquidity and funding liquidity are mutually reinforcing, leading to liquidity spirals. The model explains the empirically documented features that market liquidity (i) can suddenly dry up, (ii) has commonality across securities, (iii) is related to volatility, (iv) is subject to "flight to quality," and (v) co-moves with the market. The model provides new testable predictions, including that speculators' capital is a driver of market liquidity and risk premiums. The Author 2008. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org., Oxford University Press.
Mutual fund investors are concerned with the selection of the best fund in terms of performance among the set of alternative funds. This paper proposes an innovative mutual funds performance evaluation measure in the context of multicriteria decision making. We implement a multicriteria methodology using stochastic multicriteria acceptability analysis, on Greek domestic equity funds for the period 2000–2009. Combining a unique dataset of risk-adjusted returns such as Carhart’s alpha with funds’ cost variables,we obtain a multicriteria performance evaluation and ranking of the mutual funds, by means of an additive value function model. The main conclusion is that among employed variables, the sophisticated Carhart’s alpha plays the most important role in determining fund rankings. On the other hand, funds’ rankings are affected only marginally by operational attributes. We believe that our results could have serious implications either in terms of a fund rating system or for constructing optimal combinations of portfolios.
Identifying a suitable benchmark is essential when testing asset pricing models, measuring the performance of active investors, or providing market proxy portfolios for passive investors. Concern that increased domination of capitalization weighted stock indices by a few large firms will lead to inefficient portfolio diversification is leading some investors and researchers to argue that index providers should adjust their weighting methods to limit concentration. This study tests and rejects the hypothesis that concentration arising as a result of capitalization weights in the FTSE 100 Index increases risk, either during normal market conditions or during negative tail events in the return distribution. On the contrary, during the left tail of the return distribution, the equally weighted portfolio of FTSE 100 Index constituents exhibits higher risk and lower returns than the capitalization weighted FTSE 100 Index portfolio, a finding consistent with variations of the CAPM that allow for time varying risk premia. Post refereeing, pre-typsetting. This version is also available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1026548
We survey theoretical developments in the literature on the limits of arbitrage. This literature investigates how costs faced by arbitrageurs can prevent them from eliminating mispricings and providing liquidity to other investors. Research in this area is currently evolving into a broader agenda emphasizing the role of financial institutions and agency frictions for asset prices. This research has the potential to explain so-called \\market anomalies� and inform welfare and policy debates about asset markets. We begin with examples of demand shocks that generate mispricings, arguing that they can stem from behavioral or from institutional considerations. We next survey, and nest within a simple model, the following costs faced by arbitrageurs: (i) risk, both fundamental and non-fundamental, (ii) short-selling costs, (iii) leverage and margin constraints, and (iv) constraints on equity capital. We finally discuss implications for welfare and policy, and suggest directions for future research.
The authors present a simple overlapping generations model of an asset market in which irrational noise traders with erroneous stochastic beliefs both affect prices and earn higher expected returns. The unpredictability of noise traders' beliefs creates a risk in the price of the asset that deters rational arbitrageurs from aggressively betting against them. As a result, prices can diverge significantly from fundamental values even in the absence of fundamental risk. Moreover, bearing a disproportionate amount of risk that they themselves create enables noise traders to earn a higher expected return than rational investors do. The model sheds light on a number of financial anomalies. Copyright 1990 by University of Chicago Press.
We develop a simple rational model of active portfolio management that provides a natural benchmark against which to evaluate observed relationship between returns and fund flows. Many effects widely regarded as anomalous are consistent with this simple explanation. In the model, investments with active managers do not outperform passive benchmarks because of the competitive market for capital provision, combined with decreasing returns to scale in active portfolio management. Consequently, past performance cannot be used to predict future returns, or to infer the average skill level of active managers. The lack of persistence in actively managed returns does not imply that differential ability across managers is nonexistent or unrewarded, that gathering information about performance is socially wasteful, or that chasing performance is pointless. A strong relationship between past performance and the flow of funds exists in our model: indeed, this is the market mechanism that ensures that no predictability in performance exists. Choosing parameters to match the flow-performance relationship and survivorship rates, we find these features of the data are consistent with the vast majority (80%) of active managers having at least enough skill to make back their fees.
This paper summarizes and explains the main events of the liquidity and credit crunch in 2007-08. Starting with the trends leading up to the crisis, I explain how these events unfolded and how four different amplification mechanisms magnified losses in the mortgage market into large dislocations and turmoil in financial markets.
Using a database of more than 1.85 million retail investor transactions over 1991-1996, we show that these trades are systematically correlated-that is, individuals buy (or sell) stocks in concert. Moreover, consistent with noise trader models, we find that systematic retail trading explains return comovements for stocks with high retail concentration (i.e., small-cap, value, lower institutional ownership, and lower-priced stocks), especially if these stocks are also costly to arbitrage. Macroeconomic news and analyst earnings forecast revisions do not explain these results. Collectively, our findings support a role for investor sentiment in the formation of returns. Copyright 2006 by The American Finance Association.
Investor sentiment, defined broadly, is a belief about future cash flows and investment risks that is not justified by the facts at hand. The question is no longer whether investor sentiment affects stock prices, but how to measure investor sentiment and quantify its effects. One approach is "bottom up," using biases in individual investor psychology, such as overconfidence, representativeness, and conservatism, to explain how individual investors underreact or overreact to past returns or fundamentals. The investor sentiment approach that we develop in this paper is, by contrast, distinctly "top down" and macroeconomic: we take the origin of investor sentiment as exogenous and focus on its empirical effects. We show that it is quite possible to measure investor sentiment and that waves of sentiment have clearly discernible, important, and regular effects on individual firms and on the stock market as a whole. The top-down approach builds on the two broader and more irrefutable assumptions of behavioral finance—sentiment and the limits to arbitrage—to explain which stocks are likely to be most affected by sentiment. In particular, stocks that are difficult to arbitrage or to value are most affected by sentiment.
We study how investor sentiment affects the cross-section of stock returns. We predict that a wave of investor sentiment has larger effects on securities whose valuations are highly subjective and difficult to arbitrage. Consistent with this prediction, we find that when beginning-of-period proxies for sentiment are low, subsequent returns are relatively high for small stocks, young stocks, high volatility stocks, unprofitable stocks, non-dividend-paying stocks, extreme growth stocks, and distressed stocks. When sentiment is high, on the other hand, these categories of stock earn relatively low subsequent returns. Copyright 2006 by The American Finance Association.
We use a new database to perform a comprehensive analysis of the mutual fund industry. We find that funds hold stocks that outperform the market by 1.3 percent per year, but their net returns underperform by one percent. Of the 2.3 percent difference between these results, 0.7 percent is due to the underperformance of nonstock holdings, whereas 1.6 percent is due to expenses and transactions costs. Thus, funds pick stocks well enough to cover their costs. Also, high-turnover funds beat the Vanguard Index 500 fund on a net return basis. Our evidence supports the value of active mutual fund management. Copyright The American Finance Association 2000.
Using a sample free of survivor bias, the author demonstrates that common factors in stock returns and investment expenses almost completely explain persistence in equity mutual funds' mean and risk-adjusted returns. Darryll Hendricks, Jayendu Patel, and Richard Zeckhauser's (1993) 'hot hands' result is mostly driven by the one-year momentum effect of Narasimham Jegadeesh and Sheridan Titman (1993), but individual funds do not earn higher returns from following the momentum strategy in stocks. The only significant persistence not explained is concentrated in strong underperformance by the worst-return mutual funds. The results do not support the existence of skilled or informed mutual fund portfolio managers. Copyright 1997 by American Finance Association.
- M K Brunnermeier
- L H Pedersen
Brunnermeier, M. K., and Pedersen, L. H. 2009. Market Liquidity and Funding
Liquidity. Review of Financial Studies 22: 2201–2238.
- W J Reinhart
- M Brennan
Reinhart, W. J., and Brennan, M. 2007. Behavioral Portfolios Performance
Measurement. Financial Decisions 19: 1-15.
Risk and Liquidity. Clarendon Lectures in Finance
- H S Shin
Shin, H. S. 2010. Risk and Liquidity. Clarendon Lectures in Finance, Oxford
University Press.