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Bearing the Bear: Sentiment-based Disagreement in Multi-criteria Portfolio Optimization
Abstract
Employing a nonlinear multi-criteria optimization approach, sentiment-based disagreement is incorporated into portfolio optimization as additional risk factor. A multi-criteria trading strategy outperforms several benchmarks regarding various performance measures. Applying the strategy over a long time period including downturns and upswings, disagreement proves itself especially valuable in bear markets as it is an indicator for future volatility.
... weather effects on stock returns. Subsequent studies have supported the notion that weather, disasters, lunar phases, cloudiness, temperature, wind, etc. affect stock returns (see, for example, Hirshleifer, 2001;Hirshleifer, and Shumway, 2003;Cao and Wei, 2005;Yuan, Zheng, and Zhu, 2006;Kaplanski, and Levy, 2010;Dehaan, Madsen, and Piotroski, 2017;You, Guo, and Peng, 2017;Xu, and Zhou, 2018;Glogger et al., 2019;Erdemlioglu, and Joliet, 2019;Gao et al., in press). ...
We studied the effect of the shift from Daylight Saving Time (summer time) to Standard Time (winter time) on stock markets around the globe. Using a detailed cross-country data set of daily returns, we documented that (a) market returns on the day following the time shift were significantly lower than those on the corresponding day of the week unaffected by the change; (b) the economic magnitude of the effect was substantial, on average 5–6 times greater than the unconditional mean of market returns; and (c) the outcome was more prominent in local, relatively small markets. Furthermore, we attempted to identify the mechanism underlying the gloomy market returns accompanying the switch to winter time. Our results suggest that the mechanism underlying the effect may be based on the temporary loss of investor internal clock harmony.
This paper applies the cumulative prospect theory to improve the traditional integer subscript hesitant fuzzy linguistic term set (IS-HFLTS). The proposed variable subscript hesitant fuzzy language set (VS-HFLTS) takes into account the subjectivity and differences of investors' behavior when investing in the securities market, and extends the application of behavioral finance to multi-criteria group decision-making portfolio. To evaluate the financial products, the VS-HFLTS multi-criteria group decision-making portfolio evaluation system is constructed in the research. The hesitant fuzzy linguistic value function and the purchase appetite weight function are proposed to convert the natural linguistic evaluation into a quantitative score for the judgment of future portfolio return in the securities market. Furthermore, a variable subscript hesitant fuzzy linguistic portfolio model is put forward for the risk-averse, risk-neutral, and risk-seeking investment decision-makers. The optimal portfolio strategy is obtained by solving the equivalent non-linear model. Meanwhile, an optimized group decision-making portfolio strategy is established to better achieve the goal of increasing group decision-making investors' returns or reducing risks. Finally, numerical simulations are performed to find effective frontiers for future portfolio selection, which verifies the validity and feasibility of the models and methods proposed in this paper.
This paper investigates the impact of media pessimism on financial market returns and volatility in the long run. We hypothesize that media sentiment translates into investor sentiment. Based on the underreaction and overreaction hypotheses [Barberis, N., A. Shleifer, and R. Vishny. 1998. “A Model of Investor Sentiment.” Journal of Empirical Economics 49 (3): 307–343], we suggest that media pessimism has an effect on market performance after a lag of several months. We construct a monthly media pessimism indicator by taking the ratio of the number of newspaper articles that contain predetermined negative words to the number of newspaper articles that contain predetermined positive words in the headline and in the lead paragraph. Our results indicate that media pessimism is associated with negative (positive) market returns 14–17 (24–25) months in advance and positive market volatilities 1–20 months in advance. Our results are statistically and economically significant. We find evidence for Granger causality of media pessimism on market performance. Our media pessimism indicator possesses additional predictive power for the Baker and Wurgler [2006. “Investor Sentiment and the Cross-section of Stock Returns.” Journal of Finance 61 (4): 1645–1680] investor sentiment index and the Chicago Board Options Exchange Market Volatility Index.
In standard mean-variance bi-criterion portfolio selection, the efficient set is a frontier. While it is not yet standard for there to be additional criteria in portfolio selection, there has been a growing amount of discussion in the literature on the topic. However, should there be even one additional criterion, the efficient frontier becomes an efficient surface. Striving to parallel Merton’s seminal analytical derivation of the efficient frontier, in this paper we provide an analytical derivation of the efficient surface when an additional linear criterion (on top of expected return and variance) is included in the model addressed by Merton. Among the results of the paper there is, as a higher dimensional counterpart to the 2-mutual-fund theorem of traditional portfolio selection, a 3-mutual-fund theorem in tri-criterion portfolio selection. 3D graphs are employed to stress the paraboloidic/hyperboloidic structures present in tri-criterion portfolio selection.
The Black-Litterman (BL) model for portfolio optimization combines investors’ expectations with the Markowitz framework. The BL model is designed for investors with private information or knowledge of market behaviour. In this paper, I propose a method where investors’ expectations are based on either news sentiment using high-frequency data or on a combination of accounting variables; financial analysts’ recommendations, and corporate social network indicators with quarterly data. The results show promise when compared to a market portfolio. I also provide recommendations for trading strategies using the results of this BL model.
The building blocks of the Sharpe ratio--expected returns and volatilities--are unknown quantities that must be estimated statistically and are, therefore, subject to estimation error. This raises the natural question: How accurately are Sharpe ratios measured? To address this question, I derive explicit expressions for the statistical distribution of the Sharpe ratio using standard asymptotic theory under several sets of assumptions for the return-generating process--independently and identically distributed returns, stationary returns, and with time aggregation. I show that monthly Sharpe ratios cannot be annualized by multiplying by the square root of 12 except under very special circumstances, and I derive the correct method of conversion in the general case of stationary returns. In an illustrative empirical example of mutual funds and hedge funds, I find that the annual Sharpe ratio for a hedge fund can be overstated by as much as 65 percent because of the presence of serial correlation in monthly returns, and once this serial correlation is properly taken into account, the rankings of hedge funds based on Sharpe ratios can change dramatically.
Using a novel investor sentiment proxy extracted from Twitter, this paper investigates whether investor sentiment as expressed in daily happiness has predictive power for stock returns in 10 international stock markets. To account for complex relationships between sentiment and stock returns, a Granger non-causality test in quantiles is used. Our empirical results indicate that the causal relations vary across different quantiles. We observe that the causal relationship from happiness sentiment to stock returns exist only in high quantiles interval. The causal relationship from stock returns to happiness sentiment exists only in the tail area.
Using a new variable to measure investor sentiment we show that the sentiment of German and European investors matters for return volatility in local stock markets. A flexible empirical similarity (ES) approach is used to emulate the dynamics of the volatility process by a time-varying parameter that is created via the similarity of realized volatility and investor sentiment. Out-of-sample results show that the ES model produces significantly better volatility forecasts than various benchmark models for DAX and EUROSTOXX. Regarding other international markets no significant difference between the forecasts can be observed.
We evaluate the probability that an estimated Sharpe ratio will exceed a given threshold in the presence of nonnormal returns. We show that this new uncertainty adjusted investment skill metric (called the probabilistic Sharpe ratio) has a number of important applications. First, it allows us to establish the track-record length needed for rejecting the hypothesis that a measured Sharpe ratio is below a certain threshold with a given confidence level. Second, it models the tradeoff between track-record length and undesirable statistical features (eg, negative skewness with positive excess kurtosis). Third, it explains why track records with those undesirable traits would benefit from reporting performance with the highest sampling frequency such that the independent and identically distributed assumption is not violated. Fourth, it permits the computation of what we call the Sharpe ratio efficient frontier, which lets us optimize a portfolio under nonnormal, leveraged returns while incorporating the uncertainty derived from track-record length. Results can be validated using the Python code in the appendixes.
We provide a theoretical framework to examine how investor sentiment impacts the mean-variance tradeoff. We derive a sentiment-adjusted Markowitz efficient frontier in which investor sentiment alters the first two moments of asset returns, the minimum-variance frontier as well as the Capital Market Line. Our theoretical results are consistent with empirical findings that heightened sentiment-related noise trading activity drives perceived prices away from fundamental and increases market volatility. A rational investor neglecting the effect of investor sentiment may end up selecting a sub-optimal portfolio.
We use daily Internet search volume from millions of households to reveal market-level sentiment. By aggregating the volume
of queries related to household concerns (e.g., “recession,” “unemployment,” and “bankruptcy”), we construct a Financial and
Economic Attitudes Revealed by Search (FEARS) index as a new measure of investor sentiment. Between 2004 and 2011, we find
FEARS (i) predict short-term return reversals, (ii) predict temporary increases in volatility, and (iii) predict mutual fund
flows out of equity funds and into bond funds. Taken together, the results are broadly consistent with theories of investor
sentiment.
One of the most important factors shaping world outcomes is where investment dollars are placed. In this regard, there is the rapidly growing area called sustainable investing where environmental, social, and corporate governance (ESG) measures are taken into account. With people interested in this type of investing rarely able to gain exposure to the area other than through a mutual fund, we study a cross section of U.S. mutual funds to assess the extent to which ESG measures are embedded in their portfolios. Our methodology makes heavy use of points on the nondominated surfaces of many tri-criterion portfolio selection problems in which sustainability is modeled, after risk and return, as a third criterion. With the mutual funds acting as a filter, the question is: How effective is the sustainable mutual fund industry in carrying out its charge? Our findings are that the industry has substantial leeway to increase the sustainability quotients of its portfolios at even no cost to risk and return, thus implying that the funds are unnecessarily falling short on the reasons why investors are investing in these funds in the first place.
Our objective is to identify the trading strategy that would allow an investor to take advantage of “excessive” stock price volatility and “sentiment” fluctuations. We construct a general equilibrium “difference-of-opinion” model of sentiment in which there are two classes of agents, one of which is overconfident about a public signal, while still optimizing intertemporally. Overconfident investors overreact to the signal and introduce an additional risk factor causing stock prices to be excessively volatile. Consequently, rational investors choose a conservative portfolio; moreover, this portfolio depends not just on the current price divergence but also on their prediction about future sentiment and the speed of price convergence.
Applied researchers often test for the difference of the Sharpe ratios of two investment strategies. A very popular tool to this end is the test of Jobson and Korkie (1981), which has been corrected by Memmel (2003). Unfortunately, this test is not valid when returns have tails heavier than the normal distribution or are of time series nature. Instead, we propose the use of robust inference methods. In particular, we suggest to construct a studentized time series bootstrap confidence interval for the difference of the Sharpe ratios and to declare the two ratios different if zero is not contained in the obtained interval. This approach has the advantage that one can simply resample from the observed data as opposed to some null-restricted data. A simulation study demonstrates the improved finite
sample performance compared to existing methods. In addition, two applications to real data are provided.
Using a new data set on investor sentiment, we show that institutional and individual sentiment seem to proxy for smart money and noise trader risk, respectively. First, using bias-adjusted long-horizon regressions, we show that institutional sentiment forecasts stock market returns at intermediate horizons correctly, whereas individuals consistently get the direction wrong. Second, even the simplest possible trading strategies based on investor sentiment show clear tendencies toward being profitable after controlling for systematic risk. Finally, IV regressions show that institutional investors take into account expected individual sentiment when forming their expectations, in a way that is consistent with the view that individual investors can be a proxy for noise trader risk. However, there is evidence of structural change.
This study shows the influence of investor sentiment on the market's mean–variance tradeoff. We find that the stock market's expected excess return is positively related to the market's conditional variance in low-sentiment periods but unrelated to variance in high-sentiment periods. These findings are consistent with sentiment traders who, during the high-sentiment periods, undermine an otherwise positive mean–variance tradeoff. We also find that the negative correlation between returns and contemporaneous volatility innovations is much stronger in the low-sentiment periods. The latter result is consistent with the stronger positive ex ante relation during such periods.
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.
Wisdom of crowds: the value of stock opinions transmitted through social media
- Chen
Learning from prices and the dispersion in beliefs
- Banerjee