Subjective Return Expectations, Perceptions, and Portfolio Choice

Exploiting a representative sample of the French population by age, wealth, and asset classes, we document novel facts about their expectations and perceptions of stock market returns. Both expectations and perceptions of returns are very dispersed, significantly lower than their data counterparts, and a substantial portion of the variation in the former is explained by dispersion in the latter. Consistent with portfolio choice models under incomplete information, a conditional risk-return trade-off explains the intensive margin, while at the extensive margin, only expected returns matter. Despite accounting for survey measurement error in subjective return expectations, ’muted sensitivities’ at both portfolio choice margins obtain, getting consistently (i) bigger when excluding informed non-participants, and (ii) smaller, for inertial and professionally delegated portfolios.

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Risk-return trade-off, information diffusion, and U.S. stock market predictability

International Journal of Financial Engineering ◽

10.1142/s2424786315500383 ◽

2015 ◽

Vol 02 (04) ◽

pp. 1550038 ◽

Cited By ~ 2

Author(s):

Haibin Xie ◽

Shouyang Wang

Keyword(s):

Stock Market ◽

Information Diffusion ◽

Stock Return ◽

Empirical Studies ◽

Market Returns ◽

Complementary Information ◽

Stock Market Returns ◽

Trade Off ◽

Academic Literature ◽

Risk Return

Recent academic literature in finance documents both risk-return trade-off and gradual information diffusion (ID). Motivated by these two financial theories, this paper proposes the ARCH-M model augmented by an ID indicator to forecast the U.S. stock market returns. Empirical studies performed on the monthly S&P500 index show that our approach is useful in both statistical and economic sense. Further analysis shows that the ID provides complementary information to risk-return trade-off effect. Our findings confirm that financial theories are valuable for stock return forecasting.

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Risk Return Trade-Off in Relaxed Risk Parity Portfolio Optimization

Journal of Risk and Financial Management ◽

10.3390/jrfm13100237 ◽

2020 ◽

Vol 13 (10) ◽

pp. 237 ◽

Cited By ~ 1

Author(s):

Vaughn Gambeta ◽

Roy Kwon

Keyword(s):

Parity Violation ◽

Expected Returns ◽

Portfolio Performance ◽

Trade Off ◽

Risk Return ◽

Performance Risk ◽

Cone Model ◽

Second Order Cone ◽

Risk Parity ◽

Parity Condition

This paper formulates a relaxed risk parity optimization model to control the balance of risk parity violation against the total portfolio performance. Risk parity has been criticized as being overly conservative and it is improved by re-introducing the asset expected returns into the model and permitting the portfolio to violate the risk parity condition. This paper proposes the incorporation of an explicit target return goal with an intuitive target return approach into a second-order-cone model of a risk parity optimization. When the target return is greater than risk parity return, a violation to risk parity allocations occurs that is controlled using a computational construct to obtain near-risk parity portfolios to retain as much risk parity-like traits as possible. This model is used to demonstrate empirically that higher returns can be achieved than risk parity without the risk contributions deviating dramatically from the risk parity allocations. Furthermore, this study reveals that the relaxed risk parity model exhibits advantageous traits of robustness to expected returns, which should not deter the use of expected returns in risk parity model.

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Towards the Estimation of an Efficient Benchmark Portfolio: The Case of Croatian Emerging Market

Zagreb International Review of Economics and Business ◽

10.1515/zireb-2017-0002 ◽

2017 ◽

Vol 20 (s1) ◽

pp. 13-23 ◽

Cited By ~ 2

Author(s):

Denis Dolinar ◽

Davor Zoričić ◽

Antonija Kožul

Keyword(s):

Emerging Market ◽

Expected Returns ◽

Trade Off ◽

Risk Return ◽

Market Index ◽

Benchmark Portfolio ◽

Out Of Sample ◽

Shrinkage Method ◽

Research Findings ◽

To Come

Abstract The fact that cap-weighted indices provide an inefficient risk-return trade-off is well known today. Various research approaches evolved suggesting alternative to cap-weighting in an effort to come up with a more efficient market index benchmark. In this paper we aim to use such an approach and focus on the Croatian capital market. We apply statistical shrinkage method suggested by Ledoit and Wolf (2004) to estimate the covariance matrix and follow the work of Amenc et al. (2011) to obtain estimates of expected returns that rely on risk-return trade-off. Empirical findings for the proposed portfolio optimization include out-of-sample and robustness testing. This way we compare the performance of the capital-weighted benchmark to the alternative and ensure that consistency is achieved in different volatility environments. Research findings do not seem to support relevant research results for the developed markets but rather complement earlier research (Zoričić et al., 2014).

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Cross-Sectional Relationship Between Beta and Realized Returns in Emerging Markets

Applied Economics Quarterly ◽

10.3790/aeq.65.2.115 ◽

2019 ◽

Vol 65 (2) ◽

pp. 115-137

Author(s):

Mohammad A. Khataybeh ◽

Mohamad Abdulaziz ◽

Zyad Marashdeh

Keyword(s):

Emerging Markets ◽

Stock Exchange ◽

Expected Returns ◽

Cross Sectional ◽

Risk Return ◽

Conditional Risk ◽

Bull Markets ◽

Amman Stock Exchange ◽

Market Premium ◽

The Impact

Abstract This paper examines the conditional risk-return relationship caused by the impact of using realized returns as a proxy for expected returns, which requires a separation of negative and positive market premiums. Following the methodology of Pettengill et al. (1995), we test the cross sectional relationship between beta and realized returns on the Amman Stock Exchange (ASE) for ten beta sorted portfolio over the period of January 1993 to December 2016. The empirical results suggest that the traditional two-pass approach produces an insignificant relationship between beta and realized returns in most of the sample period. However, when adjusting for negative market premiums, the results show a significant and consistent relationship for all the testing periods and samples. However, a guaranteed reward for holding extra risk occurred only in the period 2001 –2008, which suggests an assurance of positive risk-return tradeoff during bull markets. JEL Classifications: G11, G12, G15, C21 Asset Pricing, Emerging Markets, Conditional Relationship, Beta, Market Premium

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The effect of symmetric and asymmetric information on volatility structure of crypto-currency markets

Journal of Financial Economic Policy ◽

10.1108/jfep-10-2018-0147 ◽

2019 ◽

Vol 11 (3) ◽

pp. 432-450 ◽

Cited By ~ 4

Author(s):

Anwar Hasan Abdullah Othman ◽

Syed Musa Alhabshi ◽

Razali Haron

Keyword(s):

Asymmetric Information ◽

Risk Premium ◽

Market Price ◽

Efficient Market Hypothesis ◽

Market Returns ◽

Trade Off ◽

Content Type ◽

Market Values ◽

Risk Return ◽

Crypto Currency

Purpose This paper aims to examine whether the crypto-currencies’ market returns are symmetric or asymmetric informative, through analysing the daily logarithmic returns of bitcoin currency over the period of 2011-2017. Design/methodology/approach In doing so, the symmetric informative analysis is estimated by applying the generalised auto-regressive conditional heteroscedasticity (GARCH) (1,1) model, whereas asymmetric informative or leverage effects analysis is estimated by exponential GARCH (1,1), asymmetric power ARCH (1,1) and threshold GARCH (1,1) models. In addition, the generalized autoregressive conditional heteroskedasticity in mean (GARCH-M (1,1)) was applied to examine whether the risk-return trade-off phenomenon was persistent in crypto-currencies market. Findings The main findings indicate that bitcoin market return or volatility is symmetric informative and has a long memory to persist in the future. Furthermore, the sympatric volatility is found to be more sensitive to its past values (lagged) than to the new shock of the market values. However, asymmetric informative response of volatility to the negative and the positive shocks do not exist in the bitcoin market or, in other words, there is no leverage effect. This suggests that the bitcoin market is in harmony with the efficient market hypothesis (EMH) with respect to the asymmetric information and violated the EMH with regard to the symmetric information. Hence, the market price or return of bitcoin currency could not be predicted by simply exercising such past market information in the short-run investment. In addition, the estimated coefficient of conditional variance or risk premium (λ) in the mean equation of CHARCH–M (1,1) model is positive however, statistically insignificant. This indicates the absence of risk-return trade-off, in which case the higher market risk will not essentially lead to higher market returns. This paper has proposed that an investment in the crypto-currency market is more appropriate for risk-averse investors than risk takers. Originality/value The findings of the study will provide investors with necessary information about the bitcoin market price efficiency, hedging effectiveness and risk management.

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The Standard Portfolio Choice Problem in Germany*

The Economic Journal ◽

10.1093/ej/ueab006 ◽

2021 ◽

Cited By ~ 1

Author(s):

Christoph Breunig ◽

Steffen Huck ◽

Tobias Schmidt ◽

Georg Weizsäcker

Keyword(s):

Stock Market ◽

Portfolio Choice ◽

Laboratory Experiments ◽

Task Complexity ◽

Real Life ◽

Choice Problem ◽

Market Returns ◽

Stock Market Returns ◽

German Stock ◽

German Stock Market

Abstract We study an investment experiment with a representative sample of German households. Respondents invest in a safe asset and a risky asset whose return is tied to the German stock market. Experimental investments correlate with beliefs about stock market returns and exhibit desirable external validity at least in one respect: they predict real-life stock market participation. But many households are unresponsive to an exogenous increase in the risky asset’s return. The data analysis and a series of additional laboratory experiments suggest that task complexity decreases the responsiveness to incentives. Modifying the safe asset’s return has a larger effect on behaviour than modifying the risky asset’s return.

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The VIX, the Variance Premium, and Expected Returns*

Journal of Financial Econometrics ◽

10.1093/jjfinec/nby008 ◽

2018 ◽

Vol 17 (4) ◽

pp. 517-558 ◽

Cited By ~ 5

Author(s):

Daniela Osterrieder ◽

Daniel Ventosa-Santaulària ◽

J Eduardo Vera-Valdés

Keyword(s):

Return Predictability ◽

Estimation Methods ◽

Expected Returns ◽

Economic Uncertainty ◽

Trade Off ◽

Risk Return ◽

Long Run

AbstractExisting studies find conflicting estimates of the risk–return relation. We show that the trade-off parameter is inconsistently estimated when observed or estimated conditional variances measure risk. The inconsistency arises from misspecified, unbalanced, and endogenous return regressions. These problems are eliminated if risk is captured by the variance premium (VP) instead; it is unobservable, however. We propose a 2SLS estimator that produces consistent estimates without observing the VP. Using this method, we find a positive risk–return trade-off and long-run return predictability. Our approach outperforms commonly used risk–return estimation methods, and reveals a significant link between the VP and economic uncertainty.

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Predicting stock market returns in the US: evidence from an average correlation approach

Accounting Research Journal ◽

10.1108/arj-10-2018-0168 ◽

2020 ◽

Vol 33 (2) ◽

pp. 411-433

Author(s):

Xiyang Li ◽

Bin Li ◽

Tarlok Singh ◽

Kan Shi

Keyword(s):

Stock Market ◽

Business Cycles ◽

Stock Returns ◽

Expected Returns ◽

Market Returns ◽

Average Correlation ◽

Stock Market Returns ◽

Content Type ◽

Portfolio Returns ◽

The Usa

Purpose This study aims to draw on a less explored predictor – the average correlation of pairwise returns on industry portfolios – to predict stock market returns (SMRs) in the USA. Design/methodology/approach This study uses the average correlation approach of Pollet and Wilson (2010) and predicts the SMRs in the USA. The model is estimated using monthly data for a long time horizon, from July 1963 to December 2018, for the portfolios comprising 48 Fama-French industries. The model is extended to examine the effects of a longer lag structure of one-month to four-month lags and to control for the effects of a number of variables – average variance (AV), cyclically adjusted price-to-earnings ratio (CAPE), term spread (TS), default spread (DS), risk-free rate returns (R_f) and lagged excess market returns (R_s). Findings The study finds that the two-month lagged average correlation of returns on individual industry portfolios, used individually and collectively with financial predictors and economic factors, predicts excess returns on the stock market in an effective manner. Research limitations/implications The methodology and results are of interest to academics as they could further explore the use of average correlation to improve the predictive powers of their models. Practical implications Market practitioners could include the average correlation in their asset pricing models to improve the predictions for the future trend in stock market returns. Investors could consider including average correlation in their forecasting models, along with the traditional financial ratios and economic indicators. They could adjust their expected returns to a lower level when the average correlation increases during a recession. Social implications The finding that recession periods have effects on the SMRs would be useful for the policymakers. The understanding of the co-movement of returns on industry portfolios during a recession would be useful for the formulation of policies aimed at ensuring the stability of the financial markets. Originality/value The study contributes to the literature on three counts. First, the study uses industry portfolio returns – as compared to individual stock returns used in Pollet and Wilson (2010) – in constructing average correlation. When stock market becomes more volatile on returns, the individual stocks are more diverse on their performance; the comovement between individual stock returns might be dominated by the idiosyncratic component, which may not have any implications for future SMRs. Using the industry portfolio returns can potentially reduce such an effect by a large extent, and thus, can provide more reliable estimates. Second, the effects of business cycles could be better identified in a long sample period and through several sub-sample tests. This study uses a data set, which spans the period from July 1963 to December 2018. This long sample period covers multiple phases of business cycles. The daily data are used to compute the monthly and equally-weighted average correlation of returns on 48 Fama-French industry portfolios. Third, previous studies have often ignored the use of investors’ sentiments in their prediction models, while investors’ irrational decisions could have an important impact on expected returns (Huang et al., 2015). This study extends the analysis and incorporates investors’ sentiments in the model.

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