Anchoring Bias Idiosyncratic Volatility and the Cross-section of Stock Returns PDF Download
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Author: Cedric T. Luma Mbanga
Publisher:
ISBN:
Category : Stocks
Languages : en
Pages : 120
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Book Description
Author: Cedric T. Luma Mbanga
Publisher:
ISBN:
Category : Stocks
Languages : en
Pages : 120
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Book Description
Author: Namho Kang
Publisher:
ISBN:
Category : Stocks
Languages : en
Pages : 0
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Book Description
This paper uncovers the changes in the cross-sectional distribution of idiosyncratic volatility of stocks over the period 1963--2008. The contribution of the top decile to the total market idiosyncratic volatility increased, while the contribution of the bottom decile decreased. We introduce a simple theoretical model showing that larger capital of Long/Short-Equity funds further exacerbates large idiosyncratic shocks but attenuates small idiosyncratic shocks. This effect is stronger for more illiquid stocks. Time-series and cross-sectional results are consistent with the predictions of the model. The results are robust to industry affiliation, stock liquidity, firm size, firm leverage, as well as sign of price change. These findings highlight the roll of hedge funds and other institutional investors in explaining the dynamics of extreme realizations in the cross-section of returns.
Author: Turan G. Bali
Publisher:
ISBN:
Category :
Languages : en
Pages : 29
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Book Description
This paper examines the cross-sectional relation between idiosyncratic volatility and expected stock returns. The results indicate that (i) data frequency used to estimate idiosyncratic volatility, (ii) weighting scheme used to compute average portfolio returns, (iii) breakpoints utilized to sort stocks into quintile portfolios, and (iv) using a screen for size, price and liquidity play a critical role in determining the existence and significance of a relation between idiosyncratic risk and the cross-section of expected returns. Portfolio-level analyses based on two different measures of idiosyncratic volatility (estimated using daily and monthly data), three weighting schemes (value-weighted, equal-weighted, inverse-volatility-weighted), three breakpoints (CRSP, NYSE, equal-market-share), and two different samples (NYSE/AMEX/NASDAQ and NYSE) indicate that there is no robust, significant relation between idiosyncratic volatility and expected returns.
Author: Ruslan Goyenko
Publisher:
ISBN:
Category :
Languages : en
Pages : 55
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Book Description
A number of papers document a strong negative relation between idiosyncratic volatility and risk-adjusted stock returns. Using IHS Markit data on indicative borrowing fees, we show that stocks with high idiosyncratic volatility are far more likely to be hard-to-borrow than stocks with low idiosyncratic volatility. When hard-to-borrow stocks are excluded, the relation between idiosyncratic volatility and stock returns disappears. The relation between idiosyncratic volatility and stocks returns is more accurately described as a relation between being hard-to-borrow and stock returns.
Author: Stanislav Bozhkov
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Turan G. Bali
Publisher:
ISBN:
Category :
Languages : en
Pages : 56
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Book Description
We document that stocks that experience sudden increases in idiosyncratic volatility underperform otherwise similar stocks in the future, and we propose that this phenomenon can be explained by the Miller (1977) conjecture. We show that volatility shocks can be traced to the unusual firm-level news flow, which temporarily increases the level of investor disagreement about the firm value. At the same time, volatility shocks pose a barrier to short selling, preventing pessimistic investors from expressing their views. In the presence of divergent opinions and short selling constraints, prices end up initially reflecting optimistic views but adjust down in the future as investors' opinions converge.
Author: Alexander Barinov
Publisher:
ISBN:
Category : Business enterprises
Languages : en
Pages : 290
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Author: Andrew Ang
Publisher:
ISBN:
Category : Stocks
Languages : en
Pages : 55
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Book Description
"We examine the pricing of aggregate volatility risk in the cross-section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. In addition, we find that stocks with high idiosyncratic volatility relative to the Fama and French (1993) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book-to-market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility"--National Bureau of Economic Research web site.
Author: Adam Zaremba
Publisher:
ISBN:
Category :
Languages : en
Pages : 17
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Book Description
Due to arbitrage risk asymmetries, the relationship between idiosyncratic risk and expected returns is positive (negative) among overpriced (underpriced) stocks. We offer a new active anomaly-selection strategy that capitalizes on this effect. To this end, we consider eleven equity anomalies in the U.S. market for years 1963-2016. Buying (selling) long (short) legs of the anomaly portfolios with the highest idiosyncratic volatility produces monthly abnormal returns ranging from 0.97% to 1.14% per month, outperforming a naive benchmark that equally weights all the anomalies by 45-70%. The effect cannot be subsumed by any other established anomaly-return predictor, like momentum or seasonality. The results are robust to many considerations, including different numbers of anomalies in the portfolios, subperiod analysis, as well as estimation of idiosyncratic risk from the alternative models and throughout different periods.
Author: Paul Brockman
Publisher:
ISBN:
Category :
Languages : en
Pages : 46
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Book Description
Recent research on idiosyncratic volatility has documented three main empirical findings. First, Campbell, Lettau, Malkiel, and Xu (2001) show that idiosyncratic volatility exhibits an upward trend between 1962 and 1997. Second, Goyal and Santa-Clara (2003) find that aggregate measures of idiosyncratic volatility predict one-month-ahead excess market returns from 1962 to 1999. Third, Ang, Hodrick, Xing, and Zhang (2006) report a negative and significant relation between idiosyncratic volatility and cross-sectional stock returns from 1963 to 2000. We re-examine these three findings using a 37-year holdout sample of daily returns from 1926 to 1962. We find robust empirical evidence of (1) a statistically significant downward trend in idiosyncratic volatility, (2) an insignificant relation between average idiosyncratic volatility and one-month-ahead excess market returns, and (3) a highly significant inverse relation between idiosyncratic volatility and cross-sectional stock returns. These results shed new light on the time-series behavior and pricing of idiosyncratic volatility.
