The Idiosyncratic Volatility Anomaly - Essay Example

The Idiosyncratic Volatility Anomaly BY YOU YOUR SCHOOL INFO HERE HERE The Idiosyncratic Volatility Anomaly The Idiosyncratic Volatility Anomaly (IVOL) refers to the market risk of price changes occurring in a specific common stock security, as it relates to aggregate risk assessments by investors. Asset pricing models are established for a security related to liquidity, diversification of a corporate portfolio, and recognized historical volatility of the security relative to known aggregate return shocks in the market or market capitalization of the firm. Investors look to these corporate-level indicators when determining the most viable security purchase that will facilitate effective returns and minimize risk of volatility. The IVOL comes into play when a specific security does not conform to known economic models that illustrate either inverse relationships to tangible corporate level characteristics or direct relationships to known securities in a comparable category. Various factor-model equations have been developed to establish the expected rate of return of a security, utilising complex variables such as known excess stock returns, known sensitivities to volatility risk, and certain conditional market means (averages). Consider the complexity of one such factor-model calculation to determine expected security return: Exhibit 1: Factor-Model Calculation to Determine Expected Aggregate Returns Source: Ang, et al. (2006). The cross-section of volatility and expected returns. The interchangeable variables within similar equation modelling dictate no elongated explanation of the complexity of this scientific approach to aggregate security returns. However, such models that determine not only future stock returns, but also volatility risk with a specific security or basket of securities in comparable industries, are designed to facilitate more effective and profitable security investment. The idiosyncratic volatility anomaly is an acknowledgement that not all common stock securities will produce returns that follow a logical model of analysis and computation based on known historical patterns of return and volatility. Various models for determining aggregate returns, based on corporate-level dynamics or market risks (among other criteria), should produce consistent stock returns that are in-line with mathematical expectations. The tangible market returns of a security will, at times, illustrate a direct relationship with such modelling that serves to justify these scientific methods of analyses. During other market conditions, such returns conflict these models designed to facilitate a more shrewd investment with no legitimate explanation as to why low returns occurred with the security. These are the dynamics of the idiosyncratic volatility anomaly: predictable corporate level characteristics and valuations of a firm, the known statistical significance of the model used to identify expected aggregate returns, and linear examination of historical stock trends should all serve to justify the long-term return of a security. What actually occurs in the stock market is a confliction of these predictive models, often with no concrete explanation for why the security became exposed to higher volatility, price shock, or variable returns. The IVOL is highly pervasive in domestic and global stock markets with many researchers seeking solutions for the recurring prevalence of this anomaly in the United States, the United Kingdom, and Eurasian nations (Chen, et al. 2012; Savickas and Zhao, 2012; Berrada and Hugonnier, 2009; Jiang, et al. 2007). Berrada and Hugonnier (2009) identify this routine irregularity citing disparities between the idiosyncratic volatility factor with a direct relationship to stock returns in the U.S., and Ang, et.al (2006) confirming this factor tends to hold true in other nations. The dynamics of what genuinely causes the IVOL prevalence is uncertain, as no singular method of determining its catalysts has yet been determined. However, there is speculation that it can be related to market capitalisation, improper asset pricing, non-diversified corporate portfolios, or even the phenomenon of corporate selective disclosure (Jiang, et al 2009). Corporate selective disclosure, a subjective non-ethical practice, involves disclosing information to selected investors prior to public information exchanges that would impact security pricing or market valuation movements. No singular model of incorporating IVOL catalysts into mathematical modelling has provided a solid solution for the recurring anomaly for investor security returns. Chen, et al. (2012) identify that microcap firms have the highest prevalence of this anomaly, whilst large cap businesses perform more to mathematical expectations. This would seem to be a predictable understanding, then, of expected volatility mitigation based on firm size that would be congruent over time. However, this is not always justified through various time-factor analyses, which have turned investors over to hedging practices because no singular solution has been found to explain time-factor variances that do not hold true to mathematical modelling. It is widely understood in the investment and research communities that stock returns vary over time (Ang, et al. 2006). However, there is no discernible understanding for why securities have significant volatile pricing shocks or significant drops in returns when corporate or market-level conditions have not seen variations over time. Aggregate planning models for expected returns take into consideration portfolio diversification, historical and statistical knowledge of stock performance, and even known volatility sensitivity variables, which should predict performance over time. The IVOL anomaly, when comparing stocks of differing characteristics (e.g. market cap, comparable industry conditions, etc.), refutes time as a predictable variable for dynamic shifts in stock returns. Since there is no fundamental understanding of what serves as the recurring catalyst for the anomaly, nearly all literature justifies its relevance in determining aggregate security returns. How is this most largely noticeable? Researchers draw on past modelling by other researchers that have sought solutions for the prevalence and frequency of the anomaly in the stock market, continuously building on these models to identify the most likely culprit for its idiosyncratic tendencies. Ang, et al. (2006) proposes a new mathematical model with a new factor to attempt to mimic innovations related to market volatility. Innovation should, for the sake of this discussion, be linked to higher volatility characteristics based on unpredictable market responses to innovative business practices. Even after proposing a model that attempts to factor even further unpredictability into a predictable model, idiosyncratic volatility still surfaces which cannot account for why low-risk securities achieve inconsistent results and then reverse these trends when market conditions remain constant over time. The implications of the IVOL are significant for determining a pricing model for common stock securities. According to Ang, et al. (2006, p.260), “The higher demand for assets with high systematic volatility loadings increases their price and lowers their average return”. Thus, investor preferences for accepting short-term risk or preferring higher returns are considered a variable in the asset pricing process. After considering the tangibles of corporate diversification, market volatility, human capital competencies within business governorship, and volume of investors seeking hedging strategies, effective and fair asset pricing can be established. However, introduction into this process by the idiosyncratic volatility anomaly suggests that yet-to-be-determined market forces will conflict accurate aggregate understanding of future stock security returns. In essence, an investor that is highly risk-averse could view fluctuating stock returns related to IVOL (the non-predictability of a common stock security), thus reducing demand for the security. Without having an adequate understanding of what is driving this anomaly, asset pricing become contingent on market influence rather than internalized corporate functioning, operations and strategy. Comparatively, high-performing stocks with predictable returns serve as hedging strategies for risk-averse investors and are highly dependent on corporate-level business strategies and much less market influence and speculation. Proposing the implications of the IVOL to the foundations of modern finance is not only a sophisticated task, but would require discussion of multi-dimensional domains of economics with significant emphasis on algorithmic computation. However, all of the aforementioned examples of researcher modification to existing financial models in an attempt to isolate the driver of the idiosyncratic volatility anomaly illustrate its implications to modern finance. Over-reliance on predictive or aggregate models of security returns conflicts making a legitimized investment decision as the key variable that drives recurrence of this anomaly does not seem to effectively fit within any mathematical models currently in use. In most of the contemporary research attempting to determine the IVOL catalysts, researchers are left with a considerable puzzle that appears to have cyclical elements of unpredictability. Rarely in the research is there mention of behavioural components as a potential driver of the anomaly, other than investor willingness to accept a certain level of volatility. The largest implication could well be that such reliance on computational models for aggregate security return predictions could be superseded by more sociological or psychological examination of market conditions. Using absolutely prudent judgment in analysis, it should be acknowledged that the idiosyncratic volatility anomaly is legitimately anomalous. Researchers and economists continue to ascribe computational and tangible variables within a variety of finance models to legitimize the catalysts for the IVOL and its recurrence. These efforts are meeting with minimal success in explaining a solution. Economists and researchers have, in many cases, been able to take the intangible which influence market conditions and apply these to an algorithmic variable which meets with success in a computational finance model. It would seem that the complexities of what drives the IVOL make it virtually impossible to justify using mathematical representations, thus making it a completely anomalous variable in conflicting security movements and returns. References Ang, A., Hodrick, R.J., Xing, Y. and Zhang, X. (2006), The cross-section of volatility and expected returns, The Journal of Finance, 61(1). Barrada, T. and Hugonnier, J. (2009), Incomplete information, idiosyncratic volatility and stock returns. [online] Available at: http://www.efmaefm.org/0EFMAMEETINGS/EFMA%20ANNUAL%20MEETINGS/2009-milan/EFMA2009_0201_fullpaper.pdf (accessed 10 September 2012). Chen, L.H., Jiang, G.J., Xu, D.D. and Yao, T. (2012), Dissecting the idiosyncratic volatility anomaly. [online] Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2023883 (accessed 11 September 2012). Jiang, G.J., Xu, D. and Yao, T. (2007), The information content of idiosyncratic volatility, Journal of Financial and Quantitative Analysis. [online] Available at: http://www.u.arizona.edu/~gjiang/JiangXuYao_idiorisk0705.pdf (accessed 11 September 2012). Savickas, R. and Zhao, B. (2012), Distribution effect and idiosyncratic volatility discount, George Washington University. [online] Available at: http://www.ccfr.org.cn/cicf2012/papers/20120608052155.pdf (accessed 12 September 2012). Read More