اثر نوسانات و نقش فاکتور کیفیت شرکت در بازده: شواهدی از بازار سهام هند   Volatility effect and the role of firm quality factor in returns: Evidence from the Indian stock market

Introduction The efficient market hypothesis as propounded by Fama in the 1970s has been sufficiently challenged in the last few decades by researchers around the world. Academics have found various anomalies, popularly referred to as capital asset pricing model (CAPM) anomalies, to counter the efficient market hypothesis, such as the value effect (Stattman, 1980), size effect (Banz, 1981), momentum effect (Jegadeesh & Titman, 1993), liquidity effect (Amihud, 2002) and net stock issues effect (Loughran & Ritter, 1995) to name a few. On similar lines, one of the prominent inconsistencies persisting in the past few decades has been the volatility anomaly. The volatility anomaly suggests that low volatile stocks tend to provide significant positive abnormal returns over high volatility stocks, and a long–short strategy can be adopted by traders to make riskless profits out of it. Prior studies, particularly in the U.S., have acknowledged that low volatility stocks tend to outperform high volatility stocks. Clarke, De Silva, and Thorley (2006) find that minimum variance portfolios, based on 1000 large capitalisation U.S. stocks, result in a 25% volatility reduction and provide higher returns than the market portfolio. Ang, Hodrick, Xing, and Zhang (2006) find that over the period1963–2000, U.S. stocks with high volatility earned abnormally lower returns. They based their research on a short term of the 1 month volatility measure. Blitz and Vliet (2007) extend the work of Ang et al. (2006) beyond the U.S. to other developed markets covering Europe and Japan, and use short term (1 month) as well as long term (36 months) volatility measure to test volatility anomaly. They find annual premium of 12% per year on a trading strategy which involves buying low volatility and (short) selling high volatility stocks. Further, they observe that the volatility effect cannot be explained by popular risk based models. Similarly, Baker, Bradley, and Wurgler (2011) show that contrary to basic risk principles, low volatility stocks outperform high volatility stocks. They show that such an anomaly has been in existence in the U.S. for the past four decades and provide various behavioural explanations for the same. Dutt and Humphery-Jenner (2013) confirm the presence of low volatility anomaly in developed markets outside the U.S. as well as in some emerging markets. Walkshausl (2013) tried to associate low volatility anomaly with the quality of the firm and provided a trading strategy of going long on high quality firms and short on low quality firms. Wang and Ma (2014) document a significant positive relationship between excess volatility and cross section of stock returns over a sample period of 1963–2010. Further, they show that these returns cannot be explained either by risk models using size, value and momentum factors, or by liquidity, bid-ask bounce and risk aversion related inventory effects. There have been various explanations given in the international literature for the low volatility anomaly. Blitz and Vliet (2007) provide three possible explanations for volatility effect. One reason could be that leverage restrictions in low volatility stocks may not allow investors to arbitrage away the opportunity presented by them. It has been argued that it is not possible for low volatile firms to borrow at a scale needed to exploit the opportunity offered by low volatile stocks. The second reason could be that the volatility effect may be the result of the inefficient decentralised investment approach. The approach suggests that in the institutional investment industry, an investment decision is taken in two stages: first, asset allocation decision, and second, to buy securities within an asset class. In order to beat the benchmark, and if CAPM holds, asset managers are better off buying more volatile companies which make them overpriced, and selling low volatile stocks which makes them underpriced. Further, managers tend to outperform the benchmarks during upturns rather than during downturns and thus are willing to pay more for high volatile stocks during market upturns. The third explanation could be the behavioural biases, as explained by Shefrin and Statman (2000). They argue that investors tend to overpay for risky stocks as they have a characteristic of lottery tickets and do not pay much attention to low volatile stocks. This results in overpayment for risky stocks which reduces their returns while keeping the upside potential of low volatile stocks intact.