Befinlab index
BEFINLAB INDEX JANUARY 2012
BEFINLAB INDEX CONFIRMING THE JANUARY EFFECT SEASONALITY BEATING S&P BY 1.08%. SEASONALITY WOULD SUGGEST TO LEAVE NOW AND TO COME BACK IN APRIL
We analyzed the performance of the BefinLab Index from January 3th to 31st. Our index highlights that the funds reported a positive performance of 3.8% and have been able to outperform S&P Index by 1.08% in the period considered. This result confirms not only the so called January effect but also the capacity of Behavioral Funds to positively exploit this effect.
Performance BefinLab Index January 2012
The January effect
Wachel (1942) was the first in the financial community to describe the "January effect". In particular he found that the DJ Industrial Average from 1927 to 1942 showed a "frequent bullish tendencies" from December to January. In 1976, Rozeff and Kinney published their article on stock market seasonality. They found that January stock returns were higher than in any other month. In particular they found that from 1904-74 the average amount of January returns for small firms was around 3.5%, whereas returns for all other months was closer to 0.5%. Schwert (2003) confirmed the January effect from 1980 to 2001.
There are many explanation on the origin of the January effect in the stock market. One explanation is that the surge in January returns is a result of investors selling loser stocks in December to lock in tax losses, causing returns to bounce back up in January, when investors have less incentive to sell. However Gultekin (1983) documented evidence of seasonality in January in 13 of 17 countries analyzed and in many of them there were no tax incentive. As such while the year-end tax selloff may explain some of the January effect, it does not account for the fact that the phenomenon still exists in places where capital gains taxes do not occur. An alternative explanation for the January effect is the "window dressing". Practically fund managers sell losers stock in December to not show them in the annual report but considering them as potentially appealing they buy back in January. A good "economic" explanation has been provided by Ogden (1990) saying that the cash flow coming from higher than usual year end activity is reinvested in January, part in the stock market, causing the January effect. Shiller (1999) more recently gave a psychological explanation of the December effect. He wrote that "if people view the year end as a time of reckoning and a new year as a new beginning, they may be inclined to behave differently at the turn of the year, and this may explain the January effect".
Whatever are the reason for the January effect it is pretty clear that Behavioral Finance Funds are able to exploit it, better that fundamental and macro funds. Our index highlights that the funds are able to outperform S&P by 1.08%.
Our analysis (see "Applying Behavioral Funds to Investment" by Alessandro Santoni, Lap-Publishing, 2012) suggest that after the good January Behavioral Funds are generally underperforming the relative index in February and March. Our study on seasonality (time series from 2000 to 2009) of Behavioral Funds suggest that only in April could be a good time to come back to buy back these funds.
The index and the portfolio strategy
The sample is composed by 25 funds of which 72% large cap funds, 28% small cap/multi cap. Their style is for 40% value based, 12% growth and 48% blend. The region of focus is for 68% North America, 24% Europe and the rest Asia (4%) and Develop markets (4%).
Portfolio Managers that apply behavioral finance in their investment strategy implicitly accept that excess profits are possible if the inefficiency is recognized and analyzed properly because investors behave irrationally at times and the behavior is reflected in the market price. Analyst and Investors are believed to be slow to recognize new information related to earnings surprises behaving overconfidently to their prior view with a tendency to underweight evidence that disconfirms their prior views and to overweight confirming evidence. The two main biases at the base of portfolio managers theory was overconfidence and anchoring. The most common tool used to take advantage of this inefficiencies are the exploitation of the so called winner lose effect, the trend momentum effect, the post earning effect and the insider dealing as information signal.