Over time, small capitalization stocks have been shown to outperform large-capitalization stocks. However, timing changes in the relative performance between the two groups could lead to still-better performance. In the Fall 2007 Journal of Portfolio Management, L’Her, Mouakhar and Roberge test three nonparametric techniques derived from artificial intelligence and using 20 macroeconomic and financial variables as inputs.
The three approaches are recursive partitioning, a neural network and a genetic algorithm.
Each of the three techniques outperforms a naive small-minus-big strategy, but the best results are derived from taking the consensus of the three techniques.
Posted on 10th June 2008
Under: Active Management, Asset Allocation, Economic Analysis, Institutional Investing, Investing in Stocks, Investment Returns, Momentum Strategies, Portfolio Management, Research, Risk Management | No Comments »
The momentum anomaly refers to the fact that a strategy of buying past winners and selling past losers produces returns that are not explained by the Capital Asset Pricing Model framework. Proponents of the efficient market hypothesis (EMH) attribute the momentum anomaly to risk factors that are not captured in Beta, while opponents point to the anomaly as evidence against the EMH.
In the October 2007 Journal of Finance, Avromov et. al. find that the influence of momentum is limited to a small sample (4% of market capitalization) of companies with high credit risk. This study offers support to the efficient market hypothesis and the argument that the excess returns are attributable to a risk factor (in this case, credit quality.)
Posted on 5th June 2008
Under: Fundamental Analysis, Investing in Stocks, Investment Returns, Momentum Strategies, Performance Measurement, Research | No Comments »
If the weak form of the efficient market hypothesis holds, security market information should have no relationship with future returns. Technical analysis and trading rules should not allow investors to earn excess returns.
Researchers testing weak form market efficiency generally use one of two groups of tests when studying weak-form market efficiency.
- Statistical tests of independence measure either the significance of positive or negative correlation over time (autocorrelation) or by comparing the number of runs (consecutive moves in the same direction) with that expected in a normal sample. In general, statistical tests of independence have shown no relationship between current and future price movements.
- Tests of trading rules seek to mechanically simulate various trading strategies. For example, testing whether a strategy of buying when the stock price closes above the 50 day moving average and selling when the price closes below the moving average. In general, these tests have supported the weak-form efficient market hypothesis by showing no excess returns (after trading costs, compared to a buy-and-hold strategy) from following such rules. However, the results are not unanimous – some rules have been shown to offer superior returns.
Technical analysts criticize the existing tests as being too naive or simplistic to capture the
Posted on 28th April 2008
Under: Active Management, Behavioral Finance, Investing in Stocks, Investment Returns, Momentum Strategies, Portfolio Management, Research, Security Selection, Technical Analysis | No Comments »
In the June 2007 Journal of Banking and Finance, Miffre and Rallis compare strategies for investing in commodity futures based on short-term momentum and long-term reversal, based on a variety of formation and holding periods.
Momentum strategies based on selling past losers and buying past winners generated positive and significant returns in 13 of the 16 combinations of formation and holding periods, with a significant portion of that return being derived from short positions in the losers. These strategies generate positive alpha and have low correlations with the returns on equity or fixed income securities.
The reversal strategies do not exhibit consistent outperformance in this study.
Posted on 10th January 2008
Under: Futures, Investing in Commodities, Investment Returns, Momentum Strategies, Portfolio Management, Research | No Comments »
Standardized unexpected earnings is a means of comparing earnings surprise to the company’s track record of earnings surprise. For example, Cisco was once said to consistently beat earnings estimates by a penny. Thus, if the company did beat by a penny it was hardly unexpected. A method frequently used in academic research to adjust for this factor is the standardized unexpected earnings, or SUE.
SUE = the earnings surprise at a given time divided by the standard deviation of earnings surprises measured over some historic period such as the previous 20 quarters.
Consider a stock that had a $0.03 earnings surprise, and that the standard deviation of past earnings surprises is $0.05. The surprise is smaller than normal, and the standardized earnings surprise would be $0.03/$0.05 = 0.6.
Posted on 5th January 2008
Under: Investing in Stocks, Momentum Strategies, Technical Analysis, Valuation | No Comments »