Fama-French three-factor model is an incomplete model for expected returns because its three factors miss much of the variation in average returns related to profitability and investment. The results suggest that the Fama-French three-factor model is likely to fare poorly when applied to portfolios with strong investment tilts. Motivated by those facts, the investment factor was added to the three-factor model because this factor can be utilized to enhance returns. Investment factor (CMA) is the difference between the returns on diversified portfolios of the stocks of low and high investment firms, which are called conservative and aggressive. Sorting portfolios according to their size and investment, in every size quintile, the average return on the portfolio in the lowest investment quintile is much higher than the return on the portfolio in the highest investment quintile. The aforementioned fact can be exploited to construct a profitable investment strategy that longs the portfolio with the lowest investment and, on the other hand, shorts the portfolio with the highest investment.

Fundamental reason

Past data and research proved that a conservative investment portfolio is connected with greater returns compared to an aggressive investment portfolio. To explain it briefly, aggressive investments do not improve returns in the near future, and yet it is questioned if those investments would improve returns in the future. Moreover, looking at the Size-Inv portfolios, estimates suggest that the five-factor model leaves only around 28% of the cross-section variance of expected returns unexplained. This is far less than the variance ratios produced by the Fama-French three-factor model, which are mostly greater than 50% for the Size-Inv portfolio. As a result, the investment factor could and should be used by investors.

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Markets Traded
equities

Backtest period from source paper
1963-2013

Confidence in anomaly's validity
Moderately Strong

Indicative Performance
3.54%

Notes to Confidence in Anomaly's Validity

OOS back-test shows slightly negative performance. It looks, that strategy’s alpha is deteriorating in the out-of-sample period.


Notes to Indicative Performance

Annualized average of monthly returns in excess of the one-month Treasury bill rate, data from Table 1, panel C


Period of Rebalancing
Yearly

Estimated Volatility

Notes to Period of Rebalancing

Notes to Estimated Volatility

not stated


Number of Traded Instruments
1000

Maximum Drawdown

Notes to Number of Traded Instruments

more or less, it depends on investor’s need for diversification, source paper uses a sample of 3000 stocks – all NYSE, Amex, and NASDAQ stocks on both CRSP and Compustat with share codes 10 or 11


Notes to Maximum drawdown

not stated


Complexity Evaluation
Complex strategy

Sharpe Ratio

Notes to Complexity Evaluation

Region
United States

Financial instruments
stocks

Simple trading strategy

The investment universe consists of all NYSE, Amex, and NASDAQ stocks. Firstly, stocks are allocated to five Size groups (Small to Big) at the end of each June using NYSE market cap breakpoints. Stocks are allocated independently to five Investment (Inv) groups (Low to High) still using NYSE breakpoints. The intersections of the two sorts produce 25 Size-Inv portfolios. For portfolios formed in June of year t, Inv is the growth of total assets for the fiscal year ending in t-1 divided by total assets at the end of t-1. Long portfolio with the highest Size and simultaneously with the lowest Investment. Short portfolio with the highest Size and simultaneously with the highest Investment. The portfolios are value-weighted.

Hedge for stocks during bear markets

Partially - Based on the source research paper (see Table 4), the strategy has a negative correlation to the equity market, so it is possible that strategy can be maybe used as a hedge/diversification to equity market risk factor during bear markets. An independent backtest is, however, recommended.

Source paper
Out-of-sample strategy's implementation/validation in QuantConnect's framework (chart+statistics+code)
Other papers

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