Stocks with low prices relative to their fundamental value tend to deliver returns in excess of a capitalization-weighted benchmark. Value factors reflect this correlation and are designed to provide signals to buy undervalued assets, that is, those that are relatively cheap and sell those that are overvalued and expensive. For this reason, at the core of any value strategy is a valuation model that estimates or proxies the asset's fair or fundamental value. Fair value can be defined as an absolute price level, a spread relative to other assets, or a range in which an asset should trade (for example, two standard deviations).
Value strategies rely on mean-reversion of prices to the asset's fair value. They assume that prices only temporarily move away from fair value due to either behavioral effects, such as overreaction or herding, or liquidity effects such as temporary market impact or long-term supply/demand frictions. Since value factors rely on mean-reversion, they often exhibit properties opposite to those of momentum factors. For equities, the opposite to value stocks are growth stocks with a high valuation due to growth expectations.
Value factors enable a broad array of systematic strategies including fundamental and market valuation, statistical arbitrage, and cross-asset relative value. They are often implemented as long/short portfolios without exposure to other traditional or alternative risk factors.
Fundamental value strategies derive fair asset values from economic and fundamental indicators that depend on the target asset class. In fixed income, currencies, and commodities, indicators include, for example, levels and changes in the capital account balance, economic activity, inflation, or fund flows. In equities and corporate credit, value factors go back to Graham and Dodd's Security Analysis in the 1930s, since made famous by Warren Buffet. Equity value approaches compare a stock price to fundamental metrics such as book value, top line sales, bottom line earnings, or various cash-flow metrics.
Market value strategies use statistical or machine learning models to identify mispricing due to inefficiencies in liquidity provision. Statistical and Index Arbitrage are prominent examples that capture the reversion of temporary market impacts over short time horizons (we will cover pairs trading in the next chapter). Over longer time horizons, market value trades also leverage seasonal effects in equities and commodities.
Cross-asset relative value strategies focus on the relative mispricing of different assets. For example, convertible bond arbitrage involves trades on the relative value between the stock, credit, and volatility of a single company. Relative value also includes trades between credit and equity volatility, using credit signals to trade equities or relative value trades between commodities and equities.