Sunday, May 1, 2016
Peterson, Trading on Sentiment
Peterson is the CEO of MarketPsych, a firm that in 2011 joined forces with Thomson Reuters to produce the Thomson Reuters MarketPsych Indices (TRMI), sentiment data feed covering five asset classes and 7,500 individual companies that Thomson Reuters distributes to its clients. As the Thomson Reuters website explains, these indices use “real-time linguistic and psychological analysis of news and social media to quantify how the public regards various asset classes according to dozens of sentiments including optimism, fear, trust and uncertainty.”
Odds are that, unless you’re a bank or hedge fund employee, you won’t have access to TRMI. Peterson’s book is the next best thing, although you have to realize that if you want to incorporate sentiment (not some proxy for sentiment) into your trading decisions and can’t do big data analysis yourself, you’re working with one hand tied behind your back.
Trading on Sentiment is divided into five parts: foundations, short-term patterns, long-term patterns, complex patterns and unique assets, and managing the mind.
To give a taste of this book (and to address something that affects everyone’s investments) I’ll focus on the chapter on sentiment regimes. As Peterson writes, “Context matters in financial markets. In the academic literature, differences in context are said to be a product of market regimes. A market regime is—in its most simplistic terms—a bull or a bear market. Recent academic research demonstrates that the performance of common investment strategies differs across market regimes, and these differences may be rooted in the divergent mental states of traders in each context (e.g., optimism in a bull market versus pessimism in a bear market).” (p. 270)
Regime-dependent performance may result from shifts in liquidity available to portfolio managers. “The profitability of published market strategies rises and falls in 3- to 5-year cycles based on liquidity. … The alpha to be harvested from such price patterns exists when they are largely ignored, but as capital is attracted to them, the excess returns dry up or even reverse.” (p. 272)
Peterson summarizes “the effects of sentiment regimes on the predictable returns of several market anomalies.” For instance, high-beta stocks outperform low-beta stocks “only following months of negative news sentiment.” As for post-earnings announcement drift (“the tendency of stock prices to continue moving in the direction of an earnings surprise after the event”), such drift is “significantly greater when market sentiment is opposite the direction of the earnings surprise.” (p. 273)
Trading on Sentiment will undoubtedly be seen in time as a seminal work. Much more research remains to be done on the identification and measurement of sentiment and its impact on financial markets, both on a macro and a micro level. But, even so, investors can use whatever measures they have of sentiment as potentially profitable filters in placing and managing their trades. Peterson’s work can serve as a useful guide.