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Blog Posts — October 6, 2023

Let’s talk sentiment: what to do when markets are in a mood

No market remains in strong-form efficiency all the time. Investment decisions are based on forecasts about the future, which implies a certain degree of error – the so-called ‘known unknowns’. But when the macro situation becomes highly uncertain, the size of the errors around those forecasts increases, and conversely, confidence in them decreases. During these periods, when markets adopt a weak-form efficiency, human emotions start to dominate decision-making, especially when faced with new, negative, and unexpected outcomes – the so-called ‘unknown unknowns’.

We know that when investors feel a certain way, they are more likely to behave a certain way. When bearish, they are more likely to panic in the face of negative news and rush to sell risky assets, even at a sharp discount (i.e., how crashes are formed). When they are bullish, they are more likely to be overconfident and agree to acquire more high-risk assets, even at a premium (i.e., how bubbles are formed). When investors are neutral, they are more likely to exchange risk assets at a ‘rational’ price, based on fundamental valuation models and only seek to take advantage of small perceived mispricing.

Why does sentiment matter?

Capturing this picture is essentially what the ROOF Scores were designed for; to quantitatively measure both the sign and degree of the current sentiment bias among investors in the equity market: are they bullish, neutral, or bearish? Knowing how investors ‘feel’, could help us protect our portfolios from downside overreaction to negative news when they are bearish. Conversely, knowing that investors are bullish can help us to capture more upside from overvaluation in response to positive news when overconfident investors underestimate the risks and remain willing to acquire assets at a premium to yesterday’s prices.

It is not the only indicator out there, in fact there are many sentiment indicators developed over the years, but most, like CNN’s Fear & Greed Index, combine metrics from multiple asset classes, like equities, sovereign and corporate bonds, and derivatives (index options). Some even include metrics from the commodities markets as well as macroeconomic data. The issue with methodologies that include metrics from multiple asset classes is that you lose some specificity. Some of the signal comes from bond traders, some from commodity traders, some from options trades, all of whom may have different opinions or technical reasons for their decisions, than equity only investors.

Furthermore, the specific choice of equity metric used can sometimes be restrictive and ‘miss’ important information. For example, CNN’s Fear & Greed Index uses data from the NYSE but does not capture the decisions made by NASDAQ investors. There may also be a mismatch between the derivatives used in the methodology and the broad equity investment universe investors can make decisions on – not all stocks have options for example.


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What does sentiment have to do with portfolio construction?

In general, most sentiment indicators are at the asset class level or market level, and do not provide investors with the building blocks necessary for portfolio construction. Long-only pure equity portfolio managers need actionable information about sentiment that they can directly incorporate into their portfolios. Since portfolios are constructed from the ground up, ideally this sentiment information can be made available at a level that aligns with their investment process, be it based on sectors, factors, or individual stock selection.

The goal of portfolio construction is to create a portfolio that is the best representation of the manager’s investment thesis. Knowing that sentiment in the market is bearish is not enough, If the manager’s thesis is bullish, he or she needs to be able to demonstrate how their portfolio represents the implementation of a bullish strategy, contrarian to the market now. This requires the ability to process the portfolio through the same methodology used by the market sentiment indicator so that the portfolio manager can quantify the implied sentiment of their portfolio. The ROOF Scores were specifically developed with this use-case in mind, supporting the portfolio manager in ‘scoring’ any portfolio, using the same methodology, so as to track the market sentiment alongside the portfolio’s implied sentiment.

The ROOF methodology is built from the ground up using both Style factors and Sectors to define what is risk-averse and what is risk-tolerant. It then uses observable factor and sector returns to quantify the allocation decisions made by investors between the two. When investors are bullish and their willingness to speculate increases, they will rotate into more risk-tolerant styles and sectors. Conversely, when they are turning bearish, they will rebalance their portfolios towards the more risk-averse styles and sectors. Plotting the current style and sector exposures of any active portfolio allows the methodology to quantify if the portfolio implies a more or a less risk-tolerant thesis than the market’s and ensure it stays within the right parameter. For example, investors with a bearish thesis should never have style or sector exposures that are more risk-tolerant than the market or their benchmark.

In short…

  • Focusing only on observable equity returns, rather than a diluted multi-asset class approach, Axioma can isolate the decisions of pure equity investors, without the added noise from decisions made by investors in other asset classes.
  • The bottom-up approach allows us to build optimal Risk-On and Risk-Off variants of any benchmark portfolios. These can be used to measure the performance of sentiment-aware portfolios and to quantify the sentiment risk premium the market is paying, to investors whose portfolios are aligned with the sentiment of their peers.
  • Additionally, it allows investors to measure the alpha they have captured from constructing portfolios with a contrarian sentiment, which gradually became consensus over time.

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