Identifying asset price bubbles

A new paper proposes a practical method for identifying asset price bubbles. First, one estimates deviations of prices from fundamentals based on three different approaches: a structural model, an econometric data-rich regression, and a purely statistical trend filter. Then one computes the first principal component of the three deviation series as an estimate for the common component behind them. As a general approach the method holds promise for detecting price distortions in financial markets and setback risk for ongoing trends.

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Directional predictability of daily equity returns

A new empirical paper provides evidence that the direction of daily equity returns in the Dow Jones has been predictable over the past 15 years, based on conventional short-term factors and out-of-sample selection and forecasting methods. Hit ratios have been 51-52%. The predictability has been statistically significant and consistent over time. Trading returns based on forecasting have been economically meaningful. Simple forecasting methods have outperformed more complex machine learning.

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The shadow of China’s banks

Unlike in the U.S., shadow banking in China is dominated by commercial banks, not securities markets. Regulated banks operate most shadow banking activity, take direct risks, provide implicit guarantees and use non-bank entities to shift assets off their balance sheets. That is why China’s shadow banking is called ‘the shadow of banks’ and why it is such a central factor of systemic risk in this highly leveraged economy. China’s shadow banking has important economic functions for individual savers and smaller enterprises. Outstanding ‘shadow savings’ are estimated at roughly 70% of GDP.

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The danger of volatility feedback loops

There is evidence that the financial system has adapted to low fixed income yields through an expansion of explicit and implicit short volatility strategies. These strategies earn steady premia but bear large volatility, “gamma” and correlation risks and include popular devices such as leveraged risk parity and share buybacks. The total size of explicit and implicit short-volatility strategies may have reached USD2000 billion and probably created two dangerous feedback loops. The first is a positive reinforcement between interest rates and volatility that will overshadow central banks’ attempts to normalize policy rates. The second is a positive reinforcement between measured volatility and the effective scale of short-volatility positions that has increased the risk of escalatory market volatility spirals.

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The systemic risk of highly indebted governments

The public debt ratio of the developed world has remained stuck at a 200-year record high, even with a mature global expansion and negative real interest rates. This poses a systemic threat to the global financial system for at least three reasons. First, governments’ capacity to stabilize financial and economic cycles is more limited than in past decades, which matters greatly in a highly leveraged world that has grown used to public backstops. Second, many countries have taken recourse to mild forms of “financial repression”, which puts pressure on the financial position of savers and related institutions, such as pension funds.  Third, future political changes in the direction of populist fiscal expansion can easily raise the spectres of old-fashioned inflationary monetization or even forms of debt restructuring.

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Information inefficiency in market experiments

Experimental research illustrates the mechanics of market inefficiency. If information is costly traders will only procure it to the extent that markets are seen as inefficient. In particular, when observing others’ investment in information, traders will cut their own information spending. Full information efficiency can never be reached. Moreover, business models that invest heavily in information may have higher trading profits, but still earn lower overall profits due to the costs of improving their signals. What seems crucial is high cognitive reflection so as to invest in relevant information where or when others do not.

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Basic theory of momentum strategies

Systematic momentum trading is a major alternative risk premium strategy across asset classes. Time series momentum motivates trend following; cross section momentum gives rise to ‘winners-minus-losers strategies’. Trend following is a market directional strategy that promises ‘convex beta’ and ‘good diversification’ for outright long and carry portfolios as it normally performs well in protracted good and bad times alike. It works best if the underlying assets earn high absolute (positive or negative) Sharpe ratios and display low correlation. By contrast, cross section momentum strategies benefit from high absolute correlation of underlying contracts and are more suitable for trading assets of a homogeneous class. The main pitfalls of both momentum strategies are jump events and high costs of ‘gamma trading’ conjoined with high leverage.

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Overconfidence and inattention as asset return factors

Overconfidence in personal beliefs and inattention to new trends are widespread in financial markets. If specific behavioural biases become common across investors they constitute sources of mispricing and – hence – return factors. Indeed, overconfidence and inattention can be quantified as factors to an equity market pricing model and seem to capture a wide range of pricing anomalies. This suggests that detecting sources of behavioural biases, such as attachment to ideological views or laziness in the analysis of data, offers opportunities for systematic returns.

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Policy rates and equity volatility

Measures of monetary policy rate uncertainty significantly improve forecasting models for equity volatility and variance risk premia. Theoretically, there is a strong link between the variance of equity returns in present value models and the variance short-term rates. For example, there is natural connection between recent years’ near-zero forward-guided policy rates and low equity volatility. Empirically, the inclusion of derivatives-based measures of short-term rate volatility in regression forecast models for high-frequency realized equity volatility has added significant positive predictive power at weekly, monthly and quarterly horizons.

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Clues for estimating market beta

A new empirical paper compares methods for estimating “beta”, i.e. the sensitivity of individual asset prices to changes in a broad market benchmark. It analyzes a large range of stocks and more than 50 years of history. The findings point to a useful set of initial default rules for beta estimation: [i] use a lookback window of about one year, [ii] apply an exponential moving average to the observations in the lookback window, and [iii] adjust the statistical estimates by reasonable theoretical priors, such as the similarity of betas for assets with similar characteristics.

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