Various risk parity methodologies are a popular choice for the construction of better diversified and balanced portfolios. It is notoriously hard to predict the future performance of the majority of asset classes. Risk parity approach overcomes this shortcoming by building portfolios using only assets’ risk characteristics and correlation matrix. A new research paper written by Lohre, Rother and Schafer builds on the foundation of classical risk parity methods and presents hierarchical risk parity technique. Their method uses graph theory and machine learning to build a hierarchical structure of the investment universe. Such structure allows better division of assets into clusters with similar characteristics without relying on classical correlation analysis. These portfolios then offer better tail risk management, especially for skewed assets and style factor strategies.

Authors: Lohre, Rother and Schafer

Title: Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-Asset Multi-Factor Allocations

The idea of buying an investment asset for a lower price than a fair-value is the cornerstone of value factor strategies. Various value strategies were popularized by famous investor Benjamin Graham (and his successors like Warren Buffett) and were firstly employed in the stock market. This idea of looking for investment opportunities that can be bought cheaply can also be applied in currency markets – Currency Value Factor strategy. There is, however, one catch – an investor must know the fair-value exchange rate for currencies. The most popular equilibrium exchange rate model used for this purpose is based on PPP (purchasing power parity). A new research paper written by Ca’ Zorzi, Cap, Mijakovic, and Rubaszek analyzes two additional models – Behavioral Equilibrium Exchange Rate (BEER) and the Macroeconomic Balance (MB) approach to assess which model has the best forecasting power.

Authors: Ca’ Zorzi, Cap, Mijakovic, Rubaszek

Title: The Predictive Power of Equilibrium Exchange Rate Models

Professor Robert Shiller’s work and his famous CAPE (cyclically-adjusted price-to-earnings) ratio is well known among the investment community. His methodology for assessing a valuation of the U.S. equity market is not the first one but is surely the most cited and the most discussed. There are numerous papers that tweak or adjust Shiller’s methodology to assess better if U.S. equities are under- or over-valued. We recommend the work of Wang, Ahluwalia, Aliaga-Diaz, and Davis (all from The Vanguard Group ) in which they use a combination of machine learning and a regression-based approach to obtain forecasted CAPE ratio, and subsequently, U.S. stock market returns, more accurately.

Authors: Wang, Ahluwalia, Aliaga-Diaz, Davis

Title: The Best of Both Worlds: Forecasting US Equity Market Returns using a Hybrid Machine Learning – Time Series Approach

A new research paper related to:

#118 – Time Series Momentum

Authors: Lim, Zohren, Roberts

Title: Enhancing Time Series Momentum Strategies Using Deep Neural Networks

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3369195

Abstract:

While time series momentum is a well-studied phenomenon in finance, common strategies require the explicit definition of both a trend estimator and a position sizing rule. In this paper, we introduce Deep Momentum Networks — a hybrid approach which injects deep learning based trading rules into the volatility scaling framework of time series momentum. The model also simultaneously learns both trend estimation and position sizing in a data-driven manner, with networks directly trained by optimising the Sharpe ratio of the signal. Backtesting on a portfolio of 88 continuous futures contracts, we demonstrate that the Sharpe-optimised LSTM improved traditional methods by more than two times in the absence of transactions costs, and continue outperforming when considering transaction costs up to 2-3 basis points. To account for more illiquid assets, we also propose a turnover regularisation term which trains the network to factor in costs at run-time.

Notable quotations from the academic research paper:

"While numerous papers have investigated the use of machine learning for financial time series prediction, they typically focus on casting the underlying prediction problem as a standard regression or classification task – with regression models forecasting expected returns, and classification models predicting the direction of future price movements. This approach, however, could lead to suboptimal performance in the context time-series momentum for several reasons.

Firstly, sizing positions based on expected returns alone does not take risk characteristics into account – such as the volatility or skew of the predictive returns distribution — which could inadvertently expose signals to large downside moves. This is particularly relevant as raw momentum strategies without adequate risk adjustments, such as volatility scaling, are susceptible to large crashes during periods of market panic. Furthermore, even with volatility scaling – which leads to positively skewed returns distributions and long-option-like behaviour – trend following strategies can place more losing trades than winning ones and still be profitable on the whole – as they size up only into large but infrequent directional moves. The fraction of winning trades is a meaningless metric of performance, given that it cannot be evaluated independently from the trading style of the strategy. Similarly, high classification accuracies may not necessarily translate into positive strategy performance, as profitability also depends on the magnitude of returns in each class. In light of the deficiencies of standard supervised learning techniques, new loss functions and training methods would need to be explored for position sizing – accounting for tradeoffs between risk and reward.

In this paper, we introduce a novel class of hybrid models that combines deep learning-based trading signals with the volatility scaling framework used in time series momentum strategies – which we refer to as the Deep Momentum Networks (DMNs). This improves existing methods from several angles.

Firstly, by using deep neural networks to directly generate trading signals, we remove the need to manually specify both the trend estimator and position sizing methodology – allowing them to be learnt directly using modern time series prediction architectures.

Secondly, by utilising automatic differentiation in existing backpropagation frameworks, we explicitly optimise networks for risk-adjusted performance metrics, i.e. the Sharpe ratio, improving the risk profile of the signal on the whole.

Lastly, retaining a consistent framework with other momentum strategies also allows us to retain desirable attributes from previous works – specifically volatility scaling, which plays a critical role in the positive performance of time series momentum strategies. This consistency also helps when making comparisons to existing methods, and facilitates the interpretation of different components of the overall signal by practitioners.

Referring to the cumulative returns plots for the rescaled portfolios in Exhibit 4, the benefits of direct outputs with Sharpe ratio optimisation can also be observed – with larger cumulative returns observed for linear, MLP and LSTM models compared to the reference benchmarks. Furthermore, we note the general underperformance of models which use standard regression and classification methods for trend estimation – hinting at the difficulties faced in selecting an appropriate position sizing function, and in optimising models to generate positions without accounting for risk."

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Related to all trendfollowing strategies:

Authors: Dugan, Greyserman

Title: Skew and Trend Aversion

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3315719

Abstract:

Despite evidence of the benefits to portfolio Sharpe ratio and variance, actual investor allocations to Trend Following strategies are typically 5% or less. Why is there such a significant discrepancy between the optimal allocation and actual allocation to Trend? We investigate known behavioral biases as a potential reason. While decision makers have other reasons to exclude Trend Following from their portfolios, in this paper, we explore loss aversion, recency bias, and the ambiguity effect as they pertain to Trend Following, and we call the combination of the three Trend Aversion. We quantify Trend Aversion and show that these biases are a viable explanation for suboptimal allocations to Trend. We demonstrate a direct connection between quantifications of known behavioral biases and current suboptimal allocations to Trend Following. Recognition of these relationships will help highlight the pitfalls of behavioral biases.

Notable quotations from the academic research paper:

"Investors may have reasons for excluding Trend Following from their portfolios ranging from time-horizon for performance, to drawdowns, to potential capacity issues. However, the strategy's long performance history shows that a meaningful allocation would have increased portfolio Sharpe ratio and reduced portfolio variance, and yet typical investments remain at or below 5%. Some investors have no exposure.

The strategy's quantitative nature, positive skew, and frequent but small losses act in concert to trigger loss aversion, recency bias, and the ambiguity effect. We call the combination of the three Trend Aversion.

Our results show Trend Aversion is a viable explanation of suboptimal allocations to Trend Following. Decades of psychological research show that people mentally inflate losses by a factor of two. In this paper, we demonstrated that a loss multiplier between 1.5 and 2.5 would cause the typical allocation to Trend of 5% in a simple two asset portfolio, in an 11-asset portfolio with random allocations, and in two other 11-asset portfolio constructions with dynamic allocations. We showed that loss aversion can decrease allocators Sharpe ratios by up to 50%. Using lookback windows in a dynamically allocated portfolio, we demonstrated that recency bias drives down allocations to Trend. Finally, we showed that combinations of loss aversion and recency bias also drive Trend allocations to suboptimal levels.

Many investors who are subject to Trend Aversion as a practical matter, for example due to investment committees or reporting structures, are unsure of how to balance Trend Aversion with the benets of Trend Following to reach an allocation decision. By establishing a methodology to optimize allocations under loss aversion, we provide a framework which investors can use to formalize their allocation decisions. Investors who are subject to typical loss aversion should permanently allocate at least 5% to Trend Following, while investors whose loss aversion is lower can benefit substantially by allocating materially more than 5%."

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There has been the shadow of suspicion that autocratic regimes are slightly manipulating GDP growth numbers. A recent academic paper offers interesting idea how to double check suspicious claims of growth …

Authors: Martinez

Title: How Much Should We Trust the Dictator's GDP Growth Estimates?

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3093296

Abstract:

I study the manipulation of GDP growth statistics in non-democracies by comparing the self-reported GDP figures to the nighttime lights recorded by satellites from outer space. I show that the night-lights elasticity of GDP is systematically larger in more authoritarian regimes. This autocracy gradient in the elasticity is not explained by potential differences in a large set of factors, including economic structure and levels of development, across countries with different forms of government. The gradient is larger when countries have a stronger incentive to exaggerate economic performance or when the institutions that constrain the manipulation of official statistics are weaker. I estimate that the most authoritarian regimes inflate yearly GDP growth rates on average by a factor of 1.15-1.3 and show that correcting for data manipulation provides a more nuanced view on the economic success of non-democracies in recent years.

Notable quotations from the academic research paper:

"Governments themselves usually produce economic estimates, which gives rise to a moral hazard problem, as they are constantly tempted to exaggerate just how well the economy is doing. GDP stands out in this regard as perhaps the most widely used measure of economic activity. As such, it is probably the most profitable for governments to manipulate.

This paper uses nighttime luminosity to detect and measure the manipulation of GDP growth statistics in non-democracies. GDP and nighttime lights provide complementary measures of economic activity, but while GDP is self-reported by governments and prone to manipulation, night lights are recorded by satellites from outer space and are much less vulnerable. Using panel data for 179 countries between 1992 and 2008, I study the relationship between reported GDP figures and nighttime lights across political regimes. In particular, I examine whether the same amount of growth in lights translates into systematically larger amounts of GDP growth in autocracies than in democracies.

The main result of the paper is that the night-lights elasticity of GDP is significantly larger in more autocratic regimes. In other words, I find that a constant amount of growth in lights translates into higher reported GDP growth in autocracies than in democracies. A large battery of further robustness tests indicates that the autocracy gradient in the night-lights elasticity of GDP is not driven by cross-country dierences in other factors that may be potentially correlated with regime type, including geography, economic structure or levels of development.

The observed magnitude of the variation in the night-lights elasticity of GDP across regimes is substantial. The results indicate that the most authoritarian governments inflate yearly GDP growth by a factor of 1.15-1.3. I use these estimates to adjust the long-run growth rates for data manipulation in autocracies. This adjustment has important implications for our understanding of relative economic performance at the turn of the XXI century.

"

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