Forecasting energy markets with macro data

November 7, 2020

Recent academic papers illustrate how macroeconomic data support predictions of energy market flows and prices. Valid macro indicators include shipping costs, industrial production measures, non-energy industrial commodity prices, transportation data, weather data, financial conditions indices, and geopolitical uncertainty measures. Good practices include a focus on “small” models and a reduction of the dimensionality of large datasets. Forecasts can extend to predictions of the entire probability distribution of prices and – hence – can be used to assess the probability of breakouts from price ranges.

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Fundamental trend following

October 31, 2020

Fundamental trend following uses moving averages of past fundamental data, such as valuation metrics or economic indicators, to predict future fundamentals, analogously to the conventions in price or return trend following. A recent paper shows that fundamental trend following can be applied to equity earnings and profitability indicators. One approach is to pool fundamental information across a range of popular indicators and to sequentially choose lookback windows for moving averages in accordance with past predictive power for returns. The fundamental extrapolation measure predicts future stock returns positively and would historically have generated significant profits. Most importantly, fundamental trend following returns seems to have little correlation with price trend following returns, supporting the idea that these trading styles are complementary.

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Prospect theory value as investment factor

October 17, 2020

Prospect theory value as investment factor

Prospect theory value is a valid investment factor, particularly in episodes of apparent market inefficiency. Prospect theory is a popular model of irrational decision making. It emphasizes a realistic mental representation of expected gains and losses and an individual’s evaluation of such representations. Prospect theory explains asymmetric loss aversion (view post here) and gambling preferences (view post here). Since mental representations of expected returns and volatility are often driven by price charts, prospect theory value can be estimated based on historic asset return distributions. Assets with a high prospect theory value should have low subsequent returns and vice versa. This proposition holds even if part of the market is fully rational as long as there are balance sheet and risk limits. Empirical academic papers have confirmed the prospect theory value in international equity, corporate bond and foreign exchange markets.

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Understanding international capital flows and shocks

October 3, 2020

Macro trading factors for FX must foremostly consider (gross) external investment positions. That is because modern international capital flows are mainly about financing, i.e. exchanges of money and financial assets, rather than saving, real investments and consumption (which are goods market concepts). Trades in financial assets are much larger than physical resource trades. Also, financing flows simultaneously create aggregate purchasing power, bank assets and liabilities. The vulnerability of currencies depends on gross rather than net external debt. Current account balances, which indicate current net payment flows, can be misleading. The nature and gravity of financial inflow shocks, physical saving shocks, credit shocks and – most importantly – ‘sudden stops’ all depend critically on international financing.

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R tidyverse for macro trading research

September 19, 2020

The tidyverse is a collection of packages that facilitate data science with R. It is particularly powerful for macro trading research because [a] it supports efficient and standardized work with R’s vast universe of econometric models, [b] is well adapted for analyzing data vintages (i.e. data series that change over time), and [c] supports code in form of visually clean chains of statistical operations. The tidyverse’s core and peripheral packages share common design principles that harmonize workflow for crucial tasks: [1] organizing data structures, [2] transforming the content of data structures, [3] functional programming with complex nested data sets, [4] extraction of statistical information across models in a standardized form, [5] coding and mathematics with date-time objects, [6] coding with strings and regular expressions, [7] a flexible machine learning workflow, [8] highly versatile and consistent graphics creation, and [9] connectors to financial analysis packages.

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Nowcasting with MIDAS regressions

September 5, 2020

Nowcasting macro-financial indicators requires combining low-frequency and high-frequency time series. Mixed data sampling (MIDAS) regressions explain a low-frequency variable based on high-frequency variables and their lags. For instance, the dependent variable could be quarterly GDP and the explanatory variables could be monthly activity or daily market data. The most common MIDAS predictions rely on distributed lags of higher frequency regressors to avoid parameter proliferation. Analogously, reverse MIDAS models predict a high-frequency dependent variable based on low-frequency explanatory variables. Compared to state-space models (view post here), MIDAS simplifies specification and theory-based restrictions for nowcasting. The R package ‘midasr’ estimates models for multiple frequencies and weighting schemes. In practice, MIDAS has been used for nowcasting financial market volatility, GDP growth, inflation trends and fiscal trends.

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Market-implied macro shocks

August 22, 2020

Combinations of equity returns and yield-curve changes can be used to classify market-implied underlying macro news. The methodology is structural vector autoregression. Theoretical ‘restrictions’ on unexpected changes to this multivariate linear model allow identifying economically interpretable shocks. In particular, one can distinguish news on growth, monetary policy, common risk premia and hedge premia. Monetary and growth news capture shocks to investors’ expectations of discount rates and cash flows, respectively. The common risk premium is a price for exposure to risks that drive stock and bond returns in the same direction. The hedge premium is a price for exposure to risks that drive stock and bond returns in opposite directions. Identifying shocks helps to uncover trading opportunities, including market trends and reversion of relative market returns that were inconsistent with actual macro developments.

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Measures of market risk and uncertainty

August 8, 2020

In financial markets, risk refers to the probability distribution of future returns. Uncertainty is a broader concept that encompasses ambiguity about the parameters of this probability distribution. There are various types of measures seeking to estimate risk and uncertainty: [1] realized and derivatives-implied distributions of returns across assets, [2] news-based measures of policy and political uncertainty, [3] survey-based indicators, [4] econometric measures, and [5] ambiguity indices. The benefits for macro trading are threefold. First, uncertainty measures provide a basis for comparing the market’s assessment of risk with private information and research. Second, changes in uncertainty indicators often predict near-term flows in and out of risky asset classes. Third, the level of public and market uncertainty is indicative of risk premia offered across asset classes.

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Nowcasting for financial markets

July 25, 2020

Nowcasting is a modern approach to monitoring economic conditions in real-time. It makes financial market trading more efficient because economic dynamics drive corporate profits, financial flows and policy decisions, and account for a large part of asset price fluctuations. The main technology behind nowcasting is the dynamic factor model, which condenses the information of numerous correlated ‘hard’ and ‘soft’ data series into a small number of ‘latent’ factors. A growth nowcast can be interpreted as the factor that is most correlated with a diverse representative set of growth-related data series. The state-space representation of the dynamic factor model formalizes how markets read economic data in real-time. The related estimation technique (‘Kalman filter’) generates projections for all data series and estimates for each data release a model-based surprise, called ‘news’. In recent years machine learning models, such as support vector machines, LASSO, elastic net and feed-forward artificial neural networks, have been deployed to improve the predictive power of nowcasts.

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How banks’ dollar holdings drive exchange rate dynamics

July 11, 2020

Non-U.S. financial institutions hold precautionary positions in U.S. dollar assets as protection against financial shocks. This gives rise to a safety premium on the dollar. The premium varies over time and, hence, not only accounts for contemporaneous exchange rate dynamics but also helps to predict exchange rate trends. An IMF paper measures non-U.S. banks’ dollar demand for 26 economies as the ratio of assets denominated in dollar to total assets by nationality. Demand for U.S. dollars tends to surge following negative financial market shocks and causes dollar strength. Non-U.S. holdings of dollar assets have also been a highly significant predictor of dollar trends in subsequent years. Thus, large holdings have heralded dollar depreciation in the past.

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asset management Asset Purchase Program banks bond returns bond yields bubbles capital regulation carry China collateral commodities credit returns default ECB economic data EM equity returns euro area crisis Fed financial crisis FX FX interventions FX returns government debt government deficits herding inflation information insurances Japan leverage liquidity machine learning market turmoil price factors regulation risk management shadow banking statistical methods term structure trend following variance risk premium VIX volatility ZLB
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Macrosynergy is a London based macroeconomic research and technology company whose founders have developed and employed macro quantamental investment strategies in liquid, tradable asset classes, across many markets and for a variety of different factors to generate competitive, uncorrelated investment returns for institutional investors for two decades. Our quantitative-fundamental (quantamental) computing system tracks a broad range of real-time macroeconomic trends in developed and emerging countries, transforming them into macro systematic quantamental investment strategies. In June 2020 Macrosynergy and J.P. Morgan started a collaboration to scale the quantamental system and to popularize tradable economics across financial markets.

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