Efficient market theory assumes that all market prices incorporate all information at the same time. Realistically, different market segments focus on different news flows, depending on the nature of the traded security and their research capacity. Such specialization makes it plausible that lagged

Detecting price deviations from fundamental value is challenging because the fundamental value itself is uncertain. A shortcut for doing so is to look at return time series alone and to detect “strict local martingales”, i.e. episodes when the risk-neutral return temporarily follows a random

Retail investors’ return expectations affect market momentum and risk premia. The rise of ETFs with varying and inverse leverage offers an opportunity to estimate the distribution of such expectations based on actual transactions. A new paper shows how to do this through ETFs that track the

Machine learning can improve macro trading strategies, mainly because it makes them more flexible and adaptable, and generalizes knowledge better than fixed rules or trial-and-error approaches. Within the constraints of pre-set hyperparameters machine learning is continuously and autonomously

Investment-based capital asset pricing looks at equity returns from the angle of issuers, rather than investors. It is based on the cost of capital and the net present value rule of corporate finance. The q-factor model is an implementation of investment capital asset pricing that explains many

Through R or Python we can nowadays apply a wide range of methods for predicting financial market variables. Key concepts include penalized regression, such as Ridge and LASSO, support vector regression, neural networks, standard regression trees, bagging, random forest, and gradient boosting. The

Liquidity is a critical force behind market price distortions (and related trading opportunities). First, the cost of trading in and out of a contract gives rise to a liquidity premium. Second, the risk that transaction costs will rise when market conditions necessitate trading commands a separate

Price distortions are an important source of short-term trading profits, particularly in turbulent markets. Here price distortions mean apparent price-value gaps that arise from large inefficient flows. An inefficient flow is a transaction that is not motivated by rational risk-return optimization.

The vast range of academically researched equity return anomalies can be condensed into five categories: [1] return momentum, [2] outperformance of high valuation, [3] underperformance of high investment growth, [4] outperformance of high profitability, and [5] outperformance of stocks subject to

Popular factors for government bond investment are “carry”, “momentum”, “value” and “defensive”. “Carry” depends on the steepness of the yield curve, which to some extent reflects aversion to risk and volatility. “Momentum” relates to medium-term directional trends, which in

Endogenous risk arises from the interaction of financial market participants, as opposed to traded assets’ fundamental value. It often manifests as feedback loops after some exogenous shock. An important type of endogenous market risk is setback risk, which refers to the asymmetry of the upside

Financial market prices and return indices are non-stationary time series, even in logarithmic form. This means not only that they are drifting, but also that their distribution changes overtime. The main purpose of de-trending is to mitigate the effects of non-stationarity on estimated price or

Tradable economics is a technology for building systematic trading strategies based on economic data. Economic data are statistics that – unlike market prices – directly inform on economic activity. Tradable economics is not a zero-sum game. Trading profits are ultimately paid out of the

One measure of the crowdedness of trades in a portfolio is centrality. Centrality is a concept of network analysis that measures how similar one institution’s portfolio is to its peers by assessing its importance as a network node. Empirical analysis suggests that [1] the centrality of individual

Financial markets are not information efficient with respect to macroeconomic information because data are notoriously ‘dirty’, relevant economic research is expensive, and establishing stable relations between macro data and market performance is challenging. However, statistical programming

In general, machine learning is a form of artificial intelligence that allows computers to improve the performance of a task through data, without being directly programmed. Reinforcing learning is a specialized application of (deep) machine learning that interacts with the environment and seeks to

The low-risk effect refers to the empirical finding that within an asset classes higher-beta securities fail to outperform lower-beta securities. As a result, “betting against beta”, i.e. leveraged portfolios of longs in low-risk securities versus shorts in high-risk securities, have been

Roughly speaking, a tensor is an array (generalization of a matrix) of numbers that transform according to certain rules when the array’s coordinates change. Fixed-income returns across countries can be seen as residing on tensor-like multidimensional data structures. Hence a tensor-valued

The R environment makes statistical estimation and learning accessible to portfolio management beyond the traditional quant space. Overcoming technicalities and jargon, managers can operate powerful statistical tools by learning a few lines of code and gaining some basic intuition of statistical

R is an object-oriented programming language and work environment for statistical analysis. It is not just for programmers, but for everyone conducting data analysis, including portfolio managers and traders. Even with limited coding skills R outclasses Excel spreadsheets and boosts information

Simple value and momentum strategies often end up with opposite market positions. One strategy succeeds when the other fails. There are two plausible reasons for this. First, value investors regularly bet against market trends that appear to ‘have gone too far’ by standard valuation metrics.

One of the most successful investment strategies since the turn of the century has been the risk-parity “long-long” of combined equity, credit and duration derivatives. In a simple form this trade takes continuous joint equal mark-to-market exposure in equity or credit and duration risk. A

A risk spread is a premium for bearing economic risk of an investment, paid over and above the short-term real interest rate. Over the past 30 years, risk spreads in the U.S. have increased significantly and consistently: while real interest rates on ‘safe’ bonds and deposits have collapsed,

Bad beta means market exposure that is expensive to hedge. Good beta is market exposure that is cheap to hedge. Distinguishing between these is crucial for FX trading strategies. The market sensitivity of FX positions can be decomposed into a risk premium beta (‘bad beta’) and a real rate beta

News and comments are major drivers for asset prices, maybe more so than conventional price and economic data. Yet it is impossible for any financial professional to read and analyse the vast and growing flow of written information. This is becoming the domain of natural language processing; a

Hedge funds’ capital structure is vulnerable to market shocks because most of them offer high liquidity to loss-sensitive investors. Moreover, hedge fund managers form expectations about each other based on market prices and investor flows. When industry-wide position liquidations become a

A new paper proposes to decompose the U.S. government bond yield curve by applying a ‘bootstrapping method’ that resamples observed return differences across maturities. The advantage of this method over the classical principal components approach would be greater robustness to misspecification

Allocators to systematic strategies usually trust live records far more than backtests. Given the moral hazard issues of backtesting in the financial industry, this is understandable (view post here). Unfortunately, for many systematic strategies live records can be even more misleading. First, the

Portfolio risk forecasting is subject to great parameter uncertainty, particularly for longer forward horizons. This simply reflects that large drawdowns are observed only rarely, making it hard to estimate their ‘structural’ properties. Bayesian forecasting addresses parameter uncertainty

Implicit subsidies are premia paid by large financial markets participants for reasons other than risk-return optimization (view post here). Their estimation requires skill and a strong “quantamental system”. However, implicit subsidies are behind the popularity and temporary success of many

The algorithmic strategy business likes quoting academic research to support specific trading factors, particularly in the equity space. Unfortunately, the rules of conventional academic success provide strong incentives for data mining and presenting ‘significant’ results. This greases the

Systemic financial crises arise when vulnerable financial systems meet adverse shocks. A systemic risk indicator tracks the vulnerability rather than the shocks (which are the subject of ‘stress indicators’). A systemic risk indicator is by nature slow-moving and should signal elevated

Estimating portfolio risk in extreme situations means answering two questions: First, has the market entered an extreme state? Second, how are returns likely to be distributed in such an extreme state? There are three different types of models to address these questions statistically. Conventional

Salience theory suggests that decision makers exaggerate the probability of extreme events if they are aware of their possibility. This gives rise to subjective probability distributions and undermines conventional rationality. In particular, salience theory explains skewness preference, i.e. the

The business of algorithmic trading strategies creates incentives for model overfitting and backtest embellishment: researchers must pass Sharpe ratio thresholds for their strategies to be considered, while managers lack interest in realistic simulations of ideas. Overfitting leads to bad investment

Covered interest parity is an arbitrage condition that equalizes costs of direct USD funding and of synthetic USD funding through FX swaps. Deviations are called dollar cross-currency basis and have become a common occurrence since the great financial crisis. A negative dollar basis means direct

Momentum trading, buying winning assets and selling losing assets, is a most popular trading strategy. It relies on sluggish market adjustment, allowing the trader to follow best-informed investors before the more inert part of the market does. Herding simply means that market participants imitate

Containment of drawdowns and optimization of performance ratios for multi-asset portfolios is critical for trading strategies. Alas, short data series or structural changes often render estimates of covariance matrices unreliable. A popular solution is risk-parity with volatility targeting. An

Liquidity yields are convenience yields of financial securities that typically arise from high liquidity, suitability as collateral or preferred regulatory status. New research argues that relative changes in liquidity yields on government bonds across countries have a significant impact on exchange

Fundamental value seems like a straightforward investment approach. One simply looks for assets that are “cheap” or “expensive” relative to their rationally expected risk-adjusted discounted cash flows. In reality, conscientious estimation of fundamental value gaps is one of the most

A theoretical paper shows that a downward shift in expected inflation increases equity valuations and credit default risk at the same time. The reason for this is “nominal stickiness”. A slowdown in consumer prices reduces short-term interest rates but does not immediately reduce earnings growth

There are reason and evidence for financial markets failing to be efficient with respect to macro trends. The main reason is cost: “tradable” economic research is expensive and investment firms will only invest in such research if their fees on expected incremental portfolio returns exceed their

Machine learning offers powerful tools for backtesting trading strategies. However, its computational power and convenience can also be corrosive for financial investment due to its tendency to find temporary patterns while data samples for cross validation are limited. Machine learning produces

Across assets, carry is defined as return for unchanged prices and is calculated based on the difference between spot and futures prices (view post here). Unlike other markets, commodity futures curves are segmented by obstacles to intertemporal arbitrage. The costlier the storage, the greater is

The correlation of equity and high grade sovereign bond returns is a powerful driver of portfolio construction and the term premia of interest rates. This correlation has turned from positive in the 1970s-1990s to negative in the 2000s-2010s, on the back of similar shifts in the correlation between

The term structure of sovereign credit default swaps (CDS) is indicative of country-specific financial shocks, because rising country risk affects short-dated maturities more than longer-dated ones. This feature allows disentangling global and local risk factors in sovereign CDS markets. The latter

The volatility risk premium compensates investors for taking volatility risk. Conceptually it is based on the difference between options-implied and expected realized volatility. In equity markets this premium should be positive in the long run and fluctuate overtime depending on the market’s

Public institutions have developed a wide range of methods to track systemic financial risk. What most of them have in common is reliance on financial market data. This implies that systemic risk indicators typically only show what the market has already priced, in form of correlation, volatility or

Real interest rates on ‘safe’ assets such as high-quality government bonds had been stationary around 2% for more than a century until the 1980s. Since then they have witnessed an unprecedented global decline, with most developed markets converging on the U.S. market trend. There is evidence

Variance term premia are surcharges on traded volatility that compensate for bearing volatility risk in respect to underlying asset prices over different forward horizons. The premia tend to increase in financial market distress and decrease in market expansions. Variance term premia have