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It’s been over 20 years since the academic discovery of momentum investing (Jegadeesh and Titman (1993), Asness (1994)), yet much confusion and debate remains regarding its efficacy and its use as a practical investment tool. In some cases “confusion and debate” is us attempting to be polite, as it is near impossible for informed practitioners and academics to still believe some of the myths uttered about momentum — but that impossibility is often belied by real world statements. In this article, we aim to clear up much of the confusion by documenting what we know about momentum and disproving many of the often-repeated myths. We highlight ten myths about momentum and refute them, using results from widely circulated academic papers and analysis from the simplest and best publicly available data.

There is a considerable body of research on relative strength price momentum but much less on absolute momentum, also known as time series momentum. In this paper, we explore the practical side of absolute momentum. We first explore its sole parameter - the formation, or look back, period. We then examine the reward, risk, and correlation characteristics of absolute momentum applied to stocks, bonds, and real assets. We finally apply absolute momentum to a 60/40 stock/bond portfolio and a simple risk parity portfolio. We show that absolute momentum can effectively identify regime change and add significant value as an easy-to-implement, rule-based approach with many potential uses as both a stand-alone program and trend-following overlay.

What we use today to follow up a company's profitability and value creation is inconsistent with the capital market's mechanism, and what the market considers determines value--it is therefore imprecise and irrelevant. The accounting used will not any longer be a sufficient provider of financial information. Companies will experience a demand for more precise tools, both when it comes to metrics and the tool's ingredients (relevance) due to the increasing activity among shareholders/investors. The relevance in financial management must be dramatically improved. Companies must now identify the Value Based Management (VBM) concept that will best initiate a higher degree of Shareholder Value awareness in the company. A true VBM framework is consistent with the market's mechanism and our four factors that, according to the capital markets, determine value. The metric must be precise and relevant. Not random and irrelevant as accounting is today as a decision base. This paper deals with the two VBM frameworks Economic Value Added (EVA?) and Cash Value Added (CVA?). Many things are being said about the two frameworks. I will in this paper present the result from my research and thinking surrounding the differences and similarities between them. The Cash Value Added framework discussed in this paper refers to the concept developed by Erik Ottosson and Fredrik Weissenrieder. The Economic Value Added framework discussed in this paper refers to the concept developed by Bennett Stewart.

This paper introduces the Hierarchical Risk Parity (HRP) approach. HRP portfolios address three major concerns of quadratic optimizers in general and Markowitz’s CLA in particular: Instability, concentration and underperformance.HRP applies modern mathematics (graph theory and machine learning techniques) to build a diversified portfolio based on the information contained in the covariance matrix. However, unlike quadratic optimizers, HRP does not require the invertibility of the covariance matrix. In fact, HRP can compute a portfolio on an ill-degenerated or even a singular covariance matrix, an impossible feat for quadratic optimizers. Monte Carlo experiments show that HRP delivers lower out-of-sample variance than CLA, even though minimum-variance is CLA’s optimization objective. HRP also produces less risky portfolios out-of-sample compared to traditional risk parity methods.A presentation can be found at http://ssrn.com/abstract=2713516.

Accounting in shaped by economic and political forces. It follows that increased worldwide integration of both markets and politics (driven by reductions in communications and information processing costs) makes increased integration of financial reporting standards and practice almost inevitable. But most market and political forces will remain local for the foreseeable future, so it is unclear how much convergence in actual financial reporting practice will (or should) occur. Furthermore, there is little settled theory or evidence on which to build an assessment of the advantages and disadvantages of uniform accounting rules within a country, let alone internationally. The pros and cons of IFRS therefore are somewhat conjectural, the unbridled enthusiasm of allegedly altruistic proponents notwithstanding. On the "pro" side of the ledger, I conclude that extraordinary success has been achieved in developing a comprehensive set of "high quality" IFRS standards, in persuading almost 100 countries to adopt them, and in obtaining convergence in standards with important non-adopters (notably, the U.S.). On the "con" side, I envisage problems with the current fascination of the IASB (and the FASB) with "fair value accounting." A deeper concern is that there inevitably will be substantial differences among countries in implementation of IFRS, which now risk being concealed by a veneer of uniformity. The notion that uniform standards alone will produce uniform financial reporting seems naive. In addition, I express several longer run concerns. Time will tell.

Over seventy years ago Benjamin Graham and David Dodd proposed valuing securities with earnings smoothed across multiple years. Robert Shiller popularized this method with his version of this cyclically adjusted price-to-earnings ratio (CAPE) in the late 1990s, and issued a timely warning of poor stock returns to follow in the coming years. We apply this valuation metric across over thirty foreign markets and find it both practical and useful, and indeed witness even greater examples of bubbles and busts abroad than in the United States. We then create a trading system to build global stock portfolios based on valuation, and find significant outperformance by selecting markets based on relative and absolute valuation.

In the context of risk-based portfolio construction and pro-active risk management, finding robust predictors of future realised volatility is paramount to achieving optimal performance. Volatility has been documented in economics literature to exhibit pronounced persistence with clusters of high or low volatility regimes and to mean-revert to a normal level, underpinning Nobel prize-winning work on Generalized Autoregressive Heteroskedastic (GARCH) models. From a Reinforcement Learning (RL) point of view, this process can be interpreted as a model-based RL approach where the goal of the models is twofold: first, to represent the volatility dynamics and forecast its term structure and second, to compute a resulting allocation to match a given target volatility: hence the name "volatility targeting method for risk-based portfolios". However, the resulting volatility model-based RL approaches are hard to distinguish as each model results in similar performance without a clear dominant one. We therefore present an innovative approach with an additional supervised learning step to predict the best model(s), based on historical performance ordering of RL models. Our contribution shows that adding a supervised learning overlay to decide which model(s) to use provides improvement over a naive benchmark consisting in averaging all RL models. A salient ingredient in this supervised learning task is to adaptively select features based on their significance, thanks to minimum importance filtering. This work extends our previous work on combining model-free and model-based RL. It mixes different types of learning procedures, namely model-based RL and supervised learning opening new doors to combine different machine learning approaches.

While conventional academic finance emphasizes theories such as Modern Portfolio Theory (MPT) and the Efficient Market Hypothesis (EMH), the emerging field of behavioral finance investigates the cognitive factors and emotional issues that impact the decision-making process of individuals, groups, and organizations. This paper presents an introduction to some general principles of behavioral finance including: overconfidence, cognitive dissonance, regret theory, and prospect theory. Also, this article provides strategies to assist individuals to resolve these mental errors and emotional pitfalls by recommending some important investment approaches for those who invest in stocks and mutual funds. Note: This paper serves as a very good introductory reading for an undergraduate class in finance and economics.

Can an asset manager plan the optimal timing for her/his hedging strategies given market conditions? The standard approach based on Markowitz or other more or less sophisticated financial rules aims to find the best portfolio allocation thanks to forecasted expected returns and risk but fails to fully relate market conditions to hedging strategies decision. In contrast, Deep Reinforcement Learning (DRL) can tackle this challenge by creating a dynamic dependency between market information and hedging strategies allocation decisions. In this paper, we present a realistic and augmented DRL framework that: (i) uses additional contextual information to decide an action, (ii) has a one period lag between observations and actions to account for one day lag turnover of common asset managers to rebalance their hedge, (iii) is fully tested in terms of stability and robustness thanks to a repetitive train test method called anchored walk forward training, similar in spirit to k fold cross validation for time series and (iv) allows managing leverage of our hedging strategy. Our experiment for an augmented asset manager interested in sizing and timing his hedges shows that our approach achieves superior returns and lower risk.

Can an asset manager gain knowledge from different data sources to select the right hedging strategy for his portfolio? We use Deep Reinforcement Learning (Deep RL or DRL) to extract information from not only past performances of the hedging strategies but also additional contextual information like risk aversion, correlation data, credit information and estimated earnings per shares. Our contributions are threefold: (i) the use of contextual information also referred to as augmented state in DRL, (ii) the impact of a one period lag between observations and actions that is more realistic for an asset management environment, (iii) the implementation of a new repetitive train test method called walk forward analysis, similar in spirit to cross validation for time series. Although our experiment is on trading bots, it can easily be translated to other bot environments that operate in sequential environment with regime changes and noisy data. Our experiment for an augmented asset manager interested in finding the best portfolio for hedging strategies achieves superior returns and lower risk.

In this paper we survey the literature on the Black-Litterman model. This survey is provided both as a chronology and a taxonomy as there are many claims on the model in the literature. We provide a complete description of the canonical model including full derivations from the underlying principles using both Theil's Mixed Estimation model and Bayes Theory. The various parameters of the model are considered, along with information on their computation or calibration. Further consideration is given to several of the key papers, with worked examples illustrating the concepts.

We construct an equally-weighted index of commodity futures monthly returns over the period between July of 1959 and December of 2004 in order to study simple properties of commodity futures as an asset class. Fully-collateralized commodity futures have historically offered the same return and Sharpe ratio as equities. While the risk premium on commodity futures is essentially the same as equities, commodity futures returns are negatively correlated with equity returns and bond returns. The negative correlation between commodity futures and the other asset classes is due, in significant part, to different behavior over the business cycle. In addition, commodity futures are positively correlated with inflation, unexpected inflation, and changes in expected inflation.

Gold objects have existed for thousands of years but for many investors gold has only recently become a tradable investment opportunity. Gold has been described as an inflation hedge, a “golden constant”, with a long run real return of zero. Yet over 1, 5, 10, 15 and 20 year investment horizons the variation in the nominal and real returns of gold has not been driven by realized inflation. The real price of gold is currently high compared to history. In the past, when the real price of gold was above average, subsequent real gold returns have been below average. Given this situation is it time to explore “this time is different” rationalizations? We show that new mined supply is surprisingly unresponsive to prices. In addition, authoritative estimates suggest that about three quarters of the achievable world supply of gold has already been mined. On the demand side, we focus on the official gold holdings of many countries. If prominent emerging markets increase their gold holdings to average per capita or per GDP holdings of developed countries, the real price of gold may rise even further from today’s elevated levels. As a result investors in gold face a daunting dilemma: 1) embrace a view that “those who cannot remember the past are condemned to repeat it”, there is a “golden constant” and the purchasing power of gold is likely to fall or 2) embrace a view that “this time is different” and the “golden constant” is dead. Related research: Erb and Harvey (2015), The Golden Constant Erb and Harvey (2012a), An Impressionistic View of the 'Real' Price of Gold Around the World.

If there has been a shift in corporate finance and valuation in recent years, it has been towards giving excess returns a more central role in determining the value of a business. While early valuation models emphasized the relationship between growth and value - higher growth firms were assigned higher values - more recent iterations of these models have noted that growth unaccompanied by excess returns creates no value. With this shift towards excess returns has come an increased focus on measuring and forecasting returns earned by businesses on both investments made in the past and expected future investments. In this paper, we examine accounting and cash flow measures of these returns and how best to forecast these numbers for any given business for the future.

Over the last 75 years there have been great strides in modern finance, portfolio theory and asset allocation strategies. Despite this progress the process of portfolio construction remains grounded in many theoretical concepts that can result in inappropriate or unrealistic frameworks. In this paper we provide an overview of the development of these ideas, construct a general foundation for understanding portfolio construction and produce a framework for simplifying, systematizing and streamlining the process in an attempt to establish a realistic and suitable process for portfolio construction.

Exercises and case studies for a rigorous approach to risk- and portfolio-management. This booklet stems from the review sessions of the six-day ARPM bootcamp. Contents include: Advanced multivariate statistics; copula-marginal decomposition Annualization/projection (FFT, cumulants, simulations) Pricing: exact; first order (delta/duration); second order (gamma/convexity) Quest for invariance (stationarity, volatlity clustering, cointegration) Mutlivariate estimation - Non-parametric; MLE; shrinkage; robust; Bayesian; missing data - Generalized hypothesis testing Dimension reduction - Statistical (random matrices; principal components; factor analysis) - Cross-sectional / time-series factor models - Factors on Demand Risk management - VaR/CVaR (marginal Euler decomposition; extreme value theory; Cornish-Fisher; elliptical) - Generalized objectives (p&l, return, relative return, etc) - Stochastic dominance/utility theory Classical portfolio management: mean-variance Dynamic strategies (option replication, CPPI, utlity maximization) Advanced portfolio management - Robust optimization - Black-Litterman and beyond: fully flexible views Solution code available at MATLAB Central File Exchange.

Equity risk premiums are a central component of every risk and return model in finance and are a key input in estimating costs of equity and capital in both corporate finance and valuation. Given their importance, it is surprising how haphazard the estimation of equity risk premiums remains in practice. We begin this paper by looking at the economic determinants of equity risk premiums, including investor risk aversion, information uncertainty and perceptions of macroeconomic risk. In the standard approach to estimating equity risk premiums, historical returns are used, with the difference in annual returns on stocks versus bonds over a long time period comprising the expected risk premium. We note the limitations of this approach, even in markets like the United States, which have long periods of historical data available, and its complete failure in emerging markets, where the historical data tends to be limited and volatile. We look at two other approaches to estimating equity risk premiums – the survey approach, where investors and managers are asked to assess the risk premium and the implied approach, where a forward-looking estimate of the premium is estimated using either current equity prices or risk premiums in non-equity markets. In the next section, we look at the relationship between the equity risk premium and risk premiums in the bond market (default spreads) and in real estate (cap rates) and how that relationship can be mined to generated expected equity risk premiums. We close the paper by examining why different approaches yield different values for the equity risk premium, and how to choose the “right” number to use in analysis.

We use cross-section regressions to study how a firm's value is related to dividends and debt. With a good control for profitability, the regressions can measure how the taxation of dividends and debt affects firm value. Simple tax hypotheses say that value is negatively related to dividends and positively related to debt. We find the opposite. We infer that dividends and debt convey information about profitability (expected net cash flows) missed by a wide range of control variables. This information about profitability obscures any tax effects of financing decisions.

The equity risk premium is the price of risk in equity markets, and it is a key input in estimating costs of equity and capital in both corporate finance and valuation. Given its importance, it is surprising how haphazard the estimation of equity risk premiums remains in practice. We begin this paper by looking at the economic determinants of equity risk premiums, including investor risk aversion, information uncertainty and perceptions of macroeconomic risk. In the standard approach to estimating the equity risk premium, historical returns are used, with the difference in annual returns on stocks versus bonds, over a long period, comprising the expected risk premium. We note the limitations of this approach, even in markets like the United States, which have long periods of historical data available, and its complete failure in emerging markets, where the historical data tends to be limited and volatile. We look at two other approaches to estimating equity risk premiums – the survey approach, where investors and managers are asked to assess the risk premium and the implied approach, where a forward-looking estimate of the premium is estimated using either current equity prices or risk premiums in non-equity markets. In the next section, we look at the relationship between the equity risk premium and risk premiums in the bond market (default spreads) and in real estate (cap rates) and how that relationship can be mined to generated expected equity risk premiums. We close the paper by examining why different approaches yield different values for the equity risk premium, and how to choose the “right” number to use in analysis.

We walk the reader through the Black-Litterman approach, providing all the proofs. We show how minor modifications of the original model greatly improve its range of applications. We discuss full generalizations of this and related models. MATLAB code is available through MATLAB Central

Prior academic research focuses on commodities in isolation as leading economic indicators, ignoring the message price behavior may have on other asset classes. We find that the relative movement of Lumber to Gold provides important information on economic growth and inflation expectations, which gradually diffuses with a lag to stock and bond markets. Lumber’s sensitivity to housing, a key source of domestic economic growth in the U.S., makes it a unique commodity as it pertains to macro fundamentals and risk-seeking behavior. On the opposite end of the spectrum is Gold, which is distinctive in that it historically exhibits safe-haven properties during periods of heightened volatility and stock market stress. We find that the relationship between Lumber and Gold helps to answer the critical question of when to “play defense” and when to “play offense” within the context of active portfolio management. In this paper, we show that a strategy using the signaling power of Lumber and Gold results in stronger absolute and risk-adjusted returns than a passive buy-and-hold index. This outperformance stems from being more aggressive in a portfolio during periods when Lumber is leading Gold and being more defensive during periods when Gold is leading Lumber. The results are robust to various time frames and across multiple economic and financial market cycles.

Using newly-available materiality classifications of sustainability topics, we develop a novel dataset by hand-mapping sustainability investments classified as material for each industry into firm-specific sustainability ratings. This allows us to present new evidence on the value implications of sustainability investments. Using both calendar-time portfolio stock return regressions and firm-level panel regressions we find that firms with good ratings on material sustainability issues significantly outperform firms with poor ratings on these issues. In contrast, firms with good ratings on immaterial sustainability issues do not significantly outperform firms with poor ratings on the same issues. These results are confirmed when we analyze future changes in accounting performance. The results have implications for asset managers who have committed to the integration of sustainability factors in their capital allocation decisions.

This paper surveys the theoretical and empirical literature on the economic consequences of financial reporting and disclosure regulation. We integrate theoretical and empirical studies from accounting, economics, finance and law in order to contribute to the cross-fertilization of these fields. We provide an organizing framework that identifies firm-specific (micro-level) and market-wide (macro-level) costs and benefits of firms' reporting and disclosure activities and then use this framework to discuss potential costs and benefits of regulating these activities and to organize the key insights from the literature. Our survey highlights important unanswered questions and concludes with numerous suggestions for future research.

This paper contains the statistics of the Equity Premium or Market Risk Premium (MRP) used in 2012 for 82 countries. We got answers for 93 countries, but we only report the results for 82 countries with more than 5 answers. Most previous surveys have been interested in the Expected MRP, but this survey asks about the Required MRP. The paper also contains the references used to justify the MRP, comments from persons that do not use MRP, and comments from persons that do use MRP.

Behavioral finance argues that some financial phenomena can plausibly be understood using models in which some agents are not fully rational. The field has two building blocks: limits to arbitrage, which argues that it can be difficult for rational traders to undo the dislocations caused by less rational traders; and psychology, which catalogues the kinds of deviations from full rationality we might expect to see. We discuss these two topics, and then present a number of behavioral finance applications: to the aggregate stock market, to the cross-section of average returns, to individual trading behavior, and to corporate finance. We close by assessing progress in the field and speculating about its future course.