Abstract: We analyze brokerage data and an experiment to test a cognitive dissonance based theory of trading: investors avoid realizing losses because they dislike admitting that past purchases were mistakes, but delegation reverses this effect by allowing the investor to blame the manager instead. Using individual trading data, we show that the disposition effect—the propensity to realize past gains more than past losses—applies only to nondelegated assets like individual stocks; delegated assets, like mutual funds, exhibit a robust reverse-disposition effect. In an experiment, we show that increasing investors' cognitive dissonance results in both a larger disposition effect in stocks and a larger reverse-disposition effect in funds. Additionally, increasing the salience of delegation increases the reverse-disposition effect in funds. Cognitive dissonance provides a unified explanation for apparently contradictory investor behavior across asset classes and has implications for personal investment decisions, mutual fund management, and intermediation.

Abstract: In a life-cycle model, a retiree faces stochastic health depreciation and chooses consumption, health expenditure, and the allocation of wealth between bonds, stocks, and housing. The model explains key facts about asset allocation and health expenditure across health status and age. The portfolio share in stocks is low overall and is positively related to health, especially for younger retirees. The portfolio share in housing is negatively related to health for younger retirees and falls significantly in age. Finally, out-of-pocket health expenditure as a share of income is negatively related to health and rises in age.

Abstract: Using error-free data on life-cycle portfolio allocations of a large sample of Norwegian households, we document a double adjustment as households age: a rebalancing of the portfolio composition away from stocks as they approach retirement and stock market exit after retirement. When structurally estimating an extended life-cycle model, the parameter combination that best fits the data is one with a relatively large risk aversion, a small per-period participation cost, and a yearly probability of a large stock market loss in line with the frequency of stock market crashes in Norway.

Abstract: With the rise of social media, investors have a new tool for measuring sentiment in real time. However, the nature of these data sources raises serious questions about its quality. Because anyone on social media can participate in a conversation about markets—whether the individual is informed or not—these data may have very little information about future asset prices. In this article, the authors show that this is not the case. They analyze a recurring event that has a high impact on asset prices—Federal Open Market Committee (FOMC) meetings—and exploit a new dataset of tweets referencing the Federal Reserve. The authors show that the content of tweets can be used to predict future returns, even after controlling for common asset pricing factors. To gauge the economic magnitude of these predictions, the authors construct a simple hypothetical trading strategy based on this data. They find that a tweet-based asset allocation strategy outperforms several benchmarks—including a strategy that buys and holds a market index, as well as a comparable dynamic asset allocation strategy that does not use Twitter information.

Abstract: This paper shows the importance of correcting for sample selection when investing in illiquid assets that trade endogenously. Using a sample of 32,928 paintings that sold repeatedly between 1960 and 2013, we find an asymmetric V-shaped relation between sale probabilities and returns. Adjusting for the resulting selection bias reduces average annual index returns from 8.7% to 6.3%, lowers Sharpe ratios from 0.27 to 0.11, and materially impacts portfolio allocations. Investing in a broad portfolio of paintings is not attractive, but targeting specific styles or top-selling artists may add value. The methodology naturally extends to other asset classes.

Abstract: In a continuous-time economy, we investigate the asset allocation problem among a risk-free asset and two risky assets with an ambiguous correlation between the two risky assets. The portfolio selection that is robust to the uncertain correlation is formulated as the utility maximization problem over the worst-case scenario with respect to the possible choice of correlation. Thus, it becomes a maximin problem. We solve the problem under the Black--Scholes model for risky assets with an ambiguous correlation using the theory of $G$-Brownian motions. We then extend the problem to stochastic volatility models for risky assets with an ambiguous correlation between risky asset returns. An asymptotic closed-form solution is derived for a general class of utility functions, including constant relative risk aversion and constant absolute risk aversion utilities, when stochastic volatilities are fast mean reverting. We propose a practical trading strategy that combines information from the option implied volatilit...

Abstract: We propose a composite realized kernel to estimate the ex-post covariation of asset prices. These measures can in turn be used to forecast the covariation of future asset returns. Composite realized kernels are a data-efficient method, where the covariance estimate is composed of univariate realized kernels to estimate variances and bivariate realized kernels to estimate correlations. We analyze the merits of our composite realized kernels in an ultra high-dimensional environment, making asset allocation decisions every day solely based on the previous day’s data or a short moving average over very recent days. The application is a minimum variance portfolio exercise. The dataset is tick-by-tick data comprising 437 U.S. equities over the sample period 2006–2011. We show that our estimator is able to outperform its competitors, while the associated trading costs are competitive.

Abstract: The risk parity portfolio selection problem aims to find such portfolios for which the contributions of risk from all assets are equally weighted. Portfolios constructed using the risk parity approach are a compromise between two well-known diversification techniques: minimum variance optimization and the equal weighting approach. In this paper, we discuss the problem of finding portfolios that satisfy risk parity over either individual assets or groups of assets. We describe the set of all risk parity solutions by using convex optimization techniques over orthants and we show that this set may contain an exponential number of solutions. We then propose an alternative non-convex least-squares model whose set of optimal solutions includes all risk parity solutions, and propose a modified formulation which aims at selecting the most desirable risk parity solution according to a given criterion. When general bounds are considered, a risk parity solution may not exist. In this case, the non-convex least-squar...

Abstract: We test whether ratings are comparable across asset classes over a 30-year sample. We examine default rates by initial rating, accuracy ratios, migration metrics, instantaneous upgrade and downgrade intensities, and rating changes over bonds’ entire lives in multivariate regressions. These approaches reveal substantial and persistent differences across broad asset class types, as well as across subcategories of structured finance products. Our results are best explained by variation in rating agency incentives and variation in underlying risk profiles across asset classes. We conclude that regulations requiring ratings to perform comparably across asset classes will prove difficult to enforce and we advocate instead a regulatory framework that better distinguishes risks and incentives across asset classes.

Abstract: This is the first study to investigate the impact of the choice of optimization technique when constructing Socially Responsible Investing (SRI) portfolios, and to highlight the importance of the selection of the optimization method within this specialized industry. Using data from MSCI KLD on the social responsibility of US firms, we form SRI portfolios based on six different approaches and compare their performance along the dimensions of risk, risk-return trade-off, diversification and stability. Our results indicate that the more “formal” optimization approaches (Black-Litterman, Markowitz and robust estimation) lead to portfolios that are both less risky and have superior risk-return trade-offs; but at the cost of unstable asset allocations and lower diversification. More simplistic approaches to asset allocation (naive diversification, reward-to-risk and risk-parity) are less effective in producing well-performing portfolios, but are associated with greater diversification and asset stability. While the three formal approaches have higher transactions costs, their net returns are appreciably larger than those of the three more simplistic approaches. Our main conclusions are robust to a series of tests, including the use of different estimation windows, stricter screening criteria, and 14 different metrics for evaluating portfolio performance. While there are some differences in performance when individual aspects of corporate social performance are used to select the sample companies, our conclusions are broadly confirmed.

Abstract: We construct a new scenario analysis model for the United Kingdom using ONS data from 1987 to the present. The model links decisions about real variables to credit creation in the financial sector and decisions about asset allocation among investors for a wide array of financial assets. We develop, estimate, and calibrate the model from first principles as well as describing the stock-flow coherent database we construct to validate the model. We impose several scenarios on the model to test its usefulness as a medium term scenario analysis tool, including increases in banks’ capital ratios, sudden stops, changes in investment, increases in house prices and fiscal expansions.

Abstract: This paper documents a significant time-series momentum effect that is consistent and robust across all examined conventional asset classes from 1969 to 2015 We find that the duration and magnitude of time-series momentum is different in developed and emerging markets, but this is no longer the case when controlling for the currency component We further demonstrate that time-series momentum captures a significant proportion of international mutual fund performance, but this is predominantly with respect to its long aspect Finally, the market interventions by central banks in recent years have distorted correlations across assets; this challenges the performance of such portfolios

Abstract: Regime-based asset allocation has been shown to add value over rebalancing to static weights and, in particular, reduce potential drawdowns by reacting to changes in market conditions. The predominant approach in previous studies has been to specify in advance a static decision rule for changing the allocation based on the state of financial markets or the economy. This talk proposes the use of model predictive control to dynamically optimize a portfolio based on forecasts of the mean and variance of financial returns from a hidden Markov model with time-varying parameters. There are computational advantages to using model predictive control when estimates of future returns are updated repeatedly, since the optimal control actions are reconsidered anyway every time a new observation becomes available. Results from testing the approach on market data are presented and compared with previous, rule-based approaches. Further, imposing a trading penalty that reduces the number of trades is discussed as a way to increase the robustness of the approach. (Less)

Abstract: We examine the daily activity and performance of a large panel of individual investors in Sweden's Premium Pension System. We find that active investors earn significantly higher returns and risk-adjusted returns than inactive investors. A performance decomposition analysis reveals that most of the outperformance of active investors is the result of these investors successfully timing mutual funds and asset classes. While activity is beneficial for some investors, extreme flows out of mutual funds affect funds' net asset values negatively for all investors. Financial advisors, by contributing to coordinate investments and redemptions, exacerbate these negative effects.

Abstract: Electric utilities make up an asset-intensive industry with a broad geographical spread of assets, such as poles, transformers, cables, and switchgear. The utilities face a backlog of aging assets that are pending replacement. Increasingly, a consensus has been reached on moving away from time-based maintenance planning of assets to developing a proactive and smarter asset health management program to meet the competing constraints of reducing customer downtimes, meeting regulatory standards, and managing ever-expanding infrastructure within budget. Incomplete information, fragmented data, and a diversity of asset classes collectively make a holistic assessment of the grid extremely challenging. Working with DTE Energy and Alliander N.V., IBM Research has developed advanced analytics to model asset health and network reliability by predicting the aging of assets, identifying the remaining lifecycle, and computing the network robustness. The analytics exploit data from multiple systems such as enterprise asset management, work management, geographic information systems, supervisory control and data acquisition systems, advanced metering infrastructure, weather systems, and outage management systems. The algorithms systematically evaluate asset health and prioritize preventive, proactive, and corrective maintenance strategies for all asset classes in the electrical network. We describe outcomes, summarizing an overall health score and risk ranking along with a suggested optimal maintenance strategy considering budgetary constraints.

Abstract: We propose a new framework for modeling and forecasting common financial risks based on (un)reliable realized covariance measures constructed from high-frequency intraday data. Our new approach explicitly incorporates the effect of measurement errors and time-varying attenuation biases into the covariance forecasts, by allowing the ex-ante predictions to respond more (less) aggressively to changes in the ex-post realized covariance measures when they are more (less) reliable. Applying the new procedures in the construction of minimum variance and minimum tracking error portfolios results in reduced turnover and statistically superior positions compared to existing procedures. Translating these statistical improvements into economic gains, we find that under empirically realistic assumptions a risk-averse investor would be willing to pay up to 170 basis points per year to shift to using the new class of forecasting models.

Abstract: 1 † (Corresponding Author) Senior Economist and Head of Methods Team, Surveys and Methods Unit (DECSM), Development Data Group (DECDG), World Bank, Via Labicana 110 Rome, Italy, tkilic@worldbank.org; ‡ Survey Specialist, DECSM, DECDG, World Bank, Via Labicana 110 Rome, Italy, hmoylan@worldbank.org. This document should be cited as “Kilic, T., and Moylan, H. (2016). “Methodological experiment on measuring asset ownership from a gender perspective (MEXA): technical report.” Washington, DC: World Bank. P ub lic D is cl os ur e A ut ho riz ed

Abstract: Short-term shifts in market sentiment induced by awareness of future, as yet unrealised, climate risks could lead to economic shocks, causing substantial losses in financial portfolio value within timescales that are relevant to all investors. Factors, including climate change policy, technological change, asset stranding, weather events and longerterm physical impacts may lead to financial tipping points for which investors are not presently prepared. This research shows that changing asset allocations among various asset classes and regions, combined with investing in sectors exhibiting low climate risk, can offset only half of the negative impacts on financial portfolios brought about by climate change. Climate change thus entails “unhedgeable risk” for investment portfolios. While the response to action aimed at limiting warming below 2°C is shown to be negative in its short-term economic and financial impacts, the benefits of early action lead to significantly higher economic growth rates and returns over the long run, especially when compared to a worst-case scenario of inaction. The present study shows that certain types of portfolio benefit more than others. Even in the short run, the perception of climate change represents an aggregate risk driver that must be taken into consideration when assessing the performance of asset portfolios. Our analysis provides investors with a general guide to minimising their exposure to climate sentiment risk and has the potential to stimulate a constructive dialogue between investors, governments and regulators to examine the conditions necessary to build more resilient financial markets under unprecedented environmental change.

Abstract: This paper investigates the consequences of liquidation and reorganization on the allocation and subsequent utilization of assets in bankruptcy. We identify 129,000 bankrupt establishments and construct a novel dataset that tracks the occupancy, employment and wages paid at real estate assets over time. Using the random assignment of judges to bankruptcy cases as a natural experiment that forces some firms into liquidation, we find that even after accounting for reallocation, the long-run utilization of assets of liquidated firms is lower relative to assets of reorganized firms. These effects are concentrated in thin markets with few potential users, in areas with low access to finance, and in areas with low economic growth. The results highlight that different bankruptcy approaches affect asset allocation and utilization particularly when search frictions and financial frictions are present.

Abstract: This article examines the relationship between changes in the level of investor fear (measured by VIX) and financial market returns. We document a statistically significant relationship, across asset classes, consistent with a flight to quality as investor fear increases. As VIX increase there is a decline in stock markets, bond yields, and high-yielding currencies (AUD and NZD), while the USD appreciates. Returns become more sensitive to changes in the level of investor fear during the financial crisis of 2008-09, when investor fear spikes sharply. Analysis of market returns subsequent to periods of extreme levels of investor fear suggests some predictive ability for future returns, and it is suggested that this may be used to develop a profitable trading strategy. Taken together, the results confirm that financial market returns are closely related to prevailing levels of investor fear.