Here’s MoreLiver’s Thursday Back to School research recap. Some of the summaries are truncated. Most of this is lifted from @quantivity twitterfeed and NBER. Topics this week are the Euro Zone, Portfolio Management, Anomalies and Herding & Segmentation.
EURO ZONE
The Euro and European Economic Conditions – NBER
This paper reviews (1) the reasons for these economic problems, (2) the political origins of the European Monetary Union, (3) the current attempts to solve the sovereign debt problem, (4) the long-term problem of inter-country differences of productivity growth and competitiveness, (5) the special problems of Greece and Italy, (6) and the pros and cons of a Greek departure from the Eurozone.
This paper reviews (1) the reasons for these economic problems, (2) the political origins of the European Monetary Union, (3) the current attempts to solve the sovereign debt problem, (4) the long-term problem of inter-country differences of productivity growth and competitiveness, (5) the special problems of Greece and Italy, (6) and the pros and cons of a Greek departure from the Eurozone.
Target Loans, Current Account Balances and Capital Flows: The ECB's Rescue Facility – NBER
The European Monetary Union is stuck in a severe balance-of-payments imbalance of a nature similar to the one that destroyed the Bretton Woods System. Greece, Ireland, Portugal, Spain and Italy have suffered from balance-of-payments deficits whose accumulated value, as measured by the Target balances in the national central banks’ balance sheets, was 404 billion euros in August 2011. The national central banks of these countries covered the deficits by creating and lending out additional central bank money that flowed to the euro core countries, Germany in particular, and crowded out the central bank money resulting from local refinancing operations. Thus the ECB forced a public capital export from the core countries that partly compensated for the now reluctant private capital flows to, and the capital flight from, the periphery countries.
The European Monetary Union is stuck in a severe balance-of-payments imbalance of a nature similar to the one that destroyed the Bretton Woods System. Greece, Ireland, Portugal, Spain and Italy have suffered from balance-of-payments deficits whose accumulated value, as measured by the Target balances in the national central banks’ balance sheets, was 404 billion euros in August 2011. The national central banks of these countries covered the deficits by creating and lending out additional central bank money that flowed to the euro core countries, Germany in particular, and crowded out the central bank money resulting from local refinancing operations. Thus the ECB forced a public capital export from the core countries that partly compensated for the now reluctant private capital flows to, and the capital flight from, the periphery countries.
PORTFOLIO MANAGEMENT
Time-Varying Fund Manager Skill – NBER
This paper estimates skill separately in booms and recessions and finds that the extent to which managers focus on stock picking or market timing fluctuates with the state of the economy. Stock picking is more prevalent in booms, while market timing dominates in recessions. We use this finding to develop a new methodology for detecting managerial skill. The results suggest that some but not all managers have skill.
What is the Wind Behind this Sail? Can Fund Managers Successfully Time Their Investment Styles? – SSRN
Specifically, I examine the timing activities of actively managed mutual funds within different market segments based on such established systematic risk factors as size, book-to-market, momentum, and across different fund styles such as aggressive growth, growth and income, and small company funds, etc. Mutual fund timing strategy can be viewed as the fund manager's response to his/her private information regarding future factor premiums. Instead of directly observing how fund managers make their timing decisions, an alternative approach is to look at the direct outcomes of their decisions, which are related to the factor timing loadings derived from a factor timing model.
A Risk Based Approach to Tactical Asset Allocation – SSRN
The purpose of this paper is to present an alternative and simple quantitative risk based portfolio management that improves the risk-adjusted portfolio returns across various asset classes. This approach, based on the conclusions of Brandolini D. – Colucci S. 'Backtesting Value-at-Risk: A comparison between Filtered Bootstrap and Historical Simulation', has been tested since 1974 for calibration and since 2000 in a real backtest.
The purpose of this paper is to present an alternative and simple quantitative risk based portfolio management that improves the risk-adjusted portfolio returns across various asset classes. This approach, based on the conclusions of Brandolini D. – Colucci S. 'Backtesting Value-at-Risk: A comparison between Filtered Bootstrap and Historical Simulation', has been tested since 1974 for calibration and since 2000 in a real backtest.
The Cross Section of Expected Returns with MIDAS Betas – SSRN
This paper employs mixed data sampling (MIDAS) to estimate a portfolio’s conditional beta with the market and with alternative risk factors. The market risk premium is positive and significant, and the result is robust to alternative asset pricing specifications, and model misspecification.
This paper employs mixed data sampling (MIDAS) to estimate a portfolio’s conditional beta with the market and with alternative risk factors. The market risk premium is positive and significant, and the result is robust to alternative asset pricing specifications, and model misspecification.
Composite Alpha Factor and Portfolio Rebalance on Factor Tilts – SSRN
Next, using historical data in Japanese equity market, we show numerical example of composite factor from three factors by static solution. And we show how much we have to adjust portfolio for typical factors by dynamic solution when we consider time variability of factor exposures. Our results suggest that we should use dynamic solution when we adopt factors whose exposures change fast.
Next, using historical data in Japanese equity market, we show numerical example of composite factor from three factors by static solution. And we show how much we have to adjust portfolio for typical factors by dynamic solution when we consider time variability of factor exposures. Our results suggest that we should use dynamic solution when we adopt factors whose exposures change fast.
Return Prediction and Portfolio Selection: A Distributional Approach – SSRN
This paper develops a distribution-based framework for both return prediction and portfolio selection. More specifically, a time-varying return distribution is modeled through quantile regression and copulas, using the quantile approach to extract information in marginal distributions and copulas to capture dependence structure. A nonlinear utility function is proposed for portfolio selection which utilizes the full underlying return distribution. An empirical application to US data highlights not only the predictability of the stock and bond return distributions, but also the additional information provided by the distributional approach which cannot be captured by the traditional moment-based methods.
This paper develops a distribution-based framework for both return prediction and portfolio selection. More specifically, a time-varying return distribution is modeled through quantile regression and copulas, using the quantile approach to extract information in marginal distributions and copulas to capture dependence structure. A nonlinear utility function is proposed for portfolio selection which utilizes the full underlying return distribution. An empirical application to US data highlights not only the predictability of the stock and bond return distributions, but also the additional information provided by the distributional approach which cannot be captured by the traditional moment-based methods.
Is There Any Alpha in Institutional Emerging Market Equity Funds? – SSRN
This analysis results in five major findings: (1) varieties of products have proliferated in this asset class; (2) active management works in this asset class, but investors have relied more on research to hire the good ones; (3) value managers tend to have higher alphas, but the supply of value managers is less than that of growth or market-oriented managers; (4) key risk drivers are price momentum, valuation, Asia, and resources sector; (5) active risks from this asset class are still present, so it’s important to diversify across multiple strategies and styles to mitigate unwanted risks. In sum, alphas are obtainable, but they are not free.
This analysis results in five major findings: (1) varieties of products have proliferated in this asset class; (2) active management works in this asset class, but investors have relied more on research to hire the good ones; (3) value managers tend to have higher alphas, but the supply of value managers is less than that of growth or market-oriented managers; (4) key risk drivers are price momentum, valuation, Asia, and resources sector; (5) active risks from this asset class are still present, so it’s important to diversify across multiple strategies and styles to mitigate unwanted risks. In sum, alphas are obtainable, but they are not free.
ANOMALIES
On the Anomalous Stock Price Response to Management Earnings Forecasts – SSRN
In the post-announcement period, we find a significant upward price drift for both good news forecasts and bad news forecasts. The asymmetry in the initial market response and the subsequent upward drift in stock prices are consistent with a reversal of an initial overreaction to managers’ bad news forecasts and a continuation of an initial underreaction to managers’ good news forecasts. This interpretation is supported by a negative (positive) relationship between the initial market response and the post-guidance drift in the bad news (good news) group.
Seasonal Asset Allocation: Evidence from Mutual Fund Flows – SSRN
finding strong evidence of seasonal reallocation across funds based on fund exposure to risk. We show that substantial money moves from U.S. equity to U.S. money market and government bond mutual funds in the fall, then back to equity funds in the spring, controlling for the influence of past performance, advertising, liquidity needs, capital gains overhang, and year-end influences on fund flows. We find a strong correlation between mutual fund net flows (and within-fund-family exchanges) and the onset of and recovery from seasonal depression, consistent with the hypothesis that investor risk aversion varies with the seasons. Further, we find stronger seasonality in Canadian fund flows (a more northerly location relative to the U.S., where seasonal depression is more severe), and a reverse seasonality in fund flows for Australia (where the seasons are reversed).
finding strong evidence of seasonal reallocation across funds based on fund exposure to risk. We show that substantial money moves from U.S. equity to U.S. money market and government bond mutual funds in the fall, then back to equity funds in the spring, controlling for the influence of past performance, advertising, liquidity needs, capital gains overhang, and year-end influences on fund flows. We find a strong correlation between mutual fund net flows (and within-fund-family exchanges) and the onset of and recovery from seasonal depression, consistent with the hypothesis that investor risk aversion varies with the seasons. Further, we find stronger seasonality in Canadian fund flows (a more northerly location relative to the U.S., where seasonal depression is more severe), and a reverse seasonality in fund flows for Australia (where the seasons are reversed).
Geographic Momentum – SSRN
Using geographic segment disclosures by U.S. multinational companies, I find that stock prices do not promptly incorporate information regarding changes in foreign market conditions, which in turn generates return predictability in the cross-section of firms with foreign operations. A simple trading strategy that exploits geographic information yields risk adjusted return of 139 basis points per month, or 16.68% per year.
Markets Change Every Day: Evidence from the Memory of Trade Direction – SSRN
HERDING & SEGMENTATION
Institutional Herding in Stock Markets: Empirical Evidence from French Mutual Funds – SSRN
We show that LSV herding amounts to 6.5% while FHW herding is about 2.5 times stronger. We observe that herding is stronger in small than in medium and large capitalization firms. Herding is also more severe among foreign than among UE-15 or French stocks. Moreover, French mutual funds are shown to partially use positive feedback strategies. Finally, we obtain that sell-herding have a destabilizing impact on stock prices.
Household Stock Market Beliefs and Learning – NBER
People with higher lifetime earnings, higher education, higher cognitive abilities, defined contribution as opposed to defined benefit pension plans, for example, possess beliefs that are considerably closer to what historical time series would imply.
Investing for the Long Run – SSRN
Long-horizon investors have an edge. They can ride out short-term fluctuations in risk premiums, profit from periods of elevated risk aversions and short-term mispricing, and they can pursue illiquid investment opportunities
Long-horizon investors have an edge. They can ride out short-term fluctuations in risk premiums, profit from periods of elevated risk aversions and short-term mispricing, and they can pursue illiquid investment opportunities
Information Content in Small and Large Trades – SSRN
we find that although for the majority of securities information contents in small and large trades are similar, the average PIN for small trades is significantly higher than that in large trades. We also find that trading volume and institutional trading are the primary determinants of information content in small and large trades respectively but not of both.
We find that herding present in the up-market, high trading volume, and high and low trading volatility states. We also find that past CSAD and CSAD volatility affect herding. Besides, herding is not related to size when past CSAD and CSAD volatility are included. Our results show that investors do not herd on SMB, HML and WML factors, which is consistent with the literature.
Whose Herding moves the price? – baiconference.org (pdf)
These evidences suggest that institutional investor’s herding is more fundamentals / information-driven. On the contrary, individual investors act as contrarian. They buy (sell) intensively after negative (positive) return.
Herding in trading by amateur and professional investors – Draft (word doc)
We find herding tendencies among both types of investors and that this tendency is higher for amateurs. It is shown that herding is affected by both types of factors: it is a decreasing function of the size of the firm, and an increasing function of its risk. Idiosyncratic risk tends to positively affect herding but this effect is significantly lower for professionals. Systematic risk however influences positively only the herding of professionals.
Herding and information based trading – Journal of Empirical Finance (pdf)
In general, our analyses agree with the existing literature that herding tends to be more prevalent with small stocks and in economic downturns and that investors are more likely to herd when selling rather than buying stocks. Most importantly, our results reveal the existence of informational cascades, which highlights the crucial role played by so-called fashion leaders, especially when more informed investors trade with “noise”.