Multi-year Return Research Articles

Meditation training and enduring changes in psychological functioning: a 7-year longitudinal study

ABSTRACT Considerable research has now documented the beneficial effects of mindfulness-based practices on psychological functioning. Less is known about the long-term durability of such changes following formal meditation training. In a sample of adults (N = 67, 52% female, ages 22–70 years), we examined changes in 16 measures of psychological adaptive functioning across a 3-month residential meditation intervention and across a subsequent 7-year period. We observed general training-related improvements followed by multi-year returns toward pre-training levels. However, beneficial changes in two personality attributes (agreeableness and neuroticism) were of moderate effect size (d = 0.51 and d = 0.45, respectively) and were retained across the 7-year follow-up. We further found that individual variation in changes was represented by three latent attributes: (Changes related to) Mindful Well-being, Resilient Extraversion, and Self-Compassionate Openness. These results suggest that intensive meditation training is associated with improved adaptive functioning and enduring changes in aspects of personality.

Bayesian based updating of hull and mooring structure parameters of semi-submersible platforms using monitoring data

The parameters of hull structures and mooring systems of offshore platforms will change during their long service lives at sea. Therefore, if assessments of platforms are based on the original design parameters, they will become less accurate over time. As sensor technology and data analysis methods have advanced, it has become possible to update hull body and mooring parameters based on field monitoring data. However, simultaneously updating the many parameters of the hull and mooring system of semi-submersible platform is complex and computationally demanding, and underwater structure monitoring is difficult, therefore this paper proposes a parameter updating method based on floating platform response monitoring data. First, the method conducts a sensitivity analysis based on the hydrodynamic theory of semi-submersible platforms and the parameters to be updated and an objective function for the semi-submersible platform is determined. Then, updating methods for hull structure parameters and mooring parameters are established using Bayesian theorem and surrogate models. Finally, the parameters of hull and mooring structure of a semi-submersible platform in the South China Sea were updated using monitoring data and the results assessed. The proposed method effectively reduced the statistical error between measured and simulated 6-DOF motion values by more than 20% compared with the model before updating. The updated platform model was used to re-evaluate the hydrodynamic responses of the platform under multi-year return period environmental loads. The evaluation showed that during the multi-year service of the platform, the displacement of the platform had decreased by 10%, which was the main reason for the changes in wave frequency responses of the platform.

Increased populations will be exposed to the dangerous precipitation extremes across China in the future

This study investigates the future changes in dangerous precipitation extremes with multiyear return periods and the population exposure across China at the 1.5–4°C warming levels via the latest simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results show that the simulations project more frequent dangerous precipitation extremes across China under the warmer climate regardless of the shared socioeconomic pathway (SSP), with more substantial occurrence increases at the high warming levels. Consequently, the population exposure to dangerous precipitation extremes is anticipated to increase persistently in most regions of China except for some parts of northwestern China and the Tibetan Plateau. For the events estimated to occur once every 10 years, the 1.5, 2.0, 3.0, and 4.0°C warming relative to the current state will result in approximately 29.9, 47.8, 72.9, and 84.3% increases in the aggregated population exposure over China under the SSP5-8.5 scenario, respectively. However, the exposure change is somewhat subject to the emission scenarios, with larger proportional increases under the regional-rivalry scenario of SSP3-7.0 compared to the fossil-fueled development scenario of SSP5-8.5. The increased exposure under all the scenarios is primarily attributed to the climate change factor, and the population change and their interaction component make a minor contribution. Furthermore, compared to the 2.0°C warmer climate, the 0.5°C less warming under the 1.5°C climate can trigger remarkable decreases of 16.5–20.8% for exposure to once-in-decade events over China. Additionally, the changes in the occurrence and exposure are much larger for the rarer events. Hence, our analyses indicate that limiting warming to 1.5°C is beneficial to reducing the impacts associated with precipitation extremes across China, particularly for the more extreme events.

The Stock-Bond Correlation

Investors rely on the stock-bond correlation for a variety of tasks, such as forming optimal portfolios, designing hedging strategies, and assessing risk. Most investors estimate the stock–bond correlation simply by extrapolating the historical correlation of monthly returns; they assume that this correlation best characterizes the correlation of future annual or multiyear returns, but this approach is decidedly unreliable. The authors introduce four innovations for generating a reliable prediction of the stock-bond correlation. First, they show how to represent the correlation of single-period cumulative stock and bond returns in a way that captures how the returns drift during the period. Second, they identify fundamental predictors of the stock-bond correlation. Third, they model the stock–bond correlation as a function of the path of some fundamental predictors rather than single observations. Finally, they censor their sample to include only relevant observations, in which relevance has a precise mathematical definition. <b>TOPICS:</b>Portfolio management/multi-asset allocation, risk management, statistical methods <b>Key Findings</b> ▪ The stock-bond correlation is a critical component of many investment activities, such as forming optimal portfolios, designing hedging strategies, and assessing risk. ▪ Most investors estimate the correlation of longer-interval returns by extrapolating the correlation of past shorter-interval returns, but this approach is decidedly unreliable. ▪ By applying recent advances in quantitative methods, it is possible to generate reliable predictions of the correlation of longer-horizon stock and bond returns.

Use of discrete molting grounds by migrant passerines undergoing prebasic molt in southern Quebec

We documented prebasic flight feather molt of passerines captured in fall 2013 and 2015 at McGill Bird Observatory (MBO) in Montreal, Quebec. We recorded active molt of flight feathers (remiges) in 11 species that do not breed on site. Flight feather molt was frequent among Swainson's Thrush (Catharus ustulatus; 64% of adults), Tennessee Warbler (Oreothlypis peregrina; 57%), Nashville Warbler (Leiothlypis ruficapilla; 67%), and Yellow-rumped Warbler (Setophaga coronata; 44%), and was observed less frequently in other species. The minimum stopover length of molting individuals was on average 8 times longer than that of non-molting individuals of the same species. Among Swainson's Thrushes and Yellow-rumped Warblers, far more females were undergoing molt than males, whereas for Tennessee Warblers molt was slightly more frequent among males. Frequency of molt was similar between years for most species but not Yellow-rumped Warbler, with 59% of adults captured in 2013 molting compared to none in 2015. We also observed molting site fidelity with multiyear returns of Tennessee and Nashville warblers. The use of separate breeding and molting sites is not well understood among eastern North American species, and with recent studies highlighting the importance of molt locations in western North America, we demonstrate the value in additional study of the use of discrete molt locations in the East.

Estimation of snow and ice accretion for the design of high-pressure transmission lines with multi-year return periods (case study: western highlands of Mazandaran)

یکی از مشکلات شرکت برق منطقه‌ای در مناطق سردسیر، یخ‌زدگی و نشست برف بر روی سیم‌های خطوط انتقال نیرو می‌باشد. پهنه‌بندی آب و هوایی و ارائه شرایط بارگذاری متناسب با منطقه جهت طراحی مکانیکی خطوط حائز اهمیت بوده و انتخاب صحیح این شرایط به‌لحاظ فنی و اقتصادی تاثیر بسزایی در طراحی پروژه خواهد داشت. نقشه بار برف و یخ که در گذشته توسط وزارت نیرو تدوین گردیده، کارایی و نوآوری لازم را نداشته و یا اینکه برای بسیاری از مناطق توسعه یافته جدید شهری و روستایی بازنگری نشده است در این تحقیق پارامتر آماری ضخامت یخ با دوره‌های بازگشت چند ساله، که ازجمله بارهای موثر در بارگذاری دکل انتقال نیرو هستند با استفاده از داده‌های هواشناسی (سال 2010 تا 2018 میلادی) سه ایستگاه کوهستانی بلده، کجور و سیاه بیشه در غرب مازندران مورد ارزیابی قرار گرفتند و در نهایت مقادیر اسمی جدید با دوره بازگشت‌های مختلف پیشنهاد گردیده است. نتایج نشان داد که برای طراحی خطوط انتقال نیرو با دوره بازگشت کمتر از 50 سال، منطقه بلده و کجور جزو مناطق سنگین و با دوره بازگشت بیشتر از 50 سال جزو مناطق فوق سنگین و منطقه کجور با دوره بازگشت 50 تا 100 ساله جزو مناطق متوسط و دوره بازگشت بیشتر از 100 سال جزو مناطق سنگین (مطابق با آیین نامه مشانیر) می‌باشد.

The Stock-Bond Correlation

Investors rely on the stock-bond correlation for a variety of tasks, such as forming optimal portfolios, designing hedging strategies, and assessing risk. Most investors estimate the stock-bond correlation simply by extrapolating the historical correlation of monthly returns and assume that this correlation best characterizes the correlation of future, annual or multi-year returns, but this approach is decidedly unreliable. The authors introduce four innovations for generating a reliable prediction of the stock-bond correlation. First, they show how to represent the correlation of single period cumulative stock and bond returns in a way that captures how the returns drift during the period. Second, they identify fundamental predictors of the stock-bond correlation. Third, they model the stock-bond correlation as a function of the path of some fundamental predictors rather than single observations. And fourth, they censor their sample to include only relevant observations, in which relevance has a precise mathematical definition.

Current models for strength and dynamic design of underwater structures

As one of the key met-ocean environmental loads affecting underwater structural design, current is closely related to the failure process of underwater structures such as moorings and risers. In marine structural analysis, a current model depends mainly on its speed profile corresponding to the design criterion. The conventional model of the current speed extreme (CSE) profile ignores the interlayer correlation and renders the current profile model conservative. This study is focused on the analysis of a current profile model aimed at the strength and dynamic design of underwater structures with special consideration of the interlayer correlation based on the long-term prototype measured data in the “Liuhua” oil field in the South China Sea. For the current model related to strength design, an empirical orthogonal function (EOF) decomposition is first utilized for dimensional reduction. Then, the multi-dimensional extreme-value problem is converted into an inverse reliability problem. The inverse second-order reliability method (ISORM) is applied to update the reliability index for an optimal solution. The global energy for different multi-year return periods is treated as objective functions to determine the extreme-value profiles corresponding to strength design of underwater structures. For the current model related to dynamic design, a statistical classification method based on the clustering large application algorithm (CLARA) is performed to obtain a multi-profile current model by using a large long-term prototype monitoring current dataset. An average silhouette width is found and used to determine the final classification number. Finally, a multi-profile current model is established for the underwater structural dynamic design. The presented current models can be directly applied as the current loads for underwater structural design in the South China Sea. Moreover, besides the possibility to extend the proposed method to other ocean areas, it can also be used for developing models of wind, acoustic, and other field variables.

Wave height statistical characteristic analysis

When exploring the temporal and spatial change law of ocean environment, the most common method used is using smaller-scale observed data to derive the change law for a larger-scale system. For instance, using 30-year observation data to derive 100-year return period design wave height. Therefore, the study of inherent self-similarity in ocean hydrological elements becomes increasingly important to the study of multi-year return period design wave height derivation. In this paper, we introduced multifractal to analyze the statistical characteristics of wave height series data observed from oceanic hydrological station. An improvement is made to address the existing problems of the multifractal detrended fluctuation analysis (MF-DFA) method, where trend function showed a discontinuity between intervals. The improved MF-DFA method is based on signal mode decomposition, replacing piecewise polynomial fitting used in the original method. We applied the proposed method to the wave height data collected at Chaolian Island, Shandong, China, from 1963 to 1989 and was able to conclude the wave height sequence presented weak multi-fractality. This result provided strong support to the past research on the derivation of multi-year return period design wave height with observed data. Moreover, the new method proposed in this paper also provides a new perspective to explore the intrinsic characteristic of data.

Current profile analysis and extreme value prediction in the LH11-1 oil field of the South China Sea based on prototype monitoring

Current is a key ocean-environmental factor and exhibits strong non-stationary random characteristics. The complexities of current modeling present significant challenges for deep sea oil exploitation. The multiyear return period extreme current model is one of the key factors for the reliable design of marine structures. Recently, due to limitations of design specifications and guidelines, improved methods to predict extreme values for the South China Sea based on prototype monitoring are required. In contrast to the traditional extreme value analytical method, the newly developed Average Conditional Exceedance Rate (ACER) method is robust and shows good accuracy for estimations of ocean environmental loading. The method offers good reliability for short-term prototype monitoring data. This study performs multiyear return period extreme value prediction of the current profile based on prototype monitoring data collected in the Liuhua (LH11-1) oil field that was recorded by an in-situ monitoring system. The 1-year and 10-year return period current velocity design indexes were obtained using the ACER method. The present current velocity profiles of multi-year return periods were compared with two design current load indexes of two floating platforms in Liuhua area. The consistency with comparison to TLP platform design indexes shows that the ACER method provides the accuracy and flexibility of the results needed in the construction of current load models in the South China Sea. These results could provide the basis and reference for the design of offshore structure.

A method of estimating high elevation of multi-year return periods

A method of estimating high elevation of multi-year return periods

Validation and application of multi-source altimeter wave data in China’s offshore areas

Studies of offshore wave climate based on satellite altimeter significant wave height (SWH) have widespread application value. This study used a calibrated multi-altimeter SWH dataset to investigate the wave climate characteristics in the offshore areas of China. First, the SWH measurements from 28 buoys located in China’s coastal seas were compared with an Ifremer calibrated altimeter SWH dataset. Although the altimeter dataset tended to slightly overestimate SWH, it was in good agreement with the in situ data in general. The correlation coefficient was 0.97 and the root-mean-square (RMS) of differences was 0.30 m. The validation results showed a slight difference in different areas. The correlation coefficient was the maximum (0.97) and the RMS difference was the minimum (0.28 m) in the area from the East China Sea to the north of the South China Sea. The correlation coefficient of approximately 0.95 was relatively low in the seas off the Changjiang (Yangtze River) Estuary. The RMS difference was the maximum (0.32 m) in the seas off the Changjiang Estuary and was 0.30 m in the Bohai Sea and the Yellow Sea. Based on the above evidence, it is confirmed that the multialtimeter wave data are reliable in China’s offshore areas. Then, the characteristics of the wave field, including the frequency of huge waves and the multi-year return SWH in China’s offshore seas were analyzed using the 23-year altimeter wave dataset. The 23-year mean SWH generally ranged from 0.6–2.2 m. The greatest SWH appeared in the southeast of the China East Sea, the Taiwan Strait and the northeast of the South China Sea. Obvious seasonal variation of SWH was found in most areas; SWH was greater in winter and autumn than in summer and spring. Extreme waves greater than 4 m in height mainly occurred in the following areas: the southeast of the East China Sea, the south of the Ryukyu Islands, the east of Taiwan-Luzon Island, and the Dongsha Islands extending to the Zhongsha Islands, and the frequency of extreme waves was 3%–6%. Extreme waves occurred most frequently in autumn and rarely in spring. The 100-year return wave height was greatest from the northwest Pacific seas extending to southeast of the Ryukyu Islands (9–12 m), and the northeast of the South China Sea and the East China Sea had the second largest wave heights (7–11 m). For inshore areas, the 100-year return wave height was the greatest in the waters off the east coast of Guangdong Province and the south coast of Zhejiang Province (7–8 m), whereas it was at a minimum in the area from the Changjiang Estuary to the Bohai Sea (4–6 m). An investigation of sampling effects indicates that when using the 1°×1°grid dataset, although the combination of nine altimeters obviously enhanced the time and space coverage of sampling, the accuracy of statistical results, particularly extreme values obtained from the dataset, still suffered from undersampling problems because the time sampling percent in each 1°×1°grid cell was always less than 33%.

Practical Applications of Bad Habits and Good Practice

Investors may think they are well diversified and well positioned to maximize returns, but do they understand the systematic behavioral biases that may be working against them? These biases result in bad investment habits, such as multi-year return chasing, underdiversifying and seeking comfort in investment selection. “We need to make investors behave better mainly for themselves but also for us (as managers),” says Antti Ilmanen , Principal at AQR Capital Management , speaking about his article, Bad Habits and Good Practices in The Journal of Portfolio Management . Ilmanen is co-author with David Kabiller , Founding Principal of AQR Capital Management, and Amit Goyal , Professor at the University of Lausanne in Switzerland.

Are Korean Industry-Sorted Portfolios Mean Reverting?

(ProQuest: ... denotes formulae omitted.)I. INTRODUCTIONThis paper tests the weak-form efficient market hypothesis for Korean stock markets. The weak-form efficient market hypothesis implies that stock returns are not predictable using past returns. A well-known alternative to this hypothesis is the mean reversion hypothesis stating that stock prices tend to return a trend path in the long run. In empirical finance, many studies test the efficient market hypothesis, using various empirical methods and data sets, and report mixed evidence on the predictability of stock in particular for mean reversion in long horizons.For example, Fama and French (1988, p. 538) report that 25-45 percent of the variation of 3-to 5-year stock returns is predictable from past returns, using monthly data of US stock prices in the 1926-85 period. Porteba and Summers (1988) find similar results of mean reversion over long horizons.1 In contrast, Richardson and Stock (1989) show that the univariate variance ratio tests employed in previous studies are not consistent when the return horizon is large relative to sample size and generate negative biases. Once these biases are corrected, they find little evidence of mean reversion even in long horizons in contrast to Fama and French (1988) and Porteba and Summers (1988).2 As summarized in Campbell et al. (1997), one difficulty in using long horizon returns (multi-year returns) for testing efficient market hypothesis and for detecting mean reversion is the very small sample size: standard econometric tests generally lack of power to reject the null hypothesis that stock prices follow a random walk process against the alternative of mean reversion.In this paper, we use panels of KOSPI industry group stock portfolio indexes for the period of 1988-2016 and of KOSDAQ industry group stock portfolio indexes for the period of 2001-2016. The use of panels mitigates the small sample size problems because they contain additional information in cross-industry variations. The idea of using a panel data set in testing the predictability of stock prices is from Balvers et al. (2000) who examine mean reversion using a panel of stock price indexes for 18 countries with well-developed capital markets (16 OECD countries plus Hong Kong and Singapore) in the period of 1969-1996 and find strong evidence of mean reversion. Gropp (2004) also follow Balvers et al. (2000) and employ a panel of 16 US industry-sorted portfolios for the period of 1926-1998 and find evidence of mean reversion in industry stock price indexes. Following Fama and French (1988) and Gropp (2004), we use industry group stock portfolio indexes, rather than using size-sorted portfolios (classified by market capitalization) which have been widely used in previous studies. The reason for this selection is related to one key difference between industry-sorted portfolios and size-sorted portfolios: stocks with abnormal high or low returns tend to move across portfolios from one year to next in the latter. Therefore, if abnormal performance of stocks is caused by temporary shocks, subsequent price reversals would be missed and thus detection of mean reversion would be underestimated. On the other hand, stocks in general do not move across portfolios in the former.To test the weak-form efficient market hypothesis using the panel data sets, we use panel variance ratio tests recently developed by Moon and Velasco (2014). Variance ratio tests have been widely used to detect mean reversion in long horizon returns in various asset markets such as stock and currency markets.3 However, the use of the tests has been limited only for univariate time series. Further, as Richardson and Stock (1989) and Deo and Richardson (2003) pointed out, the univariate variance ratio tests face statistical difficulties in particular for testing long horizon returns. Recently, Moon and Velasco (2014) develop the panel variance ratio tests which resolve those statistical difficulties. …

Institutionalizing Countercyclical Investment: A Framework for Long-term Asset Owners

Do portfolio shifts by the world’s largest asset owners respond procyclically to past returns, or countercyclically to valuations? And if countercyclical investment (with both market-stabilizing and return-generating properties) is a public and private good, how might asset owners be empowered to do more of it? These two questions motivate this study. Based on analysis of representative portfolios (totaling $24 trillion) for a range of asset owners (central banks, pension funds, insurers and endowments), portfolio changes typically appear procyclical. In response, I suggest a framework aimed at jointly bolstering long-term returns and financial stability should: (i) embed governance practices to mitigate ‘multi-year return chasing;’ (ii) rebalance to benchmarks with factor exposures best suited to long-term investors; (iii) minimize principal-agent frictions; (iv) calibrate risk management to minimize long-term shortfall risk (not short-term price volatility); and (v) ensure regulatory conventions do not amplify procyclicality at the worst possible times.

Bad Habits and Good Practices

Good investing results require both good investments and good investors. This article focuses on the latter, addressing some of the habits that may hinder long-term investment performance: multi-year return chasing, under-diversification, and comfort seeking. The authors also offer some insights that may help mitigate some of these pitfalls in moving toward improved investment outcomes. <b>TOPICS:</b>Analysis of individual factors/risk premia, in markets, portfolio management/multi-asset allocation

Bond Ladders and Rolling Yield Convergence

Most investment-grade bond portfolios have stable durations and can be regarded as “duration targeted” (DT). For DT portfolios, multiyear returns converge to the starting rolling yield if the yield curve undergoes a sequence of strictly parallel shifts. The theoretical convergence horizon is one year less than twice the duration target. The laddered portfolios favored by private investors are essentially DT, and surprisingly, their convergence return coincides with the starting yield of the ladder’s “top-rung” bond.

Forward Curve Shifts and Return Convergence

Most investment-grade bond portfolios have a relatively stable duration that shapes their multiyear returns. These returns are impacted by the wide variety of yield paths that can result from sequential migrations to implied forward yield curves, accompanied by parallel trend line shifts. Under these theoretical conditions, the annualized return converges, over an investment horizon that is roughly twice the portfolio’s duration, toward a starting yield curve value whose maturity just coincides with this specific horizon. Thus, a stable duration portfolio return will converge, with surprisingly minimal dispersions, back to a yield curve value that prevailed at the outset—regardless of the direction or volatility of intervening yields. These results have important implications for multiyear return estimation and for volatility assumptions involved in asset allocation. <b>TOPICS:</b>Portfolio management/multi-asset allocation, in portfolio management, commodities

Determining the Return on Investment for Evidence‐Based Practice: An Essential Skill for All Clinicians

Determining the Return on Investment for Evidence‐Based Practice: An Essential Skill for All Clinicians

Regional Meteorological–Marine Reanalyses and Climate Change Projections

A compilation of coastal weather analyses and climate change scenarios for the future for northern Europe from various sources is presented. They contain no direct measurements but results from numerical models that have been driven either by observed data in order to achieve the best possible representation of observed past conditions or by climate change scenarios for the near future. A comparison with the limited number of observational data points to the good quality of the model data in terms of long-term statistics, such as multiyear return values of wind speed and wave heights. These model data provide a unique combination of consistent atmospheric, oceanic, sea state, and other parameters at high spatial and temporal detail, even for places and variables for which no measurements have been made. In addition, coastal scenarios for the near future complement the numerical analyses of past conditions in a consistent way. The backbones of the data are regional wind, wave, and storm surge hindcasts and...