Narrow Results

This study analyzes, prices and validates global macro strategies in the real estate investment trust (REIT) industry. For REITs, global macro strategies lead to a risk-alpha conundrum, which is rare in this industry. That is, global macro strategies are different in the REIT industry and therefore, global macro within the REIT industry needs a unique and different understanding.

Motion sickness (MS) is a common experience of travelers. To provide insights into brain dynamics associated with MS, this study recruited 19 subjects to participate in an electroencephalogram (EEG) experiment in a virtual-reality driving environment. When riding on consecutive winding roads, subjects experienced postural instability and sensory conflict between visual and vestibular stimuli. Meanwhile, subjects rated their level of MS on a six-point scale. Independent component analysis (ICA) was used to separate the filtered EEG signals into maximally temporally independent components (ICs). Then, reduced logarithmic spectra of ICs of interest, using principal component analysis, were decomposed by ICA again to find spectrally fixed and temporally independent modulators (IMs). Results demonstrated that a higher degree of MS accompanied increased activation of alpha ($r=0.421$) and gamma ($r=0.478$) IMs across remote-independent brain processes, covering motor, parietal and occipital areas. This co-modulatory spectral change in alpha and gamma bands revealed the neurophysiological demand to regulate conflicts among multi-modal sensory systems during MS.

Impulsivity is a multidimensional construct often associated with unfavorable outcomes. Previous studies have implicated several electroencephalography (EEG) indices to impulsiveness, but results are heterogeneous and inconsistent. Using a data-driven approach, we identified EEG power features for the prediction of self-reported impulsiveness. To this end, EEG signals of 56 individuals (18 low impulsive, 20 intermediate impulsive, 18 high impulsive) were recorded during a risk-taking task. Extracted EEG power features from 62 electrodes were fed into various machine learning classifiers to identify the most relevant band. Robustness of the classifier was varied by stratified $k$-fold cross validation. Alpha and beta band power showed best performance in the classification of impulsiveness (accuracy = 95.18% and 95.11%, respectively) using a random forest classifier. Subsequently, a sequential bidirectional feature selection algorithm was used to estimate the most relevant electrode sites. Results show that as little as 10 electrodes are sufficient to reliably classify impulsiveness using alpha band power ($f$-measure = 94.50%). Finally, the Shapley Additive exPlanations (SHAP) analysis approach was employed to reveal the individual EEG features that contributed most to the model’s output. Results indicate that frontal as well as posterior midline alpha power seems to be of most importance for the classification of impulsiveness.

The present report describes the dynamic foundations of long-standing experimental work in the field of oscillatory dynamics in the human and animal brain. It aims to show the role of multiple oscillations in the integrative brain function, memory, and complex perception by a recently introduced conceptional framework: the super-synergy in the whole brain. Results of recent experiments related to the percept of the grandmother-face support our concept of super-synergy in the whole brain in order to explain manifestation of Gestalts and Memory-Stages. This report may also provide new research avenues in macrodynamics of the brain.

In the present work, we provide new arguments and data indicating that early ERP components are generated at least in part by evoked theta and alpha oscillations. We proceed from the general hypothesis, originally proposed by Erol Basar that ERP's are generated by a superposition of evoked oscillations with different frequencies. Based on findings about event-related desynchronization/synchronization (ERD/ERS), we analyze the following specific hypotheses. If evoked theta and alpha oscillations contribute to the generation of ERP components and if their functional role, type of reactivity and frequency specificity are similar to those of event-related oscillations (measured by ERD/ERS), we expect (i) to see the same functional relationship between frequency and cognitive processes, (ii) the same type of "reactivity" and a (iii) dependency of latency measures of evoked components on IAF. By reviewing respective data, we demonstrate that similar to research about event-related oscillations, evoked alpha reflects attention, whereas evoked theta reflects working memory processes. Furthermore, it was found that individual alpha frequency (IAF) has a significant influence on P1 latency in particular. For a better understanding of these findings, we outline a new theoretical framework. We assume that the P1–N1 complex is generated by an interplay between the synchronous activation of three neuronal network systems, a working memory, attentional, and semantic memory system, each operating with a different frequency, the first in the theta (about 6 Hz), the second in the lower alpha (about 8 Hz) and the third in the upper alpha (about 12 Hz) frequency range. The implications of this theoretical framework are discussed by reviewing research using phase sensitive measures to analyze "local" and "large scale" integration processes between different neural networks.

The decades of 1990s and 2000s in Japan are periods of economic stagnation and price deflation or the ‘lost 20’. Stock market has been anemic while the bond market has underperformed most developed markets. Hedge Funds (HFs) have played an extremely important role over the last two decades in Japan by forcing firms to increase distributions and making changes required to increase firm value. This paper looks at the performance of HFs in Japan using survivor bias free Eurkeahedge HF Index from January 2000 to September 2018 and compares them to Japanese stocks, bonds, and real estate. We find that HFs have significantly outperformed stocks and bonds but underperformed real estate. However, risk-adjusted performance (Sharpe, Sortino, and Omega Ratios) of HFs was much better compared to all other asset classes. HFs also had the smallest maximum drawdown, the shortest recovery period and have created the most wealth. HFs also had significantly positive alpha. Overall, results indicate that HFs in Japan have created lot of value compared to Japanese stocks and bonds.

This paper studied the differences of gender and left/right-handed groups from a neuroscience perspective through task-related power of alpha power changes during the generation of creative ideas. Aiming to investigate the effects of the differences, it will help understand the specific neural processes for different genders and left/right-handed groups. We used B-Alert X10^®; electroencephalography (EEG) system, computed for left and right hemispheres, to determine if EEG metrics differentiated between the gender and left/right-handed groups. This study assessed EEG power spectral density (PSD) while 17 healthy participants worked on the alternative uses (AU) task. The results showed that (1) the creativity level has no relations with the gender; there is no obvious difference between males and females on the process of creative idea generation. (2) The creativity level is high related to the cultivation of innovative ability. There is obvious higher alpha power changes in posterior regions of the right hemisphere compared to the left hemisphere of the brain for high original group, and a stronger task-related alpha synchronization showed in the right hemisphere than that in the left one for the low original group. (3) There is comparatively lower alpha power in parietal region in the left hemisphere than that in the right one for the left-handed participants, and higher alpha power in the frontal region for the left-handed and in parietal region for right-handed participants. The comparison among different genders and left/right-handed participants can help us understand more about the creative thinking manifested in the human brain.

Over the last 20 years, investors have come to approach investment decision-making in an increasingly mechanical manner. Optimizers are filled up with historical return data and the “optimal” portfolio follows almost automatically. In this paper, we argue that such an approach can be extremely dangerous, especially when alternative investments such as hedge funds are involved. Proper hedge fund investing requires a much more elaborate approach to investment decision-making than currently in use by most investors. The available data on hedge funds should not be taken at face value, but should first be corrected for various types of biases and autocorrelation. Tools like mean–variance analysis and the Sharpe ratio that many investors have become accustomed to over the years are no longer appropriate when hedge funds are involved as they concentrate on the good part while completely skipping over the bad part of the hedge fund story. Investors also have to find a way to figure in the long lock-up and advance notice periods, which makes hedge fund investments highly illiquid. In addition, investors will have to give weight to the fact that without more insight in the way in which hedge funds generate their returns it is very hard to say something sensible about hedge funds' future longer-run performance. The tools to accomplish this formally are not all there yet, meaning that more than ever investors will have to rely on common sense and doing their homework.

The hedge fund industry has grown phenomenally in terms of assets under management and number of hedge funds in the past 15 years. Hedge funds invest in all capital markets, including emerging markets, and have started to raise capital from the world stock markets. We believe the hedge fund industry has reached another phase in its life cycle.

In this chapter, we focus on the performance of hedge funds and the replication of it. We begin with a review of the extant literature on the analysis of the historical performance of hedge funds. Earlier test results on the performance of hedge funds are in general favorable, suggesting excess returns for hedge funds and positive alphas to hedge fund managers. However, studies show that performance persistence was found only in underperformed hedge funds and not in over-performed hedge funds. This suggests that hedge fund performance and hence alpha determination need to be re-evaluated given the hefty performance fees charged by hedge funds. We therefore discuss the survivor, stale-price, and backfill biases that may have caused the measurement error in hedge fund performance and alpha determination. Moreover, we discuss another area of concern in hedge fund performance evaluation — benchmarking. We introduce a sample of non-investable and investable hedge fund indices and discuss their use as benchmarks as the paradigm of performance evaluation has gradually shifted from an absolute-return basis to a relative-return basis. Finally, we introduce several approaches to replicating hedge fund returns.

This chapter studies how a well-defined environmental concerns factor influences the equity performance of US energy firms and whether or not this influence differs for renewable and non-renewable energy firms. We construct the environmental sentiment factor and seven sub-group environment-related risk factors using the Dynamic Factor Model econometric method. Then, we estimate stock abnormal returns from well-established asset pricing models to measure firm equity performance. We use an unbalanced panel of 448 US energy firms at the monthly frequency from January 2004 to October 2016. First, we find that all US energy companies show negative abnormal returns in the sampled period, that is the actual returns were lower than expected. Renewable energy firms had higher company value, since renewable energy stocks had lower abnormal returns. We also find that the abnormal returns of renewable energy stocks mainly come from environment regulation risks, while the abnormal returns of non-renewable energy stocks are more sensitive to the measure of public environmental concerns and extreme weather conditions. Consequently, we find that holding the level of environmental related risks constant, non-renewable energy stocks command relative lower abnormal returns.

In this chapter, we look at how estimation is performed on an important finance valuation model to address the problem of risk pricing and also to apply the model to evaluate investment performances.

A key foundation of finance is the tradeoff between risk and return. Assets returning higher ex-ante (or conditional expected) return should also bear higher ex-ante risk, and vice versa. Finance theory and models seek to establish the exact relationship between this return and risk. One of the earliest finance models is the Sharpe-Lintner capital asset pricing model (CAPM) that links expected return to only systematic risk. The unsystematic or residual risk can be diversified away and is, therefore, not priced. Systematic risk in the form of market risk is priced. For any asset, this market risk is reflected in the sensitivity of the asset return to the market factor movement. Higher sensitivity implies a higher beta or systematic risk. When we refer to risk, it is usually quantified in terms of standard deviation instead of variance since the former is expressed in terms of the same unit as the underlying variable. We shall begin our study of CAPM with the statistical market model.