Robust Portfolio Optimization with a Hybrid Heuristic Algorithm

Estimation errors in both the expected returns and the covariance matrix hamper the constructing of reliable portfolios within the Markowitz framework. Robust techniques that incorporate the uncertainty about the unknown parameters are suggested in the literature. We propose a modification as well as an extension of such a technique and compare both with another robust approach. In order to eliminate oversimplifications of Markowitz’ portfolio theory, we generalize the optimization framework to better emulate a more realistic investment environment. Because the adjusted optimization problem is no longer solvable with standard algorithms, we employ a hybrid heuristic to tackle this problem. Our empirical analysis is conducted with a moving time window for returns of the German stock index DAX100. The results of all three robust approaches yield more stable portfolio compositions than those of the original Markowitz framework. Moreover, the out-of-sample risk of the robust approaches is lower and less volatile while their returns are not necessarily smaller.Hybrid heuristic algorithm, Markowitz, Robust optimization, Uncertainty sets.

Development of interest and role of choice during sequential knowledge acquisition

Interest is an important motivational element for learning in the school environment. However, little research has directly addressed how interest changes over time as knowledge accumulates. To gain a better understanding of how knowledge acquisition influences intra-individual change of interest, we developed a novel paradigm in which participants gain step-by-step information about lesser known countries. After reading each piece of information, participants rated their interest in the country. Growth-curve modelling showed that interest grows during knowledge acquisition until it eventually stalls and starts to decline. We also found that the opportunity to choose information boosted the growth in interest and delayed its decline. Further analysis revealed that people disengaged from a topic (i.e. stopped accessing information about a particular country) when their interest started to decrease

Penalized Least Squares for Optimal Sparse Portfolio Selection

Markowitz portfolios often result in an unsatisfying out-of-sample performance, due to the presence of estimation errors in inputs parameters, and in extreme and unstable asset weights, especially when the number of securities is large. Recently, it has been shown that imposing a penalty on the 1-norm of the asset weights vector not only regularizes the problem, thereby improving the out-of-sample performance, but also allows to automatically select a subset of assets to invest in. Here, we propose a new, simple type of penalty that explicitly considers financial information and consider several alternative non-convex penalties, that allow to improve on the 1-norm penalization approach. Empirical results on U.S.-stock market data support the validity of the proposed penalized least squares methods in selecting portfolios with superior out-of-sample performance with respect to several state-of-art benchmarks

Meta-analysis to integrate effect sizes within a paper: possible misuse and Type-1 error inflation

In recent years an increasing number of papers have employed meta-analysis to integrate effect sizes of researchers’ own series of studies within a single paper (“internal meta-analysis”). Although this approach has the obvious advantage of obtaining narrower confidence intervals, we show that it could inadvertently inflate false-positive rates if researchers are motivated to use internal meta-analysis in order to obtain a significant overall effect. Specifically, if one decides whether to stop or continue a further replication experiment depending on the significance of the results in an internal meta-analysis, false-positive rates would increase beyond the nominal level. We conducted a set of Monte-Carlo simulations to demonstrate our argument, and provided a literature review to gauge awareness and prevalence of this issue. Furthermore, we made several recommendations when using internal meta-analysis to make a judgment on statistical significance

Cardinality versus q-Norm Constraints for Index Tracking

Index tracking aims at replicating a given benchmark with a smaller number of its constituents. Different quantitative models can be set up to determine the optimal index replicating portfolio. In this paper, we propose an alternative based on imposing a constraint on the q-norm, 0 < q < 1, of the replicating portfolios’ asset weights: the q-norm constraint regularises the problem and identifies a sparse model. Both approaches are challenging from an optimisation viewpoint due to either the presence of the cardinality constraint or a non-convex constraint on the q-norm. The problem can become even more complex when non-convex distance measures or other real-world constraints are considered. We employ a hybrid heuristic as a flexible tool to tackle both optimisation problems. The empirical analysis on real-world financial data allows to compare the two index tracking approaches. Moreover, we propose a strategy to determine the optimal number of constituents and the corresponding optimal portfolio asset weights

How Are Curiosity and Interest Different? Naive Bayes Classification of People's Beliefs

Researchers studying curiosity and interest note a lack of consensus in whether and how these important motivations for learning are distinct. Empirical attempts to distinguish them are impeded by this lack of conceptual clarity. Following a recent proposal that curiosity and interest are folk concepts, we sought to determine a non-expert consensus view on their distinction using machine learning methods. In Study 1, we demonstrate that there is a consensus in how they are distinguished, by training a Naïve Bayes classification algorithm to distinguish between free-text definitions of curiosity and interest (n = 396 definitions) and using cross-validation to test the classifier on two sets of data (main n = 196; additional n = 218). In Study 2, we demonstrate that the non-expert consensus is shared by experts and can plausibly underscore future empirical work, as the classifier accurately distinguished definitions provided by experts who study curiosity and interest (n = 92). Our results suggest a shared consensus on the distinction between curiosity and interest, providing a basis for much-needed conceptual clarity facilitating future empirical work. This consensus distinguishes curiosity as more active information seeking directed towards specific and previously unknown information. In contrast, interest is more pleasurable, in-depth, less momentary information seeking towards information in domains where people already have knowledge. However, we note that there are similarities between the concepts, as they are both motivating, involve feelings of wanting, and relate to knowledge acquisition

The role of interest in memory for trivia questions: an investigation with a large-scale database

The importance of interest for memory performance has been established in previous studies. One way to induce interest in experiments is to use trivia questions. However, previous studies have used only a limited number of trivia questions and these questions differ substantially across studies, making it difficult to ensure the comparability and generalizability of the findings. Most of these studies also have not differentiated between interest in the trivia question itself and interest in the corresponding answer. To address these issues, the current study established a normative database for 244 trivia questions with a large sample (N = 1,498) and examined how pre-answer interest (i.e., interest in the question) and post-answer interest (i.e., interest in the answer) relate to learning performance. Participants were presented with trivia questions, asked to provide their best guess for the answer, rated their confidence in the guess, and indicated their interest in learning the true answer. Following the presentation of the answer, participants indicated their post-answer interest. One week later, participants were given a memory test on the questions. A multilevel structural equation model revealed that the positive relationship between pre-answer interest and memory was mostly mediated by post-answer interest (i.e., interest in the questions’ answer). Confidence had both a direct and a mediated effect (over interest) on memory. These results provide a more fine-grained analysis of how interest can fuel learning

Spaced Retrieval Practice: Can Restudying Trump Retrieval?

We investigated spaced retrieval and restudying in 3 preregistered, online experiments. In all experiments, participants studied 40 Swahili–English word pair translations during an initial study phase, restudied intact pairs or attempted to retrieve the English words to Swahili cues twice in three spaced practice sessions, and then completed a final cued-recall test. All 5 sessions were separated by 2 days. In Experiment 1, we manipulated the response format during retrieval (covert vs. overt) and the test list structure (blocked vs. intermixed covert/overt retrieval trials). A memory rating was required on all trials (retrieval: “Was your answer correct?”; restudy: “Would you have remembered the correct translation?”). Response format had no effect on recall, but surprisingly, final test performance for restudied items exceeded both the overt and covert retrieval conditions. In Experiment 2, we manipulated the requirement to make a memory rating. If a memory rating was required, final test restudy performance exceeded retrieval performance, replicating Experiment 1. However, the pattern was descriptively reversed if no rating was required. In Experiment 3, the memory rating was removed altogether, and we examined recall performance for items restudied versus retrieved once, twice, or thrice. Performance improved with practice, and retrieval performance exceeded restudy performance in all conditions. The reversal of the typical retrieval practice effect observed in Experiments 1 and 2 is discussed in terms of theories of reactivity of memory judgments

How are curiosity and interest different? Naïve Bayes Classification of people's beliefs

Researchers studying curiosity and interest note a lack of consensus in whether and how these important motivations for learning are distinct. Empirical attempts to distinguish them are impeded by this lack of conceptual clarity. Following a recent proposal that curiosity and interest are folk concepts, we sought to determine a non-expert consensus view on their distinction using machine learning methods. In Study 1, we demonstrate that there is a consensus in how they are distinguished, by training a Naïve Bayes classification algorithm to distinguish between free-text definitions of curiosity and interest (n = 396 definitions) and using cross-validation to test the classifier on two sets of data (main n = 196; additional n = 218). In Study 2, we demonstrate that the non-expert consensus is shared by experts and can plausibly underscore future empirical work, as the classifier accurately distinguished definitions provided by experts who study curiosity and interest (n = 92). Our results suggest a shared consensus on the distinction between curiosity and interest, providing a basis for much-needed conceptual clarity facilitating future empirical work. This consensus distinguishes curiosity as more active information seeking directed towards specific and previously unknown information. In contrast, interest is more pleasurable, in-depth, less momentary information seeking towards information in domains where people already have knowledge. However, we note that there are similarities between the concepts, as they are both motivating, involve feelings of wanting, and relate to knowledge-acquisition

People’s naïve belief about curiosity and interest: a qualitative study

The purpose of this study was to critically examine how people perceive the definitions, differences and similarities of interest and curiosity, and address the subjective boundaries between interest and curiosity. We used a qualitative research approach given the research questions and the goal to develop an in-depth understanding of people’s meaning of interest and curiosity. We used data from a sample of 126 U.S. adults (48.5% male) recruited through Amazon’s Mechanical Turk (Mage = 40.7, SDage = 11.7). Semi-structured questions were used and thematic analysis was applied. The results showed two themes relating to differences between curiosity and interest; active/stable feelings and certainty/uncertainty. Curiosity was defined as an active feeling (more specifically a first, fleeting feeling) and a child-like emotion that often involves a strong urge to think actively and differently, whereas interest was described as stable and sustainable feeling, which is characterized as involved engagement and personal preferences (e.g., hobbies). In addition, participants related curiosity to uncertainty, e.g., trying new things and risk-taking behaviour. Certainty, on the other hand, was deemed as an important component in the definition of interest, which helps individuals acquire deep knowledge. Both curiosity and interest were reported to be innate and positive feelings that support motivation and knowledge-seeking during the learning process