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This paper improves the understanding of heuristics in the choice of mutual funds. We analyze the effect of price‐quality relationship and anchors as heuristics on the evaluation of the willingness‐to‐invest. We perform two studies with graduate students who possess a medium–high level of financial literacy in Chile. In the first study, we find that willingness‐to‐invest increases (decreases) when subjects observe (do not observe) in the market a positive relationship between expense ratios (price) and service quality. In the second study, in the presence of an anchor, the reference price obtained by individuals from the market information loses relevance and the anchor effect predominates. Our results confirm that participants, as consumers of financial services, apply heuristics as groundwork for their investment decisions. These heuristics as a decision making process are useful but do not always lead to the choice of the lowest cost alternative with the highest possible service quality.

This paper studies the topology of the Chilean mutual fund industry using networks methods. With the physical positions of the local equity portfolios managed during 2003.01-2017.4, we analyze their connectivity structure in both the mutual funds’ bipartite network and their one-mode projection. We estimate network measures to examine the potential effects on the topology arising from changes in the industrial environment and changes in the mutual funds’ investment strategies in their overlapped portfolios. Our main results show that changes in the bipartite network and its one-mode projection are correlated with variables related to funds’ investment strategies and with industry-specific variables. In consequence, these elements are a new potential of disturbance in the financial network conformed by stocks and mutual funds. We contribute to the existing literature, improving the understanding of the aggregate behavior of a financial sector which despite its economic importance has attracted little attention from a systemic risk perspective.

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This investigation connects two crucial economic and financial fields, financial networks, and forecasting. From the financial network’s perspective, it is possible to enhance forecasting tools, since econometrics does not incorporate into standard economic models, second-order effects, nonlinearities, and systemic structural factors. Using daily returns from July 2001 to September 2019, we used minimum spanning tree and planar maximally filtered graph techniques to forecast the stock market realized volatility of 26 countries. We test the predictive power of our core models versus forecasting benchmarks models in and out of the sample. Our results show that the length of the minimum spanning tree is relevant to forecast volatility in European and Asian stock markets, improving forecasting models’ performance. As a new contribution, the evidence from this work establishes a road map to deepening the understanding of how financial networks can improve the quality of prediction of financial variables, being the latter, a crucial factor during financial shocks, where uncertainty and volatility skyrocket.

The aim of this paper was to create a decision tree (DT) to identify personality profiles of offenders against public safety. A technique meeting this requirement was proposed that uses the C4.5 algorithm to derive decision rules for personality profiling of public safety offenders. The Mini-Mult test was used to measure the personality profiles of 238 individuals. With the test results as our database, a C4.5 DT was applied to construct rules that classify each profile into one of two groups, those without and those with records of offences against public safety. The model correctly classified 80% of the personality profiles and delivered a set of decision rules for distinguishing the profiles by group, and the principal personality profiles were interpreted. We conclude that DTs are a promising technique for analysing personality profiles by their offender or non-offender status. Finally, we believe that the development of a classifying model using DT may have practical applications in the Colombian prison syste