Portfolio Performance Measurement and Benchmarking (McGraw-Hill Finance & Investing)
Daily configurations embed high-frequency information and are thus informative, but they can be affected by market noise that influences investment behavior. It would have been possible to aggregate some information and compute the similarity matrix at a lower frequency or averaging the weights connecting each node over some dates to reduce the number of network configurations. However, longer time windows would have generated an over-smoothing effect. As a consequence, we decided to focus on daily networks.
Given our choice, the stability of the different daily communities detected across the entire period of observation is an issue. We therefore identify communities that can be considered as persistent across the d and that identify the groups of funds that behave similarly throughout the whole sample period. Other papers dealt with the problem of identifying persistent robust communities and analyze their stability and properties through time. More precisely, we adopt the following procedure.
We calculate the matrix of intersections M i , j , which quantifies the number of funds in daily community i present in persistent community j. We continue to scan the list, ignoring communities that have been already identified, until we find list i 1 t , j 1 t ; i 2 t , j 2 t ; … i 5 t , j 5 t , which identifies persistent communities with daily communities that exist on day t.
For each day we define the size S i t of persistent community i to be the number of funds in the daily community identified with it. We define the daily core of persistent community i to be the number of funds held by the persistent community that are also present in the daily community identified with it. We introduce an indicator that measures how often funds are assigned to the same community in time.
The level of cohesiveness of a certain community g , i. Thus, a homogeneous community will have a cohesiveness indicator that approaches 1.
The dark green cells are fund pairs more frequently belonging to the same community, and lighter green cells are fund pairs less frequently belonging to the same community. Using the analysis of the more frequent co-occurrences, we identify four persistent communities. The largest C 2 consists of seven funds, community C 4 consists of six funds, community C 3 consists of five funds, and community C 1 consists of three funds.
One fund is a separate singleton community C 0 for the entire period. Note that these four communities collapse into two larger aggregates when our observation of the system is less granular. The identified persistent communities are consistent across time windows, and our daily network snapshots allow us to capture behavioral signals otherwise over-smoothed in wider intervals. Communities C 1 and C 4 are stable in time and extremely cohesive with values above 0.
SI Appendix , Table S8 reports the cohesiveness values for each community averaged over the entire sample period. Behavioral communities. The plot shows the pairwise co-occurrences of funds over the period July—December Dark green values represent pairs of funds more frequently assigned to the same community high values for F i j , while lighter green cells refer to combinations less frequently assigned to the same group low values for F i j.
The first community C 1 refers to funds id6, id8, and id9; the second community C 2 is composed of funds id5, id13, id14, id15, id16, id17, and id20; the third community C 3 refers to funds id2, id7, id10, id11, and id21; and the fourth community C 4 is composed of funds id1, id4, id12, id18, id19, and id In the first 75 d the communities remain relatively stable.
At day 75 corresponding to September 8, their sizes and cores begin to fluctuate, indicating a change in manager behavior. On days 90 and 91 November 3, and November 4, community C 1 merges with C 2 and community C 3 merges with C 4. While these exogenous shocks in the autumn of may have pushed some managers to temporarily adopt a different behavior, the original set of communities returns at the end of Sizes and cores of the persistent communities.
Thick lines show the daily sizes number of funds of the daily communities identified with one of the persistent communities. Thin lines show the daily cores of the persistent communities, i. One can see that around day 80 communities C 1 and C 3 disappear, with their constituent funds joining persistent communities C 2 and C 4 , which significantly increase their sizes. The detailed analysis shows that on days 90 and 91 all funds from community C 1 join community C 2 , while all funds from community C 3 join C 4. Over time, some funds never change their persistent community, while others switch from one community to another.
We define the loyalty of a fund to a persistent community as the percentage of observations in which the fund belongs to the community. Note that the sum of the loyalties of a fund is not always 1 because on some days it may be assigned to a daily community not identified with any persistent community.
The loyalty of funds to their persistent communities is always greater than 0. The only exception is id15, which is 0. We summarize the characteristics of the four persistent communities we have identified by examining the average daily values of the vector components. Often, the attributes linked to portfolio composition alone, although important, do not clearly characterize a community. Marked differences between communities emerge instead when we consider the whole set of indicators. Funds in communities C 1 and C 4 adjust their allocations less frequently and display lower TI values, but those in C 2 and C 3 display a more volatile portfolio allocation behavior.
Funds in C 1 and C 4 are less sensitive to net flow dynamics and rely less on liquidity as a buffer to stabilize portfolios. In contrast, funds in C 2 and C 3 trade more frequently when faced with additional liquidity.
Amazon Price History
The HC is relatively high in C 2 , although its members have on average a low equity exposure, but funds in C 3 with a similar level of equity exposure have a very low average HC. Funds in C 1 have minimal HC despite a consistent equity position. In contrast, C 4 has an average portfolio composition similar to C 1 but very high HC. This is due more to manager investment attitude than to sector type or geographical market. Similarly, the HHIs indicate diversified or concentrated investments in similar portfolio compositions, dependent on the asset class composition.
Finally, funds in C 4 respond to changes in market volatility by adjusting their positions, while investments in the other communities seem less sensitive to market dynamics. Note that funds belonging to different behavioral classes differ in some ways and not in others. This confirms the importance of our identification strategy, which builds upon granular, multidimensional, data. Although portfolio composition is an important feature that characterizes funds, our analysis highlights that more weight should be given to fund manager behavior.
To find whether the communities we identified are distinct, we apply nonparametric tests to the distributions of behavioral indicators. We use the Kruskal—Wallis nonparametric equality-of-medians test to verify whether at least two communities have differing median values for each feature.
Results indicate that this is the case for the majority of the medians. The Dunn posthoc multiple-pairwise comparison test also supports the presence of distinct communities. While portfolio composition values display few notable differences among the four communities, the other attributes indicate peculiar patterns among them. This confirms that our approach is able to better capture heterogeneity in investment manager behavior and provide richer information about the allocation decision process. The heatmap exhibits the distributions of the attributes for the members of each community.
We consider average values computed over the daily observations along the interval July—December Negative and low values are shown in red—yellow colors, while positive and high ones are in gray—blue. The list of behavioral attributes not related to portfolio compositions is highlighted in the box on the right. We do not find statistically relevant differences in the performances of our communities, which emerge independently of market results.
We propose a taxonomy for the communities we detect. Community C 1 has low values for portfolio TI and high levels of resilience against external signals. Finally, communities C 2 and C 3 often change their portfolio allocations, showing high TI values and positive correlation between trading intensity and net flows. SI Appendix , Fig. S3 shows that all communities, apart from the reactive one, have an inelastic relation between daily stock trades and returns.
Communities and regularities in the behavior of investment fund managers
In other words, on average, they do not react differently to positive or negative price swings. C 4 shows instead a slightly negative relation between stock returns and holding changes, highlighting that members of this community tend to buy sell when prices go down up , behaving as negative feedback traders in this particular period. Interestingly enough, our analysis enables us to show that behavioral attitudes are influenced by exogenous shocks.
Turbulence became more intense on November 3 and November 4, when community C 1 merged with C 2 and community C 3 merged with C 4.
Afterward, the original configuration of communities emerged again. Note that this transitory shift happened in correspondence with a series of relevant macroevents that occurred during the second part of our sample period. The Greek legislative election took place on September 20 and Syriza won by 7. The new austerity package was enacted on November 19 by the Greek government.
Portfolio Performance Measurement and Benchmarking : Jon A. Christopherson :
Monetary policy actions by both the European Central Bank ECB and the Federal Reserve FED took place at the end of this sample period and hit markets that had been experiencing a long period of stability. The Eurozone debt crisis and the Greek instability resulted in high credit spreads on government bonds throughout , and during the summer and autumn the effect was especially severe. All these events heavily affected the decisions of managers and may have concurred with the reduced heterogeneity in manager behaviors we observe when the communities merge. When the effects of the exogenous shocks vanished, the original configuration returned.
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This finding, apart from supporting the presence of persistent commonalities in the way fund managers allocate their portfolios, opens a perspective in the analysis of the interdependence between observed behaviors and the emergence and resolution of phases of systemic instability. Agents tend to apply complex decision-making mechanisms, but formal rules of rational choice can be overturned by subjective views, beliefs, and habits, which generate personal mental models that affect their decision processes.
Expert fund managers are a unique sample that we can use to investigate how investment decisions are affected by behavioral heuristics. Professional market participants are expert decision makers whose decision processes are affected by competing preferences, are conditioned by a limited set of opportunities, suffer from bounded rationality, and rely on routines, all of which we understand to be investment behavioral features.
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Behavioral attitudes contribute to induce manager allocation strategies that go beyond traditional classifications based on portfolio compositions. The goal of our approach is to quantify financial indicators that may be related to well-known patterns detected in behavioral finance, e. First, our evidence suggests that mental models, personal preferences, and routines play an important role in expert decision making.
Although our analysis highlights their importance, further research is needed to disentangle the effects of behavioral traits and other fund characteristics that we could not observe, such as management fees, the structure of transaction costs, or business constraints, on the adoption of specific strategies.
Second, we show that exogenous shocks temporarily alter the configurations of communities. During the out-of-equilibrium phase, expert investors seem to converge toward more similar strategies, and then the system returns to its precrisis configuration.
If confirmed by future investigations on other datasets, this pattern might contribute to our understanding of the emergence and evolution of market instabilities. We are very grateful to Sara Zaltron, Francesco De Matteis, and the team at Azimut Analytics for insightful discussions and for providing us with the dataset. The authors declare no conflict of interest.