Discretionary funds

As we have seen so far in the section on discretionary trading, the discretionary ap-proach to investing involves the use of mechanical trading rules as is shown by Fung and Hsieh (1999), but the actual process of making decisions is done by humans as is shown by Harvey et al. (2017). Therefore, while the growth in technology is also seen as impact-ing the discretionary approach, these types of funds can be seen as usimpact-ing technology solely as helpful tools and not as a means to automate their entire processes.

Discretionary funds are therefore hedge funds that utilize the discretionary trading style and as such they are the closest to the methodologies present ever since the start of the industry. Discretionary funds can be seen as placing a higher emphasis and weight on their managers and therefore especially the professionalism and skill of the manager is

important to avoid some of the behavioral biases seen before and to showcase true out-performance as is shown by Kooli and Stetsyuk (2020).

The Preqin (2021, p. 96) report details some of the key figures relating to discretionary funds, noting that 6 960 such funds are currently active, managed by 2 636 fund manag-ers and invested in by 1 152 investors. It can also be seen from the report that discre-tionary funds are a lot more common when compared against their systematic counter-parts.

As is noted by Harvey et al. (2017), discretionary funds exhibit a stronger exposure against the well-known risk factors present in factor models. They also note the increas-ing investments made by these funds towards some of the latest technologies as a means of helping their day-to-day investment processes. In their research paper the au-thors also detail the inherent preferences that some investors show towards investing in discretionary funds, as the returns of these funds are considered to be less homogenous and their strategies more easily understandable when compared against systematic funds.

Also, the effects of algorithm aversion uncovered by Dietvorst et al. (2015) show that some investors are vary of investing in more technology driven funds due to general fears relating to the use of algorithms and wrong perceptions that are not backed by evidence, as algorithms are shown to be better forecasters. As such the greater popularity of dis-cretionary funds can be explained as being caused by a wide variety of reasons, but it is especially due to their longer history as opposed to the other trading styles.

Discretionary funds are human-driven, but as discussed there are still multiple places where technologies are being used. Treleaven et al. (2013) detail different types of pro-cesses related to the pre-trade analysis, such as data cleaning and signal generation and the authors note that these are highly automated even in discretionary methodologies, where the human involvement truly begins after this analysis of raw data is carried out.

The usage of fundamental analysis is also seen as important in discretionary funds, with Treleaven et al. (2013) noting that this entails the usage of different types of data sources in addition to technical analysis. Using a more fundamental approach would then render a fund more focused on external variables and factors affecting the prices of securities instead of only focusing on the prices of the securities themselves.

Therefore, discretionary funds can be seen as also focused on both macroeconomic and company specific external factors, but modern systematic funds can be noted as taking similar factors as a part of their automated processes. Therefore, as detailed by Harvey et al. (2017) the historical differences between the two are being reduced, at least as far as data is concerned.

The differences in data can also be hypothesized similarly as was done before in the dis-cussion related to discretionary trading, as it can be easily seen that humans are more adaptive and can for example take advantage of direct conversations with the manage-ment of companies, different gossips and rumors and other such varying sources to fur-ther their understanding. For algorithms ofur-ther than AI, this would naturally need to be specifically programmed, removing such flexibility. With such adaptability would natu-rally also come possible behavioral biases, as a discretionary fund manager might weight some information to a too large extent.

One of the main factors setting discretionary funds apart from more automated funds is their reduced use of different types of mathematical quantitative models as a part of their trading strategy. Preqin (2021, p. 106-109) notes this reduced focus on models, as discretionary funds are seen as more skill focused. As a consequence, this creates some inherent differences in terms of their return dynamics.

Based on Preqin (2021, p. 96) data, discretionary funds can be seen as being more vola-tile than systematic funds and this finding is especially interesting, when considering that they are operated by human managers, and still aim to apply similar rule-based

strategies. Therefore, one could assume that behavioral factors are the cause for this added volatility in the returns of these funds, as some deviation from these trading plans seems a likely explanation.

Additionally, Chincarini (2014) note that discretionary funds are more illiquid, and issue more strict restriction clauses to their investors in regard to withdrawals. While these two factors are connected as having a stable asset base is essential for investing in illiquid securities, Aragon (2007) notes that these practices are also linked to higher average returns for hedge funds, as they earn a premium as a compensation for taking on these illiquid securities.

Therefore, discretionary funds can be concluded as being on the whole very similar to their systematic counterparts, as the differences are mostly driven by the execution pro-cess of trading strategies. Still multiple differences can be noted amongst them, with the models, philosophies and general data consumption often being different.