Elias L. Ohneberg
Hello, I am Elias...

I hold a PhD in Finance from the University of Cambridge and am a postdoctoral research associate in Finance at the Centre for Endowment Asset Management (CEAM) at the University of Cambridge Judge Business.

My research is empirical and revolves around topics in asset management. As an empiricist, I employ data analysis in my investigations into the mutual fund industry. My latest working papers examine:

  • the relationship between within-firm connections and portfolio manager Incentives and mutual fund performance.
  • the effect of a happy workforce on employee performance in the mutual fund industry.
  • the role of expertise and market thickness in mutual fund outsourcing.

  • For my empirical analysis, I predominantly use the Julia Programming Language.

    As an empiricist, I believe important (financial) decisions should be based on hard data and facts. Therefore I have created an interface in Julia to access financial data from Yahoo Finance – YFinance.jl. The package allows users to access practically all data displayed by Yahoo Finance easily.

    Data items include but are not limited to: Stock, currency, and futures prices, company fundamental data, option chains, ESG ratings, up- and down-grades, analyst ratings, insider transactions, and much more.

    This unofficial API is for personal use only, and one should check Yahoo's terms of service before downloading the data.

    Working Papers
    The Hidden Costs of Networking: The Consequences on Mutual Fund Manager Incentives and Performance
    Solo Author
    This paper examines the impact of within-firm connections promotion and turnover decisions, employee behaviour, and fund performance. I compute connectedness using a novel measure of within-firm networks based on 13,357 mutual fund managers across 26 years. Well-connected managers within the fund family face lower performance-turnover and -promotion sensitivities. This advantageous treatment of better-connected managers is associated with lower risk-taking and less distinctively managed funds. Funds of better-connected managers deviate less from their peers in systematic factor and sector exposures and exhibit lower risk-adjusted performance. Mutual fund investors are unaware of this phenomenon, illustrated by the lack of a flow differential.
    Satisfied Employees, Satisfied Investors: How Employee Well-being Impacts Mutual Fund Returns
    with Dr Pedro Saffi (University of Cambridge)
    This paper uses proprietary data on self-reported employee reviews from Glassdoor.com to study the relationship between employee satisfaction and mutual funds’ performance. Using the staggered adoption of Anti-SLAPP (Strategic Lawsuits Against Public Participation) laws in the U.S. and variation from mergers between asset management companies to identify exogenous variation in job satisfaction, we find that employee satisfaction is positively linked to fund performance and size but that only performance-critical employees' satisfaction matters. A one-point increase on the 5-point scale of employee satisfaction leads to a 36bps increase in abnormal fund performance. Finally, while there is a positive effect of employee satisfaction on risk-taking, we cannot establish a causal relationship.
    Market Thickness and Mutual Fund Outsourcing
    with Dr David Chambers (University of Cambridge) and Dr Richard B. Evans (University of Virginia)
    We investigate the effect of market thickness, defined as the number of subadvisors the fund family could contract with, on outsourcing in the mutual fund industry. Our empirical analysis shows that market thickness is a key driver in the decision to enter an outsourcing relationship and impacts the way fee revenues are shared. We link the impact of market thickness on the relative power of both parties in the outsourcing relationship to the threat of dismissal of the subadvisor and show that outsourced funds operating in thick markets perform better.
    Research Reports
    Dividends, Share Repurchases and Stock Returns
    with Dr David Chambers (University of Cambridge) and Dr Adam Reed (University of North Carolina)
    The connection between dividends and stock returns is fundamental to finance. Recent changes in the investment landscape have nevertheless challenged this past relationship. A small number of highly visible firms have had strong earnings growth and continued to outperform the market in the past years despite offering no dividends. More broadly the stark increase in share repurchases could have weekend the longstanding relationship between returns and dividends. Using a sample that spans over 91 years, we first show that dividends still play a vital role for stock returns and subsequently contrast portfolios formed on dividend information alone with portfolios that also look at repurchases as a substitute for dividends. Our findings indicate that the inclusion of repurchases in portfolio construction leads to higher turnover, trading costs, and volatility, while offering only a limited upside in returns. Furthermore, we confirm that share buybacks are much more cyclical and volatile in nature than dividend payments. Therefore, share buybacks do not offer a good alternative for investors looking for regular and consistent cash payments. Moreover, we investigate the relationship between dividend yields and forward earnings growth. While we find a stark difference in earnings growth between low and high dividend yield portfolios looking backwords (2.64% 5-year), once we investigate a forward-looking measure the difference between earnings growth becomes substantially smaller at 1.74% (5-year).

    Excel Files

    MV Optimization with 5 Assets

    I use a slighly different version of this to illustrate Mean Variance Optimization in one of my session with the Masters in Finance programme.

    Some of my students might find it useful to see a more automated version of the sheet used in class, using some simple macros.

    Multiple Regression Output

    This file allows the user to perform a multiple-regression that updates dynamically with changes in the input data by using the linest function.

    Some of the students might find this useful, specifically doing their robustness on the impact of different estimation windows in factor regressions.