tommijohnsen

About Tommi Johnsen, PhD

Tommi Johnsen is the former Director of the Reiman School of Finance and an Emeritus Professor at the Daniels College of Business at the University of Denver. She has worked extensively as a research consultant and investment advisor for institutional investors and wealth managers in quantitative methods and portfolio construction. She taught at the graduate and undergraduate levels and published research in several areas including: capital markets, portfolio management and performance analysis, financial applications of econometrics and the analysis of equity securities. In 2019, Dr. Johnsen published “Smarter Investing” with Palgrave/Macmillan, a top 10 in business book sales for the publisher.  She received her Ph.D. from the University of Colorado at Boulder, with a major field of study in Investments and a minor in Econometrics.  Currently, Dr. Johnsen is a consultant to wealthy families/individuals, asset managers, and wealth managers.

Short Campaigns by Hedge Funds

Our analysis highlights the importance of short campaigns for understanding the economic impact of activist hedge funds.

Robo-advisors: A well-researched topic

Along with the rapid growth in the utilization of robo-advisors, there has been similar growth in academic interest about robo-advisors.   What is the current state and what are the main research streams in the literature?

Can ChatGPT Improve Your Stock Picks?

One use of the NLP (natural language processing) features of ChatGPT is to search out patterns in the immense amounts of news, data and other sources of information about specific stocks, and then efficiently convert them into summaries valuable for all types of investors.  Can this be accomplished with useful results? The authors use the Q2_2023 period to test performance around earnings announcements. Earnings announcements and earnings surprises are informationally rich as well as challenging events for investors to analyze.

Professional Athletes and Money Skills

Until that framework is defined, an assessment of the financial acumen of professional athletes will remain unfocused.  This research addresses that gap.

Are stock returns predictable at different points in time?

For many benchmark predictor variables, short-horizon return predictability in the U.S. stock market is local in time as short periods with significant predictability (“pockets”) are interspersed with long periods with no return predictability.

A new twist on momentum strategies: Utilize overlapping momentum portfolios

Momentum investors utilize different timeframes to identify high momentum equities: past 6, 9, 12 months as an example. Obviously, there is a significant degree of overlap in momentum stocks identified across various past time frames. However, there has been little research focused on understanding the characteristics of momentum stocks formed on six and 12 months that overlap one another. The authors refer to the subset as “overlapping” stocks and suggest they constitute the largest proportion of the profitability of the momentum strategy.

Value versus Values in ESG Investing

The relationship between financial markets and ESG investing is obscured by the lack of clarity regarding motivations for investing in ESG strategies. Is the motive to align the investor’s values with the ESG theme? Or is the ESG term a misnomer for a set of stocks that are systematically undervalued, for some reason as a function of its ESG characteristics? 

Factor Investors: Momentum is Everywhere

The Jegadeesh and Titman (1993) paper on momentum established that an equity trading strategy consisting of buying past winners and selling past losers, reliably produced risk-adjusted excess returns.  The Jegadeesh results have been replicated in international markets and across asset classes. As this evidence challenged and contradicted widely accepted notions of weak-form market efficiency, the academic community took notice and started churning out research.  As a result, a very large number of academic studies were published on momentum. The article summarized here has conveniently summarized 47 articles deemed as the highest quality and published in either the Journal of Finance, the Review of Financial Studies, or the Journal of Financial Economics, all three considered premier journals in the finance discipline. It is difficult to understate the importance of having a well-curated summary of momentum research. Keep it in your library.

The democratization of investing and the evolution of ETFs

The implications of the competitive landscape for ETFs are mixed. On one hand, they have truly democratized investing. Investors now have access to the benefits of financial markets in one instrument that provides diversification at very low fees. Recently advertised fees on broad-based bond funds have fallen to 3bps. On the other hand, ETF providers have been able to satisfy investor demand for increasingly specialized products even though the evidence suggests they underperform. Are investors becoming worse off due to the effectiveness of the marketing strategies by providers of specialized ETFs?

Do Short-Term Factor Strategies Survive Transaction Costs?

Short term return anomalies are generally dismissed in the academic literature "because they seemingly do not survive after accounting for market frictions.” In this research, short term “factors” are taken seriously and the authors argue the standard parameters may not apply for short horizons.

Investor demand, rating reform and equity returns

The traditional financial theory attributes security returns to market- or factor-based risk, with no role ascribed to other influences. In this research, the authors argue for including investor demand as an additional variable in explaining returns.  Can changes in investor demand generate systematic changes in security returns?

Female execs bring more accuracy to analysts’ earnings forecasts

The results of this research extend the literature in a number of areas including: the analyst forecast literature; the literature on behavioral accounting and finance with respect to corporate decision-making all in the context of gender; and the dominant role of the CEO on information transparency.

Where Large Language Models and Finance Meet

BloombergGPT is a large language model (LLM) developed specifically for financial tasks. The authors trained the LLM on a large body of financial textual data, evaluated it on several financial language processing tasks and found it performed at a significantly higher level than several other state-of-the-art LLMs.

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