There has been a changing focus of industrial organization and the theory of the firm over my career that also may tell us something about the hedge fund industry. The hedge fund and money management industries have evolved with changes in the demand for their product and through competitive threats from other firms.

The study of industrial organization has evolved, and that evolution will help provide a framework for explaining hedge funds. I will start with my undergraduate course in industrial organization that used the textbook by Frederic Scherer. This was the dominant thinking on the topic and used the SCP framework for the empirical analysis on industries. Scherer was a discipline of Schumpeter and focused on competition, change, and innovation. 

The SCP framework, which was foundational for most business schools, looked at Structure-Conduct-Performance as the three drivers for understanding a firm and industry. The market structure, the number of firms and barriers to entry will determine the conduct of the firm based such as pricing and products which then lead to performance as measured by profitability. The Harvard Business School advanced Scherer to new generations through the five forces of Porter as the dominant view for thinking about firm, industry, and strategy. 

The SCP framework focused on the external behavior of the firm, but there was a growing interest in the internal structure of the firm that was explored through the work of Oliver Wiliamson, Ronald Coase, and Alan Alchian. There was a focus on how firms are structured based on minimizing transactions costs and information flows. The transaction cost view looked at market versus hierarchical structures to solve transaction costs problem. Along with the contracting of the firm, Jensen and Meckling applied the idea of the corporation as a set of contracts. The contracting view solved problems of asymmetric information and moral hazard. There was an explosion of research on principal-agent problems. While this work on the internal structure of the firm solved problems with how firms and contracts were formed, there was another school or direction of thinking about how firms interacted in a competitive environment. 

The work of Meyer, Milgrom and Roberts (MMR) applied a game-theoretic approach to the competition across firms and strategic behavior. This approach focused on information and incentives issues between firms. The work on information asymmetries helped explain strategic decisions. Like the work in finance to explain incentives for shareholders and managers, MMR applied this asymmetric information thinking for explaining the actions of firms. Firms will make strategic decisions to influence the behavior of their competitors. There also was work focused on competitive threats and whether industries were contestable as a means of determining whether there was a monopoly. 

The work on information, game theory, and contracting was linked to the more classic work in industrial organization through Jean Tirole who developed a more comprehensive framework for industrial organization to explain oligopolies, monopolies, monopsonies, and competition. This unified framework also provided a framework for price discrimination, the vertical integration of the firm, and tackled the reason for government intervention and regulation.

So, what does this have to do with hedge funds? The hedge fund industry is not special. It is subject to the same issues and problems of any other industry. Firms attempt to solve information and transaction costs problem to increase profitability or provide alpha for clients. Hedge funds compete for funds, so they have to focus on strategic decisions to improve their position relative to other firms. The hedge fund industry may have started out as a group of artisans, skilled managers, but are now vertically integrated and structured to gain an information edge and will become more horizontally integrated to improve their diversification and stabilize cash flows. We can learn from other industries to explain hedge fund and money manager behavior.

AI-Powered Financial Scholarship - This is scary

 

This is one scary paper for anyone in academic finance, AI-Powered (Finance) Scholarship. It is scary not because of a specific conclusion but because an AI program can not only generate and test new investment signals but because it can also wrap it up in a nice working paper suitable for publication. These AI papers may be better written and presented than papers done by humans. Ouch, this is a game changer. 

This paper destroys a lot of academic finance and is the ultimate data mining paper delivered in nice publishable form. Think about it. We have gone from the search for factors that form a factor zoo to now mining all indicators. The authors mine over 30,000 potential stock return predictors form accounting data and apply the "assaying anomalies" protocol to generate 96 signals that pass the protocol test and benchmarks these results against a large group of known anomalies. 

We have moved from a world of p-hacking and atheoretical research which does not need careful thought and formation of hypotheses to a world where we can have AI run the research and write the paper. This will put academics out of work - sort of. They will have to reinvent who they are and what they do. 

The LLM generates papers that given signals creative names, custom introductions to provide a justification for the signals, and incorporate citations to support the work. The authors provide a cautionary note that this is the ultimate form of HARKing - Hypothesizing After Results are Known.

Conditional beta using LASSO adds value

We know that betas are not stable. They are time varying and may change based on fundamental factors associated with the firm as well as changing risk across the business cycle, yet we do not seem to focus on conditional betas. In the new paper, "Conditional Betas", the authors focus on comparing several econometric and machine-learning method to test asset pricing models and market anomalies. If we better measure or control beta, we should see fewer market anomalies and an increase in explanatory power for asset models. These tests will allow us to measure the relative valued-added of choosing better machine-learning technics. 

The comparisons shows that firm-level LASSO method provide improvement over more traditional econometric techniques. The LASSO method is a technique that add a penalty term to a linear regression that forces or encourages to some coefficient to be zero and focuses on the most important values. The penalty is one the absolute value of the slope. (Ridge regression will have a penalty based on the squared value of the slope.) If we penalize the marginal coefficients, we will reduce overfitting and make the overall regression easier to interpret. This will be helpful when there are a lot of potential predictors.

A LASSO regression is easy to interpret and generate and when applied to panel data, it generates coefficients for the three and four factor models that make intuitive sense.

Different forms of backtests - A review

There are different forms of backtesting and a short paper provides the pros and cons of each, See "The Three Types of Backtests". This is a good summary of the types as well as the quality of simulations and backtests through several key issues. It provides a reasonable checklist of what an investor should do when conducting or reviewing a backtest. 

This serves as a good complement to my paper “I Have Never Seen a Bad Backtest”: Modeling Reality in Quantitative Investing in The Journal of Investing.