Active Research

Current finance research in Portfolio Computational Methods, Expanded Portfolio Theory, and HiFuPi asset-model backtesting.

Finance research • computational portfolios • 5thN Financial

Active Research

Robert Fithen’s current research direction has shifted decisively toward finance: applying decades of work in numerical methods, computational modeling, machine learning and empirical simulation to portfolio construction and historical-data portfolio testing.

Research Publications Career Foundation Contact

Current professional focus: CEO of 5thN Financial, with active work centered on Portfolio Computational Methods, Expanded Portfolio Theory, and historical backtesting through the HiFuPi asset model.

Research affiliation: The finance research described here is pursued through 5thN Financial, LLC, an Arkansas-registered investment adviser. It is conducted independently of Robert Fithen’s university employment and is not sponsored or endorsed by Arkansas Tech University.

Research pivot

From computational engineering to finance

Finance research roots: Interest in finance began in 1986 at the Texas A&M library, after finding Portfolio and Investment Selection: Theory and Practice by Levy and Sarnat. That interest led to several Texas A&M continuing-education courses focused on portfolio theory and option pricing, and eventually to the present work in Portfolio Computational Methods, Expanded Portfolio Theory, and HiFuPi backtesting.

The same computational habits that drive finite element analysis, CFD, adaptive numerical methods, and high-performance simulation now guide the finance research program: specify the model, define constraints, run the computation, test the assumptions, and evaluate results through repeatable historical tests.

This work seeks to make portfolio research systematic, documented, and computationally testable. Rather than treating finance as a purely qualitative judgment problem, the research frames portfolio design as an applied computational problem with explicit inputs, constraints, objective functions, and validation steps.

Portfolio Computational Methods

Methods for building, testing, and refining portfolios through numerical optimization, historical simulation, machine learning signals, turnover constraints, robustness checks, and repeatable implementation rules.

Expanded Portfolio Theory

EPT extends the portfolio-theory research path toward a broader computational framework for portfolio selection, constraint handling, risk evaluation, and implementation-sensitive allocation.

HiFuPi Backtesting

Current backtesting work applies EPT ideas using the new HiFuPi asset model, emphasizing structured historical tests, asset-selection logic, and computationally reproducible portfolio experiments.

5thN Financial

The research direction is connected to Robert Fithen’s role as CEO of 5thN Financial, where finance, computation, portfolio methodology, and practical implementation come together.

Core framework

Expanded Portfolio Theory / EPT

Expanded Portfolio Theory is the central finance-research theme. The work builds from classical portfolio theory but places greater emphasis on the computational details that determine whether a portfolio method can be implemented, tested, and repeated in realistic settings.

Key themes include:

• constraint-aware portfolio construction,
• robust and stable allocation rules,
• portfolio behavior under turnover and holding restrictions,
• empirical simulation and historical simulation,
• machine-learning-informed asset signals,
• implementation discipline rather than ad hoc portfolio changes.

Why EPT fits the research path

EPT is a natural extension of a career spent in engineering mechanics and scientific computing. It treats portfolio construction as an engineering-style problem: identify the governing assumptions, compute the solution, test sensitivity, and evaluate model behavior under controlled historical experiments.

What makes it practical

The work emphasizes algorithms that can be backtested, audited, and implemented. This helps connect theory to actual portfolio-management decisions at 5thN Financial while preserving the discipline of a research program.

Backtesting program

HiFuPi asset-model testing

A major current effort is backtesting EPT with the new HiFuPi asset model. The goal is to evaluate how an EPT-driven process behaves when portfolio construction is tied to a defined asset model, a repeatable selection process, and historical market data.

The backtesting program is organized around a research pipeline:

1. define the portfolio question,
2. build the HiFuPi asset universe and input model,
3. apply EPT allocation and constraint logic,
4. run historical backtests,
5. evaluate risk, return, turnover, stability, and implementation quality,
6. refine the model only through documented research rules.

Current questions

Questions guiding the work

• How does EPT behave when realistic constraints are applied?
• Which parts of portfolio performance come from asset selection, allocation rules, turnover discipline, or the HiFuPi model itself?
• Can backtesting be made transparent enough that each performance claim is tied to an explicit computational process?
• How can portfolio methodology preserve the rigor of engineering computation while remaining practical for real-world advisory work?

Research limitations: Expanded Portfolio Theory and HiFuPi are subjects of ongoing research and development. Historical simulations examine model behavior under selected assumptions and constraints; they cannot establish how an investment method will perform in future markets. Model uncertainty, changing market conditions, trading costs, and implementation constraints can materially affect actual investment results. Securities investing involves the risk of loss, including loss of principal.

This page describes ongoing research, not realized client investment performance or a guarantee of results. Individualized advisory services are provided separately through 5thN Financial, LLC.