Fair Value Accounting in Algorithmic Markets: Rethinking Measurement Neutrality, Bias, and Financial Reporting Integrity

The introduction of algorithmic trading, together with high-frequency trading systems and data-driven predictive modelling
methods, has resulted in fundamental changes to financial markets. Automated systems and proprietary trading algorithms,
together with recursive feedback mechanisms, now control price movements in financial markets rather than relying on human
market participants to establish prices. Fair value accounting methods still depend on market prices because they serve as
objective measurement standards according to accounting principles. The study asserts that algorithmic market microstructures
require researchers to rethink the foundational knowledge that supports fair value accounting. Accounting theory investigates
liquidity stress and procyclicality issues, but it has not yet examined how algorithmic bias, system opacity, and feedback loops
will change the market price information system. Researchers have treated value relevance evidence as a basis for determining
neutrality instead of studying it as a construct that involves societal and technical dimensions. The presence of hidden financial
algorithms that produce market prices creates situations where financial reports deliver technological distortions while people
think these reports present unbiased information. The Algorithmic Fair Value Risk Framework developed in this paper addresses
existing research gaps by showing how algorithmic input bias and model opacity together produce market distortions that impact
financial statements. The research study defines neutrality through its dependence on technology while showing how this
relationship affects digital market governance, audit practices, and regulatory contro