Global Journal of Economic and Finance Research

During the COVID-19 pandemic, low-friction trading platforms, mobile-first broking interfaces, and more social information flows all came together to revolutionise how retail investors interact with financial markets in a big way. This study investigates the impact of post-pandemic experiences and behavioural mechanisms on retail investors' investment strategy choices by creating a comprehensive analytical framework grounded in robust econometric techniques. The research issue addresses a significant knowledge deficiency on the interplay between behavioural biases and democratised market access in influencing portfolio decisions. We develop an integrated empirical model with complete mathematical specification of all estimators, identification conditions, and asymptotic properties, building upon fundamental behavioural finance theories such as prospect theory (Kahneman & Tversky, 1979), overconfidence bias (Barber & Odean, 2000), and attention-driven trading (Barber et al., 2022). We offer an outcome-consistent estimation strategy that uses fractional response regression for portfolio allocations, ordered response models for risk tolerance, multinomial logit regression for categorical strategy selection, and negative binomial models for trading frequency counts.We give formal proofs of consistency and asymptotic normality for each estimator under the regularity conditions we set out. The empirical analysis combines data from four official U.S. sources: the Survey of Household Economics and Decisionmaking (SHED), the National Financial Capability Study (NFCS), the Survey of Consumer Finances (SCF), and the Consumer Price Index for All Urban Consumers (CPI-U) for the years 2015 to 2024. Results show that measures of financial resilience always predict how people will invest, and the spike in inflation in 2022 happened at the same time as household financial indicators getting worse.

retail investors, behavioral finance, COVID-19, multinomial logit, fractional response models, maximum likelihood estimation, asymptotic theory, prospect theory, financial resilience

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DATA SOURCES

1. Board of Governors of the Federal Reserve System. Survey of Household Economics and Decisionmaking (SHED). Available at: https://www.federalreserve.gov/consumerscommunities/shed.htm
2. FINRA Investor Education Foundation. National Financial Capability Study (NFCS). Available at: https://www.usfinancialcapability.org/
3. Board of Governors of the Federal Reserve System. Survey of Consumer Finances (SCF). Available at: https://www.federalreserve.gov/econres/scfindex.htm
4. U.S. Bureau of Labor Statistics. Consumer Price Index for All Urban Consumers (CPI-U). Available at: https://www.bls.gov/cpi/