Improving Returns on Strategy Decisions through Integration of Neural Networks for the Valuation of Asset Pricing: The Case of Taiwanese Stock

Abstract

Most of the growth forecasts in analysts’ evaluation reports rely on human judgment, which leads to the occurrence of bias. A back-propagation neural network (BPNN) is a financial technique that learns a multi-layer feedforward network. This study aims to integrate BPNN and asset pricing models to avoid artificial forecasting errors. In terms of evaluation, financial statements and investor attention were used in this case study, demonstrating that modern analysts should incorporate the evaluation advantages of big data to provide more reasonable and rational investment reports. We found that assessments of revenue, index returns, and investor attention suggest that stock prices are prone to undervaluation The levels of risk-taking behaviors were used in the classification of robustness analysis. This study showed that when betas range from 1% to 5%, both risk-taking levels of investors can hold buying strategies for the long term. However, for lower risk-taking preferences, only when the change exceeds 10 percent, the stock price is prone to overvaluation, indicating that investors can sell or adopt a more cautious investment strategy.

On Static and Dynamic Strategic Approaches for Allocating and Managing Asset Values in Presence of Financial Inflation

“…Our current purposes of study are primarily oriented on zero-risk investing and management, while other type of the precious metals such as gold or platinum might bring about higher rate of earnings than those of silver dollars, their potential risks for investment are also increasing. Besides, machine learning and deep learning-based schemes on financial forecasting (Chen et al 2022;Ali et al 2023), as well as some artificial intelligence (AI) related topics on financial performance (Shiyyab et al 2023) are with the scope of our subsequent investigation.…”

“…While individual welfare loss and socio-economic cost directly result from inflation, its hidden cost is also difficult to recognize, indicating that the main body of economic behavior, shows deviation from reality when predicting inflation, and hence, inflation and the uncertainty of expecting inflation are generated (Chen and Chen, 2011). Open discussions towards the various impacts of such kind of uncertainty on national finance and the global economy, can be chronologically divided into four categories: Friedman hypothesized that a higher inflation rate may increase its uncertainty, where its model is reshaped by Ball (Friedman and Ball, 1977), and the positive correlations between inflation and its uncertainty in US were later verified by ARIMA-GARCH time series model testing (Payne, 2009) and mechanism transformation based ARCH model (Chang and He, 2010). Opposite views suggested that the increased uncertainty of inflation may also generate stronger fluctuations in the inflation rate, leading to an increase in its level (Cukeriman and Meltzer, 1986).…”