Enhancing Fairness and Transparency in Deep Learning- Based Stock Market Forecasting
Keywords:
Deep learning, Fairness, Stock market forecasting, LSTM, AI ethics, Bias mitigation, Interpretability, Statistical fairness evaluation, Error parity, Calibration curves, Transparency, Responsible AI, Ethical financial forecastingAbstract
Over the past few years, Deep Learning Models have gained prominence as a very effective means of predicting future financial markets because these types of models are capable of identifying and modelling complex non-linear data relationships that may otherwise be missed by human analysts. By analyzing huge amounts (millions of observations) of historic data about the market, the technical indicators and sentiment derived from news articles, social media or analyst reports and/or any other data collected about the financial market, the predictive accuracy of Deep Learning Models can be exceptionally high. Consequently, Deep Learning Models have provided a valuable resource for investors, financial institutions and policymakers who rely on the use of accurate data for informed investment decision-making. However, Predictive Accuracy is one of many factors that need to be considered before any conclusion can be reached regarding the reliability, fairness and trustworthiness of any system. Inadequate consideration of Fairness and Transparency during Model Development and Evaluation is one of the major limitations of all of the current approaches. That is why all of today's Benchmark Tests focus primarily on Prediction Performance, while ignoring the inherent systemic biases that typically favours certain types of companies (e.g. Large Capitalization. However, due to the limited availability of training and testing datasets for smaller companies, Environmental Agencies, or Emerging Market Investors, deep learning models may tend to favor larger companies due to the larger number of training datasets available to the models used to create them. However, deep learning models may also overlook the role that small and/or environmentally conscious companies are playing in promoting sustainability, particularly as these companies continue to grow in importance to the sustainable finance sector. Lastly, because most Deep Learning Models are developed using Black Box technology, the Stakeholders of Deep Learning Models cannot audit the Black Box to ascertain how predictions are generated which creates additional difficulties in detecting and eliminating potential biases that may exist within the Deep Learning Models.
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