Leveraging Explainable AI to Support Cryptocurrency Investors

Fior, Jacopo; Cagliero, Luca; Garza, Paolo

doi:10.3390/fi14090251

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Inamdar et al [8] were the pioneers in demonstrating how investors utilized cryptocurrency sentiment analysis with Explainable AI to inform their investment decisions. Subsequently, Jackob et al [9] illustrated explainable AI algorithms can leverage cryptocurrency predictions and aid crypto investors with practical guidance. Additionally, in digital currency, positive sentiment tweets are considered a crucial factor in determining market trends, as demonstrated in the studies conducted by Wolk et al [10].…”

Section: Introductionmentioning

confidence: 99%

Investigate How Market Behaves: Toward an Explanatory Multitasking Based Analytical Model for Financial Investments

Das,

Chowdhury,

Lijin

et al. 2024

IEEE Access

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In recent years, the intersection between financial market behaviour and social media has emerged as a sought-after source of information, meeting the requirements of investors, institutions, regulators, researchers, and policymakers. Assessing sentiment and emotions aids in evaluating public psychology on particular stocks, assets, or the overall market, with shifts often aligning with market movements. Previously, machine learning, both traditional and deep learning methods, targeted discerning stock market sentiment and emotion without conducting studies to offer comprehensive explanations for these behavioural factors. This study introduces a multitasking sequence-to-sequence model that integrates financial investment analysis with sentiment and emotion analysis from tweets upheld by an explanation mechanism. We also present the FinEMA dataset, featuring sentiment, emotion, and cause labels on financial stock market changes. Our study highlights how joint learning improves performance in discerning sentiment and emotion by utilizing interrelated features, enhancing task effectiveness. Our proposed model, the Emotion-Sentiment Attention Network (ESAN), achieved 89% accuracy in sentiment identification and 79% accuracy in emotion recognition, outperforming conventional machine learning methods. Furthermore, our findings indicate a positive outlook for the stock market in the latter half of 2023, which has intensified investor optimism, though some individuals still harbour uncertainties. Conclusively, our results suggest that regenerating existing computational tools can open up new research opportunities to address relevant novel tasks. Disclaimer: The primary aim of this study is to elucidate the diverse dimensions of financial market behaviour and offer explanatory insights for the research community. The authors maintain impartiality towards specific stocks. It's essential to note that stock market investments inherently carry market risks and potential losses. The market information within the research findings remains independent of the authors' viewpoints.

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Section: Introductionmentioning

confidence: 99%

Investigate How Market Behaves: Toward an Explanatory Multitasking Based Analytical Model for Financial Investments

Das,

Chowdhury,

Lijin

et al. 2024

IEEE Access

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“…Among all the various forecasting algorithms in ML, Linear Regression (LR) model is one of the common ML algorithms which also includes Ridge Regressions (RR) and Lasso Regressions (LaR) [10,11]. Other commonly used ML approaches are Decision Tree (DT) [12][13][14], Random Forest (RF) [15][16][17], Support Vector Machine (SVM) [18][19][20][21][22], Gradient Boosting Machine (GBM) [23][24][25][26], K Nearest Neighbors (KNN) [27][28][29][30], and Artificial Neural Network (ANN) [31][32][33][34]. Recently, Successful ML models using out-of-sample data set over predicted housing pricing, including support vector regression, regression tree, random forecast, bagging, boosting, Ridge and Lasso, and ensemble learning, have been discovered to be more efficient and realistic [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Machine Learning Regressors for Estimating Real Estate House Prices

Khosravi¹,

Arif²,

Ghaseminejad³

et al. 2022

Preprint

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Real estate market analysis and place-based decision-making can both benefit from understanding house price development. Although considerable amounts of interest have been devoted to housing price modelling, the assessment of house price fluctuation still requires further comparing studies. Housing price prediction is challenging as contributing factors are quite dynamic and subject to a variety of regulating elements. The future understanding of the housing market trends not only provides sufficient customers’ investment trust potential but also enables the financial support to progress more realistic in advance. In this study, a comprehensive data-informed framework is developed to investigate and anticipate real estate house prices using historical data by combining explanatory features. We examined about 500 houses in the Boston area as a case study and discussed how the increase in housing prices could vary by each of the contributing components. Fourteen Machine Learning (ML) regressors imply to the dataset and lead to a comparative study of the accuracy of all the models. ML-based regressors forecast real estate home prices as a function of thirteen influencing factors. The most informative features were also selected by conducting the Permutation Feature Importance technique on all the features The study provides a comprehensive tool for evaluating the robustness and efficiency of ML models for housing price predictions. The results highlighted Random Forest as the best model has an R2 equals to 0.88 and Voting Regressor as the second highest rated model has R2 equals to 0.87. Results of multivariate exploratory data analysis also implied that the average number of rooms and percentage of the lower status of the population have the most significant impact on the price range predictions.

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Explainable artificial intelligence (XAI) in finance: a systematic literature review

Černevičienė,

Kabašinskas

2024

Artif Intell Rev

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As the range of decisions made by Artificial Intelligence (AI) expands, the need for Explainable AI (XAI) becomes increasingly critical. The reasoning behind the specific outcomes of complex and opaque financial models requires a thorough justification to improve risk assessment, minimise the loss of trust, and promote a more resilient and trustworthy financial ecosystem. This Systematic Literature Review (SLR) identifies 138 relevant articles from 2005 to 2022 and highlights empirical examples demonstrating XAI's potential benefits in the financial industry. We classified the articles according to the financial tasks addressed by AI using XAI, the variation in XAI methods between applications and tasks, and the development and application of new XAI methods. The most popular financial tasks addressed by the AI using XAI were credit management, stock price predictions, and fraud detection. The three most commonly employed AI black-box techniques in finance whose explainability was evaluated were Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGBoost), and Random Forest. Most of the examined publications utilise feature importance, Shapley additive explanations (SHAP), and rule-based methods. In addition, they employ explainability frameworks that integrate multiple XAI techniques. We also concisely define the existing challenges, requirements, and unresolved issues in applying XAI in the financial sector.

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Leveraging Explainable AI to Support Cryptocurrency Investors

Cited by 5 publications

References 24 publications

Investigate How Market Behaves: Toward an Explanatory Multitasking Based Analytical Model for Financial Investments

Investigate How Market Behaves: Toward an Explanatory Multitasking Based Analytical Model for Financial Investments

Performance Evaluation of Machine Learning Regressors for Estimating Real Estate House Prices

Explainable artificial intelligence (XAI) in finance: a systematic literature review

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