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on Big Data |
| By: | Xintong Wu; Peiting Tsai; Jing Yuan; Michael Yu; Greg Sun; Luyao Zhang |
| Abstract: | Decentraland, a decentralized virtual reality platform operating within the expanding Metaverse ecosystem, utilizes its native MANA token to facilitate virtual asset transactions and governance. This study investigates the integration of Discord community sentiment with multi-modal financial data to enhance cryptocurrency price prediction within virtual world economies. We address: (1) identifying sentiment patterns within Decentraland's Discord community, and (2) evaluating the impact of multi-modal features on token return forecasting. Using a BERT-based large language model for sentiment analysis, we develop two LSTM architectures: a baseline incorporating historical prices and a multi-modal variant integrating sentiment scores, trading volume, and market capitalization. Results indicate predominantly neutral community sentiment with a positive skew. The multi-modal model significantly outperforms the price-only baseline in prediction accuracy. These findings demonstrate the predictive value of community-derived signals for virtual economy forecasting and establish a foundation for future research at the intersection of immersive virtual environments, natural language processing, and cryptocurrency market analysis. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.20192 |
| By: | Manuel Noseda; Nathan Soldati; Marco Paina |
| Abstract: | Predicting stock price movements during Earnings Announcements (EAs) is a significant challenge due to market noise and high-impact price discontinuities. In this study, we evaluate whether pre-announcement news sentiment, firm fundamentals, and recent market dynamics jointly predict the directional price movement of equities on EA days. We construct a multi-modal feature space combining 15 fundamental metrics, 3 price-based technical indicators and sentiment scores derived from financial news articles processed using FinBERT. We compare a Long Short-Term Memory (LSTM) network and a Transformer-based architecture against a logistic regression baseline, and further assess all models with and without sentiment features to quantify their incremental value. Our results indicate that while the LSTM demonstrates higher precision through a conservative safe-bet strategy, the Transformer model exhibits superior sensitivity in identifying volatile movements, achieving a higher macro F1-score, with ablation experiments showing a consistent benefit from incorporating news sentiment. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.25894 |
| By: | Griebsch, Susanne; Röthig, Andreas |
| Abstract: | This paper applies machine learning methods and anomaly detection to sudden stop analysis of portfolio flows. Using the isolation forest methodology, univariate as well as bivariate sudden stops of equity and bond fund flows to emerging markets are generated. An anomaly score and an anomaly classification are provided. The results point to an increase in anomalous portfolio flows to emerging markets in recent years. In addition, the isolation forest methodology appears to yield better results than the traditional approach to sudden stop analysis in classifying anomalies connected with the recent capital flow volatility related to the outbreak of the COVID-19 pandemic as well as the interest rate reversal in advanced economies in recent years. The bivariate approach to anomaly detection is better able to identify anomalous episodes of financial stress, where both equity and bond markets are simultaneously affected. Most of the classified anomalies are related to fund flow stops (i.e. simultaneous stops to both equity and bond flows) or surges (i.e. surges in both equity and bond flows). In general, univariate and bivariate anomaly detection using machine learning techniques can play an important part and lead to a better understanding of sudden stops and surges. |
| Keywords: | Capital Flows, Portfolio Flows, Sudden Stops, Emerging Markets, Machine Learning, Isolation Forest, Anomaly Detection |
| JEL: | E32 F30 F32 G15 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bubdps:341638 |
| By: | Pierre Brugière (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique); Gabriel Turinici (CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | Option pricing theory, in particular the model of Black & Scholes (1973), provides an explicit solution for constructing a perfectly hedged portfolio in continuous time. However, in practice, trading occurs in dis- crete time and is subject to transaction costs, making the direct applica- tion of continuous-time models often suboptimal. Previous studies, such as Buehler et al. (2018), Buehler et al. (2019), and Cao et al. (2019), have shown that deep learning and reinforcement learning can yield superior hedging strategies compared to traditional continuous-time approaches. However, these methods typically rely on a large number of simulated trajectories (on the order of 10^5 to 10^6) for effective training. In this work, we show that it is possible to train a deep hedging neural network using as few as 256 independent trajectories and still outperform both the classical Black & Scholes model and the Leland model in a Ge- ometric Brownian Motion setting. The Leland model is often considered one of the most effective explicit frameworks for incorporating transac- tion costs, yet it is surpassed by our data-efficient neural network when transaction costs are high. Going one step further, we demonstrate that even 256 overlapping sequences can beat the Leland formula when transaction costs are high and that a single trajectory, consisting of 31 or 91 points and augmented with a random drift (our Random Drift Augmentation method) is sufficient to roughly calibrate our neural network. These results highlight the potential for low-data implementations of deep hedging models in practical financial applications |
| Keywords: | Deep hedging, Machine Learning, Leland, Options, Optimal Strategy, Transaction costss |
| Date: | 2026–06–03 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05642615 |
| By: | Marie Soehl Coolsaet; Roberto Gallardo; Zhen Gao |
| Abstract: | This research aims to leverage machine learning to improve stock price prediction and support informed investment decisions related to buying, selling, and holding assets. Specifically, this work investigates transformer-based models for stock prediction and examines the impact of pre-training strategies on forecasting performance. A transformer model was first pre-trained on the Toronto Stock Exchange Index (TSX) to predict intra-day return direction and subsequently fine-tuned on individual TSX stocks. The model was further adapted for return-value regression tasks. Performance was benchmarked against Long Short-Term Memory (LSTM) and XGBoost models. Pre-training on the market index improved the binary cross-entropy loss for individual stock prediction from 0.69 to 0.64. The fine-tuned transformer regression model achieved lower mean squared error than the benchmark models, although the ensemble and XGBoost models achieved higher average daily returns. In addition, a practical application was developed to deliver real-time stock predictions for trading support. Future work will focus on increasing transformer model capacity, incorporating broader global technical indicators, and filtering out stocks with low predictability. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.23962 |
| By: | Hainaut, Donatien (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | This article introduces a novel framework for explaining regional mortality differences across European NUTS-2 areas using macroeconomic indicators. Because regional death rates are substantially noisier than national aggregates, we model age-specific mortality as a smooth B-spline surface whose coefficients are predicted by a feed-forward neural network. The network takes as inputs a set of interpretable regional factors, including GDP per capita, purchasing power, employment rate, educational attainment, and NO2 emissions. Model parameters are estimated via maximization of the Poisson log-likelihood, and the methodology is applied to French regional mortality data. Compared with the LiLee multi-population framework, the proposed approach offers several advantages. First, it provides an interpretable link between economic conditions and mortality, allowing the impact of policy-relevant variables to be quantied. Second, the combination of neural networks with B-splines yields smooth, stable mortality curves and avoids the overtting often observed in non-parametric regional models. Finally, the model is suciently robust for long-term mortality forecasting and actuarial applications such as life expectancy projections and annuity valuation. |
| Keywords: | Mortality ; neural networks ; Lee-Carter model ; multi-group mortality ; life insurance |
| Date: | 2026–04–08 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2026009 |
| By: | Yiqing Wang; Dehao Dai; Ding Ma; Kerui Geng |
| Abstract: | We test whether large language models (LLMs) add value in commodity portfolio construction when the information set and implementation rules are held fixed across strategies. A Hawkish Agent (inflation-tightening prior), a Dovish Agent (growth-easing prior), a Debate Agent, and a deterministic z-score Rule Agent each receive identical FRED macro z-scores and route their tilt signals through the same portfolio engine. Across 124 weekly rebalancing dates spanning the 2023 U.S. rate peak and the 2024-2025 soft landing, all three LLM strategies outperform the Rule Agent in Sharpe terms; the Hawkish and Debate Agents record the largest gains (\Delta Sharpe = +0.044 and +0.040, both p |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.08283 |
| By: | Achintya Ranjan; Uma Ranjan |
| Abstract: | The GDP of a country is modelled as the relative interaction between two agents - working hours, reflecting the social choice of a population, and Total Factor Productivity, reflecting the collective investment in productivity enhancers. It is shown that a Random Forest model can accu- rately predict the GDP from these two factors. The differences in the choices made by Germany and USA are analysed though Gini importance, SHAP plots and partial dependency. It is shown that the differences in the social structure of the countries are reflected in the relative contribution of working hours and productivity to the GDP. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.01234 |
| By: | Magdalena Cornejo (Universidad Torcuato Di Tella - CONICET); Walter Sosa Escudero (Universidad de San Andrés - CONICET) |
| Abstract: | This paper studies forecasting in dynamic panel data models with fixed effects. We compare the forecasting accuracy of conventional estimators—pooledOLS, fixed effects, Anderson–Hsiao, and Arellano–Bond—against shrinkage and regularization methods such as Ridge, LASSO, ElasticNet, empirical Bayes maximum likelihood and the recent unbiased risk estimation of Kwon (2026). Monte Carlo evidence shows that shrinkage methods substantially improve out-of-sample accuracy. An empirical application to firm-level leverage dynamics using Compustat data confirms the relevance of these findings for forecasting in corporate finance. Machine learning regularization can improve forecasting performance in dynamic panel settings while preserving the structural framework. |
| Keywords: | Forecasting, Dynamic panel data, Machine learning, Regularization, Corporate finance. |
| JEL: | C53 C58 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:sad:wpaper:183 |
| By: | Pablo Rodriguez Manzi |
| Abstract: | We study the reconstruction of implied volatility surfaces from sparse and noisy option quotes using deep learning models under no-arbitrage constraints. We compare multiple neural architectures, including multilayer perceptrons, convolutional networks, U-Nets, variational autoencoders, and Transformer-based models against classical SVI parameterizations on option market data. Results show that Transformer and U-Net architectures achieve strong reconstruction accuracy, particularly under sparse observation regimes, while soft arbitrage penalties significantly reduce arbitrage violations with moderate impact on reconstruction error. We further analyze the trade-off between accuracy and arbitrage consistency across architectures and regularization strengths. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.24031 |
| By: | John R. Graham; Campbell R. Harvey; Manish Jha |
| Abstract: | Business sentiment is a closely watched economic signal, but measuring it is slow and costly: surveys reach only a few hundred firms, arrive periodically, and take time to compile. We show that large language models hold the potential to address these shortcomings. We prompt an LLM to role-play as the CFO of a specific company at a specific date and focus on the economic-optimism question on the Duke-Federal Reserve CFO Survey over 2002-2025. We find that the LLM reproduces individual human responses: the predicted optimism score significantly forecasts the CFO's actual answer, surviving firm and year-quarter fixed effects and a control for the most recent prior response. Predictive accuracy increases with the amount of information supplied, as both respondent history and firm characteristics improve fit, and the relationship persists under quarterly aggregation. With appropriate conditioning, LLMs may be able to serve as credible digital twins of executives, offering scalable, high-frequency expectations data for financial research and policy. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.13812 |
| By: | Antoine Pesenti; Aidan O'Sullivan |
| Abstract: | Electricity markets are inherently complex systems characterised by strong nonlinearities, high-dimensional interactions, and increasing interdependence across regions. While deep neural networks (DNNs) have demonstrated strong predictive capabilities for electricity prices, their lack of interpretability limits their usefulness for understanding the underlying drivers of price formation. This paper addresses this gap by combining DNN models with explainable artificial intelligence (XAI) techniques to analyse the determinants of electricity prices across 39 European bidding zones. We employ SHAP (SHapley Additive exPlanations) to quantify feature contributions and apply and extend SSHAP, an aggregation framework to improve interpretability in high-dimensional settings. The analysis identifies that renewable energy sources, particularly solar, play a disproportionately important role in price formation despite their lower share in total power generation. Gas prices remain a dominant and consistent driver across electricity markets, while interconnections significantly shape price dynamics, highlighting the strong interdependence of European electricity systems. In addition, a synthetic EU-wide electricity market is constructed to explore the counterfactual scenario of a fully integrated market with a single price. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.19118 |