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on Big Data |
| By: | Hrishikesh Singh; Aarti Sharma; Millie Pant |
| Abstract: | In modern financial markets, more than 3/4th of the large volume trade is executed by algorithmic trading based on advanced trading strategies to attain higher precision and speed than traditional human traders. The conventional strategies focused on price-based indicators are inadequate in capturing the market dynamism arising from real-world events such as social sentiments, political instability, or sudden economic shifts. With the rise of Deep Reinforcement Learning (DRL) and data availability, there is a requirement for more efficient algorithms that can incorporate these external factors in trading strategies. DRL has shown potential in simulating real-world scenarios and in simplifying complex decision-making since DRL agents can automate the trading process i.e. executing trades in a manner comparable to human traders. They have shown reliable performance since they can update their policies based on risk and quantitative factors. In this paper, we propose a DRL approach based on partial and deep-infusion of sentimental analysis. It features distinct policy designs of actor-critic-based agents: only policy optimization (A2C & PPO) and policy optimization along with Q-learning principles (DDPG, TD3 & SAC). Our findings reveal, that the agents based on policy optimization combined with Q-learning, significantly and consistently demonstrate improved performance upon sentiment deep-infusion in profit maximization as well as risk-averse trading. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.32237 |
| By: | Adrian Gadient; Delia Haag; Nicolas Gygax |
| Abstract: | Housing costs are an important indicator of affordability and financial pressure on households. This project develops a housing cost estimation model for the canton of Aargau by combining survey data (Structural Survey 2019–2022) with public register data (e.g., population and buildings). To capture the relationship between predictor variables and monthly net rent we used random forest and an elastic net regression models. The random forest regression model achieved the better out-of-sample performance (RMSE = CHF 315, MAE = CHF 224, R2 = 0.55). The most important predictor was floor area, followed by building construction period and number of rooms. Model performance was stable across regions and dwelling sizes. The geographical variation of the monthly net rent corresponded to patterns known from surveys and market analyses. Robustness checks indicated that the results were stable across alternative model settings. These findings suggest that data from public registers and surveys can be effectively combined to estimate housing costs at scale. Since this approach relies on existing data, it avoids costly additional data collection and is therefore useful for public administration, research, monitoring, and planning. Since the necessary data are available for all Swiss cantons, the model can be applied nationwide. |
| Keywords: | housing costs, rent estimation, machine learning, household budget, affordability |
| JEL: | R21 R31 C52 C53 |
| Date: | 2026–10–13 |
| URL: | https://d.repec.org/n?u=RePEc:bss:wpaper:63 |
| By: | Bhagwat, Vineet; Cookson, J. Anthony; Dim, Chukwuma Chijioke; Niessner, Marina |
| Abstract: | Can large language models (LLMs) reveal how investors disagree? We endow an LLM with 216 representative investor personas, elicit buy/hold/sell responses to 1.25 million S&P 500 news headlines, and measure disagreement as within-headline dispersion of responses. Disagreement is highest for socially oriented news and lowest for fundamentals, and predicts elevated same- and next-day abnormal trading volume, even after the LLM's training cutoff. Disagreement rooted in income, education, and age is most strongly related to trading, whereas political disagreement, though largest, is least related. Persona responses reflect non-pecuniary rationales, align with human-survey benchmarks, and are stable across prompt variations. |
| Keywords: | Investor Disagreement, LLMs, Beliefs, Sentiment, Narratives, Trading |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:cbscwp:344253 |
| By: | Derun Zhong; Guolian Wang; Shiqing Wang; Hongjie Feng |
| Abstract: | Recent advances in financial machine learning have prompted growing interest in applying deep neural networks to convertible bond valuation and forecasting. However, existing studies often focus on in-sample valuation fit or cross-sectional return rankings, leaving out-of-sample next-day price forecasting insufficiently benchmarked against persistence. This paper provides a comprehensive empirical reality check on cross-bond panel learning for Chinese convertible bonds. Using a daily panel of 73, 806 observations across 100 A-share convertible bonds from 2022 to 2025, we evaluate whether cross-bond neural architectures reliably outperform the random walk under strict temporal splitting, boundary purging, fold-specific preprocessing, and dependence-aware inference. On the 29, 514-observation holdout, the random walk achieves an RMSE of 2.3577 CNY. Five-seed ensembles of small and large price-change MLPs yield RMSEs of 2.3619 and 2.3645 CNY, with negative out-of-sample R^2 values (-0.0035 and -0.0057) and target-date clustered HAC p-values of 0.453 and 0.248. Ten-day circular block-bootstrap intervals span zero, while direct price-level training degrades performance substantially. Although cross-bond pooling provides a variance-stabilizing shrinkage effect over single-bond estimation (lowering test RMSE for 79 of 90 individual bonds), it fails to produce an aggregate forecast advantage over persistence. Error decomposition reveals that failure is not uniform: five highly volatile, clause-sensitive bonds account for 74.4% of the model's total excess squared error. Without contemporaneous equity market information or explicit contractual event conditioning, deep panel architectures do not deliver incremental next-day predictability over persistence. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.33453 |
| By: | Mahony, Christopher Brian; Vemuru, Varalakshmi; Rahim, Aly; Owen, Daniel P. |
| Abstract: | AI- and machine learning–based social risk forecasting and explanation are applied across fragile and climate-affected contexts to predict change in conflict (DRC), population movement (Horn of Africa), and crime (a SIDS), using large multimodal datasets (satellite imagery, NLP on news/social media, economic and climate indicators, geospatial data). Three proof-of-concept models achieve out-of-sample accuracies of 63–76% (conflict) and 70–74% (population change), and generate crime risk proxies where official statistics are scarce. Key associated factors include language about sensitive topics and political actors, economic pressures and prices, climate stress, and social perceptions. Results illustrate how social science–informed AI complements conventional analytics and supports policy and operations for risk monitoring, early action, and targeted resource allocation |
| Date: | 2026–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:hdnspu:209567 |
| By: | Yiru Chen; Muhammad Ali Nasir; Xingjie Wei |
| Abstract: | Given the important role of the Federal Reserve Chair, their communications may generate substantial cross-border effects on international financial markets. Building on this perspective, this article investigates whether emotional signals conveyed by the Fed Chair during FOMC post-meeting press conferences generate emotional spillovers to global financial markets. Using press conferences held between 2011 and 2025, we construct multimodal emotional indices from textual, vocal, and facial cues using machine learning techniques, and examine their high-frequency effects on exchange rates and equity markets across six major economies. Using local projection methods with minute-level event windows, the results show that Fed Chair emotional signals have immediate and economically significant impacts on global financial markets. Among different modalities, facial and textual emotions exhibit stronger and more robust effects than vocal signals. In particular, positive emotional cues are generally associated with increases in equity ETF returns, while exchange rate responses display substantial cross-country heterogeneity, with emerging market currencies tending to appreciate. We also provide evidence that the effects of emotional cues are non-additive and depend on cross-modal interactions, with vocal signals conditioning the impact of textual and facial emotions. Moreover, the findings show that the transmission of emotional spillovers is shaped by macro-financial vulnerability and global risk conditions. On the one hand, economies with higher vulnerability exhibit stronger responses to emotional shocks, indicating that macro-financial vulnerability acts as a moderating mechanism. However, the effects of emotional signals, especially facial and textual emotion, are amplified during periods of higher global uncertainty. Our main findings remain robust across several robustness checks. In particular, for vocal and facial emotions, coefficient equality is not rejected in 85.8% and 78.3% of the market horizon comparisons, respectively, indicating that the main findings are not systematically driven by the choice of emotion extraction algorithms. |
| Keywords: | central bank communication, multimodal signals, emotional spillover, high-frequency identification, machine learning, facial emotion analysis |
| JEL: | E44 E52 E58 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2026-86 |
| By: | Severin Joho; Enzo Rossi |
| Abstract: | We compiled and processed a novel, comprehensive dataset of 1, 063 speeches delivered by high-ranking Swiss National Bank officials between 1928 and 2024. Beyond tracking their quantitative evolution, we conducted an in-depth analysis of the length, readability and thematic content. To ensure robust results, we benchmarked traditional Python-based NLP text mining against generative artificial intelligence using ChatGPT. Our findings highlight four primary policy insights. First, the frequency of annual speeches has followed an upward trajectory since the mid-1950s, accelerating notably after the transition to flexible exchange rates. Second, a clear shift toward brevity is evident. Third, the language has become increasingly accessible since the 1960s. Fourth, inflation has moved to the forefront of the thematic discourse in the era of flexible exchange rates, while cyclical economic development and unemployment have received less emphasis. Finally, we outline key methodological lessons for deploying LLMs to analyze central bank communication, emphasizing the complementary relationship between probabilistic language models and deterministic Python frameworks, while addressing inherent algorithmic pitfalls and identifying critical open questions for future research. |
| Keywords: | Central bank speeches, Linguistic Analysis, Natural Language Processing (NLP), ChatGPT |
| JEL: | E52 E58 D83 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:snb:snbwpa:2026-13 |
| By: | Jin Huang; Diego Ferreras Garrucho; Yutong Xie; Walter M. Yuan; Qiaozhu Mei; Chen Lian; Jonathon Hazell |
| Abstract: | Large language models (LLMs) have the potential to meet a key goal in economics: a quantitative model of household decision making, across a variety of settings. Yet existing evaluations cover few surveys and outcomes, and do not study how households adjust to changing economic conditions. We introduce a new evaluation, HouseholdBench, which unites 6 U.S. household surveys and 32 prediction tasks spanning numeric, categorical and probabilistic outcomes, related to consumption, income, labor, expectations, and housing. Using past behavior, demographics and macroeconomic conditions, the tasks test whether LLMs predict behavior, including how households adjust to changes in various policies. We evaluate 13 proprietary and open-weight LLMs against a no-change baseline and a gradient boosted tree model. Most LLMs outperform the no-change baseline, including for policy response tasks—with the best model lowering error for numeric outcomes by 12.2%. Across most tasks, gradient-boosted trees rank first; leading proprietary LLMs approach their performance, but open-weight models lag. LLMs exhibit systematic over- and underprediction across different tasks. We identify methods that enable a 4 billion parameter open-weight model to match proprietary models’ performance: fine-tuning and aggregating 16 predictions per observation. Improvements generalize to policy-response tasks, which are excluded from fine-tuning. We release our datasets, code, and leaderboard on our website. |
| JEL: | C45 C53 D12 E21 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35927 |
| By: | Chae, Jinwoo |
| Abstract: | This study examines how tourists perceive and describe authentic travel experiences in South Korea through text mining of online travel comments. Unlike survey-based studies that measure authenticity using predetermined scales, this study explores tourists’ spontaneously expressed perceptions in naturally occurring online discussions. The dataset comprised 10, 000 English-language travel comments about destinations and tourism experiences in South Korea. The comments were analyzed using keyword frequency, co-occurrence analysis, sentiment analysis, and thematic coding, focusing on local culture, traditional experiences, food, interactions with residents, natural environments, commercialization, and tourist congestion. The findings indicate that authenticity was frequently associated with local food, traditional culture, interactions with residents, and less-commercialized places. Conversely, commercialization and overcrowding were linked to negative perceptions of authenticity. These findings suggest that tourists construct authenticity not only through historical or cultural characteristics but also through perceptions of whether experiences feel local, distinctive, personal, and relatively unaffected by mass tourism. The study demonstrates the potential of text mining to examine tourist perceptions through naturally generated online travel content. |
| Keywords: | tourist authenticity, South Korea, text mining, online travel comments, tourist experience, local culture |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esrepo:344186 |
| By: | Scielzo Ortiz, Fabio; Grané Chávez, Aurea; Díaz Gorfinkiel, Magdalena |
| Abstract: | Measuring digital polarization around geopolitical conflicts raises two methodological problems that are rarely addressed jointly: how to extract sociologically meaningful constructs from unstructured text at scale, and which representation of that text allows unsupervised methods to recover ideological structure. We study both on a corpus of 76, 816 English-language Reddit comments on the Gaza conflict, collected from 18 subreddits over one year. Five discursive variables (political stance, sentiment, discourse tone, thematic frame and argument quality) are extracted with a large language model through zero-shot, rule-based prompting and validated against expert-coded ground truth. We then benchmark five clustering configurations that cross the feature space (validated discursive variables, dense semantic embeddings, or both) with the clustering architecture (standard k-means versus a robust mixed-data framework combining a Robust Generalized Gower distance with Fast k-medoids), and compare them through a Pairwise Separability Index that measures how distinguishable every pair of clusters is on the discursive variables. Embedding-based configurations conflate rhetorical register with political stance and fail to recover the ideological axis, a failure that persists when 90% rather than 50% of embedding variance is retained. Restricting the feature space to the validated variables is necessary but not sufficient: on the same variables, k-means yields an asymmetric partition that leaves one ideological bloc internally undifferentiated, whereas the robust mixed-data framework recovers a symmetric four-group structure. Substantively, the resulting partition reveals a bidimensional structure in which both the pro-Israel and the pro-Palestine blocs split into a non-hostile faction and a hostile, reactive one, so that ideological position and deliberative-affective register operate as distinct structuring dimensions of the debate. |
| Keywords: | Computational Social Science; Llm Feature Extraction; Mixed-Data Robust Clustering; Gaza Conflict; Polarization; Public Opinion Analysis |
| Date: | 2026–10–14 |
| URL: | https://d.repec.org/n?u=RePEc:cte:wsrepe:51001 |