Market Prediction

Hybrid Quantum-Classical Ensemble Learning for S&P 500 Directional Prediction ArXiv ID: 2512.15738 “View on arXiv” Authors: Abraham Itzhak Weinberg Abstract Financial market prediction is a challenging application of machine learning, where even small improvements in directional accuracy can yield substantial value. Most models struggle to exceed 55–57% accuracy due to high noise, non-stationarity, and market efficiency. We introduce a hybrid ensemble framework combining quantum sentiment analysis, Decision Transformer architecture, and strategic model selection, achieving 60.14% directional accuracy on S&P 500 prediction, a 3.10% improvement over individual models. Our framework addresses three limitations of prior approaches. First, architecture diversity dominates dataset diversity: combining different learning algorithms (LSTM, Decision Transformer, XGBoost, Random Forest, Logistic Regression) on the same data outperforms training identical architectures on multiple datasets (60.14% vs.\ 52.80%), confirmed by correlation analysis ($r>0.6$ among same-architecture models). Second, a 4-qubit variational quantum circuit enhances sentiment analysis, providing +0.8% to +1.5% gains per model. Third, smart filtering excludes weak predictors (accuracy $<52%$), improving ensemble performance (Top-7 models: 60.14% vs.\ all 35 models: 51.2%). We evaluate on 2020–2023 market data across seven instruments, covering diverse regimes including the COVID-19 crash and inflation-driven correction. McNemar’s test confirms statistical significance ($p<0.05$). Preliminary backtesting with confidence-based filtering (6+ model consensus) yields a Sharpe ratio of 1.2 versus buy-and-hold’s 0.8, demonstrating practical trading potential. ...

Cross-Modal Temporal Fusion for Financial Market Forecasting ArXiv ID: 2504.13522 “View on arXiv” Authors: Unknown Abstract Accurate forecasting in financial markets requires integrating diverse data sources, from historical prices to macroeconomic indicators and financial news. However, existing models often fail to align these modalities effectively, limiting their practical use. In this paper, we introduce a transformer-based deep learning framework, Cross-Modal Temporal Fusion (CMTF), that fuses structured and unstructured financial data for improved market prediction. The model incorporates a tensor interpretation module for feature selection and an auto-training pipeline for efficient hyperparameter tuning. Experimental results using FTSE 100 stock data demonstrate that CMTF achieves superior performance in price direction classification compared to classical and deep learning baselines. These findings suggest that our framework is an effective and scalable solution for real-world cross-modal financial forecasting tasks. ...

Contrastive Similarity Learning for Market Forecasting: The ContraSim Framework ArXiv ID: 2502.16023 “View on arXiv” Authors: Unknown Abstract We introduce the Contrastive Similarity Space Embedding Algorithm (ContraSim), a novel framework for uncovering the global semantic relationships between daily financial headlines and market movements. ContraSim operates in two key stages: (I) Weighted Headline Augmentation, which generates augmented financial headlines along with a semantic fine-grained similarity score, and (II) Weighted Self-Supervised Contrastive Learning (WSSCL), an extended version of classical self-supervised contrastive learning that uses the similarity metric to create a refined weighted embedding space. This embedding space clusters semantically similar headlines together, facilitating deeper market insights. Empirical results demonstrate that integrating ContraSim features into financial forecasting tasks improves classification accuracy from WSJ headlines by 7%. Moreover, leveraging an information density analysis, we find that the similarity spaces constructed by ContraSim intrinsically cluster days with homogeneous market movement directions, indicating that ContraSim captures market dynamics independent of ground truth labels. Additionally, ContraSim enables the identification of historical news days that closely resemble the headlines of the current day, providing analysts with actionable insights to predict market trends by referencing analogous past events. ...

Modeling News Interactions and Influence for Financial Market Prediction ArXiv ID: 2410.10614 “View on arXiv” Authors: Unknown Abstract The diffusion of financial news into market prices is a complex process, making it challenging to evaluate the connections between news events and market movements. This paper introduces FININ (Financial Interconnected News Influence Network), a novel market prediction model that captures not only the links between news and prices but also the interactions among news items themselves. FININ effectively integrates multi-modal information from both market data and news articles. We conduct extensive experiments on two datasets, encompassing the S&P 500 and NASDAQ 100 indices over a 15-year period and over 2.7 million news articles. The results demonstrate FININ’s effectiveness, outperforming advanced market prediction models with an improvement of 0.429 and 0.341 in the daily Sharpe ratio for the two markets respectively. Moreover, our results reveal insights into the financial news, including the delayed market pricing of news, the long memory effect of news, and the limitations of financial sentiment analysis in fully extracting predictive power from news data. ...

MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction ArXiv ID: 2409.05698 “View on arXiv” Authors: Unknown Abstract It is widely acknowledged that extracting market sentiments from news data benefits market predictions. However, existing methods of using financial sentiments remain simplistic, relying on equal-weight and static aggregation to manage sentiments from multiple news items. This leads to a critical issue termed ``Aggregated Sentiment Homogenization’’, which has been explored through our analysis of a large financial news dataset from industry practice. This phenomenon occurs when aggregating numerous sentiments, causing representations to converge towards the mean values of sentiment distributions and thereby smoothing out unique and important information. Consequently, the aggregated sentiment representations lose much predictive value of news data. To address this problem, we introduce the Market Attention-weighted News Aggregation Network (MANA-Net), a novel method that leverages a dynamic market-news attention mechanism to aggregate news sentiments for market prediction. MANA-Net learns the relevance of news sentiments to price changes and assigns varying weights to individual news items. By integrating the news aggregation step into the networks for market prediction, MANA-Net allows for trainable sentiment representations that are optimized directly for prediction. We evaluate MANA-Net using the S&P 500 and NASDAQ 100 indices, along with financial news spanning from 2003 to 2018. Experimental results demonstrate that MANA-Net outperforms various recent market prediction methods, enhancing Profit & Loss by 1.1% and the daily Sharpe ratio by 0.252. ...

Predicting public market behavior from private equity deals ArXiv ID: 2407.01818 “View on arXiv” Authors: Unknown Abstract We process private equity transactions to predict public market behavior with a logit model. Specifically, we estimate our model to predict quarterly returns for both the broad market and for individual sectors. Our hypothesis is that private equity investments (in aggregate) carry predictive signal about publicly traded securities. The key source of such predictive signal is the fact that, during their diligence process, private equity fund managers are privy to valuable company information that may not yet be reflected in the public markets at the time of their investment. Thus, we posit that we can discover investors’ collective near-term insight via detailed analysis of the timing and nature of the deals they execute. We evaluate the accuracy of the estimated model by applying it to test data where we know the correct output value. Remarkably, our model performs consistently better than a null model simply based on return statistics, while showing a predictive accuracy of up to 70% in sectors such as Consumer Services, Communications, and Non Energy Minerals. ...

Detection of financial opportunities in micro-blogging data with a stacked classification system ArXiv ID: 2404.07224 “View on arXiv” Authors: Unknown Abstract Micro-blogging sources such as the Twitter social network provide valuable real-time data for market prediction models. Investors’ opinions in this network follow the fluctuations of the stock markets and often include educated speculations on market opportunities that may have impact on the actions of other investors. In view of this, we propose a novel system to detect positive predictions in tweets, a type of financial emotions which we term “opportunities” that are akin to “anticipation” in Plutchik’s theory. Specifically, we seek a high detection precision to present a financial operator a substantial amount of such tweets while differentiating them from the rest of financial emotions in our system. We achieve it with a three-layer stacked Machine Learning classification system with sophisticated features that result from applying Natural Language Processing techniques to extract valuable linguistic information. Experimental results on a dataset that has been manually annotated with financial emotion and ticker occurrence tags demonstrate that our system yields satisfactory and competitive performance in financial opportunity detection, with precision values up to 83%. This promising outcome endorses the usability of our system to support investors’ decision making. ...

Introduction of L0 norm and application of L1 and C1 norm in the study of time-series ArXiv ID: 2401.05423 “View on arXiv” Authors: Unknown Abstract Four markets are considered: Cryptocurrencies / South American exchange rate / Spanish Banking indices and European Indices and studied using TDA (Topological Data Analysis) tools. These tools are used to predict and showcase both strengths and weakness of the current TDA tools. In this paper a new tool $L0$ norm is defined and complemented with the already existing $C1$ norm. ...

Media Moments and Corporate Connections: A Deep Learning Approach to Stock Movement Classification ArXiv ID: 2309.06559 “View on arXiv” Authors: Unknown Abstract The financial industry poses great challenges with risk modeling and profit generation. These entities are intricately tied to the sophisticated prediction of stock movements. A stock forecaster must untangle the randomness and ever-changing behaviors of the stock market. Stock movements are influenced by a myriad of factors, including company history, performance, and economic-industry connections. However, there are other factors that aren’t traditionally included, such as social media and correlations between stocks. Social platforms such as Reddit, Facebook, and X (Twitter) create opportunities for niche communities to share their sentiment on financial assets. By aggregating these opinions from social media in various mediums such as posts, interviews, and news updates, we propose a more holistic approach to include these “media moments” within stock market movement prediction. We introduce a method that combines financial data, social media, and correlated stock relationships via a graph neural network in a hierarchical temporal fashion. Through numerous trials on current S&P 500 index data, with results showing an improvement in cumulative returns by 28%, we provide empirical evidence of our tool’s applicability for use in investment decisions. ...