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Showing new listings for Monday, 20 January 2025
- [1] arXiv:2501.09760 [pdf, html, other]
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Title: Boosting the Accuracy of Stock Market Prediction via Multi-Layer Hybrid MTL StructureSubjects: Statistical Finance (q-fin.ST); Machine Learning (cs.LG)
Accurate stock market prediction provides great opportunities for informed decision-making, yet existing methods struggle with financial data's non-linear, high-dimensional, and volatile characteristics. Advanced predictive models are needed to effectively address these complexities. This paper proposes a novel multi-layer hybrid multi-task learning (MTL) framework aimed at achieving more efficient stock market predictions. It involves a Transformer encoder to extract complex correspondences between various input features, a Bidirectional Gated Recurrent Unit (BiGRU) to capture long-term temporal relationships, and a Kolmogorov-Arnold Network (KAN) to enhance the learning process. Experimental evaluations indicate that the proposed learning structure achieves great performance, with an MAE as low as 1.078, a MAPE as low as 0.012, and an R^2 as high as 0.98, when compared with other competitive networks.
- [2] arXiv:2501.09773 [pdf, html, other]
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Title: The Structure of ScenariosComments: 18 pages, 8 figures, 4 tablesSubjects: General Economics (econ.GN)
Scenarios elicit possibilities that may be ignored otherwise, as well as causal relations between them. Even when too little information is available to assess reliable probabilities, the structure of linkages between evoked alternatives and perceived consequences can be analyzed by highlighting shared consequences of different alternatives or, conversely, diverging consequences of apparently similar alternatives. While according to current practice this structure is analyzed by exploring four possibilities obtained by crossing two macro-features, I illustrate the wider possibilities enabled by hypergraph analysis. An application is discussed.
- [3] arXiv:2501.09911 [pdf, other]
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Title: Institutional Adoption and Correlation Dynamics: Bitcoin's Evolving Role in Financial MarketsSubjects: Portfolio Management (q-fin.PM)
Bitcoin, widely recognized as the first cryptocurrency, has shown increasing integration with traditional financial markets, particularly major U.S. equity indices, amid accelerating institutional adoption. This study examines how Bitcoin exchange-traded funds and corporate Bitcoin holdings affect correlations with the Nasdaq 100 and the S&P 500, using rolling-window correlation, static correlation coefficients, and an event-study framework on daily data from 2018 to this http URL levels intensified following key institutional milestones, with peaks reaching 0.87 in 2024, and they vary across market regimes. These trends suggest that Bitcoin has transitioned from an alternative asset toward a more integrated financial instrument, carrying implications for portfolio diversification, risk management, and systemic stability. Future research should further investigate regulatory and macroeconomic factors shaping these evolving relationships.
- [4] arXiv:2501.09917 [pdf, html, other]
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Title: The Evolution of Unobserved Skill Returns in the U.S.: A New Approach Using Panel DataComments: 124 pages, 42 figuresSubjects: General Economics (econ.GN)
Economists disagree about the factors driving the substantial increase in residual wage inequality in the US over the past few decades. To identify changes in the returns to unobserved skills, we make a novel assumption about the dynamics of skills rather than about the stability of skill distributions across cohorts, as is standard. We show that our assumption is supported by data on test score dynamics for older workers in the HRS. Using survey data from the PSID and administrative data from the IRS and SSA, we estimate that the returns to unobserved skills $declined$ substantially in the late-1980s and 1990s despite an increase in residual inequality. Accounting for firm-specific pay differences yields similar results. Extending our framework to consider occupational differences in returns to skill and multiple unobserved skills, we further show that skill returns display similar patterns for workers employed in each of cognitive, routine, and social occupations. Finally, our results suggest that increasing skill dispersion, driven by rising skill volatility, explains most of the growth in residual wage inequality since the 1980s.
- [5] arXiv:2501.10001 [pdf, other]
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Title: Applying AHP and FUZZY AHP Management Methods to Assess the Level of Financial and Digital InclusionJournal-ref: Economic Computation and Economic Cybernetics Studies and Research, 55(4), 2021, 165-182Subjects: General Economics (econ.GN)
In today's world, marked by social distancing and lockdowns, the development of digital financial services is becoming increasingly important, but there is little empirical work documenting the most important factors that contribute to the process of financial and digital inclusion. Because the speed with which states adapt to digital financial services is critical, we must ask how prepared states are for this transition and how far they have progressed in terms of financial and digital inclusion. In this context, the goal of this article is, on the one hand, to propose a financial responsibility process framework capable of raising awareness of the most important harmonized key levels of financial and digital inclusion process that, when properly managed, can lead to achieving an optimal level of financial responsibility, and, on the other hand, to assess the financial and digital inclusion process of two different age groups of individuals who are active in the financial environment (15-34 and 35-59 age groups). The Analytical Hierarchy Process AHP and Fuzzy AHP approaches are proposed as a framework for assessing the mechanism of financial and digital inclusion in five East Central European countries. The findings reflect differences between the analyzed countries in terms of the key levels of financial and digital inclusion (where digital and financial education are the most important levels), with Croatia, Czech Republic, and Poland being the most integrated and Romania being the least. According to the findings, as a country or region's level of financial and digital inclusion increases, so does its level of financial responsibility. This research can be a useful tool in raising awareness about the importance of directed behavior for financial responsibility, particularly for policymakers.
New submissions (showing 5 of 5 entries)
- [6] arXiv:2405.08101 (replaced) [pdf, other]
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Title: Can machine learning unlock new insights into high-frequency trading?Comments: 66 pages, 6 figures, 11 tablesSubjects: Computational Finance (q-fin.CP); Machine Learning (cs.LG)
We design and train machine learning models to capture the nonlinear interactions between financial market dynamics and high-frequency trading (HFT) activity. In doing so, we introduce new metrics to identify liquidity-demanding and -supplying HFT strategies. Both types of HFT strategies increase activity in response to information events and decrease it when trading speed is restricted, with liquidity-supplying strategies demonstrating greater responsiveness. Liquidity-demanding HFT is positively linked with latency arbitrage opportunities, whereas liquidity-supplying HFT is negatively related, aligning with theoretical expectations. Our metrics have implications for understanding the information production process in financial markets.
- [7] arXiv:2410.19107 (replaced) [pdf, html, other]
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Title: What Drives Liquidity on Decentralized Exchanges? Evidence from the Uniswap ProtocolSubjects: Trading and Market Microstructure (q-fin.TR)
We study liquidity on decentralized exchanges (DEXs), identifying factors at the platform, blockchain, token pair, and liquidity pool levels with predictive power for market depth metrics. We introduce the v2 counterfactual spread metric, a novel criterion which assesses the degree of liquidity concentration in pools using the ``concentrated liquidity'' mechanism, allowing us to decompose the effect of a factor on market depth into two channels: total value locked (TVL) and concentration. We further explore how external liquidity from competing DEXs and private inventory on DEX aggregators influence market depth. We find that (i) gas prices, returns, and a DEX's share of trading volume affect liquidity through concentration, (ii) internalization of order flow by private market makers affects TVL but not the overall market depth, and (iii) volatility, fee revenue, and markout affect liquidity through both channels.
- [8] arXiv:2412.02446 (replaced) [pdf, html, other]
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Title: An Integral Equation in Portfolio Selection with Time-Inconsistent PreferencesSubjects: Mathematical Finance (q-fin.MF)
This paper discusses a nonlinear integral equation arising from portfolio selection with a class of time-inconsistent preferences. We propose a unified framework requiring minimal assumptions, such as right-continuity of market coefficients and square-integrability of the market price of risk. Our main contribution is proving the existence and uniqueness of the square-integrable solution for the integral equation under mild conditions. Illustrative applications include the mean-variance portfolio selection and the utility maximization with random risk aversion.
- [9] arXiv:2202.02300 (replaced) [pdf, html, other]
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Title: From Semi-Infinite Constraints to Structured Robust Policies: Optimal Gain Selection for Financial SystemsComments: Submitted for possible publicationSubjects: Systems and Control (eess.SY); Optimization and Control (math.OC); Computational Finance (q-fin.CP); Mathematical Finance (q-fin.MF)
This paper studies the robust optimal gain selection problem for financial trading systems, formulated within a \emph{double linear policy} framework, which allocates capital across long and short positions. The key objective is to guarantee \emph{robust positive expected} (RPE) profits uniformly across a range of uncertain market conditions while ensuring risk control. This problem leads to a robust optimization formulation with \emph{semi-infinite} constraints, where the uncertainty is modeled by a bounded set of possible return parameters. We address this by transforming semi-infinite constraints into structured policies -- the \emph{balanced} policy and the \emph{complementary} policy -- which enable explicit characterization of the optimal solution. Additionally, we propose a novel graphical approach to efficiently solve the robust gain selection problem, drastically reducing computational complexity. Empirical validation on historical stock price data demonstrates superior performance in terms of risk-adjusted returns and downside risk compared to conventional strategies. This framework generalizes classical mean-variance optimization by incorporating robustness considerations, offering a systematic and efficient solution for robust trading under uncertainty.
- [10] arXiv:2310.12671 (replaced) [pdf, other]
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Title: Neural networks for insurance pricing with frequency and severity data: a benchmark study from data preprocessing to technical tariffSubjects: Machine Learning (cs.LG); Risk Management (q-fin.RM)
Insurers usually turn to generalized linear models for modeling claim frequency and severity data. Due to their success in other fields, machine learning techniques are gaining popularity within the actuarial toolbox. Our paper contributes to the literature on frequency-severity insurance pricing with machine learning via deep learning structures. We present a benchmark study on four insurance data sets with frequency and severity targets in the presence of multiple types of input features. We compare in detail the performance of: a generalized linear model on binned input data, a gradient-boosted tree model, a feed-forward neural network (FFNN), and the combined actuarial neural network (CANN). The CANNs combine a baseline prediction established with a GLM and GBM, respectively, with a neural network correction. We explain the data preprocessing steps with specific focus on the multiple types of input features typically present in tabular insurance data sets, such as postal codes, numeric and categorical covariates. Autoencoders are used to embed the categorical variables into the neural network, and we explore their potential advantages in a frequency-severity setting. Model performance is evaluated not only on out-of-sample deviance but also using statistical and calibration performance criteria and managerial tools to get more nuanced insights. Finally, we construct global surrogate models for the neural nets' frequency and severity models. These surrogates enable the translation of the essential insights captured by the FFNNs or CANNs to GLMs. As such, a technical tariff table results that can easily be deployed in practice.
- [11] arXiv:2501.09636 (replaced) [pdf, html, other]
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Title: LLM-Based Routing in Mixture of Experts: A Novel Framework for TradingComments: Accepted by AAAI 2025 Workshop on AI for Social Impact - Bridging Innovations in Finance, Social Media, and Crime PreventionSubjects: Machine Learning (cs.LG); Trading and Market Microstructure (q-fin.TR)
Recent advances in deep learning and large language models (LLMs) have facilitated the deployment of the mixture-of-experts (MoE) mechanism in the stock investment domain. While these models have demonstrated promising trading performance, they are often unimodal, neglecting the wealth of information available in other modalities, such as textual data. Moreover, the traditional neural network-based router selection mechanism fails to consider contextual and real-world nuances, resulting in suboptimal expert selection. To address these limitations, we propose LLMoE, a novel framework that employs LLMs as the router within the MoE architecture. Specifically, we replace the conventional neural network-based router with LLMs, leveraging their extensive world knowledge and reasoning capabilities to select experts based on historical price data and stock news. This approach provides a more effective and interpretable selection mechanism. Our experiments on multimodal real-world stock datasets demonstrate that LLMoE outperforms state-of-the-art MoE models and other deep neural network approaches. Additionally, the flexible architecture of LLMoE allows for easy adaptation to various downstream tasks.