An Advanced Ensemble Deep Learning Framework for Stock Price Prediction Using VAE, Transformer, and LSTM Model ArXiv ID: 2503.22192 “View on arXiv” Authors: Unknown Abstract This research proposes a cutting-edge ensemble deep learning framework for stock price prediction by combining three advanced neural network architectures: The particular areas of interest for the research include but are not limited to: Variational Autoencoder (VAE), Transformer, and Long Short-Term Memory (LSTM) networks. The presented framework is aimed to substantially utilize the advantages of each model which would allow for achieving the identification of both linear and non-linear relations in stock price movements. To improve the accuracy of its predictions it uses rich set of technical indicators and it scales its predictors based on the current market situation. By trying out the framework on several stock data sets, and benchmarking the results against single models and conventional forecasting, the ensemble method exhibits consistently high accuracy and reliability. The VAE is able to learn linear representation on high-dimensional data while the Transformer outstandingly perform in recognizing long-term patterns on the stock price data. LSTM, based on its characteristics of being a model that can deal with sequences, brings additional improvements to the given framework, especially regarding temporal dynamics and fluctuations. Combined, these components provide exceptional directional performance and a very small disparity in the predicted results. The present solution has given a probable concept that can handle the inherent problem of stock price prediction with high reliability and scalability. Compared to the performance of individual proposals based on the neural network, as well as classical methods, the proposed ensemble framework demonstrates the advantages of combining different architectures. It has a very important application in algorithmic trading, risk analysis, and control and decision-making for finance professions and scholars. ...

Equilibrium Reward for Liquidity Providers in Automated Market Makers ArXiv ID: 2503.22502 “View on arXiv” Authors: Unknown Abstract We find the equilibrium contract that an automated market maker (AMM) offers to their strategic liquidity providers (LPs) in order to maximize the order flow that gets processed by the venue. Our model is formulated as a leader-follower stochastic game, where the venue is the leader and a representative LP is the follower. We derive approximate closed-form equilibrium solutions to the stochastic game and analyze the reward structure. Our findings suggest that under the equilibrium contract, LPs have incentives to add liquidity to the pool only when higher liquidity on average attracts more noise trading. The equilibrium contract depends on the external price, the pool reference price, and the pool reserves. Our framework offers insights into AMM design for maximizing order flow while ensuring LP profitability. ...

Dynamic Asset Pricing Theory for Life Contingent Risks ArXiv ID: 2503.21256 “View on arXiv” Authors: Unknown Abstract Although the valuation of life contingent assets has been thoroughly investigated under the framework of mathematical statistics, little financial economics research pays attention to the pricing of these assets in a non-arbitrage, complete market. In this paper, we first revisit the Fundamental Theorem of Asset Pricing (FTAP) and the short proof of it. Then we point out that discounted asset price is a martingale only when dividends are zero under all random states of the world, using a simple proof based on pricing kernel. Next, we apply Fundamental Theorem of Asset Pricing (FTAP) to find valuation formula for life contingent assets including life insurance policies and life contingent annuities. Last but not least, we state the assumption of static portfolio in a dynamic economy, and clarify the FTAP that accommodates the valuation of a portfolio of life contingent policies. ...

From Deep Learning to LLMs: A survey of AI in Quantitative Investment ArXiv ID: 2503.21422 “View on arXiv” Authors: Unknown Abstract Quantitative investment (quant) is an emerging, technology-driven approach in asset management, increasingy shaped by advancements in artificial intelligence. Recent advances in deep learning and large language models (LLMs) for quant finance have improved predictive modeling and enabled agent-based automation, suggesting a potential paradigm shift in this field. In this survey, taking alpha strategy as a representative example, we explore how AI contributes to the quantitative investment pipeline. We first examine the early stage of quant research, centered on human-crafted features and traditional statistical models with an established alpha pipeline. We then discuss the rise of deep learning, which enabled scalable modeling across the entire pipeline from data processing to order execution. Building on this, we highlight the emerging role of LLMs in extending AI beyond prediction, empowering autonomous agents to process unstructured data, generate alphas, and support self-iterative workflows. ...

A Causal Perspective of Stock Prediction Models ArXiv ID: 2503.20987 “View on arXiv” Authors: Unknown Abstract In the realm of stock prediction, machine learning models encounter considerable obstacles due to the inherent low signal-to-noise ratio and the nonstationary nature of financial markets. These challenges often result in spurious correlations and unstable predictive relationships, leading to poor performance of models when applied to out-of-sample (OOS) domains. To address these issues, we investigate \textit{“Domain Generalization”} techniques, with a particular focus on causal representation learning to improve a prediction model’s generalizability to OOS domains. By leveraging multi-factor models from econometrics, we introduce a novel error bound that explicitly incorporates causal relationships. In addition, we present the connection between the proposed error bound and market nonstationarity. We also develop a \textit{“Causal Discovery”} technique to discover invariant feature representations, which effectively mitigates the proposed error bound, and the influence of spurious correlations on causal discovery is rigorously examined. Our theoretical findings are substantiated by numerical results, showcasing the effectiveness of our approach in enhancing the generalizability of stock prediction models. ...

Equity Risk Premiums (ERP): Determinants, Estimation, and Implications – The 2025 Edition ArXiv ID: ssrn-5168609 “View on arXiv” Authors: Unknown Abstract The equity risk premium is the price of risk in equity markets, and it is not only a key input in estimating costs of equity and capital in both corporate Keywords: equity risk premium, cost of equity, valuation, corporate finance, risk and return, Equities Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 6.0/10 Quadrant: Street Traders Why: The paper focuses on practical estimation methods (historical, survey, implied) and uses empirical data from multiple markets, but relies on conceptual frameworks and regression analysis rather than advanced mathematical derivations. flowchart TD A["Research Goal: Determine 2025 Equity Risk Premium"] --> B["Methodology & Data Inputs"] B --> C["Computational Processes"] C --> D["Key Findings & Implications"] subgraph B ["Methodology & Data Inputs"] B1["Historical Market Returns"] B2["Inflation & Treasury Yields"] B3["Valuation Multiples<br>P/E, Dividend Yields"] end subgraph C ["Computational Processes"] C1["Historical Averages"] C2["Build-Up Models<br>ERP = RiskFree + Equity Risk Compensation"] C3["Inverse P/E Implied ERP"] end subgraph D ["Key Findings & Implications"] D1["Updated Cost of Equity<br>Estimates"] D2["Valuation Adjustments<br>for 2025"] D3["Strategic Asset Allocation<br>Guidance"] end

Relative portfolio optimization via a value at risk based constraint ArXiv ID: 2503.20340 “View on arXiv” Authors: Unknown Abstract In this paper, we consider $n$ agents who invest in a general financial market that is free of arbitrage and complete. The aim of each investor is to maximize her expected utility while ensuring, with a specified probability, that her terminal wealth exceeds a benchmark defined by her competitors’ performance. This setup introduces an interdependence between agents, leading to a search for Nash equilibria. In the case of two agents and CRRA utility, we are able to derive all Nash equilibria in terms of terminal wealth. For $n>2$ agents and logarithmic utility we distinguish two cases. In the first case, the probabilities in the constraint are small and we can characterize all Nash equilibria. In the second case, the probabilities are larger and we look for Nash equilibria in a certain set. We also discuss the impact of the competition using some numerical examples. As a by-product, we solve some portfolio optimization problems with probability constraints. ...

Asset pre-selection for a cardinality constrained index tracking portfolio with optional enhancement ArXiv ID: 2503.18609 “View on arXiv” Authors: Unknown Abstract An index tracker is a passive investment reproducing the return and risk of a market index, an enhanced index tracker offers a return greater than the index. We consider the selection of a portfolio of given cardinality to track an index, both without and with enhancement. We divide the problem into two steps - (1) pre-selection of assets; (2) estimation of weights on the assets chosen. The eight pre-selection procedures considered use: forward selection (FS) or backward elimination (BE); implemented using ordinary least squares (OLS) or least absolute deviation (LAD) regression; with a regression constant (c) or without (n). The two-step approach avoids the NP-hard problem arising when asset selection and asset weight computation are combined, leading to the selection of a cardinality constrained index tracking portfolio by computer intensive heuristic procedures with many examples in the literature solving for portfolios of 10 or fewer assets. Avoiding these restrictions, we show that out-of-sample tracking errors are roughly proportional to 1/sqrt(cardinality). We find OLS more effective than LAD; BE marginally more effective than FS; (n) marginally more effective than (c). For index tracking, both without and with enhancement, we use BE-OLS(n) in sensitivity analyses on the periods used for selection and evaluation. For a S&P 500 index tracker, we find that out-of-sample tracking error, transaction volume and return-risk ratios all improve as cardinality increases. By contrast for enhanced returns, cardinalities of the order 10 to 20 are most effective. The S&P 500 data used from 3/1/2005 to 29/12/2023 is available to researchers. ...

Cryptocurrency Time Series on the Binary Complexity-Entropy Plane: Ranking Efficiency from the Perspective of Complex Systems ArXiv ID: 2504.01974 “View on arXiv” Authors: Unknown Abstract We report the first application of a tailored Complexity-Entropy Plane designed for binary sequences and structures. We do so by considering the daily up/down price fluctuations of the largest cryptocurrencies in terms of capitalization (stable-coins excluded) that are worth $circa ,, 90 %$ of the total crypto market capitalization. With that, we focus on the basic elements of price motion that compare with the random walk backbone features associated with mathematical properties of the Efficient Market Hypothesis. From the location of each crypto on the Binary Complexity-Plane (BiCEP) we define an inefficiency score, $\mathcal I$, and rank them accordingly. The results based on the BiCEP analysis, which we substantiate with statistical testing, indicate that only Shiba Inu (SHIB) is significantly inefficient, whereas the largest stake of crypto trading is reckoned to operate in close-to-efficient conditions. Generically, our $\mathcal I$-based ranking hints the design and consensus architecture of a crypto is at least as relevant to efficiency as the features that are usually taken into account in the appraisal of the efficiency of financial instruments, namely canonical fiat money. Lastly, this set of results supports the validity of the binary complexity analysis. ...

QubitSwap: The Informational Edge in Decentralised Exchanges ArXiv ID: 2504.06281 “View on arXiv” Authors: Unknown Abstract Decentralised exchanges (DEXs) have transformed trading by enabling trustless, permissionless transactions, yet they face significant challenges such as impermanent loss and slippage, which undermine profitability for liquidity providers and traders. In this paper, we introduce QubitSwap, an innovative DEX model designed to tackle these issues through a hybrid approach that integrates an external oracle price with internal pool dynamics. This is achieved via a parameter $z$, which governs the balance between these price sources, creating a flexible and adaptive pricing mechanism. Through rigorous mathematical analysis, we derive a novel reserve function and pricing model that substantially reduces impermanent loss and slippage compared to traditional DEX frameworks. Notably, our results show that as $z$ approaches 1, slippage approaches zero, enhancing trading stability. QubitSwap marks a novel approach in DEX design, delivering a more efficient and resilient platform. This work not only advances the theoretical foundations of decentralised finance but also provides actionable solutions for the broader DeFi ecosystem. ...