EarnMore uses masked stock representations to train RL agents for customizable stock pools efficiently.
problem Training RL agents for customizable stock pools (CSPs) is computationally expensive and unstable.
method EarnMore introduces a mechanism to mask out stocks outside the target pool, learns meaningful stock representations, and uses a re-weighting mechanism to focus on favorable stocks.
result EarnMore significantly outperforms state-of-the-art baselines in profit metrics with over 40% improvement.
Model predicts stock prices using GAN and RoI Pooling.
problem Predicting stock prices influenced by macroeconomic factors.
method Markov Decision Process, GAN, RoI Pooling.
result Identifies macroeconomic factors' influence on stock prices.
This paper predicts stock prices using LLMs and news embeddings.
problem Predicting stock prices with high accuracy and relevance.
method Integrates LLMs with stock name embeddings and attention mechanisms for news filtering.
result Reduces MAE by 7.11% compared to baseline.
This paper investigates the impact of dark pools on price discovery (the efficiency of prices on stock exchanges to aggregate information). Assets are traded in either an exchange or a dark pool, with the dark pool offering better prices but lower execution rates. Informed traders receive noisy and heterogeneous signal…
Crowded trades cluster investors, affecting stock price stability.
problem Crowded trades lead to price instability and systemic risk.
method Market clustering measure using granular trading data.
result Market clustering has a causal effect on stock return distribution tails, especially positive tail.
We study the problem of allocating stocks to dark pools. We propose and analyze an optimal approach for allocations, if continuous-valued allocations are allowed. We also propose a modification for the case when only integer-valued allocations are possible. We extend the previous work on this problem to adversarial sce…
Although the threshold network is one of the most used tools to characterize the underlying structure of a stock market, the identification of the optimal threshold to construct a reliable stock network remains challenging. In this paper, the concept of dynamic consistence between the threshold network and the stock ma…
Develops a new class of forward performance processes for investment pools.
problem Investment performance in market models with continuous semimartingale stock prices.
method Constructs a broad class of forward performance processes with power mixture initial conditions.
result Characterizes and derives properties of two-power mixture forward performance processes.
A universal LSTM model outperforms asset-specific models in forecasting stock volatilities.
problem Forecasting stock volatilities across different assets.
method Trained an LSTM network on a pooled dataset of liquid stocks to forecast daily realized volatilities.
result The LSTM model consistently outperforms other asset-specific parametric models in volatility forecasting.
We argue that an important contributing factor into market inefficiency is the lack of a robust mechanism for the stock price to rise if a company has good earnings, e.g., via buybacks/dividends. Instead, the stock price is prone to volatility due to rather random perception/interpretation of earnings announcements (am…
The study uses machine learning to forecast stock volatility, showing superior performance over traditional methods.
problem Forecasting stock volatility using machine learning.
method Pooling stock data, using a proxy for market volatility, and applying neural networks.
result The proposed methodology yields superior out-of-sample forecasts over traditional methods.
Long-term investors, different from short-term traders, focus on examining the underlying forces that affect the well-being of a company. They rely on fundamental analysis which attempts to measure the intrinsic value an equity. Quantitative investment researchers have identified some value factors to determine the cos…
Introduces GA-P/E, a growth-adjusted stock valuation measure.
problem Evaluating stock value and predicting future returns.
method Computes a payback period adjusted for earnings growth, using a sorted portfolio methodology.
result Low GA-P/E stocks outperform high GA-P/E stocks in absolute and risk-adjusted returns.
ChatGPT snapshots predict future stock returns.
problem Predicting future stock returns using pre-cutoff text.
method Extracted LLM outlook scores from OpenAI snapshots.
result Outlook scores positively correlate with future stock returns.
Paper detects social media influencers affecting financial markets.
problem Impact of social media influencers on financial markets.
method Developed an early warning system for detecting suspicious social network activity.
result Discrepancy in meme and non-meme stocks' reactions to social networks.
This paper uses deep reinforcement learning to optimize stock portfolios considering transaction costs and risks.
problem Optimizing stock portfolios with transaction costs and risks.
method Formulated stock portfolio optimization as a reinforcement learning problem, applied DDPG, GDPG, and PPO algorithms, and used Wavelet Transform.
result DDPG and GDPG algorithms outperformed PPO in continuous action space.
Research shows franchised fast food companies' stock prices decline more during recessions.
problem Impact of recession on franchised fast food companies' stock prices.
method Analyzed stock price data with Weibull distribution.
result Recessions have a more severe impact on franchised fast food companies' stock prices.
New methods for equity fund selection and portfolio construction using mutual fund top holdings.
problem Classic equity fund selection and portfolio construction problems.
method Propose an easy-to-implement framework to produce a long-short portfolio from mutual fund top holdings.
result Generate impressive results and show statistical evidence.
We present the OpenAI Remote Rendering Backend (ORRB), a system that allows fast and customizable rendering of robotics environments. It is based on the Unity3d game engine and interfaces with the MuJoCo physics simulation library. ORRB was designed with visual domain randomization in mind. It is optimized for cloud de…
Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of electronic market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary price formation mechanism relating the dynamics of supply and demand for a s…
We model the impact costs of a strategy that trades a basket of correlated instruments, by extending to the multivariate case the linear propagator model previously used for single instruments. Our specification allows us to calibrate a cost model that is free of arbitrage and price manipulation. We illustrate our resu…
The performance of financial market prediction systems depends heavily on the quality of features it is using. While researchers have used various techniques for enhancing the stock specific features, less attention has been paid to extracting features that represent general mechanism of financial markets. In this pape…
Proposes a new cost function for neural networks to improve prediction interval quality.
problem Uncertainty-guided neural network training convergence issues and suboptimal prediction intervals.
method Proposes a customizable smooth cost function for NNs to optimize prediction intervals.
result Significant improvement in prediction interval quality, convergence, and reliability.
Convolutional neural networks (CNNs) have achieved remarkable performance in many applications, especially in image recognition tasks. As a crucial component of CNNs, sub-sampling plays an important role for efficient training or invariance property, and max-pooling and arithmetic average-pooling are commonly used sub-…
Deep-n-Cheap automates deep learning model search for low complexity.
problem Finding efficient deep learning models for various datasets.
method Automated search framework for architecture and hyperparameters, including search transfer.
result Models offer comparable performance to state-of-the-art but are faster to train.
Global catastrophe risk pools increase financial resilience by diversifying risk and including more countries.
problem Low- to middle-income countries rely heavily on foreign aid for recovery from extreme weather events, which is slow and uncertain.
method Developed a method to form global catastrophe risk pools that maximize risk diversification and select countries with low bilateral correlations or low shares in the pool risk.
result Global pooling increases risk diversification, lowers countries' shares in the pool risk, and increases the number of countries benefiting from risk pooling.
In most convolution neural networks (CNNs), downsampling hidden layers is adopted for increasing computation efficiency and the receptive field size. Such operation is commonly so-called pooling. Maximation and averaging over sliding windows (max/average pooling), and plain downsampling in the form of strided convoluti…
New image classifier uses hierarchical max-pooling with local pooling.
problem Improving image classification accuracy with variable spatial relationships.
method Introduces a hierarchical max-pooling model with additional local pooling for convolutional neural networks.
result Demonstrates improved performance in estimating image features.
We seek to improve deep neural networks by generalizing the pooling operations that play a central role in current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in (1) learning a pooling function via (…
Optimizes diversification in catastrophe risk pooling using asymptotic analysis.
problem Maximizing diversification benefit from catastrophic events in insurance pools.
method Asymptotic analysis to solve high-dimensional optimization problem.
result Derives an asymptotically optimal pool that approximates practical optimal pool.
An efficient algorithm identifies labels from sparse pooled data.
problem Identify unknown labels from condensed measurements of items.
method Design an efficient algorithm based on novel pooling scheme.
result Resolves statistical-to-computational gap in minimum pools needed.
This research simplifies lending pools in decentralized finance for better understanding and security.
problem Complexity and lack of executable models make lending pools hard to understand and predict.
method Developed a formal model to reflect common features of lending pools and proved general properties.
result Proved correct handling of funds and described vulnerabilities and attacks.
Graph Neural Network (GNN) research has concentrated on improving convolutional layers, with little attention paid to developing graph pooling layers. Yet pooling layers can enable GNNs to reason over abstracted groups of nodes instead of single nodes. To close this gap, we propose a graph pooling layer relying on the …
Existing approaches for automatically generating mathematical word problems are deprived of customizability and creativity due to the inherent nature of template-based mechanisms they employ. We present a solution to this problem with the use of deep neural language generation mechanisms. Our approach uses a Character …
Develops a novel global pooling framework using optimal transport.
problem Sub-optimal performance in global pooling operations.
method Regularized Optimal Transport (ROT) for generalized global pooling.
result ROTP layers can improve performance in various machine learning scenarios.
We consider interactive algorithms in the pool-based setting, and in the stream-based setting. Interactive algorithms observe suggested elements (representing actions or queries), and interactively select some of them and receive responses. Pool-based algorithms can select elements at any order, while stream-based algo…
In this work we compute lower Lipschitz bounds of ℓp pooling operators for p=1,2,∞ as well as ℓp pooling operators preceded by half-rectification layers. These give sufficient conditions for the design of invertible neural network layers. Numerical experiments on MNIST and image patches confirm tha…
We propose a novel graph pooling operation using cliques as the unit pool. As this approach is purely topological, rather than featural, it is more readily interpretable, a better analogue to image coarsening than filtering or pruning techniques, and entirely nonparametric. The operation is implemented within graph con…
Improved privacy-preserving statistical estimates with customizable noise reduction.
problem Balancing privacy and accuracy in statistical estimation.
method Introducing the Brownian mechanism, which adds Gaussian noise to a sequence of estimates, gradually reducing it based on the practitioner's needs.
result The Brownian mechanism produces more accurate estimates while maintaining strong privacy guarantees, outperforming existing methods.
Proposes a graph pooling method leveraging node proximity for hierarchical graph representation learning.
problem Efficiently exploiting the geometry of graph data for hierarchical representation learning.
method Combines node proximity with kernel representation of topology and node features for adaptive node signal similarities evaluation.
result Achieves state-of-the-art performance on graph classification benchmark datasets.
Global pooling, such as max- or sum-pooling, is one of the key ingredients in deep neural networks used for processing images, texts, graphs and other types of structured data. Based on the recent DeepSets architecture proposed by Zaheer et al. (NIPS 2017), we introduce a Set Aggregation Network (SAN) as an alternative…
Graph neural networks, which generalize deep neural network models to graph structured data, have attracted increasing attention in recent years. They usually learn node representations by transforming, propagating and aggregating node features and have been proven to improve the performance of many graph related tasks…
Study characterizes Uniswap v3 liquidity pools using transaction graphs and identifies ideal trading conditions.
problem Computational expense in analyzing the full Uniswap v3 ecosystem.
method Extracted and analyzed a sub-universe of liquidity pools, using transaction graphs and graph2vec algorithm.
result Identified seven clusters of liquidity takers with similar trading preferences and introduced an ideal crypto law.
A novel approach predicts long-term stock price trends using 2D-convolutional encoders and semantic segmentation.
problem Predicting long-term daily stock price changes with deep learning models.
method Proposes a hierarchical CNN structure with Atrous Spatial Pyramid Pooling blocks to capture both long and short-term temporal relationships.
result Achieved overall accuracy and AUC of 78.18% and 0.88 for predicting trends over the next 20 days.
Optimal rebalancing strategy improves AMM pool performance by 25%.
problem Optimizing the sequence of weights in dynamic AMM pools to minimize rebalancing costs.
method Using optimal interpolation and a cheap-to-compute approximation to achieve nearly optimal rebalancing.
result Approximately-optimal weight changes lead to significant increases in pool performance (up to 25%) under various conditions.
High-fee pools attract more liquidity but execute less volume; low-fee pools have more stable LPs.
problem Optimal liquidity supply and execution on decentralized exchanges with fixed gas costs.
method Analysis of Uniswap data to compare high- and low-fee pools.
result Fragmented liquidity leads to more LPs and competition, improving overall market efficiency.
Study optimal liquidation strategies in lit and dark pools with and without regulation.
problem Optimal liquidation strategies in dark and lit pools with execution uncertainty.
method Design optimal make-take fee policies, solve HJB-Fokker-Planck systems, use BSDEs.
result Explicit solutions for optimal strategies in both competitive and regulated markets.
Deep neural networks reduce portfolio tail-risk by 99% in crisis-era simulations.
problem Managing tail risk in financial portfolios.
method Parameterizing convex-risk minimization with deep neural networks.
result Significant reduction in one-day 99% CVaR.