We reduce variance in monetization metrics for ranking experiments.
problem Heavy-tailed monetization metrics lead to unreliable conclusions in A/B experiments.
method Post-stratification combined with CUPED.
result Significant reduction in variance and improved decision stability.
This paper introduces strategies to maximize arbitrage profits in decentralized exchanges.
problem Maximizing profits from arbitrage loops in decentralized exchanges.
method Three strategies: MaxPrice, MaxMax, and Convex Optimization.
result The Convex Optimization strategy yields the highest monetized arbitrage profit in theory and practice.
In this small article one compromise monetization strategy is proposed, which hopefully may lead to a more satisfactory coexistence of IP manufacturers and consumers. The motto is "fair exchange": you use our IP-product, we use your product (in form of money); when you do not need our product any more, we change back.
MONET debiases graph embeddings by training on metadata-orthogonal dimensions.
problem Graph embeddings can be biased by node attributes, affecting fairness and interpretability.
method MONET trains embeddings on a hyperplane orthogonal to node metadata.
result MONET effectively removes bias from node embeddings, improving fairness and interpretability.
This paper monetizes customer load data to boost energy retailer profits.
problem Improving load forecasts to reduce energy imbalance costs.
method Cooperative game theory approach to quantify and distribute profits.
result Retailer gains significant profit from customer load data.
The ability to decompose scenes in terms of abstract building blocks is crucial for general intelligence. Where those basic building blocks share meaningful properties, interactions and other regularities across scenes, such decompositions can simplify reasoning and facilitate imagination of novel scenarios. In particu…
Study finds consumers are more price-sensitive before livestreams than after.
problem Understanding consumer demand during livestreaming lifecycle.
method Examined consumer demand for live events and recorded versions using data from a livestreaming platform.
result Demand is more price-sensitive before livestreams than after.
Predicting which players will convert to paying users in video games.
problem Retaining premium players in free-to-play games.
method Survival analysis techniques, Cox regression, random survival forest, conditional inference survival ensembles.
result Conditional inference survival ensembles method corrects bias in RSF models and predicts conversion.
Model analyzes OTC market making with reputation feedback.
problem Optimizing electronic OTC liquidity provision considering reputation.
method Developed a stochastic-control model with feedback loops.
result Policy alternates between reputation-building and franchise monetization phases.
Model analyzes how reputation feedback affects OTC market making.
problem Understanding and optimizing OTC market making strategies.
method Developed a stochastic-control model with feedback loops.
result Policy alternates between reputation building and franchise monetization.
Proposes Equity2Vec for cross-sectional asset pricing.
problem Sub-optimal performance due to missing cross-sectional effects and heterogeneous data.
method End-to-end deep learning framework with Equity2Vec for graph-based interactions and all alpha sources.
result Outperforms state-of-the-art approaches in real-world stock market datasets.
The emergence of mobile games has caused a paradigm shift in the video-game industry. Game developers now have at their disposal a plethora of information on their players, and thus can take advantage of reliable models that can accurately predict player behavior and scale to huge datasets. Churn prediction, a challeng…
LORL learns object-centric representations from vision and language.
problem Learning disentangled, object-centric scene representations from vision and language.
method LORL integrates unsupervised object discovery and segmentation with language input to learn object-centric concepts.
result LORL improves unsupervised object discovery methods and aids downstream tasks.
Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation opti…
GENESIS generates and samples 3D scenes by capturing object interactions.
problem Lack of models that explicitly capture object interactions in scene generation.
method Object-centric latent variables, spatial GMM, amortized inference, autoregressive prior.
result First object-centric generative model of 3D visual scenes.
Maximizing product use is a central goal of many businesses, which makes retention and monetization two central analytics metrics in games. Player retention may refer to various duration variables quantifying product use: total playtime or session playtime are popular research targets, and active playtime is well-suite…
Deep learning predicts cryptocurrency price movements from trade data.
problem Predicting short-term price changes in cryptocurrencies.
method Long Short-term Memory Network (LSTM) trained on trade-by-trade data.
result Optimal LSTM model achieves over 60% accuracy on out-of-sample test periods.
Chebyshev polynomials analyze Czech enterprises' stock dynamics.
problem Analyzing stock dynamics of enterprises not following normal distribution.
method Chebyshev polynomial decomposition of stock time series.
result Allows effective analysis of stock dynamics without variance and correlation.
Financial market created for wellbeing indices to mitigate socioeconomic risks.
problem Risk mitigation in financial indices of socioeconomic wellbeing.
method Developed new quantitative measure, created financial market, and implemented insurance instruments.
result Optimal portfolio weights and efficient frontiers for wellbeing indices.
Survival strategy for crypto firms in bear markets using BTC-to-sats payments rail.
problem Downside risk in crypto reserves during bear markets.
method Conservative treasury policy, operating line monetizing holdings, BTC-to-sats payments rail.
result Sustained mNAV premium through cycles with disclosed KPIs.
The paper introduces a new financial market for environmental indices to attract investors.
problem Inherent risks and sustainability concerns in environmental investments.
method Quantitative measures, econometric analysis, dynamic asset pricing tools, and financial options.
result Monetization and construction of country-specific environmental indices as dollar-denominated assets.
In E-commerce advertising, where product recommendations and product ads are presented to users simultaneously, the traditional setting is to display ads at fixed positions. However, under such a setting, the advertising system loses the flexibility to control the number and positions of ads, resulting in sub-optimal p…
The paper finds optimal levels for traders in mean-reverting markets.
problem Determining optimal levels for traders in mean-reverting markets.
method Analytical framework using heat potentials.
result Developed an analytical solution for optimal levels.
The paper shows how overreactions in stock prices can be predicted and used for trading.
problem Predicting and monetizing overreactions in stock prices as momentum signals.
method High-frequency data from Twitter, machine learning models (XGBoost, Random Forests, Deep Neural Networks, Bidirectional LSTMs), and SHAP for explainability.
result Machine learning models significantly outperform traditional overreaction rules at ultra short horizons.
New approach improves AI's handling of incomplete data.
problem Improving AI's ability to work with incomplete data.
method Proposes a new likelihood-free EM algorithm for faster, more efficient inference.
result More statistically efficient than masking approach and faster than conventional EM.
Paper evaluates whether AI is a bubble or a productivity revolution.
problem Determining if AI investments are a bubble or a sustainable technology.
method Hybrid review and diagnostic framework combining asset pricing foundations and modern econometric methods.
result AI investments show both genuine fundamentals and bubble-like fragilities.
New piracy-resistant watermarks for deep neural networks prevent false claims.
problem Protecting deep learning models from false claims by embedding pirate watermarks.
method Null embedding, a new approach to build watermarks that resist removal and addition.
result Null embedding achieves piracy resistance and other watermark properties over various tasks and models.
Our study proposes a new currency system to protect wealth from over-issued fiat and stablecoins.
problem The over-issuance of fiat and stablecoins undermines the stability of currency purchasing power.
method We introduce a parallel monetary system based on redeemable self-decaying money (RSDM) to provide a stable currency alternative.
result A parallel monetary system including RSDM, domestic fiat, and major reserve currencies can safeguard wealth and prevent the reverse Gresham law.