Satellite imagery and ML improve livelihood measurements and estimate electrification's impact.
problem Sparse data hinders policy development and evaluation.
method Satellite imagery, machine learning, and ML inference techniques.
result Grid access improves rural asset wealth by 0.17 standard deviations.
Study examines market risks on pension system sustainability.
problem Impact of market risks on pension corpus sustainability.
method Monte Carlo simulations with historical data.
result Market risks significantly impact pension corpus sustainability.
Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce. Previous work has shown that it is possible to measure local-level economic livelihoods using high-resolution satellite imagery. However, such imagery is relatively expensive to …
Research uses SWT and BDLSTM to forecast stock and oil prices amid COVID-19.
problem Impact of COVID-19 on stock and oil prices forecasting.
method Integrates Stationary Wavelet Transform and Bidirectional Long Short-Term Memory networks.
result BDLSTM+WT-ADA achieved satisfactory results in Crude Oil price forecasting.
Optimizes lockdown strategies to balance economic activities and virus spread.
problem Balancing economic activities and virus spread during lockdowns.
method Modeling the pandemic as SEIR, applying Granovetter threshold model for social distancing, and using NSGA-II optimization.
result Optimal lockdown policies for ten weeks to minimize infections and economic impact.
Study predicts doubling of U.S. maize insurance claims due to climate change.
problem Climate change increases U.S. maize loss probability, impacting insurance claims.
method Neural Network Monte Carlo simulations to predict crop loss metrics.
result Doubling of annual probability of maize Yield Protection insurance claims by mid-century.
Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact. Self-driving vehicles (SDVs) are expected to prevent road accidents and save millions of lives while improving the liveli…
Machine learning models are increasingly used to automate decisions that affect humans - deciding who should receive a loan, a job interview, or a social service. In such applications, a person should have the ability to change the decision of a model. When a person is denied a loan by a credit score, for example, they…
Project forecasts crop prices to help Indian farmers choose optimal crops for better ROI.
problem Indian farmers struggle to choose crops that fetch decent profits due to lack of scientific decision-making.
method Price forecasting to create an optimal portfolio of crops for better ROI.
result Data-driven decision-making for crop selection leads to higher estimated ROI.
Study predicts global trade impacts using deep learning during the COVID-19 period.
problem Forecasting global trade impacts during the COVID-19 pandemic.
method Developed a sustainable prediction process using Long-Short Term Memory (LSTM) deep learning model.
result Accurately predicted daily imports and exports for the next 180 days during the pandemic.
Study forecasts food security trends using real-time data.
problem Food insecurity prediction for sub-national regions.
method Quantitative methodology combining various machine learning models.
result Reservoir Computing model performs best in food security prediction.
LightGBM outperforms other models in predicting pH values in Georgia, USA.
problem Accurate water quality prediction for effective resource management and pollution mitigation.
method Five distinct predictive models (linear regression, Random Forest, XGBoost, LightGBM, MLP neural network) were assessed for pH value forecasting in Georgia, USA.
result LightGBM achieved the highest average precision in predicting pH values.
The paper models CBF dynamics using queueing theory and insurance risk models.
problem Understanding and optimizing the operation of community bail funds.
method Combining queueing theory with classic insurance risk models.
result A fluid limit for the blocking model of CBF operations.
New method uses impact IRR to assess impact investments.
problem Determining financial returns of impact investments remains challenging.
method Adapts modern portfolio theory and financial tools to evaluate impact investments.
result Demonstrates the feasibility and utility of impact IRR for optimizing impact investments.
Droughts, with their increasing frequency of occurrence, continue to negatively affect livelihoods and elements at risk. For example, the 2011 in drought in east Africa has caused massive losses document to have cost the Kenyan economy over $12bn. With the foregoing, the demand for ex-ante drought monitoring systems is…
Theory of price impact on bond term structure.
problem Understanding price impact in interest rate markets.
method Formulated instantaneous and transient price impact on bonds with different maturities, connecting to no-arbitrage theory.
result Price impact can be embedded in the pricing measure and no-arbitrage preserved.
We study the problem of what causes prices to change. We define the mechanical impact of a trading order as the change in future prices in the absence of any future changes in decision making, and its it informational impact as the remainder of the total impact once mechanical impact is removed. We introduce a method o…
The price impact for a single trade is estimated by the immediate response on an event time scale, i.e., the immediate change of midpoint prices before and after a trade. We work out the price impacts across a correlated financial market. We quantify the asymmetries of the distributions and of the market structures of …
Study liquidity impact on spread option pricing.
problem Imperfect liquidity in stock markets affects option pricing.
method Developed partial-impact and full-impact models to analyze European spread options.
result Full impact model leads to higher option prices due to increased stock buying.
Paper examines costs of using wrong price impact models in trading.
problem Misspecifying price impact models in trading predictions.
method Derives formulas for misspecification costs and applies to trading data.
result Misspecification costs are asymmetric, affecting profits and losses.
There are two schools of thought regarding market impact modeling. On the one hand, seminal papers by Almgren and Chriss introduced a decomposition between a permanent market impact and a temporary (or instantaneous) market impact. This decomposition is used by most practitioners in execution models. On the other hand,…
Transient market impact explained via Nash equilibrium in a game.
problem Understanding the transient nature of market impact.
method Analyzing a game between a trader and an arbitrageur, deriving decay kernels.
result Implied transient impact can be derived from trader behavior at Nash equilibrium.
Defines hybrid systems on principal bundles and studies impact effects.
problem Understanding impact effects in hybrid mechanical systems.
method Defines hybrid systems on principal bundles, studies underlying geometry, and finds conditions for impact preservation.
result Conditions for preservation of both exterior and interior impacts by mechanical connections.
This study examines how market makers balance risk and impact in foreign exchange markets.
problem Balancing risk management with market impact in foreign exchange markets.
method An intermediate scenario approach considering both instantaneous and permanent market impact components.
result Transient market impact is more prevalent than previously thought, challenging traditional market impact models.
Decomposing market impact into diffusive components
problem Market impact scaling
method Decomposing impact into realized and counterfactual returns
result Implication of square-root law in information-neutral regime
The notion of market impact is subtle and sometimes misinterpreted. Here we argue that impact should not be misconstrued as volatility. In particular, the so-called ``square-root impact law'', which states that impact grows as the square-root of traded volume, has nothing to do with price diffusion, i.e. that typical p…
Modeling market makers' quoting strategies to understand price impact.
problem Understanding how price impact arises from market makers' quoting strategies.
method Modeling market making as a dynamic auction using Stochastic Differential Games and finding Nash Equilibrium.
result The price impact function derived from market makers' strategies matches the Almgren-Chriss model.
Estimates cross-impact on derivatives markets using E-Mini futures and options.
problem Empirical estimation of cross-impact on complex financial instruments like derivatives.
method Modeling derivatives prices as a function of stochastic factors and trades on both factors and derivatives.
result Simple framework successfully captures cross-impact on derivatives markets.
Two models are identified for robust cross-impact analysis.
problem Developing and validating cross-impact models that fit data and are well-behaved.
method Classified cross-impact models according to desirable properties and evaluated them on three asset classes.
result Only one model satisfies all desirable properties and is suitable for applications.
Study shows integrating OFI from multiple levels improves price impact explanation but not forecasting.
problem Explaining and forecasting price movements in equity markets using OFI.
method Systematic approach to combine OFIs from multiple levels into an integrated variable, testing multi-asset models with and without cross-impact terms.
result Lagged cross-asset OFIs improve future return forecasting but not contemporaneous price impact.
This paper investigates the market impact of passive orders.
problem Understanding the market impact of passive orders executed through limit orders.
method Developed a microstructure model linking liquidity dynamics and price moves, replacing the constant information content assumption with a function dependent on available volume.
result Derived useful approximations for market impact curves, leading to closed-form formulas.
The asymmetric price impact between the institutional purchases and sales of 32 liquid stocks in Chinese stock markets in year 2003 is carefully studied. We analyze the price impact in both drawup and drawdown trends with consecutive positive and negative daily price changes, and test the dependence of the price impact…
Develops a framework for valuing Asian options with market impact.
problem Valuation of Asian options under price impact.
method Discrete-time quote-level model, continuous-time limits, Hamilton-Jacobi-Bellman equations, CRR-style tree-based Bellman algorithm.
result Endogenous trading volumes feed into prices and costs, leading to nontrivial bid-ask spreads.
Artificial intelligence has impacted many aspects of human life. This paper studies the impact of artificial intelligence on economic theory. In particular we study the impact of artificial intelligence on the theory of bounded rationality, efficient market hypothesis and prospect theory.
Develops SPT with price impact, deriving formulas for wealth and arbitrage conditions.
problem Tackles price impact in high-dimensional markets.
method Incorporates nonlinear price impact and impact decay models.
result Derives master formula for trading strategies and wealth dynamics.
Study examines market impact of small orders in futures contracts.
problem Understanding market impact of small orders in financial markets.
method Empirical study using tick data, normalizing results, proposing a simple linear model.
result Market impact of small orders is either linear or concave, depending on the instrument.
We develop a theory for the market impact of large trading orders, which we call metaorders because they are typically split into small pieces and executed incrementally. Market impact is empirically observed to be a concave function of metaorder size, i.e., the impact per share of large metaorders is smaller than that…
We extend the "No-dynamic-arbitrage and market impact"-framework of Jim Gatheral [Quantitative Finance, 10(7): 749-759 (2010)] to the multi-dimensional case where trading in one asset has a cross-impact on the price of other assets. From the condition of absence of dynamical arbitrage we derive theoretical limits for t…
Market impact has become a subject of increasing concern among academics and industry experts. We put forward a price impact model which considers the heteroscedasticity of price in the time dimension and dependency between permanent impact and temporary impact. We discuss and derive the extremum of the expectation of …
CCVA adjusts for climate change impacts on financial valuation.
problem Climate change impacts on financial valuation are currently ignored.
method Flexible parameterization to capture climate impacts on hazard rates.
result Significant impacts on interest rate swaps even with slow climate change.
Study finds price impact follows a 'double' square-root law, suggesting mechanical origin.
problem Understanding the origin of price impact in markets.
method Detailed dataset of Tokyo Stock Exchange orders, analyzing single and metaorders.
result Price impact follows a 'double' square-root law, indicating mechanical origin rather than information.
Proposes a new model to measure trade impact and information content in fluctuating markets.
problem Measuring price impact and information content of trades in a time-varying market setting.
method Non-linear observation-driven model for dynamically estimating market impact and information content.
result Market impact shows intraday patterns with large fluctuations, some of which are exogenous.
DeepSIP predicts network failures' impact using CNN from syslog and traffic data.
problem Predicting service impact from network failures.
method Temporal multimodal CNN for predicting time to recovery and traffic loss.
result DeepSIP reduced prediction error by approximately 50%.
Study utility indifference pricing in a Bachelier model with small linear price impact.
problem Utility indifference pricing in a model with linear price impact.
method Analyzes the Bachelier model with exponential utility indifference prices for vanilla European options.
result Computes the scaling limit of utility indifference prices for a vanishing price impact inversely proportional to risk aversion.
Investors' strategies in a market influenced by price impact are analyzed, showing aggressive behavior when impact exceeds a critical point.
problem Strategic interaction and Nash equilibria of investors in a financial market with price impact.
method Analysis of Nash equilibria for relative investors with CRRA and CARA utility functions in a Brownian motion-driven market, considering both linear and non-linear price impacts.
result Investors' aggressive behavior is observed when price impact exceeds a critical parameter.
Investigates cross-impact kernels for financial asset prices.
problem Understanding and parameterizing cross-impact kernels for financial asset prices.
method Examined martingale-admissible and no-statistical-arbitrage-admissible kernels, determined their overlap, and provided calibration formulas.
result Identified the overlap between martingale-admissible and no-statistical-arbitrage-admissible kernels and provided formulas for their calibration.
The paper uses regression models to predict COVID-19 spread and its stock market impact.
problem Predicting and understanding the impact of COVID-19 on stock markets.
method Logistic curve model with Bayesian regression for predictive analytics.
result Different crises have different impacts on the same stocks.
We propose a general framework to describe the impact of different events in the order book, that generalizes previous work on the impact of market orders. Two different modeling routes can be considered, which are equivalent when only market orders are taken into account. One model posits that each event type has a te…