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on Big Data |
| By: | Pulikandala Nithish Kumar; Nneka Umeorah; Alex Alochukwu |
| Abstract: | Volatility forecasting is essential for risk management and decision-making in financial markets. Traditional models like Generalized Autoregressive Conditional Heteroskedasticity (GARCH) effectively capture volatility clustering but often fail to model complex, non-linear interdependencies between multiple indices. This paper proposes a novel approach using Graph Neural Networks (GNNs) to represent global financial markets as dynamic graphs. The Temporal Graph Attention Network (Temporal GAT) combines Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) to capture the temporal and structural dynamics of volatility spillovers. By utilizing correlation-based and volatility spillover indices, the Temporal GAT constructs directed graphs that enhance the accuracy of volatility predictions. Empirical results from a 15-year study of eight major global indices show that the Temporal GAT outperforms traditional GARCH models and other machine learning methods, particularly in short- to mid-term forecasts. The sensitivity and scenario-based analysis over a range of parameters and hyperparameters further demonstrate the significance of the proposed technique. Hence, this work highlights the potential of GNNs in modeling complex market behaviors, providing valuable insights for financial analysts and investors. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.16858 |
| By: | Jesús Fernández-Villaverde (University of Pennsylvania, CEPR and NBER); Galo Nuno (Banco de Espana, CEPR, CEMFI); Jesse Perla (University of British Columbia) |
| Abstract: | We argue that deep learning provides a promising avenue for taming the curse of dimensionality in quantitative economics. We begin by exploring the unique challenges posed by solving dynamic equilibrium models, especially the feedback loop between individual agents’ decisions and the aggregate consistency conditions required by equilibrium. Following this, we introduce deep neural networks and demonstrate their application by solving the stochastic neoclassical growth model. Next, we compare deep neural networks with traditional solution methods in quantitative economics. We conclude with a survey of neural network applications in quantitative economics and offer reasons for cautious optimism. |
| Keywords: | Deep learning, quantitative economics |
| JEL: | C61 C63 E27 |
| Date: | 2024–10–29 |
| URL: | https://d.repec.org/n?u=RePEc:pen:papers:24-034 |
| By: | Sinha, Pankaj; Kumar, Amit; Biswas, Sumana; Gupta, Chirag |
| Abstract: | The outcome of the US presidential election is one of the most significant events that impacts trade, investment, and geopolitical policies on the global stage. It also sets the direction of the world economy and global politics for the next few years. Hence, it is of prime importance not just for the American population but also to shape the future well-being of the masses worldwide. Therefore, this study aims to forecast the popular vote share of the incumbent party candidate in the Presidential election of 2024. The study applies the regularization-based machine learning algorithm of Lasso to select the most important economic and non-economic indicators influencing the electorate. The variables identified by lasso were further used with lasso (regularization), random forest (bagging) and gradient boosting (boosting) techniques of machine learning to forecast the popular vote share of the incumbent party candidate in the 2024 US Presidential election. The findings suggest that June Gallup ratings, average Gallup ratings, scandal ratings, oil price indicator, unemployment indicator and crime rate impact the popular vote share of the incumbent party candidate. The prediction made by Lasso emerges as the most consistent estimate of the popular vote share forecast. The lasso-based prediction model forecasts that Kamala Harris, the Democratic Party candidate, will receive a popular vote share of 47.04% in the 2024 US Presidential Election. |
| Keywords: | US Presidential Election, Machine Learning, Lasso, Random Forest |
| JEL: | C1 C10 C15 C6 C63 G0 |
| Date: | 2024–10–20 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122490 |
| By: | Ashley Davey; Harry Zheng |
| Abstract: | We consider the portfolio optimisation problem where the terminal function is an S-shaped utility applied at the difference between the wealth and a random benchmark process. We develop several numerical methods for solving the problem using deep learning and duality methods. We use deep learning methods to solve the associated Hamilton-Jacobi-Bellman equation for both the primal and dual problems, and the adjoint equation arising from the stochastic maximum principle. We compare the solution of this non-concave problem to that of concavified utility, a random function depending on the benchmark, in both complete and incomplete markets. We give some numerical results for power and log utilities to show the accuracy of the suggested algorithms. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.05524 |
| By: | Ahmad Makinde |
| Abstract: | Several studies have discussed the impact different optimization techniques in the context of time series forecasting across different Neural network architectures. This paper examines the effectiveness of Adam and Nesterov's Accelerated Gradient (NAG) optimization techniques on LSTM and GRU neural networks for time series prediction, specifically stock market time-series. Our study was done by training LSTM and GRU models with two different optimization techniques - Adam and Nesterov Accelerated Gradient (NAG), comparing and evaluating their performance on Apple Inc's closing price data over the last decade. The GRU model optimized with Adam produced the lowest RMSE, outperforming the other model-optimizer combinations in both accuracy and convergence speed. The GRU models with both optimizers outperformed the LSTM models, whilst the Adam optimizer outperformed the NAG optimizer for both model architectures. The results suggest that GRU models optimized with Adam are well-suited for practitioners in time-series prediction, more specifically stock price time series prediction producing accurate and computationally efficient models. The code for the experiments in this project can be found at https://github.com/AhmadMak/Time-Series-Optimization-Research Keywords: Time-series Forecasting, Neural Network, LSTM, GRU, Adam Optimizer, Nesterov Accelerated Gradient (NAG) Optimizer |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.01843 |
| By: | Carl von Havighorst; Vincil Bishop III |
| Abstract: | This research presents a novel approach to predicting option movements by analyzing residual transactions, which are trades that deviate from standard hedging activities. Unlike traditional methods that primarily focus on open interest and trading volume, this study argues that residuals can reveal nuanced insights into institutional sentiment and strategic positioning. By examining these deviations, the model identifies early indicators of market trends, providing a refined framework for forecasting option prices. The proposed model integrates classical machine learning and regression techniques to analyze patterns in high frequency trading data, capturing complex, non linear relationships. This predictive framework allows traders to anticipate shifts in option values, enhancing strategies for better market timing, risk management, and portfolio optimization. The model's adaptability, driven by real time data processing, makes it particularly effective in fast paced trading environments, where early detection of institutional behavior is crucial for gaining a competitive edge. Overall, this research contributes to the field of options trading by offering a strategic tool that detects early market signals, optimizing trading decisions based on predictive insights derived from residual trading patterns. This approach bridges the gap between conventional metrics and the subtle behaviors of institutional players, marking a significant advancement in options market analysis. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.16563 |
| By: | Julius Range; Benedikt Gloria; Albert Erasmus Grafe |
| Abstract: | The rapid increasing demand for freight transport has precipitated a critical need for expanded infrastructure, particularly in Germany, where a significant crisis in Heavy Goods Vehicle (HGV) parking facilities is emerging. Our study aims to determine the optimum supply of HGV parking lots required to mitigate this problem. Utilizing state-of-the-art object detection techniques in satellite imagery, we conduct a comprehensive analysis to assess the current availability of HGV parking spaces. Our machine learning-based approach enables an accurate and large-scale evaluation, revealing a considerable undersupply of HGV parking lots across Germany. These findings underscore the severity of the infrastructure deficit in the context of increasing freight transport demands. In a next step, we conduct a location analysis to determine regions, which are impacted acutely. Our results therefore deliver valuable insights to specialized real-estate developers seeking to cater to the demand and profit from this deficit. Based on the results, we develop industry and policy recommendations aimed at addressing this shortfall. |
| Keywords: | Machine Learning; satellite image analysis; specialized real estate; Transportation |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2024-164 |
| By: | Mengyu Wang; Shay B. Cohen; Tiejun Ma |
| Abstract: | The diffusion of financial news into market prices is a complex process, making it challenging to evaluate the connections between news events and market movements. This paper introduces FININ (Financial Interconnected News Influence Network), a novel market prediction model that captures not only the links between news and prices but also the interactions among news items themselves. FININ effectively integrates multi-modal information from both market data and news articles. We conduct extensive experiments on two datasets, encompassing the S&P 500 and NASDAQ 100 indices over a 15-year period and over 2.7 million news articles. The results demonstrate FININ's effectiveness, outperforming advanced market prediction models with an improvement of 0.429 and 0.341 in the daily Sharpe ratio for the two markets respectively. Moreover, our results reveal insights into the financial news, including the delayed market pricing of news, the long memory effect of news, and the limitations of financial sentiment analysis in fully extracting predictive power from news data. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.10614 |
| By: | Sohyeon Kwon; Yongjae Lee |
| Abstract: | In the financial sector, a sophisticated financial time series simulator is essential for evaluating financial products and investment strategies. Traditional back-testing methods have mainly relied on historical data-driven approaches or mathematical model-driven approaches, such as various stochastic processes. However, in the current era of AI, data-driven approaches, where models learn the intrinsic characteristics of data directly, have emerged as promising techniques. Generative Adversarial Networks (GANs) have surfaced as promising generative models, capturing data distributions through adversarial learning. Financial time series, characterized 'stylized facts' such as random walks, mean-reverting patterns, unexpected jumps, and time-varying volatility, present significant challenges for deep neural networks to learn their intrinsic characteristics. This study examines the ability of GANs to learn diverse and complex temporal patterns (i.e., stylized facts) of both univariate and multivariate financial time series. Our extensive experiments revealed that GANs can capture various stylized facts of financial time series, but their performance varies significantly depending on the choice of generator architecture. This suggests that naively applying GANs might not effectively capture the intricate characteristics inherent in financial time series, highlighting the importance of carefully considering and validating the modeling choices. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.09850 |
| By: | Xinyu Li |
| Abstract: | This paper explores how ad platforms can utilize Bayesian persuasion within blockchain-based auction systems to strategically influence advertiser behavior despite increased transparency. By integrating game-theoretic models with machine learning techniques and the principles of blockchain technology, we analyze the role of strategic information disclosure in ad auctions. Our findings demonstrate that even in environments with inherent transparency, ad platforms can design signals to affect advertisers' beliefs and bidding strategies. A detailed case study illustrates how machine learning can predict advertiser responses to different signals, leading to optimized signaling strategies that increase expected revenue. The study contributes to the literature by extending Bayesian persuasion models to transparent systems and providing practical insights for auction design in the digital advertising industry. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.07392 |
| By: | Schemm, Jochen (Department of Economics of the Duesseldorf University of Applied Sciences); Schwarz, Christian (Department of Economics of the Duesseldorf University of Applied Sciences); Stickrodt, Marc (WER GmbH) |
| Abstract: | Die Arbeit entwickelt systematisch ein Machine-Learning-Modell zur Prognose von Kundenabwanderungen im Werbeartikelhandel. Im Fokus steht die WER GmbH, ein mittelständischer Werbeartikelhändler, der jährlich signifikante Umsatzverluste durch Kundenabwanderung in der Streckenabwicklung verzeichnet und diese durch effektive Bindungsmaßnahmen reduzieren möchte. Die Ausgangsbasis für ein proaktives Kundenbindungsmanagement bildet ein Modell zur Identifikation abwanderungsgefährdeter Kunden. Das in einem Vergleich von insgesamt 15 Verfahren ausgewählte heterogene Machine-Learning-Ensemble nutzt eine Vielzahl transaktions-, leistungs-, kunden- und interaktionsbezogener Merkmale und liefert signifikant bessere Abwanderungsprognosen als einfachere Vergleichsverfahren. Zusätzlich zur inhaltlichen Interpretation des Modells und der relevantesten Merkmale beschreibt die Arbeit die praktische Integration in den Geschäftsablauf des Unternehmens. Sie liefert damit eine empirische Fallstudie zur Entwicklung eines Abwanderungsprognosemodells in nicht-vertraglichen B2B-Kundenbeziehungen und demonstriert die Leistungsfähigkeit datengetriebener Verfahren des maschinellen Lernens in der praktischen Anwendung. |
| Abstract: | The report systematically develops a machine learning model for predicting customer churn in the promotional products trade sector. The company concerned is the WER GmbH, a medium-sized promotional product distributor, which records significant annual sales losses due to customer churn in its drop shipment business and would like to reduce these through effective retention measures. The starting point for proactive customer retention management is a quantitative model for identifying customers at risk of churning. The heterogeneous machine learning ensemble selected in a comparison of a total of 15 methods uses a large number of transaction-, performance-, customer- and interaction-related features and delivers significantly better churn forecasts than simpler baseline methods. In addition to the explanative interpretation of the model and the most relevant features, the report describes the practical integration into the company's business process. It thus provides an empirical case study for the development of a churn prediction model in non-contractual B2B customer relationships and demonstrates the performance of data-driven machine learning methods in practical application. |
| Keywords: | Maschinelles Lernen, Kundenforschung, Kundenbeziehungsmanagement, Abwanderungsprognose, Machine Learning, Business-to-Business, Churn Prediction |
| JEL: | M39 G20 |
| URL: | https://d.repec.org/n?u=RePEc:ddf:wpaper:60 |
| By: | M. Hashem Pesaran; Hayun Song |
| Abstract: | This document is a follow up to the paper by Ahmed and Pesaran (2020, AP) and reports state-level forecasts for the 2024 US presidential election. It updates the 3, 107 county level data used by AP and uses the same machine learning techniques as before to select the variables used in forecasting voter turnout and the Republican vote shares by states for 2024. The models forecast the non-swing states correctly but give mixed results for the swing states (Nevada, Arizona, Wisconsin, Michigan, Pennsylvania, North Carolina, and Georgia). Our forecasts for the swing states do not make use of any polling data but confirm the very close nature of the 2024 election, much closer than AP’s predictions for 2020. The forecasts are too close to call. |
| Keywords: | voter turnout, popular and electoral college votes, simultaneity and recursive identification, high dimensional forecasting models, Lasso, OCMT |
| JEL: | C53 C55 D72 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11415 |
| By: | Yingjie Niu; Lanxin Lu; Rian Dolphin; Valerio Poti; Ruihai Dong |
| Abstract: | Accurate and robust stock trend forecasting has been a crucial and challenging task, as stock price changes are influenced by multiple factors. Graph neural network-based methods have recently achieved remarkable success in this domain by constructing stock relationship graphs that reflect internal factors and relationships between stocks. However, most of these methods rely on predefined factors to construct static stock relationship graphs due to the lack of suitable datasets, failing to capture the dynamic changes in stock relationships. Moreover, the evaluation of relationship graphs in these methods is often tied to the performance of neural network models on downstream tasks, leading to confusion and imprecision. To address these issues, we introduce the SPNews dataset, collected based on S\&P 500 Index stocks, to facilitate the construction of dynamic relationship graphs. Furthermore, we propose a novel set of financial relationship graph evaluation methods that are independent of downstream tasks. By using the relationship graph to explain historical financial phenomena, we assess its validity before constructing a graph neural network, ensuring the graph's effectiveness in capturing relevant financial relationships. Experimental results demonstrate that our evaluation methods can effectively differentiate between various financial relationship graphs, yielding more interpretable results compared to traditional approaches. We make our source code publicly available on GitHub to promote reproducibility and further research in this area. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.07216 |
| By: | Roman Belavkin; Panos Pardalos; Jose Principe |
| Abstract: | The advances and development of various machine learning techniques has lead to practical solutions in various areas of science, engineering, medicine and finance. The great choice of algorithms, their implementations and libraries has resulted in another challenge of selecting the right algorithm and tuning their parameters in order to achieve optimal or satisfactory performance in specific applications. Here we show how the value of information (V(I)) can be used in this task to guide the algorithm choice and parameter tuning process. After estimating the amount of Shannon's mutual information between the predictor and response variables, V(I) can define theoretical upper bound of performance of any algorithm. The inverse function I(V) defines the lower frontier of the minimum amount of information required to achieve the desired performance. In this paper, we illustrate the value of information for the mean-square error minimization and apply it to forecasts of cryptocurrency log-returns. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.01831 |
| By: | Robert Lasser; Fabian Hollinetz |
| Abstract: | In the realm of Automated Valuation Models (AVM) for real estate, incorporating nuanced features can significantly enhance the accuracy of property valuation. We are introducing a novel feature in our AVM framework aimed at capturing the impact of heating energy demand on the market value of real estate properties. Leveraging a combination of machine learning techniques and statistical modeling, our approach involves two key steps.First, utilizing a robust dataset of real estate transactions, we employ XGBoost models to predict heating energy demand for properties lacking such information. This imputation process enables us to generate comprehensive estimates of heating energy demand across a diverse range of properties.Secondly, we integrate tensor interaction effects within Generalized Additive Models (GAM) to analyze the relationship between heating energy demand and property value, considering crucial factors such as the construction year of the real estate objects. By incorporating tensor interaction effects, we are able to capture complex nonlinear relationships and interactions, allowing for a more nuanced understanding of how heating energy demand influences property valuation over time.Through the implementation of this advanced feature, our AVM framework offers real estate practitioners and stakeholders a more comprehensive tool for accurately assessing property values. This research contributes to the evolving landscape of real estate valuation methodologies, demonstrating the efficacy of combining machine learning with statistical modeling techniques to capture multifaceted influences on property value. |
| Keywords: | Automated Valuation Models (AVM); Heating Energy Demand, ; Machine Learning; Real Estate Valuation |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2024-196 |
| By: | Chad Brown |
| Abstract: | This paper establishes statistical properties of deep neural network (DNN) estimators under dependent data. Two general results for nonparametric sieve estimators directly applicable to DNNs estimators are given. The first establishes rates for convergence in probability under nonstationary data. The second provides non-asymptotic probability bounds on $\mathcal{L}^{2}$-errors under stationary $\beta$-mixing data. I apply these results to DNN estimators in both regression and classification contexts imposing only a standard H\"older smoothness assumption. These results are then used to demonstrate how asymptotic inference can be conducted on the finite dimensional parameter of a partially linear regression model after first-stage DNN estimation of infinite dimensional parameters. The DNN architectures considered are common in applications, featuring fully connected feedforward networks with any continuous piecewise linear activation function, unbounded weights, and a width and depth that grows with sample size. The framework provided also offers potential for research into other DNN architectures and time-series applications. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.11113 |
| By: | Rodolfo Monfilier Peres; Onofre Alves Simões |
| Abstract: | In Brazil, hospital admissions account for nearly 50% of the total cost of health insurance claims, while representing only 1% of total medical procedures. Therefore, modeling hospital admissions is useful for insurers to evaluate costs in order to maintain their solvency. This article analyzes the use of the Lee-Carter model to predict hospital admissions in the state of São Paulo, Brazil, and contrasts it with the Long Short Term Memory (LSTM) neural network. The results showed that the two approaches have similar performance. This was not a disappointing result, on the contrary: from now on, future work can further test whether LSTM models are able to give a better result than Lee-Carter, for example by working with longer data sequences or by adapting the models. |
| Keywords: | Hospital Admissions; Lee-Carter; Neural Networks; LSTM; Brazil. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp03492024 |
| By: | Laura Gabrielli; Aurora Greta Ruggeri; Massimiliano Scarpa |
| Abstract: | The energy performance of buildings has emerged as a critical factor in the real estate sector, intertwining environmental sustainability with market pricing. Therefore, this study aims to explore the relationship between a building's energy performance, as indicated by its energy class, and its market value. Leveraging a web-parsing automated procedure, the authors gathered approximately 200, 000 observations of properties currently listed for sale across Italy, capturing both asking prices and energy class specifications. Through the analysis of this extensive dataset, an Artificial Neural Network was trained to develop a predictive tool for estimating property market values based on various building characteristics, with particular emphasis on understanding the impact of energy class on market prices. In conclusion, this research opens the debate on the significance of energy class in evaluating the market value of buildings, especially within the context of the European Green Homes Directive. |
| Keywords: | Artificial Neural Network; Energy class; Market Value; Property Valuation |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2024-198 |
| By: | Mehdi Hosseini Chagahi; Niloufar Delfan; Saeed Mohammadi Dashtaki; Behzad Moshiri; Md. Jalil Piran |
| Abstract: | Detecting credit card fraud (CCF) holds significant importance due to its role in safeguarding consumers from unauthorized transactions that have the potential to result in financial detriment and negative impacts on their credit rating. It aids financial institutions in upholding the reliability of their payment mechanisms and circumventing the expensive procedure of compensating for deceitful transactions. The utilization of Artificial Intelligence methodologies demonstrated remarkable efficacy in the identification of credit card fraud instances. Within this study, we present a unique attention-based ensemble model. This model is enhanced by adding an attention layer for integration of first layer classifiers' predictions and a selection layer for choosing the best integrated value. The attention layer is implemented with two aggregation operators: dependent ordered weighted averaging (DOWA) and induced ordered weighted averaging (IOWA). The performance of the IOWA operator is very close to the learning algorithm in neural networks which is based on the gradient descent optimization method, and performing the DOWA operator is based on weakening the classifiers that make outlier predictions compared to other learners. Both operators have a sufficient level of complexity for the recognition of complex patterns. Accuracy and diversity are the two criteria we use for selecting the classifiers whose predictions are to be integrated by the two aggregation operators. Using a bootstrap forest, we identify the 13 most significant features of the dataset that contribute the most to CCF detection and use them to feed the proposed model. Exhibiting its efficacy, the ensemble model attains an accuracy of 99.95% with an area under the curve (AUC) of 1. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.09069 |
| By: | Daniel Albert; Stephan Billinger |
| Abstract: | In this study, we propose LLM agents as a novel approach in behavioral strategy research, complementing simulations and laboratory experiments to advance our understanding of cognitive processes in decision-making. Specifically, we reproduce a human laboratory experiment in behavioral strategy using large language model (LLM) generated agents and investigate how LLM agents compare to observed human behavior. Our results show that LLM agents effectively reproduce search behavior and decision-making comparable to humans. Extending our experiment, we analyze LLM agents' simulated "thoughts, " discovering that more forward-looking thoughts correlate with favoring exploitation over exploration to maximize wealth. We show how this new approach can be leveraged in behavioral strategy research and address limitations. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.06932 |
| By: | Francesco Audrino; Jessica Gentner; Simon Stalder |
| Abstract: | This paper presents an innovative method for measuring uncertainty via large language models (LLMs), which offer greater precision and contextual sensitivity than the conventional methods used to construct prominent uncertainty indices. By analysing newspaper texts with state-of-the-art LLMs, our approach captures nuances often missed by conventional methods. We develop indices for various types of uncertainty, including geopolitical risk, economic policy, monetary policy, and financial market uncertainty. Our findings show that shocks to these LLM-based indices exhibit stronger associations with macroeconomic variables, shifts in investor behaviour, and asset return variations than conventional indices, underscoring their potential for more accurately reflecting uncertainty. |
| Keywords: | Uncertainty measurement, Large language models, Economic policy, Geopolitical risk, Monetary policy, Financial markets |
| JEL: | C45 C55 E44 G12 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:snb:snbwpa:2024-12 |
| By: | Luca Lalor; Anatoliy Swishchuk |
| Abstract: | We develop a deep reinforcement learning (RL) framework for an optimal market-making (MM) trading problem, specifically focusing on price processes with semi-Markov and Hawkes Jump-Diffusion dynamics. We begin by discussing the basics of RL and the deep RL framework used, where we deployed the state-of-the-art Soft Actor-Critic (SAC) algorithm for the deep learning part. The SAC algorithm is an off-policy entropy maximization algorithm more suitable for tackling complex, high-dimensional problems with continuous state and action spaces like in optimal market-making (MM). We introduce the optimal MM problem considered, where we detail all the deterministic and stochastic processes that go into setting up an environment for simulating this strategy. Here we also give an in-depth overview of the jump-diffusion pricing dynamics used, our method for dealing with adverse selection within the limit order book, and we highlight the working parts of our optimization problem. Next, we discuss training and testing results, where we give visuals of how important deterministic and stochastic processes such as the bid/ask, trade executions, inventory, and the reward function evolved. We include a discussion on the limitations of these results, which are important points to note for most diffusion models in this setting. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.14504 |
| By: | Alba Miñano-Mañero |
| Abstract: | This paper explores how geography shapes the legacy of redlining, the systemic mortgage lending bias against minority us neighborhoods. On average, redlined neighborhoods lag behind adjacent, less-discriminated areas in home values, income, and racial composition. Yet, redlined neighborhoods near parks and water fare better. To help understand convergence, we inventory waterfront renovations, apply machine learning to historical imagery to track tree canopy changes, and instrument such changes exploiting tree replacements due to geographic variation in tree plagues and susceptible species. Findings suggest that enhancing waterfronts and increasing tree canopy can mitigate the long-lasting effects of institutionalized discrimination. |
| Keywords: | redlining, geography, natural amenities, waterfronts, tree canopy. |
| JEL: | R23 |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp03532024 |
| By: | Yuzhe Yang; Yifei Zhang; Yan Hu; Yilin Guo; Ruoli Gan; Yueru He; Mingcong Lei; Xiao Zhang; Haining Wang; Qianqian Xie; Jimin Huang; Honghai Yu; Benyou Wang |
| Abstract: | This paper introduces the UCFE: User-Centric Financial Expertise benchmark, an innovative framework designed to evaluate the ability of large language models (LLMs) to handle complex real-world financial tasks. UCFE benchmark adopts a hybrid approach that combines human expert evaluations with dynamic, task-specific interactions to simulate the complexities of evolving financial scenarios. Firstly, we conducted a user study involving 804 participants, collecting their feedback on financial tasks. Secondly, based on this feedback, we created our dataset that encompasses a wide range of user intents and interactions. This dataset serves as the foundation for benchmarking 12 LLM services using the LLM-as-Judge methodology. Our results show a significant alignment between benchmark scores and human preferences, with a Pearson correlation coefficient of 0.78, confirming the effectiveness of the UCFE dataset and our evaluation approach. UCFE benchmark not only reveals the potential of LLMs in the financial sector but also provides a robust framework for assessing their performance and user satisfaction.The benchmark dataset and evaluation code are available. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.14059 |
| By: | Farley Ishaak; Peng Liu; Egbert Hardeman; Hilde Remoy |
| Abstract: | Recent literature has shown how dynamic factor models (DFM) can be used successfully to predict real estate price returns. In this paper, we take it a step further. In a two-step approach we estimate (1) a dynamic factor model over multiple markets to extract a few common trends, and (2) estimate a per-market Autoregressive Distributed Lag (ARDL) model including the dynamic factors, in a LASSO framework. In total we estimate 7 different variants (for example by also utilizing macroeconomic explanatory variables) of this model for rents and prices for a selection of Polish cities. Compared to a vanilla ARDL model, our LASSO-DFM augmented ARDL, reduces the prediction error by more than 60% on average. What is more, the prediction errors are relatively "stable." With this we mean that the size of the error is comparable over time and over markets, without any large outliers. This holds true even for forecasts over very long horizons. |
| Keywords: | Autoregressive Distributed Lag; LASSO; Poland |
| JEL: | R3 |
| Date: | 2024–01–01 |
| URL: | https://d.repec.org/n?u=RePEc:arz:wpaper:eres2024-207 |