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on Computational Economics |
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Issue of 2024‒06‒10
eighteen papers chosen by |
| By: | Pedro Duarte Gomes |
| Abstract: | This article introduces the groundbreaking concept of the financial differential machine learning algorithm through a rigorous mathematical framework. Diverging from existing literature on financial machine learning, the work highlights the profound implications of theoretical assumptions within financial models on the construction of machine learning algorithms. This endeavour is particularly timely as the finance landscape witnesses a surge in interest towards data-driven models for the valuation and hedging of derivative products. Notably, the predictive capabilities of neural networks have garnered substantial attention in both academic research and practical financial applications. The approach offers a unified theoretical foundation that facilitates comprehensive comparisons, both at a theoretical level and in experimental outcomes. Importantly, this theoretical grounding lends substantial weight to the experimental results, affirming the differential machine learning method's optimality within the prevailing context. By anchoring the insights in rigorous mathematics, the article bridges the gap between abstract financial concepts and practical algorithmic implementations. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.01233&r= |
| By: | Hulusi Mehmet Tanrikulu; Hakan Pabuccu |
| Abstract: | Forecasting cryptocurrencies as a financial issue is crucial as it provides investors with possible financial benefits. A small improvement in forecasting performance can lead to increased profitability; therefore, obtaining a realistic forecast is very important for investors. Successful forecasting provides traders with effective buy-or-hold strategies, allowing them to make more profits. The most important thing in this process is to produce accurate forecasts suitable for real-life applications. Bitcoin, frequently mentioned recently due to its volatility and chaotic behavior, has begun to pay great attention and has become an investment tool, especially during and after the COVID-19 pandemic. This study provided a comprehensive methodology, including constructing continuous and trend data using one and seven years periods of data as inputs and applying machine learning (ML) algorithms to forecast Bitcoin price movement. A binarization procedure was applied using continuous data to construct the trend data representing each input feature trend. Following the related literature, the input features are determined as technical indicators, google trends, and the number of tweets. Random forest (RF), K-Nearest neighbor (KNN), Extreme Gradient Boosting (XGBoost-XGB), Support vector machine (SVM) Naive Bayes (NB), Artificial Neural Networks (ANN), and Long-Short-Term Memory (LSTM) networks were applied on the selected features for prediction purposes. This work investigates two main research questions: i. How does the sample size affect the prediction performance of ML algorithms? ii. How does the data type affect the prediction performance of ML algorithms? Accuracy and area under the ROC curve (AUC) values were used to compare the model performance. A t-test was performed to test the statistical significance of the prediction results. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.19324&r= |
| By: | Shuochen Bi; Wenqing Bao; Jue Xiao; Jiangshan Wang; Tingting Deng |
| Abstract: | With the continuous development of artificial intelligence technology, using machine learning technology to predict market trends may no longer be out of reach. In recent years, artificial intelligence has become a research hotspot in the academic circle, and it has been widely used in image recognition, natural language processing and other fields, and also has a huge impact on the field of quantitative investment. As an investment method to obtain stable returns through data analysis, model construction and program trading, quantitative investment is deeply loved by financial institutions and investors. At the same time, as an important application field of quantitative investment, the quantitative investment strategy based on artificial intelligence technology arises at the historic moment.How to apply artificial intelligence to quantitative investment, so as to better achieve profit and risk control, has also become the focus and difficulty of the research. From a global perspective, inflation in the US and the Federal Reserve are the concerns of investors, which to some extent affects the direction of global assets, including the Chinese stock market. This paper studies the application of AI technology, quantitative investment, and AI technology in quantitative investment, aiming to provide investors with auxiliary decision-making, reduce the difficulty of investment analysis, and help them to obtain higher returns. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.18184&r= |
| By: | Pierre Beck; Pablo Garcia-Sanchez; Alban Moura; Julien Pascal; Olivier Pierrard |
| Abstract: | This technical report provides an introduction to solving economic models using deep learning techniques. We offer a simple yet rigorous overview of deep learning methods and their applicability to economic modeling. We illustrate these concepts using the benchmark of modern macroeconomic theory: the stochastic growth model. Our results emphasize how various choices related to the design of the deep learning solution affect the accuracy of the results, providing some guidance for potential users of the method. We also provide fully commented computer codes. Overall, our hope is that this report will serve as an accessible, useful entry point to applying deep learning techniques to solve economic models for graduate students and researchers interested in the field. |
| Keywords: | Solutions of DSGE models, deep learning, artificial neural networks |
| JEL: | C45 C60 C63 E13 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:bcl:bclwop:bclwp184&r= |
| By: | Alicia Vidler; Toby Walsh |
| Abstract: | Exploring complex adaptive financial trading environments through multi-agent based simulation methods presents an innovative approach within the realm of quantitative finance. Despite the dominance of multi-agent reinforcement learning approaches in financial markets with observable data, there exists a set of systematically significant financial markets that pose challenges due to their partial or obscured data availability. We, therefore, devise a multi-agent simulation approach employing small-scale meta-heuristic methods. This approach aims to represent the opaque bilateral market for Australian government bond trading, capturing the bilateral nature of bank-to-bank trading, also referred to as "over-the-counter" (OTC) trading, and commonly occurring between "market makers". The uniqueness of the bilateral market, characterized by negotiated transactions and a limited number of agents, yields valuable insights for agent-based modelling and quantitative finance. The inherent rigidity of this market structure, which is at odds with the global proliferation of multilateral platforms and the decentralization of finance, underscores the unique insights offered by our agent-based model. We explore the implications of market rigidity on market structure and consider the element of stability, in market design. This extends the ongoing discourse on complex financial trading environments, providing an enhanced understanding of their dynamics and implications. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.02849&r= |
| By: | Prabhu Prasad Panda; Maysam Khodayari Gharanchaei; Xilin Chen; Haoshu Lyu |
| Abstract: | The paper examines the performance of regression models (OLS linear regression, Ridge regression, Random Forest, and Fully-connected Neural Network) on the prediction of CMA (Conservative Minus Aggressive) factor premium and the performance of factor timing investment with them. Out-of-sample R-squared shows that more flexible models have better performance in explaining the variance in factor premium of the unseen period, and the back testing affirms that the factor timing based on more flexible models tends to over perform the ones with linear models. However, for flexible models like neural networks, the optimal weights based on their prediction tend to be unstable, which can lead to high transaction costs and market impacts. We verify that tilting down the rebalance frequency according to the historical optimal rebalancing scheme can help reduce the transaction costs. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.18017&r= |
| By: | Peyman Khezr; Kendall Taylor |
| Abstract: | Understanding bidding behavior in multi-unit auctions remains an ongoing challenge for researchers. Despite their widespread use, theoretical insights into the bidding behavior, revenue ranking, and efficiency of commonly used multi-unit auctions are limited. This paper utilizes artificial intelligence, specifically reinforcement learning, as a model free learning approach to simulate bidding in three prominent multi-unit auctions employed in practice. We introduce six algorithms that are suitable for learning and bidding in multi-unit auctions and compare them using an illustrative example. This paper underscores the significance of using artificial intelligence in auction design, particularly in enhancing the design of multi-unit auctions. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.15633&r= |
| By: | Silvia Garc\'ia-M\'endez; Francisco de Arriba-P\'erez; Ana Barros-Vila; Francisco J. Gonz\'alez-Casta\~no |
| Abstract: | Microblogging platforms, of which Twitter is a representative example, are valuable information sources for market screening and financial models. In them, users voluntarily provide relevant information, including educated knowledge on investments, reacting to the state of the stock markets in real-time and, often, influencing this state. We are interested in the user forecasts in financial, social media messages expressing opportunities and precautions about assets. We propose a novel Targeted Aspect-Based Emotion Analysis (TABEA) system that can individually discern the financial emotions (positive and negative forecasts) on the different stock market assets in the same tweet (instead of making an overall guess about that whole tweet). It is based on Natural Language Processing (NLP) techniques and Machine Learning streaming algorithms. The system comprises a constituency parsing module for parsing the tweets and splitting them into simpler declarative clauses; an offline data processing module to engineer textual, numerical and categorical features and analyse and select them based on their relevance; and a stream classification module to continuously process tweets on-the-fly. Experimental results on a labelled data set endorse our solution. It achieves over 90% precision for the target emotions, financial opportunity, and precaution on Twitter. To the best of our knowledge, no prior work in the literature has addressed this problem despite its practical interest in decision-making, and we are not aware of any previous NLP nor online Machine Learning approaches to TABEA. |
| Date: | 2024–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.08665&r= |
| By: | Yupeng Cao; Zhi Chen; Qingyun Pei; Prashant Kumar; K. P. Subbalakshmi; Papa Momar Ndiaye |
| Abstract: | In the realm of financial analytics, leveraging unstructured data, such as earnings conference calls (ECCs), to forecast stock performance is a critical challenge that has attracted both academics and investors. While previous studies have used deep learning-based models to obtain a general view of ECCs, they often fail to capture detailed, complex information. Our study introduces a novel framework: \textbf{ECC Analyzer}, combining Large Language Models (LLMs) and multi-modal techniques to extract richer, more predictive insights. The model begins by summarizing the transcript's structure and analyzing the speakers' mode and confidence level by detecting variations in tone and pitch for audio. This analysis helps investors form an overview perception of the ECCs. Moreover, this model uses the Retrieval-Augmented Generation (RAG) based methods to meticulously extract the focuses that have a significant impact on stock performance from an expert's perspective, providing a more targeted analysis. The model goes a step further by enriching these extracted focuses with additional layers of analysis, such as sentiment and audio segment features. By integrating these insights, the ECC Analyzer performs multi-task predictions of stock performance, including volatility, value-at-risk (VaR), and return for different intervals. The results show that our model outperforms traditional analytic benchmarks, confirming the effectiveness of using advanced LLM techniques in financial analytics. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.18470&r= |
| By: | Marco Bardoscia (Bank of England.); Adrian Carro (Banco de España.); Marc Hinterschweiger (Bank of England.); Mauro Napoletano (Université Côte D’Azur, CNRS, GREDEG.); Lilit Popoyan (Queen Mary University of London.); Andrea Roventini (Scuola Superiore Sant’Anna.); Arzu Uluc (Bank of England) |
| Abstract: | We develop a macroeconomic agent-based model to study the joint impact of borrower- and lender-based prudential policies on the housing and credit markets and the economy more widely. We perform three experiments: (i) an increase of total capital requirements; (ii) an introduction of a loan-to-income (LTI) cap on mortgages to owner-occupiers; and (iii) a joint introduction of both experiments at the same time. Our results suggest that tightening capital requirements leads to a sharp decrease in commercial and mortgage lending, and housing transactions. When the LTI cap is in place, house prices fall sharply relative to income, and the homeownership rate decreases. When both policy instruments are combined, we find that housing transactions and prices drop. Both policies have a positive impact on real GDP and unemployment, while there is no material impact on inflation and the real interest rate. |
| Keywords: | Prudential policies; Housing market; Macroeconomy; Agent-based models. |
| JEL: | C63 D1 D31 E58 G21 G28 R2 R21 R31 |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:cgs:wpaper:118&r= |
| By: | Shuochen Bi; Wenqing Bao |
| Abstract: | With the rapid growth of technology, especially the widespread application of artificial intelligence (AI) technology, the risk management level of commercial banks is constantly reaching new heights. In the current wave of digitalization, AI has become a key driving force for the strategic transformation of financial institutions, especially the banking industry. For commercial banks, the stability and safety of asset quality are crucial, which directly relates to the long-term stable growth of the bank. Among them, credit risk management is particularly core because it involves the flow of a large amount of funds and the accuracy of credit decisions. Therefore, establishing a scientific and effective credit risk decision-making mechanism is of great strategic significance for commercial banks. In this context, the innovative application of AI technology has brought revolutionary changes to bank credit risk management. Through deep learning and big data analysis, AI can accurately evaluate the credit status of borrowers, timely identify potential risks, and provide banks with more accurate and comprehensive credit decision support. At the same time, AI can also achieve realtime monitoring and early warning, helping banks intervene before risks occur and reduce losses. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.18183&r= |
| By: | Lukasz Szpruch; Tanut Treetanthiploet; Yufei Zhang |
| Abstract: | Combining model-based and model-free reinforcement learning approaches, this paper proposes and analyzes an $\epsilon$-policy gradient algorithm for the online pricing learning task. The algorithm extends $\epsilon$-greedy algorithm by replacing greedy exploitation with gradient descent step and facilitates learning via model inference. We optimize the regret of the proposed algorithm by quantifying the exploration cost in terms of the exploration probability $\epsilon$ and the exploitation cost in terms of the gradient descent optimization and gradient estimation errors. The algorithm achieves an expected regret of order $\mathcal{O}(\sqrt{T})$ (up to a logarithmic factor) over $T$ trials. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.03624&r= |
| By: | Tsay, Calvin |
| Abstract: | Two-stage stochastic programming is a popular framework for optimization under uncertainty, where decision variables are split between first-stage decisions, and second-stage (or recourse) decisions, with the latter being adjusted after uncertainty is realized. These problems are often formulated using Sample Average Approximation (SAA), where uncertainty is modeled as a finite set of scenarios, resulting in a large “monolithic” problem, i.e., where the model is repeated for each scenario. The resulting models can be challenging to solve, and several problem-specific decomposition approaches have been proposed. An alternative approach is to approximate the expected second-stage objective value using a surrogate model, which can then be embedded in the first-stage problem to produce good heuristic solutions. In this work, we propose to instead model the distribution of the second-stage objective, specifically using a quantile neural network. Embedding this distributional approximation enables capturing uncertainty and is not limited to expected-value optimization, e.g., the proposed approach enables optimization of the Conditional Value at Risk (CVaR). We discuss optimization formulations for embedding the quantile neural network and demonstrate the effectiveness of the proposed framework using several computational case studies including a set of mixed-integer optimization problems. |
| Keywords: | Optimization under uncertainty; Stochastic programming; Neural networks; Mixed-Integer Programming (MIP) |
| Date: | 2024–03–19 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wsrepe:43773&r= |
| By: | Huan-Yi Su; Ke Wu; Yu-Hao Huang; Wu-Jun Li |
| Abstract: | Recently, many works have proposed various financial large language models (FinLLMs) by pre-training from scratch or fine-tuning open-sourced LLMs on financial corpora. However, existing FinLLMs exhibit unsatisfactory performance in understanding financial text when numeric variables are involved in questions. In this paper, we propose a novel LLM, called numeric-sensitive large language model (NumLLM), for Chinese finance. We first construct a financial corpus from financial textbooks which is essential for improving numeric capability of LLMs during fine-tuning. After that, we train two individual low-rank adaptation (LoRA) modules by fine-tuning on our constructed financial corpus. One module is for adapting general-purpose LLMs to financial domain, and the other module is for enhancing the ability of NumLLM to understand financial text with numeric variables. Lastly, we merge the two LoRA modules into the foundation model to obtain NumLLM for inference. Experiments on financial question-answering benchmark show that NumLLM can boost the performance of the foundation model and can achieve the best overall performance compared to all baselines, on both numeric and non-numeric questions. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.00566&r= |
| By: | Minwu Kim |
| Abstract: | This work develops a predictive model to identify potential targets of activist investment funds, which strategically acquire significant corporate stakes to drive operational and strategic improvements and enhance shareholder value. Predicting these targets is crucial for companies to mitigate intervention risks, for activists to select optimal targets, and for investors to capitalize on associated stock price gains. Our analysis utilizes data from the Russell 3000 index from 2016 to 2022. We tested 123 variations of models using different data imputation, oversampling, and machine learning methods, achieving a top AUC-ROC of 0.782. This demonstrates the model's effectiveness in identifying likely targets of activist funds. We applied the Shapley value method to determine the most influential factors in a company's susceptibility to activist investment. This interpretative approach provides clear insights into the driving forces behind activist targeting. Our model offers stakeholders a strategic tool for proactive corporate governance and investment strategy, enhancing understanding of the dynamics of activist investing. |
| Date: | 2024–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2404.16169&r= |
| By: | Leonardo Ciambezi; Mattia Guerini; Mauro Napoletano; Andrea Roventini |
| Abstract: | In this work, we develop a macroeconomic agent-based model to study the role of demand and supply factors in the determination of inflation dynamics. The model is characterized by local interactions of heterogeneous firms and households in the labor and goods markets. Imperfect information implies that market selection is imperfect, as it does not depend only on relative prices but also on firm size. We show that our model is able to generate realistic inflation dynamics, as well as a non-linear Phillips curve in line with the empirical evidence. We then find that the traditional demand-led explanation of inflation stemming from a tight labor market only holds when markets are competitive and efficient. Finally, we study the response of inflation to shocks impacting on consumption, labor productivity or energy costs. The results show that only demand shocks lead to wage-led inflation surges. Productivity shocks are entirely passed-through to prices without affecting the income distribution. Energy shocks, instead, induce sellers' inflation after changes in both firms' cost structure and profit margins. This is in line with the recent empirical evidence for the Euro Area. |
| Keywords: | Inflation, agent-based models, market structure, mark-up rates, sellers' inflation |
| Date: | 2024–05–10 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2024/15&r= |
| By: | Nugroho, Rizqi Ilma; Gnann, Till; Speth, Daniel; Purwanto, Widodo Wahyu; Hanafi, Jessica; Soehodho, Sutanto |
| Abstract: | Battery electric vehicles (BEV) present a promising approach to decarbonizing the transportation sector. This extends beyond electric passenger cars, such as electric motorcycles that hold significant potential in emerging markets with high population density and income disparities. However, providing access to infrastructure remains a challenge in increasing BEV adoption. This research endeavours to determine BEV passenger cars (BEV-PC) and motorcycles (BEV-MC) market diffusion within an emerging market city, focusing on the Greater Jakarta Area, utilizing an Agent-Based Model that considers charging infrastructure availability. Findings indicate that BEV-PC diffusion could attain about 9% of the total vehicle stock by 2030 and almost 75% by 2050 under the Current Policy. Similarly, BEV-MC adoption rates may reach 39% by 2030 and 80% by 2050. Introducing a vehicle purchase subsidy along with full abolishment of fossil fuel subsidies could amplify the diffusion of BEV-PC and BEV-MC to almost triple and double in 2030, respectively. |
| Keywords: | Battery electric vehicles (BEV), BEV passenger cars (BEV-PC), BEV motorcycles (BEV-MC) |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:fisisi:294823&r= |
| By: | Kevin Xin; Lizhi Xin |
| Abstract: | Forecasting, to estimate future events, is crucial for business and decision-making. This paper proposes QxEAI, a methodology that produces a probabilistic forecast that utilizes a quantum-like evolutionary algorithm based on training a quantum-like logic decision tree and a classical value tree on a small number of related time series. By using different cycles of the Dow Jones Index (yearly, monthly, weekly, daily), we demonstrate how our methodology produces accurate forecasts while requiring little to none manual work. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.03701&r= |