nep-cmp New Economics Papers
on Computational Economics
Issue of 2023‒10‒02
thirteen papers chosen by
Stan Miles, Thompson Rivers University

  1. Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making By Patrick Rehill; Nicholas Biddle
  2. Mutual Information Maximizing Quantum Generative Adversarial Network and Its Applications in Finance By Mingyu Lee; Myeongjin Shin; Junseo Lee; Kabgyun Jeong
  3. Econometrics of Machine Learning Methods in Economic Forecasting By Andrii Babii; Eric Ghysels; Jonas Striaukas
  4. Long-term Effects of Temperature Variations on Economic Growth: A Machine Learning Approach By Eugene Kharitonov; Oksana Zakharchuk; Lin Mei
  5. Predicting Re-Employment: Machine Learning versus Assessments by Unemployed Workers and by Their Caseworkers By van den Berg, Gerard J.; Kunaschk, Max; Lang, Julia; Stephan, Gesine; Uhlendorff, Arne
  6. Harnessing the Power of Artificial Intelligence to Forecast Startup Success: An Empirical Evaluation of the SECURE AI Model By Morande, Swapnil; Arshi, Tahseen; Gul, Kanwal; Amini, Mitra
  7. Analysis of Optimal Portfolio Management Using Hierarchical Clustering By Kapil Panda
  8. Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market By Timothy DeLise
  9. Recurrent Neural Networks with more flexible memory: better predictions than rough volatility By Damien Challet; Vincent Ragel
  10. ATMS: Algorithmic Trading-Guided Market Simulation By Song Wei; Andrea Coletta; Svitlana Vyetrenko; Tucker Balch
  11. The roots of inequality: estimating inequality of opportunity from regression trees and forests By Brunori, Paolo
  12. Predicting Financial Market Trends using Time Series Analysis and Natural Language Processing By Ali Asgarov
  13. Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance By Lefteris Loukas; Ilias Stogiannidis; Prodromos Malakasiotis; Stavros Vassos

  1. By: Patrick Rehill; Nicholas Biddle
    Abstract: Causal machine learning methods which flexibly generate heterogeneous treatment effect estimates could be very useful tools for governments trying to make and implement policy. However, as the critical artificial intelligence literature has shown, governments must be very careful of unintended consequences when using machine learning models. One way to try and protect against unintended bad outcomes is with AI Fairness methods which seek to create machine learning models where sensitive variables like race or gender do not influence outcomes. In this paper we argue that standard AI Fairness approaches developed for predictive machine learning are not suitable for all causal machine learning applications because causal machine learning generally (at least so far) uses modelling to inform a human who is the ultimate decision-maker while AI Fairness approaches assume a model that is making decisions directly. We define these scenarios as indirect and direct decision-making respectively and suggest that policy-making is best seen as a joint decision where the causal machine learning model usually only has indirect power. We lay out a definition of fairness for this scenario - a model that provides the information a decision-maker needs to accurately make a value judgement about just policy outcomes - and argue that the complexity of causal machine learning models can make this difficult to achieve. The solution here is not traditional AI Fairness adjustments, but careful modelling and awareness of some of the decision-making biases that these methods might encourage which we describe.
    Date: 2023–09
  2. By: Mingyu Lee; Myeongjin Shin; Junseo Lee; Kabgyun Jeong
    Abstract: One of the most promising applications in the era of NISQ (Noisy Intermediate-Scale Quantum) computing is quantum machine learning. Quantum machine learning offers significant quantum advantages over classical machine learning across various domains. Specifically, generative adversarial networks have been recognized for their potential utility in diverse fields such as image generation, finance, and probability distribution modeling. However, these networks necessitate solutions for inherent challenges like mode collapse. In this study, we capitalize on the concept that the estimation of mutual information between high-dimensional continuous random variables can be achieved through gradient descent using neural networks. We introduce a novel approach named InfoQGAN, which employs the Mutual Information Neural Estimator (MINE) within the framework of quantum generative adversarial networks to tackle the mode collapse issue. Furthermore, we elaborate on how this approach can be applied to a financial scenario, specifically addressing the problem of generating portfolio return distributions through dynamic asset allocation. This illustrates the potential practical applicability of InfoQGAN in real-world contexts.
    Date: 2023–09
  3. By: Andrii Babii; Eric Ghysels; Jonas Striaukas
    Abstract: This paper surveys the recent advances in machine learning method for economic forecasting. The survey covers the following topics: nowcasting, textual data, panel and tensor data, high-dimensional Granger causality tests, time series cross-validation, classification with economic losses.
    Date: 2023–08
  4. By: Eugene Kharitonov; Oksana Zakharchuk; Lin Mei
    Abstract: This study investigates the long-term effects of temperature variations on economic growth using a data-driven approach. Leveraging machine learning techniques, we analyze global land surface temperature data from Berkeley Earth and economic indicators, including GDP and population data, from the World Bank. Our analysis reveals a significant relationship between average temperature and GDP growth, suggesting that climate variations can substantially impact economic performance. This research underscores the importance of incorporating climate factors into economic planning and policymaking, and it demonstrates the utility of machine learning in uncovering complex relationships in climate-economy studies.
    Date: 2023–06
  5. By: van den Berg, Gerard J. (University of Groningen); Kunaschk, Max (Institute for Employment Research (IAB), Nuremberg); Lang, Julia (Institute for Employment Research (IAB), Nuremberg); Stephan, Gesine (Institute for Employment Research (IAB), Nuremberg); Uhlendorff, Arne (CREST)
    Abstract: Predictions of whether newly unemployed individuals will become long-term unemployed are important for the planning and policy mix of unemployment insurance agencies. We analyze unique data on three sources of information on the probability of re-employment within 6 months (RE6), for the same individuals sampled from the inflow into unemployment. First, they were asked for their perceived probability of RE6. Second, their caseworkers revealed whether they expected RE6. Third, random-forest machine learning methods are trained on administrative data on the full inflow, to predict individual RE6. We compare the predictive performance of these measures and consider whether combinations improve this performance. We show that self-reported and caseworker assessments sometimes contain information not captured by the machine learning algorithm.
    Keywords: unemployment, expectations, prediction, random forest, unemployment insurance, information
    JEL: J64 J65 C55 C53 C41 C21
    Date: 2023–09
  6. By: Morande, Swapnil; Arshi, Tahseen; Gul, Kanwal; Amini, Mitra
    Abstract: This pioneering study employs machine learning to predict startup success, addressing the long-standing challenge of deciphering entrepreneurial outcomes amidst uncertainty. Integrating the multidimensional SECURE framework for holistic opportunity evaluation with AI's pattern recognition prowess, the research puts forth a novel analytics-enabled approach to illuminate success determinants. Rigorously constructed predictive models demonstrate remarkable accuracy in forecasting success likelihood, validated through comprehensive statistical analysis. The findings reveal AI’s immense potential in bringing evidence-based objectivity to the complex process of opportunity assessment. On the theoretical front, the research enriches entrepreneurship literature by bridging the knowledge gap at the intersection of structured evaluation tools and data science. On the practical front, it empowers entrepreneurs with an analytical compass for decision-making and helps investors make prudent funding choices. The study also informs policymakers to optimize conditions for entrepreneurship. Overall, it lays the foundation for a new frontier of AI-enabled, data-driven entrepreneurship research and practice. However, acknowledging AI’s limitations, the synthesis underscores the persistent relevance of human creativity alongside data-backed insights. With high predictive performance and multifaceted implications, the SECURE-AI model represents a significant stride toward an analytics-empowered paradigm in entrepreneurship management.
    Date: 2023–08–29
  7. By: Kapil Panda
    Abstract: Portfolio optimization is a task that investors use to determine the best allocations for their investments, and fund managers implement computational models to help guide their decisions. While one of the most common portfolio optimization models in the industry is the Markowitz Model, practitioners recognize limitations in its framework that lead to suboptimal out-of-sample performance and unrealistic allocations. In this study, I refine the Markowitz Model by incorporating machine learning to improve portfolio performance. By using a hierarchical clustering-based approach, I am able to enhance portfolio performance on a risk-adjusted basis compared to the Markowitz Model, across various market factors.
    Date: 2023–08
  8. By: Timothy DeLise
    Abstract: Modern financial electronic exchanges are an exciting and fast-paced marketplace where billions of dollars change hands every day. They are also rife with manipulation and fraud. Detecting such activity is a major undertaking, which has historically been a job reserved exclusively for humans. Recently, more research and resources have been focused on automating these processes via machine learning and artificial intelligence. Fraud detection is overwhelmingly associated with the greater field of anomaly detection, which is usually performed via unsupervised learning techniques because of the lack of labeled data needed for supervised learning. However, a small quantity of labeled data does often exist. This research article aims to evaluate the efficacy of a deep semi-supervised anomaly detection technique, called Deep SAD, for detecting fraud in high-frequency financial data. We use exclusive proprietary limit order book data from the TMX exchange in Montr\'eal, with a small set of true labeled instances of fraud, to evaluate Deep SAD against its unsupervised predecessor. We show that incorporating a small amount of labeled data into an unsupervised anomaly detection framework can greatly improve its accuracy.
    Date: 2023–08
  9. By: Damien Challet; Vincent Ragel
    Abstract: We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or with highly disparate time scales. We compare the ability of vanilla and extended long short term memory networks (LSTMs) to predict asset price volatility, known to have a long memory. Generally, the number of epochs needed to train extended LSTMs is divided by two, while the variation of validation and test losses among models with the same hyperparameters is much smaller. We also show that the model with the smallest validation loss systemically outperforms rough volatility predictions by about 20% when trained and tested on a dataset with multiple time series.
    Date: 2023–08
  10. By: Song Wei; Andrea Coletta; Svitlana Vyetrenko; Tucker Balch
    Abstract: The effective construction of an Algorithmic Trading (AT) strategy often relies on market simulators, which remains challenging due to existing methods' inability to adapt to the sequential and dynamic nature of trading activities. This work fills this gap by proposing a metric to quantify market discrepancy. This metric measures the difference between a causal effect from underlying market unique characteristics and it is evaluated through the interaction between the AT agent and the market. Most importantly, we introduce Algorithmic Trading-guided Market Simulation (ATMS) by optimizing our proposed metric. Inspired by SeqGAN, ATMS formulates the simulator as a stochastic policy in reinforcement learning (RL) to account for the sequential nature of trading. Moreover, ATMS utilizes the policy gradient update to bypass differentiating the proposed metric, which involves non-differentiable operations such as order deletion from the market. Through extensive experiments on semi-real market data, we demonstrate the effectiveness of our metric and show that ATMS generates market data with improved similarity to reality compared to the state-of-the-art conditional Wasserstein Generative Adversarial Network (cWGAN) approach. Furthermore, ATMS produces market data with more balanced BUY and SELL volumes, mitigating the bias of the cWGAN baseline approach, where a simple strategy can exploit the BUY/SELL imbalance for profit.
    Date: 2023–09
  11. By: Brunori, Paolo
    Abstract: We propose the use of machine learning methods to estimate inequality of opportunity and to illustrate that regression trees and forests represent a substantial improvement over existing approaches: they reduce the risk of ad hoc model selection and trade off upward and downward bias in inequality of opportunity estimates. The advantages of regression trees and forests are illustrated by an empirical application for a cross-section of 31 European countries. We show that arbitrary model selection might lead to significant biases in inequality of opportunity estimates relative to our preferred method. These biases are reflected in both point estimates and country rankings.
    Keywords: equality of opportunity; machine learning; random forests; Equality of opportunity; Wiley deal
    JEL: J1
    Date: 2023–02–20
  12. By: Ali Asgarov
    Abstract: Forecasting financial market trends through time series analysis and natural language processing poses a complex and demanding undertaking, owing to the numerous variables that can influence stock prices. These variables encompass a spectrum of economic and political occurrences, as well as prevailing public attitudes. Recent research has indicated that the expression of public sentiments on social media platforms such as Twitter may have a noteworthy impact on the determination of stock prices. The objective of this study was to assess the viability of Twitter sentiments as a tool for predicting stock prices of major corporations such as Tesla, Apple. Our study has revealed a robust association between the emotions conveyed in tweets and fluctuations in stock prices. Our findings indicate that positivity, negativity, and subjectivity are the primary determinants of fluctuations in stock prices. The data was analyzed utilizing the Long-Short Term Memory neural network (LSTM) model, which is currently recognized as the leading methodology for predicting stock prices by incorporating Twitter sentiments and historical stock prices data. The models utilized in our study demonstrated a high degree of reliability and yielded precise outcomes for the designated corporations. In summary, this research emphasizes the significance of incorporating public opinions into the prediction of stock prices. The application of Time Series Analysis and Natural Language Processing methodologies can yield significant scientific findings regarding financial market patterns, thereby facilitating informed decision-making among investors. The results of our study indicate that the utilization of Twitter sentiments can serve as a potent instrument for forecasting stock prices, and ought to be factored in when formulating investment strategies.
    Date: 2023–08
  13. By: Lefteris Loukas; Ilias Stogiannidis; Prodromos Malakasiotis; Stavros Vassos
    Abstract: We propose the use of conversational GPT models for easy and quick few-shot text classification in the financial domain using the Banking77 dataset. Our approach involves in-context learning with GPT-3.5 and GPT-4, which minimizes the technical expertise required and eliminates the need for expensive GPU computing while yielding quick and accurate results. Additionally, we fine-tune other pre-trained, masked language models with SetFit, a recent contrastive learning technique, to achieve state-of-the-art results both in full-data and few-shot settings. Our findings show that querying GPT-3.5 and GPT-4 can outperform fine-tuned, non-generative models even with fewer examples. However, subscription fees associated with these solutions may be considered costly for small organizations. Lastly, we find that generative models perform better on the given task when shown representative samples selected by a human expert rather than when shown random ones. We conclude that a) our proposed methods offer a practical solution for few-shot tasks in datasets with limited label availability, and b) our state-of-the-art results can inspire future work in the area.
    Date: 2023–08

This nep-cmp issue is ©2023 by Stan Miles. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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