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on Computational Economics |
| By: | Tejas Ramdas; Martin T. Wells |
| Abstract: | In this study, we leverage powerful non-linear machine learning methods to identify the characteristics of trades that contain valuable information. First, we demonstrate the effectiveness of our optimized neural network predictor in accurately predicting future market movements. Then, we utilize the information from this successful neural network predictor to pinpoint the individual trades within each data point (trading window) that had the most impact on the optimized neural network's prediction of future price movements. This approach helps us uncover important insights about the heterogeneity in information content provided by trades of different sizes, venues, trading contexts, and over time. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.05192 |
| By: | Chen Qiang (Shandong University) |
| Abstract: | Traditional methods for estimating treatment effects generally assume strong functional forms and are only applicable when the covariates are low-dimensional data. However, using machine learning methods directly often leads to "regularization bias". The recently emerging "double/debiased machine learning" provides an effective estimation method without assuming a functional form and is suitable for high-dimensional data. This presentation will introduce the principles of dual machine learning in a simple way and demonstrate the corresponding Stata operations with classic cases. |
| Date: | 2024–10–02 |
| URL: | https://d.repec.org/n?u=RePEc:boc:chin23:03 |
| By: | Boris Ter-Avanesov; Homayoon Beigi |
| Abstract: | We explore the performance of various artificial neural network architectures, including a multilayer perceptron (MLP), Kolmogorov-Arnold network (KAN), LSTM-GRU hybrid recursive neural network (RNN) models, and a time-delay neural network (TDNN) for pricing European call options. In this study, we attempt to leverage the ability of supervised learning methods, such as ANNs, KANs, and gradient-boosted decision trees, to approximate complex multivariate functions in order to calibrate option prices based on past market data. The motivation for using ANNs and KANs is the Universal Approximation Theorem and Kolmogorov-Arnold Representation Theorem, respectively. Specifically, we use S\&P 500 (SPX) and NASDAQ 100 (NDX) index options traded during 2015-2023 with times to maturity ranging from 15 days to over 4 years (OptionMetrics IvyDB US dataset). Black \& Scholes's (BS) PDE \cite{Black1973} model's performance in pricing the same options compared to real data is used as a benchmark. This model relies on strong assumptions, and it has been observed and discussed in the literature that real data does not match its predictions. Supervised learning methods are widely used as an alternative for calibrating option prices due to some of the limitations of this model. In our experiments, the BS model underperforms compared to all of the others. Also, the best TDNN model outperforms the best MLP model on all error metrics. We implement a simple self-attention mechanism to enhance the RNN models, significantly improving their performance. The best-performing model overall is the LSTM-GRU hybrid RNN model with attention. Also, the KAN model outperforms the TDNN and MLP models. We analyze the performance of all models by ticker, moneyness category, and over/under/correctly-priced percentage. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.06724 |
| By: | Anthony Coache; Sebastian Jaimungal |
| Abstract: | In a reinforcement learning (RL) setting, the agent's optimal strategy heavily depends on her risk preferences and the underlying model dynamics of the training environment. These two aspects influence the agent's ability to make well-informed and time-consistent decisions when facing testing environments. In this work, we devise a framework to solve robust risk-aware RL problems where we simultaneously account for environmental uncertainty and risk with a class of dynamic robust distortion risk measures. Robustness is introduced by considering all models within a Wasserstein ball around a reference model. We estimate such dynamic robust risk measures using neural networks by making use of strictly consistent scoring functions, derive policy gradient formulae using the quantile representation of distortion risk measures, and construct an actor-critic algorithm to solve this class of robust risk-aware RL problems. We demonstrate the performance of our algorithm on a portfolio allocation example. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.10096 |
| By: | Kevin Cedric Guyard; Michel Deriaz |
| Abstract: | The Foreign Exchange market is a significant market for speculators, characterized by substantial transaction volumes and high volatility. Accurately predicting the directional movement of currency pairs is essential for formulating a sound financial investment strategy. This paper conducts a comparative analysis of various machine learning models for predicting the daily directional movement of the EUR/USD currency pair in the Foreign Exchange market. The analysis includes both decorrelated and non-decorrelated feature sets using Principal Component Analysis. Additionally, this study explores meta-estimators, which involve stacking multiple estimators as input for another estimator, aiming to achieve improved predictive performance. Ultimately, our approach yielded a prediction accuracy of 58.52% for one-day ahead forecasts, coupled with an annual return of 32.48% for the year 2022. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.04471 |
| By: | Kim, Hayeon; Lee, Sang Woo; Seo, Sungwoo |
| Keywords: | Generative Artificial Intelligence, Hallucination, Politeness Strategy, Attribution Strategy, Anthropomorphism |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:itsb24:302511 |
| By: | Neel Rajani; Lilli Kiessling; Aleksandr Ogaltsov; Claus Lang |
| Abstract: | Although powerful, current cutting-edge LLMs may not fulfil the needs of highly specialised sectors. We introduce KodeXv0.1, a family of large language models that outclass GPT-4 in financial question answering. We utilise the base variants of Llama 3.1 8B and 70B and adapt them to the financial domain through a custom training regime. To this end, we collect and process a large number of publicly available financial documents such as earnings calls and business reports. These are used to generate a high-quality, synthetic dataset consisting of Context-Question-Answer triplets which closely mirror real-world financial tasks. Using the train split of this dataset, we perform RAG-aware 4bit LoRA instruction tuning runs of Llama 3.1 base variants to produce KodeX-8Bv0.1 and KodeX-70Bv0.1. We then complete extensive model evaluations using FinanceBench, FinQABench and the withheld test split of our dataset. Our results show that KodeX-8Bv0.1 is more reliable in financial contexts than cutting-edge instruct models in the same parameter regime, surpassing them by up to 9.24%. In addition, it is even capable of outperforming state-of-the-art proprietary models such as GPT-4 by up to 7.07%. KodeX-70Bv0.1 represents a further improvement upon this, exceeding GPT-4's performance on every tested benchmark. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.13749 |
| By: | Zhengyang Chi; Junbin Gao; Chao Wang |
| Abstract: | This study introduces a global stock market volatility forecasting model that enhances forecasting accuracy and practical utility in real-world financial decision-making by integrating dynamic graph structures and encompassing the union of active trading days of different stock markets. The model employs a spatial-temporal graph neural network (GNN) architecture to capture the volatility spillover effect, where shocks in one market spread to others through the interconnective global economy. By calculating the volatility spillover index to depict the volatility network as graphs, the model effectively mirrors the volatility dynamics for the chosen stock market indices. In the empirical analysis, the proposed model surpasses the benchmark model in all forecasting scenarios and is shown to be sensitive to the underlying volatility interrelationships. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.15320 |
| By: | Wolfgang Keller; Carol H. Shiue; Sen Yan |
| Abstract: | Primary historical sources are often by-passed for secondary sources due to high human costs of accessing and extracting primary information–especially in lower-resource settings. We propose a supervised machine-learning approach to the natural language processing of Chinese historical data. An application to identifying different forms of social unrest in the Veritable Records of the Qing Dynasty shows that approach cuts dramatically down the cost of using primary source data at the same time when it is free from human bias, reproducible, and flexible enough to address particular questions. External evidence on triggers of unrest also suggests that the computer-based approach is no less successful in identifying social unrest than human researchers are. |
| JEL: | C8 N45 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32982 |
| By: | Oh, Ryeong; Kim, Seongcheol |
| Abstract: | Misusing AI in video production, such as creating deepfakes, leads to significant social losses and crimes. While individual users are exposed to losses and crimes, little research examines the factors influencing their intention to misuse AI in video production. To fill the gap, this study aims to identify the key factors influencing individuals' intention to misuse AI and their resistance to AI. An online survey was conducted in June 2023, and data was collected from 400 respondents in Korea. The analysis of mediation has substantiated the impact of perceived risk on the misuse of AI voices as well as the favorability towards AI humans to resistance. In addition, this study successfully established the mediating effects of attitudes and subjective norms on the relationships between risk perception, favorability, misuse, and resistance. This study revealed that to reduce intentions of misuse, it is imperative to enhance the perception of risk and cultivate negative attitudes toward the misuse of AI. Notably, this study also identified variations in these effects across different modalities. Furthermore, it emphasizes the importance of improving humans' perceptions of risk to artificial intelligence to address the potential for misuse. |
| Keywords: | AI misuse, disinformation, AI video production, deepfakes |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:itsb24:302478 |