nep-cmp New Economics Papers
on Computational Economics
Issue of 2022‒08‒08
ten papers chosen by
Stan Miles
Thompson Rivers University

  1. Predicting Stock Price Movement after Disclosure of Corporate Annual Reports: A Case Study of 2021 China CSI 300 Stocks By Fengyu Han; Yue Wang
  2. Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning By Vladimir Skavysh; Sofia Priazhkina; Diego Guala; Thomas Bromley
  3. Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Network By Frensi Zejnullahu; Maurice Moser; Joerg Osterrieder
  4. Charging the macroeconomy with an energy sector: an agent-based model By Emanuele Ciola; Enrico Turco; Andrea Gurgone; Davide Bazzana; Sergio Vergalli; Francesco Menoncin
  5. Development of a hybrid method for stock trading based on TOPSIS, EMD and ELM By Elivelto Ebermam; Helder Knidel; Renato A. Krohling
  6. Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial News By Yiqi Deng; Siu Ming Yiu
  7. Safe-FinRL: A Low Bias and Variance Deep Reinforcement Learning Implementation for High-Freq Stock Trading By Zitao Song; Xuyang Jin; Chenliang Li
  8. On the universality of the volatility formation process: when machine learning and rough volatility agree By Mathieu Rosenbaum; Jianfei Zhang
  9. A Data Science Pipeline for Algorithmic Trading: A Comparative Study of Applications for Finance and Cryptoeconomics By Luyao Zhang; Tianyu Wu; Saad Lahrichi; Carlos-Gustavo Salas-Flores; Jiayi Li
  10. Using Artificial Intelligence in the workplace: What are the main ethical risks? By Angelica Salvi del Pero; Peter Wyckoff; Ann Vourc'h

  1. By: Fengyu Han; Yue Wang
    Abstract: In the current stock market, computer science and technology are more and more widely used to analyse stocks. Not same as most related machine learning stock price prediction work, this work study the predicting the tendency of the stock price on the second day right after the disclosure of the companies' annual reports. We use a variety of different models, including decision tree, logistic regression, random forest, neural network, prototypical networks. We use two sets of financial indicators (key and expanded) to conduct experiments, these financial indicators are obtained from the EastMoney website disclosed by companies, and finally we find that these models are not well behaved to predict the tendency. In addition, we also filter stocks with ROE greater than 0.15 and net cash ratio greater than 0.9. We conclude that according to the financial indicators based on the just-released annual report of the company, the predictability of the stock price movement on the second day after disclosure is weak, with maximum accuracy about 59.6% and maximum precision about 0.56 on our test set by the random forest classifier, and the stock filtering does not improve the performance. And random forests perform best in general among all these models which conforms to some work's findings.
    Date: 2022–06
  2. By: Vladimir Skavysh; Sofia Priazhkina; Diego Guala; Thomas Bromley
    Abstract: Computational methods both open the frontiers of economic analysis and serve as a bottleneck in what can be achieved. Using the quantum Monte Carlo (QMC) algorithm, we are the first to study whether quantum computing can improve the run time of economic applications and challenges in doing so. We identify a large class of economic problems suitable for improvements. Then, we illustrate how to formulate and encode on quantum circuit two applications: (a) a bank stress testing model with credit shocks and fire sales and (b) a dynamic stochastic general equilibrium (DSGE) model solved with deep learning, and further demonstrate potential efficiency gain. We also present a few innovations in the QMC algorithm itself and in how to benchmark it to classical MC.
    Keywords: Business fluctuations and cycles; Central bank research; Econometric and statistical methods; Economic models; Financial stability
    Date: 2022–06
  3. By: Frensi Zejnullahu; Maurice Moser; Joerg Osterrieder
    Abstract: This paper presents a Double Deep Q-Network algorithm for trading single assets, namely the E-mini S&P 500 continuous futures contract. We use a proven setup as the foundation for our environment with multiple extensions. The features of our trading agent are constantly being expanded to include additional assets such as commodities, resulting in four models. We also respond to environmental conditions, including costs and crises. Our trading agent is first trained for a specific time period and tested on new data and compared with the long-and-hold strategy as a benchmark (market). We analyze the differences between the various models and the in-sample/out-of-sample performance with respect to the environment. The experimental results show that the trading agent follows an appropriate behavior. It can adjust its policy to different circumstances, such as more extensive use of the neutral position when trading costs are present. Furthermore, the net asset value exceeded that of the benchmark, and the agent outperformed the market in the test set. We provide initial insights into the behavior of an agent in a financial domain using a DDQN algorithm. The results of this study can be used for further development.
    Date: 2022–06
  4. By: Emanuele Ciola (Fondazione Eni Enrico Mattei and Università degli Studi di Brescia); Enrico Turco (Fondazione Eni Enrico Mattei and Università Cattolica del Sacro Cuore); Andrea Gurgone (Fondazione Eni Enrico Mattei and Università Cattolica del Sacro Cuore); Davide Bazzana (Fondazione Eni Enrico Mattei and Università degli Studi di Brescia); Sergio Vergalli (Fondazione Eni Enrico Mattei and Università degli Studi di Brescia); Francesco Menoncin (Fondazione Eni Enrico Mattei and Università degli Studi di Brescia)
    Abstract: The global energy crisis that began in fall 2021 and the following spike in energy price constitute a major challenge for the world economy which risks undermining the post-COVID-19 recovery. In this paper, we develop and validate a new macroeconomic agent-based model with an endogenous energy sector to analyse the role of energy in the functioning of a complex adaptive system and assess the effects of energy shocks on the economic dynamics. The economic system is populated by heterogeneous agents, i.e., households, firms and banks, who take optimal decision rules and interact in decentralized markets characterized by limited information. After calibrating the model on US quarterly macroeconomic data, we investigate the economic and distributional effects of different types of energy shocks, that is an exogenous increase in the price of natural resources such as oil or gas and a decrease in the energy firms' productivity. We find that whereas the two energy shocks entail similar effects at the aggreagate level, the distribution of gains and losses across sectors is largely driven by the subsequent impact on the relative energy price, which varies depending on the type of shock. Our results suggest that, in order to design effective measures in response to energy crises, policymakers need to carefully take into account the nature of energy shocks and the resulting distributional effects.
    Keywords: Energy Sector, Energy Shocks, Agent-Based Models, Macroeconomic Dynamics
    JEL: C63 O13 Q41 Q43
    Date: 2022–03
  5. By: Elivelto Ebermam; Helder Knidel; Renato A. Krohling
    Abstract: Deciding when to buy or sell a stock is not an easy task because the market is hard to predict, being influenced by political and economic factors. Thus, methodologies based on computational intelligence have been applied to this challenging problem. In this work, every day the stocks are ranked by technique for order preference by similarity to ideal solution (TOPSIS) using technical analysis criteria, and the most suitable stock is selected for purchase. Even so, it may occur that the market is not favorable to purchase on certain days, or even, the TOPSIS make an incorrect selection. To improve the selection, another method should be used. So, a hybrid model composed of empirical mode decomposition (EMD) and extreme learning machine (ELM) is proposed. The EMD decomposes the series into several sub-series, and thus the main omponent (trend) is extracted. This component is processed by the ELM, which performs the prediction of the next element of component. If the value predicted by the ELM is greater than the last value, then the purchase of the stock is confirmed. The method was applied in a universe of 50 stocks in the Brazilian market. The selection made by TOPSIS showed promising results when compared to the random selection and the return generated by the Bovespa index. Confirmation with the EMD-ELM hybrid model was able to increase the percentage of profit tradings.
    Date: 2022–06
  6. By: Yiqi Deng; Siu Ming Yiu
    Abstract: A major source of information can be taken from financial news articles, which have some correlations about the fluctuation of stock trends. In this paper, we investigate the influences of financial news on the stock trends, from a multi-instance view. The intuition behind this is based on the news uncertainty of varying intervals of news occurrences and the lack of annotation in every single financial news. Under the scenario of Multiple Instance Learning (MIL) where training instances are arranged in bags, and a label is assigned for the entire bag instead of instances, we develop a flexible and adaptive multi-instance learning model and evaluate its ability in directional movement forecast of Standard & Poors 500 index on financial news dataset. Specifically, we treat each trading day as one bag, with certain amounts of news happening on each trading day as instances in each bag. Experiment results demonstrate that our proposed multi-instance-based framework gains outstanding results in terms of the accuracy of trend prediction, compared with other state-of-art approaches and baselines.
    Date: 2022–06
  7. By: Zitao Song; Xuyang Jin; Chenliang Li
    Abstract: In recent years, many practitioners in quantitative finance have attempted to use Deep Reinforcement Learning (DRL) to build better quantitative trading (QT) strategies. Nevertheless, many existing studies fail to address several serious challenges, such as the non-stationary financial environment and the bias and variance trade-off when applying DRL in the real financial market. In this work, we proposed Safe-FinRL, a novel DRL-based high-freq stock trading strategy enhanced by the near-stationary financial environment and low bias and variance estimation. Our main contributions are twofold: firstly, we separate the long financial time series into the near-stationary short environment; secondly, we implement Trace-SAC in the near-stationary financial environment by incorporating the general retrace operator into the Soft Actor-Critic. Extensive experiments on the cryptocurrency market have demonstrated that Safe-FinRL has provided a stable value estimation and a steady policy improvement and reduced bias and variance significantly in the near-stationary financial environment.
    Date: 2022–06
  8. By: Mathieu Rosenbaum; Jianfei Zhang
    Abstract: We train an LSTM network based on a pooled dataset made of hundreds of liquid stocks aiming to forecast the next daily realized volatility for all stocks. Showing the consistent outperformance of this universal LSTM relative to other asset-specific parametric models, we uncover nonparametric evidences of a universal volatility formation mechanism across assets relating past market realizations, including daily returns and volatilities, to current volatilities. A parsimonious parametric forecasting device combining the rough fractional stochastic volatility and quadratic rough Heston models with fixed parameters results in the same level of performance as the universal LSTM, which confirms the universality of the volatility formation process from a parametric perspective.
    Date: 2022–06
  9. By: Luyao Zhang; Tianyu Wu; Saad Lahrichi; Carlos-Gustavo Salas-Flores; Jiayi Li
    Abstract: Recent advances in Artificial Intelligence (AI) have made algorithmic trading play a central role in finance. However, current research and applications are disconnected information islands. We propose a generally applicable pipeline for designing, programming, and evaluating the algorithmic trading of stock and crypto assets. Moreover, we demonstrate how our data science pipeline works with respect to four conventional algorithms: the moving average crossover, volume-weighted average price, sentiment analysis, and statistical arbitrage algorithms. Our study offers a systematic way to program, evaluate, and compare different trading strategies. Furthermore, we implement our algorithms through object-oriented programming in Python3, which serves as open-source software for future academic research and applications.
    Date: 2022–06
  10. By: Angelica Salvi del Pero (OECD); Peter Wyckoff (OECD); Ann Vourc'h
    Abstract: Artificial Intelligence (AI) systems are changing workplaces. AI systems have the potential to improve workplaces, but ensuring trustworthy use of AI in the workplace means addressing the ethical risks it can raise. This paper reviews possible risks in terms of human rights (privacy, fairness, agency and dignity); transparency and explainability; robustness, safety and security; and accountability. The paper also reviews ongoing policy action to promote trustworthy use of AI in the workplace. Existing legislation to ensure ethical workplaces must be enforced effectively, and serve as the foundation for new policy. Economy- and society-wide initiatives on AI, such as the EU AI Act and standard-setting, can also play a role. New workplace-specific measures and collective agreements can help fill remaining gaps.
    JEL: J01 J08 J2 J7 O3
    Date: 2022–07–08

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