Computational Economics最新文献

This paper illustrates the power of Julia language for the solution and estimation of Dynamic Stochastic General Equilibrium models. We document large gains of the Julia implementation of Perturbation solution (first and higher orders) and Bayesian estimation using two workhorse models in the literature: the Real Business Cycle Model and a medium scale New-Keynesian Model. We release a companion package that implements 1st, 2nd a 3rd order approximation of Dynamic Stochastic General Equilibrium models and allows for estimation of (log-)linearized models using Sequential Monte-Carlo Methods. Our examples highlight that Julia has low entry costs and it is a language where it is easy to deal with parallelization.

Recent advances in Machine Learning and, especially, Deep Learning, have led to applications of these areas in different fields of knowledge, with great emphasis on stock market prediction. There are two main approaches in the literature to predict future prices in the stock market: (1) considering historical stock prices; and (2) considering news or social media documents. Despite the recent efforts to combine these two approaches, the literature lacks works in which both strategies are performed with Deep Learning, which has led to state-of-art results in many regression and classification tasks. To overcome these limitations, in this work we proposed a new Deep Learning-based approach to predict the Brazilian stock market combining the use of historical stock prices, financial technical indicators, and financial news. The experiments were performed considering the period from 2010 to 2019 with the Ibovespa index and the historical prices of the following Brazilian companies: Banco do Brasil, Itaú, Ambev, and Gerdau, which have significant contribution to the Ibovespa index. Our results show that the combination of stock prices, technical indicators and news improves the stock market prediction considering both the prediction error and return-of-investment.

Every market leaves its fingerprint in prices time series. The Efficient Market Hypothesis (EMH), considers that prices behave as random walks, a property that has been tested on whole data sets of both formal and informal markets. Here we extend this idea studying the Cuban informal exchange market using two standard tests, the Wald-Wolfowitz runs test and the Variance ratio test. Moreover, while these tests are usually done in the whole data set, we check whether different intervals of the series and the series on different time scales fulfill the EMH. Therefore, we repeated the tests in the fast components of the market obtained from an Empirical Mode Decomposition of the data and on separated time intervals defined through a Hidden Markov Model with two latent variables. We concluded that in all cases the Efficient Market Hypothesis is violated. We finish our work discussing some possible causes and consequences of this inefficiency.

In this paper, we propose a new hybrid model based on a deep learning network to predict the prices of financial assets. The study addresses two key limitations in existing research: (1) the lack of standardized datasets, time scales, and evaluation metrics, and (2) the focus on prediction return. The proposed model employs a two-stage preprocessing approach utilizing Principal Component Analysis (PCA) for dimensionality reduction and de-noising, followed by Independent Component Analysis (ICA) for feature extraction. A Long Short-Term Memory (LSTM) network with five layers is fed with this preprocessed data to predict the price of the next day using a 5 day time horizon. To ensure comparability with existing literature, experiments employ an 18 year dataset of the Standard & Poor's 500 (S&P500) index and include over 40 technical indicators. Performance evaluation encompasses six metrics, highlighting the model's superiority in accuracy and return rates. Comparative analyses demonstrate the superiority of the proposed PCA-ICA-LSTM model over single-stage statistical methods and other deep learning architectures, achieving notable improvements in evaluation metrics. Evaluation against previous studies using similar datasets corroborates the model's superior performance. Moreover, extensions to the study include adjustments to dataset parameters to account for the COVID-19 pandemic, resulting in improved return rates surpassing traditional trading strategies. PCA-ICA-LSTM achieves a 220% higher return compared to the “hold and wait” strategy in the extended S&P500 dataset, along with a 260% higher return than its closest competitor in the comparison. Furthermore, it outperformed other models in additional case studies.

Graphical Abstract

Cryptocurrencies have garnered significant attention recently due to widespread investments. Additionally, researchers have increasingly turned to social media, particularly in the context of financial markets, to harness its predictive capabilities. Investors rely on platforms like Twitter to analyze investments and detect trends, which can directly impact the future price movements of Bitcoin. Understanding and analyzing Twitter sentiments can potentially provide insights into future Bitcoin price movements and can shed light on how investor sentiment affects cryptocurrency markets. In this study, we explore the correlation between Twitter activity and Bitcoin prices by examining tweets related to Bitcoin price sentiments. Our proposed model consists of two distinct networks. The first network exclusively utilizes historical price data, which is further decomposed into various components using the Empirical Mode Decomposition method. This decomposition helps mitigate the impact of irregular fluctuations on Bitcoin price predictions. Each of these components is then separately processed by Long Short-Term Memory (LSTM) networks. The second network focuses on modeling user sentiments and emotions in conjunction with Bitcoin market data. User opinions are categorized into positive and negative classes and are integrated with historical data to predict the next-day price using LSTM networks. Finally, the outputs of each network are combined to form the ultimate prediction values. Experimental results demonstrate that Twitter sentiment can effectively helps us predict Bitcoin price trends. Furthermore, to validate our proposed model, we compared it with several state-of-the-art methods. The results indicate that our approach outperforms these existing models in terms of accuracy.

This study focuses on the Black–Scholes American call option model as a moving boundary problem. Using a front-fixing approach, the model is derived as a fixed domain nonlinear parabolic problem, and the uniqueness of both the call option price and critical stock price is established. An iterative approach is established to numerically solve the problem, and the convergence of the iterative method is proved. For computational implementation, a finite difference scheme in conjunction with a second-order Runge–Kutta method is conducted. Finally, the numerical results for two test problems are reported in order to confirm our theoretical achievements.