Computational Economics最新文献

Algorithmic trading has become increasingly prevalent in financial markets, and traders and investors seeking to leverage computational techniques and data analysis to gain a competitive edge. This paper presents a comprehensive analysis of algorithmic trading strategies, focusing on the efficacy of technical indicators in predicting market trends and generating profitable trading signals. The research framework outlines a systematic process for investigating and evaluating stock market investment strategies, beginning with a clear research objective and a comprehensive review of the literature. Data collected from various stock exchanges, including the S&P 500, undergo rigorous preprocessing, cleaning, and transformation. The subsequent stages involve generating investment signals, calculating relevant indicators such as RSI, EMAs, and MACD, and conducting backtesting to compare the strategy's historical performance to benchmarks. The key findings reveal notable returns generated by the indicators analyzed, though falling short of benchmark performance, highlighting the need for further refinement. The study underscores the importance of a multi-indicator approach in enhancing the interpretability and predictive accuracy of algorithmic trading models. This research contributes to understanding of algorithmic trading strategies and provides valuable information for traders and investors looking to optimize their investment decisions in financial markets.

It is prone to overfitting and poor generalization ability for imbalanced small sample datasets in modeling. Auxiliary data is an effective solution. However, there may be data distribution differences between auxiliary data and small sample data, and the presence of noise samples affects the prediction performance. To address this issue, we propose an ensemble resampling based transfer AdaBoost (TrAdaBoost) algorithm for imbalanced small sample credit classification. The proposed algorithm framework has two stages: ensemble resampling dataset generation and weight adaptive transfer AdaBoost (WATrA) model prediction. In the first stage, neighborhood-based resampling technique is proposed to filter source data and reduce noise samples, followed by bagging resampling to balance the filtered source data. In the second stage, a weight adaptive TrAdaBoost model is utilized to address small sample with class imbalance issues and improve the effectiveness of the proposed method. We validate the proposed algorithm on two small sample credit datasets with class imbalance, and observe significant improvements in performance compared to traditional supervised machine learning methods and resampling methods based on the main evaluation criteria.

Due to overly optimistic economic and statistical assumptions, the classical option pricing model frequently falls short of ideal predictions. Rapid progress in artificial intelligence, the availability of massive datasets, and the rise in computational power in machines have all created an environment conducive to the development of complex methods for predicting financial derivatives prices. This study proposes a hybrid deep learning (DL) based predictive model for accurate and prompt prediction of option prices by fusing a one-dimensional convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM). A set of 15 predictive factors is carefully built under the umbrella of fundamental market data and technical indicators. Our proposed model is compared with other DL-based models using six evaluation metrics-root mean square error (RMSE), mean absolute percentage error, mean percentage error, determination coefficient ((R^2)), maximum error and median absolute error. Further, statistical analysis of models is also done using one-way ANOVA and posthoc analysis using the Tukey HSD test to demonstrate that the CNN-BiLSTM model outperforms competing models in terms of fit and prediction accuracy.

Fuzzy inference systems emerged as a machine learning model that provides accurate and explainable results. Two fuzzy inference systems are reported in the literature, Mamdani and Takagi–Sugeno–Kang. Mamdani implements fuzzy sets in the consequent part and provides more explainable results. On the other hand, Takagi–Sugeno–Kang is more suitable for modeling more complex data because it uses polynomial functions. However, there is no unique method to design Takagi–Sugeno–Kang rules in the literature, and some limitations can be found in the proposed models, such as no direct control over the number of rules, many hyper-parameters and increased complexity due to hybridization to form Takagi–Sugeno–Kang rules. To overcome these shortcomings, this paper proposes a new Takagi–Sugeno–Kang. The user can define the number of rules in the introduced model considering the accuracy-interpretability trade-off. Furthermore, the model has a lower number of hyper-parameters. Two filtering approaches are implemented to compute the consequent parameters, the recursive least squares, and the weighted recursive least squares. The model is applied to six relevant financial series, S &P 500, NASDAQ, TAIEX, CSI 300, KOSPI 200, and NYSE. The concept of interval-valued data is implemented to estimate the volatility of the economic series as a complement to classical forecasting. The results support that predictions of interval-valued data can be implemented as a complement to crisp prediction in defining decision-making strategies. The proposed approach’s results are compared with those of classical models and evolving Fuzzy Systems, and the model presented satisfactory results. The code of the proposed models is given at https://github.com/kaikerochaalves/NTSK.git.

Due to the continuous worldwide conflicts, the prices of corn and wheat have fluctuated greatly in recent years, which has led countries to focus more on concerns related to food security. In order to study the dynamic characteristics and evolution law of price volatility in the international grain futures market and improve the price linkage trend of grain identification. This study builds a directed weighted network of corn and wheat futures prices based on the distributed lag model and examines the linkage relationship between corn and wheat futures prices. We discover that most of the price linkages between corn and wheat futures between 2013 and 2023 form some significant and relatively consistent relationship patterns. Through the analysis of complex network, it has been discovered that the prices of corn and wheat futures are relatively stable over time and that the frequent occurrence of high centrality nodes has a regular pattern that is directly related to the fundamental conditions of the global market. Our results are useful in determining the trend of change in the linkage impact of agricultural product prices, which is crucial for enhancing the safety of grain futures.

Numerous researchers have used various techniques to predict housing prices, but the results have been mixed. This article forecasts housing prices based on their stationary (level) and nonstationary (growth rate) presentations. Our study uses five classes of univariate time series techniques: autoregressive moving average (ARMA) modeling, generalized autoregression (GAR) modeling, generalized autoregressive conditional heteroskedasticity (GARCH) modeling, time-varying Kalman filtering with random autoregressive (KAR) presentation, and Markov chain Monte Carlo (MCMC) simulations. We assigned optimal weights to each technique to minimize the mean square error (MSE) of our forecasts. Our dynamic forecasting method shows superior out-of-sample performance based on the nonstationary presentation one to three quarters ahead, while reducing the average MSE by 37%. For four-quarter horizons, the average MSE of our dynamic forecasts decreased by 11% when we used a stationary presentation of housing prices and included lagged values for four economic leading indicators: the shadow federal funds rate, 1-year expected inflation, the 10-year Treasury Minus 3-Month Treasury Constant Maturity term spread (TERM), and the Brave-Butters-Kelley Leading Index.

The emergence of Islamic finance as an alternative financial investment area and the increasing political and economic uncertainty around the world necessitated an examination of the relationship between these two factors. This study examines the impact of four important global uncertainty and risk indicators “Global Economic Policy Uncertainty-GEPU, Geopolitical Risk Index-GPR, World Uncertainty Index-WUI, and CBOE Volatility Index-VIX” on two important Islamic stock market indices (Dow Jones Islamic Market Index and Bist Participation 100) using wavelet coherence (WTC) and asymmetric Fourier TY analyzes Quarterly data for the period March 2011–June 2023 were used in the study. The results of the analysis show that economic instability indicators impact Islamic equity market indices (both in Turkey and the world). This effect is determined as VIX, GEPU, GPR, and WUI. In addition, the fact that the GPR and WUI indices, which have an impact on conventional markets, have truly little and only a partial impact on Islamic equity markets is an important finding. The results of this study make important contributions to the literature and provide important findings for investors and policy makers.

Taking risk management as an independent department and comparable factor, we set up three parallel departments (credit, risk management, and investment) in a bank. To resolve the problem of common resource allocation, this study is the first to combine the three parallel departments and the optimal common resource allocation in the banking framework. The empirical results show the following. (1) The efficiency and ranking of banks with risk management are better than that without risk management. (2) Banks that share common resources in an optimal way have higher efficiency than banks that share resources in a non-optimal way.

This paper investigates the efficacy of various heterogeneous autoregressive models (HAR) in forecasting volatility across the U.S. financial markets. We address potential data measurement errors and leverage a comprehensive dataset of 22 years of tick-by-tick data encompassing three major stock indices: the S&P500, the Dow Jones Industrial Average (DJI), and the Nasdaq. Our analysis reveals several key findings: (1) Long-term (monthly) realized volatility (RV) has a stronger influence on future volatility compared to short-term (daily and weekly) RV. This aligns with the Heterogeneous Market Hypothesis, suggesting all market participants prioritize long-term volatility due to its impact on market direction. (2) Daily jumps have a short-term negative impact on future volatility, while aggregated monthly jumps have a positive effect due to their influence on market direction. The transient nature of jumps implies that the persistence of volatility stems from its continuous component. (3) The leverage effect is present and persists for up to 1 week. Models incorporating this effect demonstrate significantly better performance. (4) Across all models, forecast accuracy peaks at the 1-week horizon. More general models offer superior predictive power for short-term forecasts. For longer horizons, while there is no statistically significant difference among models, the loss function shows a slight improvement for more general models. (5) All models are able to confirm the theoretical link between expected return and volatility by identifying a positive correlation between return and risk in the data.