Forecasting financial asset prices and quantifying associated risks are critical challenges in computational finance. This paper presents an optimal forecast combination framework by integrating advanced time series models and machine learning techniques to enhance prediction accuracy and risk assessment. A novel data-driven risk measure (DDRisknew) based on price differences is introduced, and sign correlation is used to capture risk and mitigate the limitations of traditional risk metrics. Our approach incorporates state-of-the-art forecasting models, including ARIMA, Neural Network Autoregressive, Long Short-Term Memory, XGBoost, and Random Forest, alongside two combination methods: equally weighted averages (FComp_SA) and datadriven optimal weighted averages (FComp_Weighted). Experimental results conducted on a dataset of stocks from ten diverse sectors during the highly volatile COVID-19 period demonstrate the superior performance of FComp_Weighted in minimizing forecasting errors across multiple metrics (RMSE, MAE, MAPE). Moreover, the RMSE of asset price and DDRisknewforecasts using FComp_Weighted models is always lower than the RMSE obtained using the FComp_SA model. This study underscores the importance of combining forecasts and provides a robust, computationally efficient framework for predictive modeling in financial markets. These findings have implications for algorithmic trading, portfolio optimization, and risk management, paving the way for future applications in broader domains like energy and cryptocurrency markets.

The climate crisis has sparked a global shift toward renewable energy sources, increasing the demand for models that provide accurate risk assessment and forecasting to support informed investment decisions. The novelty of this study is to analyze renewable energy investments through three methodological approaches: risk assessment, deep learning-based volatility forecasting, and renewable energy demand forecasting. First, risk profiles of two renewable energy stocks (NextEra Energy and Iberdrola) and two traditional energy stocks (ExxonMobil and Chevron) are evaluated for the time period 2020 to 2024. Second, deep learning (DL) models such as recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent unit (BiGRU) are employed, and it is shown that renewable energy stocks have more stable performance across all DL models, with BiLSTM revealed as the most robust architecture. Third, UK liquid biofuel consumption trends are analyzed using multiple forecasting methods, including Mean, Naïve, Seasonal Naïve, Drift, and machine learning models. It is shown that the neural network autoregressive (NNAR) model has the highest forecast accuracy, and among the combined forecast models, constrained least squares achieved the highest forecast accuracy. The results indicate that renewable energy produces lower risk exposure and more stable returns, and improves predictability despite short-term volatility.

This study investigates data-driven financial correlation networks with a particular focus on modeling log returns using a data-driven t-distribution. A key contribution is showing that the covariance matrix of absolute log returns, used in volatility networks can be expressed as a function of the sign correlation and the covariance matrix of log returns. Fuzzy adjacency matrices are introduced to account for the uncertainty inherent in correlation estimates that depend on the degrees of freedom. Using data from 55 stocks across 13 sectors and S&P500 (big data), we compare four different networks based on correlations of log returns, volatility of log returns, data-driven volatility of log returns and sign(±) of returns. Three dynamic networks based on price correlations, ARIMA innovation correlations of the price and neuro innovation correlations of the price are also considered. All seven networks are evaluated using metrics such as average node degree, sparsity, and maximum eigenvalue. Unlike the existing work, the novelty of this study is demonstrating a relationship between data driven volatility networks and recently proposed volatility correlation networks without using distributional properties. Three different community detection algorithms demonstrate that the proposed data-driven volatility network proposed in this paper has a higher modularity score.

Recently, there has been a growing interest in studying both long-term and short-term forecasts of electricity demand using dynamic regression models with seasonal ARIMA (SARIMA) errors and neural network autoregression (NNAR) models. Most of the electricity demand forecasting models investigated in the literature involved two features: temperature and day type, and only the point forecasts of temperature are used to obtain forecasts of electricity demand. However, it is crucial to acknowledge that temperature fluctuates throughout the day, and it is more appropriate to incorporate the forecast error variability and use the fuzzy forecasts of the temperature as an input to forecast electricity demand. This paper uses a novel fuzzy two-step approach to generate fuzzy forecasts of electricity demand. In step 1, fuzzy forecasts of temperature are obtained by incorporating additional features such as precipitation, irradiance, snowfall, snow mass, cloud cover, and air density. Thirteen distinct models, including neural network regression models and Facebook industrial Prophet models, are fitted to temperature data, and the best forecasting model for temperature is selected based on forecast accuracy measures. In step 2, the fuzzy forecasts of the temperature are used as a feature with day type (weekday/weekend/holiday) to obtain fuzzy forecasts of electricity demand. The superior performance of neural network fuzzy forecasts of electricity demand in terms of forecast accuracy is demonstrated for Ontario electricity demand data.

Recently, there has been a growing interest in using neuro volatility models in fuzzy forecasting and fuzzy option pricing. Neuro volatility models are used to model and predict financial market volatility by extending the neural network autoregressive (NNAR) model for nonlinear nonstationary times series data. In financial risk forecasting, various risk forecasting models for volatility are used to obtain the volatility forecasts, and the model risk ratio based on all the models is calculated to assess the stability of the financial system. However, the recently proposed neuro volatility models (based on neural networks such as LSTM, NNAR, etc.) are not used in evaluating the model risk. In this paper, novel 'neuro model risk forecasts' are obtained by including recently proposed neuro volatility models, and the resiliency of the financial system is studied. Unlike the existing model risk ratio forecasting based on linear volatility models, the driving idea is to use more appropriate nonlinear nonstationary neuro volatility forecasting models to obtain the model risk forecasts. The proposed model risk forecasts in this paper have been evaluated through extensive experiments, and it is shown that the model risk ratio can effectively serve as a metric for assessing the resilience and stability of the targeted financial system.

For many Markov chains that arise in applications (health, finance, etc.), state spaces are huge, and existing matrix methods may not be practical or even not possible to implement. In the literature, the expected waiting time for Markov chain (with a smaller number of states) generated patterns are obtained by finding an appropriate pattern matrix and solving a set of linear equations. In this paper, a fuzzy transition probability (TP) matrix is introduced, and a data-driven fuzzy pattern mining algorithm is proposed for sequence data of any length. The proposed algorithm, which avoids the inversion of the pattern matrix, is applicable to Markov chains with huge state spaces. The proposed algorithm studies two examples involving DNA sequence data with 3954 base pairs and patterns generated by the log-returns of the stocks/cryptocurrencies. Expected weighting times are compared with the traditional matrix approach. Incorporating stochastic variation in the TP estimates through fuzzy matrices, the new approach provides an alternative path to produce$\alpha$-cuts for TP matrices. The main contribution of this paper is to fit an appropriate MC model to a given sequence data and use the proposed fuzzy pattern mining algorithm to obtain resilient probabilistic forecasts and expected waiting time to reach patterns of interest.

Recently there has been a growing interest in studying fuzzy option pricing using Monte Carlo (MC) methods for diffusion models. The traditional volatility estimator has a larger asymptotic variance. In this paper, data-driven neuro-volatility estimates with smaller variances are used to obtain direct volatility forecasts. Asymmetric nonlinear adaptive fuzzy numbers are used to address ambiguity and vagueness associated with volatility estimates. This study uses fuzzy set theory and data-driven volatility forecasts to study call option prices of the S&P 500 index. Four modeling approaches have been considered, Black-Scholes (BS) model, Monte Carlo option pricing with normal / t errors, and the Jump-Diffusion (JD) model. Fuzzy α-cuts of option prices are presented and discussed under different parameter values. Our experimental study suggests that the JD model predicts the call option price more accurately compared to BS, normal errors, and t errors using the volatility estimate obtained using the Bayesian approach.

Supply chain literature reveals that study of resilient supply chains and bullwhip effect (BE) have been receiving special attention during pandemic for supply chains with seasonal as well as nonseasonal demand components. The BE phenomenon has been detected in various industries and sectors, and causes multiple inefficiencies such as higher costs of producing more than needed, wastage and transportation costs. As a result, BE forecast is of great importance for academics and supply chain managers. Despite the multitude of studies that have emerged addressing this issue, the impact of the quality of dynamic forecasts on the BE has received limited coverage in the literature. Optimal dynamic forecasts of the demand could allow managers to mitigate the upstream amplification of orders (and thus the BE), as well as reduce unnecessary inventory costs. Order quantity and BE in a supply chain depend on the forecast of the future demand. Usually minimum mean square error (MMSE) forecasts of the future demand are obtained by fitting an appropriate seasonal auto-regressive moving average (ARMA) time series model. However, a major drawback of the MMSE forecasting method is that it does not provide the associated risk forecasts. In this paper, a simple yet effective machine learning demand forecasting approach without fitting any time series model is presented.Specifically, a novel data driven machine learning algorithm that bypasses traditional forecasting steps and allows forecast weights to be optimized by minimizing the one-step ahead forecast error sum of squares (FESS) is proposed. A novel stability metric of a supply chain is proposed as the risk adjusted forecast of the future demand. It is shown that the risk adjusted forecasts can be used to check whether a given supply chain is resilient. In order to be more resilient and competitive in the current market, business leaders around the world agree that it is necessary to modernize and make major changes to their supply chain strategies. Demand risk forecasts obtained by the proposed machine learning approach allow supply chain managers to enhance the forecasting power of the order quantity and construct more resilient supply chains. The performance of proposed approach is evaluated through numerical experiments using simulated data and weekly demand data of two products. The results show that the performance of the proposed forecasts and risk adjusted forecasts of the future demand are better than the commonly used MMSE forecasts of the future demand.

Algorithmic trading uses a computer program that follows a defined set of instructions (an algorithm) to place a trade and can generate profits at a speed and frequency that is impossible for a human trader. Current state-of-the-art in algorithmic trading uses Kalman filtering (KF) and maximum informative resilient filtering (MIRF) that allow traders to enhance the predictive power of statistical models and improve trading strategies. There has been a growing interest in using MIRF in pairs trading, and a major drawback is that a threshold value (assumed to be one) is selected in an ad hoc way rather than maximizing the Sharpe ratio. In this paper, a novel random weights innovation volatility forecasting (RWIVF) algorithm is introduced to obtain the optimal data-driven weights of past observed volatilities instead of the equal weights by extending the Bollinger bands algo-tradinng strategy. RWIVF algorithm is also compared with the commonly used KF algorithm. Autocorrelations of the absolute values of the innovations in multiple trading are used to demonstrate that the innovations are non-normal with time-varying volatility. Performance of the RWIVF algorithm is shown using the experiments on cointegrated exchange-traded funds (ETFs). Analyses also explain how our approach can improve the performance of the trading strategies. The proposed novel resilient (robustness to initial values) trading strategies to the volatile stock market are also discussed in some detail.

Recently, there has been a growing interest in portfolio optimization using graphical LASSO (GL) machine learning method, by assuming normality for asset returns. However, a major drawback is that most of the asset returns follow non-normal distributions and sample percentiles are used to study the portfolio optimization with Value-at-Risk (VaR) as a risk measure. In this paper, a data-driven random weights algorithm (RWA) and a sign correlation based portfolio return distribution are used to study the fuzzy portfolio optimization. The superiority of RWA over the commonly used genetic algorithm (GA) in computing the optimal portfolio weights is demonstrated by comparing the computing time. When comparing the estimate of the risk tolerance coefficient and the theoretical value for tangency portfolios with volatility as a risk measure, RWA outperforms (smaller absolute error) the GA. The novelty of this paper is the use of RWA and GA to calculate the fuzzy estimates (interval estimates) of the risk tolerance coefficient/optimal weights and using the sign correlation to obtain the data-driven distribution of the portfolio returns. More specifically the novelty is to obtain the fuzzy estimates of the risk tolerance coefficient and portfolio weights by modelling the portfolio volatility as an asymmetric triangular fuzzy number from the data-driven observed portfolio volatilities. In particular, the proposed RWA as well as GA lead to machine learning solutions for the portfolio optimization problems without a closed form solution and provide fuzzy estimates of the risk tolerance coefficient and the optimal portfolio weights.

Electricity is a special commodity that has to be kept available at all times. In fact, power plants need to have accurate forecast of electricity demand in order to provide enough electricity for customers. Final customers are able to establish their own power plants to decrease their dependency on the grid. For example rooftop photovoltaic panels are getting more popular among residential customers. It seems that meteorological variables such as solar irradiance play an important role in load forecasting. Moreover, temperature is also a main determinant of electricity demand. In this paper, we propose a model for short-term load forecasting which consists of hourly weather data (including seasonal variation as well) and historical load data. Machine learning algorithms such as support vector regression (SVR), least absolute shrinkage and selection operator (LASSO) regression and a multilayer neural network (NN) are used for short-term load forecasting. In order to improve the forecast accuracy (smaller mean absolute error) of NN, we propose a dual phase forecasting method. In the first phase, data driven double exponential smoothing (DDDES) is used to generate electricity load forecasts. In the second phase, the results of first phase forecasting are fed into a multilayer NN to have more accurate forecasts of electricity demand. It is shown that NN outperforms the other two methods. Our data analysis shows a significant improvement in terms of performance where maximum mean absolute error (MAE) decreases from 367.26 to 115.30.

Many of the challenges and opportunities of data science in finance involve recursive smoothing, forecasting, filtering and pattern mining. Recently there has been a growing interest in using filtered estimates for dynamic hedge ratios for pairs trading. Moreover, rolling estimates and forecasts are used for pattern mining in technical analysis. Kalman filtering algorithms were successfully applied in pairs trading with only two co-integrated assets. In this paper, pairs trading strategy is extended to multiple trading (with more than two assets) strategy. Recently proposed non-Gaussian maximum informative filtering algorithms for dynamic state space models are used to obtain the filtered estimates of hedge ratios and applied in multiple trading. It is shown that the proposed multiple trading strategy outperforms (with higher profits) the commonly used pairs trading strategy using real data. A data-driven approach for selecting a parameter which maximizes the Sharpe ratio (SR) to generate optimal trading signals is also discussed in some detail.

Regularization methods allow data scientists and risk managers to enhance the predictive power of a statistical model and improve the quality of risk forecasts. Financial risk forecasting is about forecasting volatility, Value at Risk (VaR), expected shortfall (ES) and model risk ratio. While regularized estimates have been shown to perform well in model selection and parameter estimation, their applications in financial risk forecasting has not yet been studied. In this paper, regularized adaptive forecasts and computationally efficient forecasting algorithms for volatility, VaR, ES and model risk are studied using various regularization methods such as ridge, lasso and elastic net. Sample sign correlation of standardized log returns (standardized by volatility forecasts) is used to identify the conditional distribution of the log returns series and provide regularized interval forecasts as well as regularized probability forecasts. Superiority of the regularized risk forecasts is demonstrated using different volatility models including a recently proposed generalized data-driven volatility model in [8]. Validation of the regularized risk forecasts using real financial data is given. Regularized probabilistic forecasts for stationary time series models are also discussed in some detail.

Quantitative finance has been evolving over last several decades and combining randomness and fuzziness of the parameters has found growing interest among researchers to solve forecasting problems. Superiority of the fuzzy forecasting method over the minimum mean square forecasting had been demonstrated for fuzzy coefficient (linear as well as nonlinear) time series models in Thavaneswaran et al. [5]. However, many proposed fuzzy forecasting methods remain difficult to use in practice and there is a need for data-driven approach to fit the fuzzy coefficient volatility models. A neural network (NN) system can uniformly approximate any real nonlinear function on a compact domain to any degree of accuracy. Artificial NN (ANNs) have been applied to finance problems such as stock index prediction and bankruptcy prediction. In this paper, we introduce a novel direct data-driven neuro predictive model for conditional volatility and study the fuzzy value-at-risk (VaR) forecasts. We apply this model to forecast VaR with actual financial data. Our model shows considerable promise as a decision making and risk managing tool.