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on Forecasting |
| By: | Kevin Xin; Lizhi Xin |
| Abstract: | Forecasting, to estimate future events, is crucial for business and decision-making. This paper proposes QxEAI, a methodology that produces a probabilistic forecast that utilizes a quantum-like evolutionary algorithm based on training a quantum-like logic decision tree and a classical value tree on a small number of related time series. By using different cycles of the Dow Jones Index (yearly, monthly, weekly, daily), we demonstrate how our methodology produces accurate forecasts while requiring little to none manual work. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.03701&r= |
| By: | Etienne Theising |
| Abstract: | This paper introduces an approach to reference class selection in distributional forecasting with an application to corporate sales growth rates using several co-variates as reference variables, that are implicit predictors. The method can be used to detect expert or model-based forecasts exposed to (behavioral) bias or to forecast distributions with reference classes. These are sets of similar entities, here firms, and rank based algorithms for their selection are proposed, including an optional preprocessing data dimension reduction via principal components analysis. Forecasts are optimal if they match the underlying distribution as closely as possible. Probability integral transform values rank the forecast capability of different reference variable sets and algorithms in a backtest on a data set of 21, 808 US firms over the time period 1950 - 2019. In particular, algorithms on dimension reduced variables perform well using contemporaneous balance sheet and financial market parameters along with past sales growth rates and past operating margins changes. Comparisions of actual analysts' estimates to distributional forecasts and of historic distributional forecasts to realized sales growth illustrate the practical use of the method. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.03402&r= |
| By: | Domenico Delli Gatti; Filippo Gusella; Giorgio Ricchiuti |
| Abstract: | Given the unobserved nature of expectations, this paper employs latent variable analysis to examine three financial instability models and assess their out-of-sample forecasting accuracy. We compare a benchmark linear random walk model, which implies exogenous instability phenomena, with a linear state-space model and a nonlinear Markov regime-switching model, both of which postulate endogenous fluctuations phenomena due to heterogeneous behavioral heuristics. Using the S&P 500 dataset from 1990 to 2019, results confirm complex endogenous dynamics and suggest that the inclusion of behavioral nonlinearities improves the model’s predictability both in the short, medium, and long run. |
| Keywords: | endogenous instability, exogenous instability, behavioral model, forecasting analysis |
| JEL: | C13 C51 E37 G10 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_11082&r= |
| By: | Yihang Fu; Mingyu Zhou; Luyao Zhang |
| Abstract: | In the distributed systems landscape, Blockchain has catalyzed the rise of cryptocurrencies, merging enhanced security and decentralization with significant investment opportunities. Despite their potential, current research on cryptocurrency trend forecasting often falls short by simplistically merging sentiment data without fully considering the nuanced interplay between financial market dynamics and external sentiment influences. This paper presents a novel Dual Attention Mechanism (DAM) for forecasting cryptocurrency trends using multimodal time-series data. Our approach, which integrates critical cryptocurrency metrics with sentiment data from news and social media analyzed through CryptoBERT, addresses the inherent volatility and prediction challenges in cryptocurrency markets. By combining elements of distributed systems, natural language processing, and financial forecasting, our method outperforms conventional models like LSTM and Transformer by up to 20\% in prediction accuracy. This advancement deepens the understanding of distributed systems and has practical implications in financial markets, benefiting stakeholders in cryptocurrency and blockchain technologies. Moreover, our enhanced forecasting approach can significantly support decentralized science (DeSci) by facilitating strategic planning and the efficient adoption of blockchain technologies, improving operational efficiency and financial risk management in the rapidly evolving digital asset domain, thus ensuring optimal resource allocation. |
| Date: | 2024–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2405.00522&r= |