Numerous machine learning models have been developed to achieve the ‘real-life’ financial objective of optimising the risk/return profile of investment strategies. In the current article: (a) we present and classify the most popular performance metrics used in 190 articles analysed. We noticed that, in most articles, no attention is devoted to the criteria used to compare the algorithms. (b) We evaluate the ability of the metrics used in the literature to assess the efficiency of algorithms to improve investments results. We demonstrate that many of the most popular metrics, like mean squared error (MSE) or root mean squared error (RMSE), are inappropriate for this purpose while others, like accuracy or F1, are just weak. We explain why risk-adjusted return-based metrics are best-in-class, although they suffer from statistical limitations and do not allow easy comparison of algorithms across assets or over time. (c) We propose a new discriminant metric that measures the efficiency of AI models to optimize the risk-adjusted return, which is statistically more robust, and which can test the effectiveness and the stability of models over time and across assets. 

http://manuscript.elsevier.com/S0957417422003967/pdf/S0957417422003967.pdf

Machine learning models predicting returns: Why most popular performance metrics are misleading and proposal for an efficient metric

Forecasts for international financial series with VMD algorithms

Abstract Stocks are an attractive investment option because they can generate large profits compared to other businesses. The movement of stock price patterns in the capital market is very dynamic. Therefore, accurate data modeling is needed to forecast stock prices with a low error rate. Forecasting models using Deep Learning are believed to be able to predict stock price movements accurately with time-series data input, especially the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. Unfortunately, several previous studies and investigations of LSTM/GRU implementation have not yielded convincing performance results. This paper proposes eight new architectural models for stock price forecasting by identifying joint movement patterns in the stock market. The technique is to combine the LSTM and GRU models with four neural network block architectures. Then, the proposed architectural model is evaluated using three accuracy measures obtained from the loss function Mean Absolute Percentage Error (MAPE), Root Mean Squared Percentage Error (RMSPE), and Rooted Mean Dimensional Percentage Error (RMDPE). The three accuracies, MAPE, RMSPE, and RMDPE, represent lower accuracy, true accuracy, and higher accuracy in using the model. 

/pdf/implementation-of-long-short-term-memory-and-gated-recurrent-cj0boouh.pdf

Implementation of Long Short-Term Memory and Gated Recurrent Units on grouped time-series data to predict stock prices accurately

An exploratory data analysis approach for analyzing financial accounting data using machine learning

Abstract Social media is used to categorise products or services, but analysing vast comments is time-consuming. Researchers use sentiment analysis via natural language processing, evaluating methods and results conventionally through literature reviews and assessments. However, our approach diverges by offering a thorough analytical perspective with critical analysis, research findings, identified gaps, limitations, challenges and future prospects specific to deep learning-based sentiment analysis in recent times. Furthermore, we provide in-depth investigation into sentiment analysis, categorizing prevalent data, pre-processing methods, text representations, learning models, and applications. We conduct a thorough evaluation of recent advances in deep learning architectures, assessing their pros and cons. Additionally, we offer a meticulous analysis of deep learning methodologies, integrating insights on applied tools, strengths, weaknesses, performance results, research gaps, and a detailed feature-based examination. Furthermore, we present in a thorough discussion of the challenges, drawbacks, and factors contributing to the successful enhancement of accuracy within the realm of sentiment analysis. A critical comparative analysis of our article clearly shows that capsule-based RNN approaches give the best results with an accuracy of 98.02% which is the CNN or RNN-based models. We implemented various advanced deep-learning models across four benchmarks to identify the top performers. Additionally, we introduced the innovative CRDC (Capsule with Deep CNN and Bi structured RNN) model, which demonstrated superior performance compared to other methods. Our proposed approach achieved remarkable accuracy across different databases: IMDB (88.15%), Toxic (98.28%), CrowdFlower (92.34%), and ER (95.48%). Hence, this method holds promise for automated sentiment analysis and potential deployment. 

"Challenges and future in deep learning for sentiment analysis: a comprehensive review and a proposed novel hybrid approach"

Stock market prediction with deep learning: The case of China

It is a challenge to online obtain the time-varying temperature distribution of self-heating lithium-ion battery (SHLB), which results in a lack of theoretical basis for heating strategies to optimize heating time and temperature distribution uniformity. To reveal the evolution of temperature distribution and improve the temperature distribution uniformity of SHLB during heating, a variable duty cycle heating strategy with online two-dimensional model is proposed. Distributed thermal equivalent circuit model coupled with electrical model can obtain the two-dimensional temperature distribution of battery in real-time. The results show that the maximum temperature gap between the proposed model and the finite element model (FEM) is only 1.2 °C, while 90 % of the calculation time is saved. The proposed model can calculate the temperature distribution almost as accurately as FEM under different conditions. Based on the proposed model, heating currents of different values and duty cycles are applied to investigate their effects on the temperature distribution evolution of SHLB. Finally, we propose a variable duty cycle heating strategy that can automatically switch the duty cycle of the heating current according to the heating uniformity requirements in order to consider both heating time and the temperature distribution uniformity. The results show that compared with constant duty cycle heating, the variable duty cycle heating strategy can reduce the temperature difference by 35 % and limit the temperature difference to the pre-set boundary. The proposed strategy and method have considerable potential for use as improved temperature uniformity in battery management system. • An online two-dimensional model for self-heating lithium-ion battery • High accuracy but reduced complexity compared to finite element model • The effect of current on the temperature distribution evolution is explored. • A variable duty cycle heating strategy to improve temperature uniformity significantly with less influence of heating time 

Variable duty cycle heating strategy based online two-dimensional model for self-heating lithium-ion battery

Lithium-ion battery heating technology is a crucial enabler for electric vehicles to operate in cold climates. Ensuring the delicate balance between temperature rise rate and temperature uniformity faces unprecedented challenges due to the poor thermal diffusivity of batteries. In order to overcome the challenges, first of all, this study proposes a sandwich self-heating structure for lithium-ion batteries and establishes a temperature uniformity model during the heating process of sandwich self-heating structure batteries. Then, the effects of duty cycle on temperature uniformity and heating rate are analyzed. Finally, a rapid warm-up strategy considering temperature uniformity is proposed to reconcile the contradiction between heating rate and temperature uniformity. The proposed rapid warm-up strategy can shorten the heating time by 16 % under three typical heating scenarios. Furthermore, the proposed strategy is applied to two other sandwich self-heating structures with different batteries, and the results show that the rapid warm-up strategy can significantly reduce the heating time and has the potential to be promoted in different types of batteries. 

Rapid warm-up strategy for lithium-ion batteries based on temperature uniformity model of sandwich self-heating structure in multiple scenarios

Variable horizon multivariate driving pattern recognition framework based on vehicle-road two-dimensional information for electric vehicle

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Stock market prediction with deep learning: The case of China

Variable duty cycle heating strategy based online two-dimensional model for self-heating lithium-ion battery

Rapid warm-up strategy for lithium-ion batteries based on temperature uniformity model of sandwich self-heating structure in multiple scenarios

Variable horizon multivariate driving pattern recognition framework based on vehicle-road two-dimensional information for electric vehicle