Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.

Feature Selection with Optimal Variational Auto Encoder for Financial Crisis Prediction

Financial crisis prediction(FCP)received significant attention in the financial sector for decision-making.Proper forecasting of the number of firms possible to fail is important to determine the growth index and strength of a nation’s economy.Conventionally,numerous approaches have been developed in the design of accurate FCP processes.At the same time,classifier efficacy and predictive accuracy are inadequate for real-time applications.In addition,several established techniques carry out well to any of the specific datasets but are not adjustable to distinct datasets.Thus,there is a necessity for developing an effectual prediction technique for optimum classifier performance and adjustable to various datasets.This paper presents a novel multi-vs.optimization(MVO)based feature selection(FS)with an optimal variational auto encoder(OVAE)model for FCP.The proposed multi-vs.optimization based feature selection with optimal variational auto encoder(MVOFS-OVAE)model mainly aims to accomplish forecasting the financial crisis.For achieving this,the proposed MVOFS-OVAE model primarily pre-processes the financial data using min-max normalization.In addition,the MVOFS-OVAE model designs a feature subset selection process using the MVOFS approach.Followed by,the variational auto encoder(VAE)model is applied for the categorization of financial data into financial crisis or non-financial crisis.Finally,the differential evolution(DE)algorithm is utilized for the parameter tuning of the VAE model.A series of simulations on the benchmark dataset reported the betterment of the MVOFS-OVAE approach over the recent state of art approaches.

Optimized Stacked Autoencoder for IoT Enabled Financial Crisis Prediction Model

Recently,Financial Technology(FinTech)has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or firm.Financial crisis prediction(FCP)is an essential topic in business sector that finds it useful to identify the financial condition of a financial institution.At the same time,the development of the internet of things(IoT)has altered the mode of human interaction with the physical world.The IoT can be combined with the FCP model to examine the financial data from the users and perform decision making process.This paper presents a novel multi-objective squirrel search optimization algorithm with stacked autoencoder(MOSSA-SAE)model for FCP in IoT environment.The MOSSA-SAE model encompasses different subprocesses namely preprocessing,class imbalance handling,parameter tuning,and classification.Primarily,the MOSSA-SAE model allows the IoT devices such as smartphones,laptops,etc.,to collect the financial details of the users which are then transmitted to the cloud for further analysis.In addition,SMOTE technique is employed to handle class imbalance problems.The goal of MOSSA in SMOTE is to determine the oversampling rate and area of nearest neighbors of SMOTE.Besides,SAE model is utilized as a classification technique to determine the class label of the financial data.At the same time,the MOSSA is applied to appropriately select the‘weights’and‘bias’values of the SAE.An extensive experimental validation process is performed on the benchmark financial dataset and the results are examined under distinct aspects.The experimental values ensured the superior performance of the MOSSA-SAE model on the applied dataset.

An Empirical Analysis on the Prediction of Chinese Financially Distressed Listed Companies

This paper presents an in-depth analysis of financially distressed listed companies in China between 1998 and 2002. We compare the predictive power of multiple discriminant analysis （MDA）, logistic regression, and neural network models. We design and implement 126 different forecasting models using different predictive methods, different sample proportions, and different initial independent variables. The aim is to determine which model（s） and variables are best applicable for the short-term prediction of financial distress in China. We find that logistic regression models are superior to multiple discriminant analysis models in terms of prediction accuracy rate, restriction of sample distribution or prediction cost, but the neural network models show promise in their low Type I and Type II errors. The paper also inherently tests the applicability of variables traditionally used for bankruptcy prediction to the purpose of financial distress prediction in China.

Predicting Firms' Financial Failure Using Financial Ratios: an Empirical Studies of Chinese Firms

The stock market has played an important role in today’s China economy. Due to the fact that a number of the listed companies did not generate the return that the investors expected, the research on the causes of financial failure and predicting such financial failure becomes very important for future capital market development. The paper selected 43 ST(Special Treat) and 43 Non-ST companies as sample, using financial ratio and the factor analysis model to compare and analyse. This study especially analyses profitability, operation efficiency and development ability of firms and focuses on the design of an early warning system for possible financial failure of the listed companies by using factor analysis.

Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model

Due to global financial crisis,risk management has received significant attention to avoid loss and maximize profit in any business.Since the financial crisis prediction(FCP)process is mainly based on data driven decision making and intelligent models,artificial intelligence(AI)and machine learning(ML)models are widely utilized.This article introduces an intelligent feature selection with deep learning based financial risk assessment model(IFSDL-FRA).The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise.In addition,the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection(WSOA-FS)manner to an optimum selection of feature subsets.Moreover,Deep Random Vector Functional Link network(DRVFLN)classification technique was applied to properly allot the class labels to the financial data.Furthermore,improved fruit fly optimization algorithm(IFFOA)based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN model.For enhancing the better performance of the IFSDL-FRA technique,an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches.

Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science

In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their businesses.Data science integrates the conventions of econometrics with the technological elements of data science.It make use of machine learning(ML),predictive and prescriptive analytics to effectively understand financial data and solve related problems.Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations.At the same time,it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial crisis.AI becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time problems.With this motivation,this paper presents a new AI based optimal functional link neural network(FLNN)based financial crisis prediction(FCP)model forSMEs.The proposed model involves preprocessing,feature selection,classification,and parameter tuning.At the initial stage,the financial data of the enterprises are collected and are preprocessed to enhance the quality of the data.Besides,a novel chaotic grasshopper optimization algorithm(CGOA)based feature selection technique is applied for the optimal selection of features.Moreover,functional link neural network(FLNN)model is employed for the classification of the feature reduced data.Finally,the efficiency of theFLNNmodel can be improvised by the use of cat swarm optimizer(CSO)algorithm.A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented model.The experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction accuracy of 98.830%,92.100%,and 95.220%on the applied Polish dataset Year I-III respectively.

Forecasting Financial Distress of Chinese High-tech Manufacturing Companies Based on a Hybrid Model of GA-SVM

Owing to the radical changing of Chinese economy, it is essential to build an effective financial distress prediction model. In this paper, we present a genetic algorithm （GA） approach for optimizing parameters of support vector machine （SVM）. We validate the proposed model on datasets of Chinese high-tech manufacturing industry. Experimental results reveal that the proposed GAo SVM model can compare to and even outperform other exiting classifiers. Compared to grid-search algorithm, the proposed GA-based takes less time to optimize SVM parameter without degrading the prediction accuracy of SVM.