Financial Data Modeling by Using Asynchronous Parallel Evolutionary Algorithms

In this paper, the high-level knowledge of financial data modeled by ordinary differential equations (ODEs) is discovered in dynamic data by using an asynchronous parallel evolutionary modeling algorithm (APHEMA). A numerical example of Nasdaq index analysis is used to demonstrate the potential of APHEMA. The results show that the dynamic models automatically discovered in dynamic data by computer can be used to predict the financial trends.

Reflections on Applications of International Standards for Financial Data Exchange in China Securities and Futures Markets

1.1. Development of international data exchange standards in securities field Securities market involves a large number of participants, like investors, securities companies, exchanges, clearingcorporations and so on. Businesses among the participants are completed via data exchange. Therefore, the data exchange protocols serve an important factor to determine and promote the sate and rapid development of the securities market.

Using Python to Analyze Financial Big Data

As technology and the internet develop,more data are generated every day.These data are in large sizes,high dimensions,and complex structures.The combination of these three features is the“Big Data”[1].Big data is revolutionizing all industries,bringing colossal impacts to them[2].Many researchers have pointed out the huge impact that big data can have on our daily lives[3].We can utilize the information we obtain and help us make decisions.Also,the conclusions we drew from the big data we analyzed can be used as a prediction for the future,helping us to make more accurate and benign decisions earlier than others.If we apply these technics in finance,for example,in stock,we can get detailed information for stocks.Moreover,we can use the analyzed data to predict certain stocks.This can help people decide whether to buy a stock or not by providing predicted data for people at a certain convincing level,helping to protect them from potential losses.

An extended sparsemax-linearmoving model with application to high-frequency financial data

There continues to be unfading interest in developing parametric max-stable processes for modelling tail dependencies and clustered extremes in time series data.However,this comes with some difficulties mainly due to the lack of models that fit data directly without transforming the data and the barriers in estimating a significant number of parameters in the existing models.In thiswork,we study the use of the sparsemaxima ofmovingmaxima(M3)process.After introducing random effects and hidden Fréchet type shocks into the process,we get an extended maxlinear model.The extended model then enables us to model cases of tail dependence or independence depending on parameter values.We present some unique properties including mirroring the dependence structure in real data,dealing with the undesirable signature patterns found in most parametricmax-stable processes,and being directly applicable to real data.ABayesian inference approach is developed for the proposed model,and it is applied to simulated and real data.

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.

The Credit-Risk Decision Mechanism on Fixed Loan Interest Rate with Imperfect Information

In this paper, decision mechanism of credit-risk for banks is studied when the loan interest rate is fixed with asymmetry information in credit market. We give out the designs of rationing and non-rationing on credit risky decision mechanism when collateral value provided by an entrepreneur is not less than the minimum demands of the bank. It shows that under the action of the mechanism, banks could efficiently identify the risk size of the project. Finally, the condition of the project investigation of bank is given over again.

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and re-al-time data transactions, the stock market has increased vulnerability to at-tacks. This paper aims to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipula-tions are not available for training. This paper discovers a competitive alterna-tive to the leading approach and investigates the effects of combining AIS with clustering algorithms;Kernel Density Estimation, Self-Organized Maps, Densi-ty-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.

Baidu index and predictability of Chinese stock returns

A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns.The empirical results show that 1)the Search Frequency of Baidu Index(SFBI)can predict next day’s price changes;2)the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks;3)the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs.These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.

Multilingual Financial News Retrieval and Smart Recommendation Based on Big Data

In view of the study of finance and economics information, we research on the real-time financial news posted on the authority sites in the world's major advanced economies. Analyzing the massive financial news of different information sources and language origins, we come up with a basic theory model and its algorithm on financial news, which is capable of intelligent collection, quick access, deduplication, correction and integration with financial news' backgrounds. Furthermore, we can find out connections between financial news and readers' interest. So we can achieve a real-time and on-demand financial news feed, as well as provide a theoretical basis and verification of the scientific problems on real-time processing of massive information. Finally, the simulation experiment shows that the multilingual financial news matching technology can give more help to distinguish the similar financial news in different languages than the traditional method.

Implementation of a drainage information, analysis and management system

An integrated drainage information, analysis and management system （DIAMS） was developed and implemented for the New Jersey Department of Transportation （N/DOT）. The purpose of the DIAMS is to provide a useful tool for managers to evaluate drainage infrastructure, to facilitate the determination of the present costs of preserving those in- frastructures, and to make decisions regarding the optimal use of their infrastructure budgets. The impetus for DIAMS is the culvert information management system （CIMS）, which is developed to manage the data for culvert pipes. DIAMS maintains and summa- rizes accumulated inspection data for all types of drainage infrastructure assets, including pipes, inlet/outlet structures, outfalls and manufactured treatment devices. DIAMS capa- bilities include identifying drainage infrastructure, maintaining inspection history, map- ping locations, predicting service life based on the current condition states, and assessing present asset value. It also includes unit cost values of 72 standard items to estimate the current cost for new assets with the ability to adjust for future inflation. In addition, DIAMS contains several different repair, rehabilitation and replacement options to remedy the drainage infrastructure. DIAMS can analyze asset information and determine decisions to inspect, rehabilitate, replace or do nothing at the project and network levels by comparing costs with risks and failures. Costs may be optimized to meet annual maintenance budget allocations by pfioritizing drainage infrastructure needing inspection, cleaning and repair. DIAMS functional modules include vendor data uploading, asset identification, system administration and financial analysis. Among the significant performance feature of DIAMS is its proactive nature, which affords decision makers the means of conducting a comprehensive financial analysis to determine the optimal proactive schedule for the proper maintenance actions and to prioritize them accordingly. Benefits of DIAMS include long-term savings that accrue by adopting optimized preventive maintenance strategies and facilitating compliance with Governmental Accounting Standards Board （GASB） and federal storm water regulations.

Quantitative Analysis of the Target Related toCash Flow Statement

With the example of the trend analysis method opplied to the cash flow statement, the authors study thequantitative analysis method of the target related to cash flow statement.So the statement user can knows the currentand previous financial condition in the enterprises, correctly evaluate the current andfuture abilities to pay andrepay,find out the problems in financial affairs, and scientifically calculate the future financial conditions. Anadequate, efficient basis is provided for scientific decision.

Credit Risk Analysis of Chinese Companies by Applying the CAFÉApproach

It is known that the current Credit Rating in financial markets of China is facing at least three problems:1)the rating is falsely high;2)the differentiation of credit rating is insufficient;and 3)the poor performance of predicting early warning,thus we must consider how to create a reasonable new credit risk analysis approach to deal with issues for financial markets in China for those listed companies’performance.This report shows that by using a newmethod called the“Hologram approach”as a tool,we are able to establish a so-called“CAFÉRisk Analysis System”(in short,“CAFÉApproach”,or“CAFÉ”)to resolve three issues for credit rating in China.In particular,the main goal in this paper is to give a comprehensive report for credit risk assessments for eight selected list companies by applying our“CAFÉ”from different industry sectors against actual market performance with the time period from the past one to three years through our one-by-one interpretation for event screening and true occurrence and related events.In this way,we show how“CAFÉ”is able to resolve current three major problems of“rating is falsely high,the differentiation of credit rating grades is insufficient,and the poor performance of predicting early warning”in the current credit market in China’s financial industry in practice.