A Decentralized Prediction Market Platform Based on Blockchain and Masternode Technologies

Prediction market can help to trade the outcome of events,which is very useful for risk hedging,especially when people are making business ventures.However,the traditional prediction market is centralized,which means the platform is completely controlled by just one person or team.This centralization has bad influence on the platform to obtaining trust from massive users.So it is important for the prediction market industry to decentralize the traditional platform into a decentralized one,and get its control from few person and give it back to a lot of people in the prediction market community.Blockchain is a very promising technology to make it true as it is a popular decentralizing technology.However,existing blockchain technologies like Bitcoin or Ethernum bring about new problems like high energy consumption,expensive fee,and very low system capacity,which is not suitable for the current prediction market.To solve these problems,we propose to combine masternode technology together with blockchain to serve as a decentralized node,and each masternode is deployed and running on an edge server in mobile edge network.The network of masternodes serves together as the decentralized prediction market platform.As we know,it is not easy to run a masternode.So the number of masternodes is much less than the number of complete nodes of the blockchain.As a result,it is easier to reach the consensus among these masternodes.Theoretical analysis and experimental results show that our proposed method is useful.

An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination

In this study,the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based,deep-learning(LSTM)and ensemble learning(Light-GBM)models.These models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure metrics.While the first experiments directly used the own stock features as the model inputs,the second experiments utilized reduced stock features through Variational AutoEncoders(VAE).In the last experiments,in order to grasp the effects of the other banking stocks on individual stock performance,the features belonging to other stocks were also given as inputs to our models.While combining other stock features was done for both own(named as allstock_own)and VAE-reduced(named as allstock_VAE)stock features,the expanded dimensions of the feature sets were reduced by Recursive Feature Elimination.As the highest success rate increased up to 0.685 with allstock_own and LSTM with attention model,the combination of allstock_VAE and LSTM with the attention model obtained an accuracy rate of 0.675.Although the classification results achieved with both feature types was close,allstock_VAE achieved these results using nearly 16.67%less features compared to allstock_own.When all experimental results were examined,it was found out that the models trained with allstock_own and allstock_VAE achieved higher accuracy rates than those using individual stock features.It was also concluded that the results obtained with the VAE-reduced stock features were similar to those obtained by own stock features.

Indian stock market prediction using artificial neural networks on tick data

Introduction:Nowadays,the most significant challenges in the stock market is to predict the stock prices.The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature.Case description:Support Vector Machines(SVM)and Artificial Neural Networks(ANN)are widely used for prediction of stock prices and its movements.Every algorithm has its way of learning patterns and then predicting.Artificial Neural Network(ANN)is a popular method which also incorporate technical analysis for making predictions in financial markets.Discussion and evaluation:Most common techniques used in the forecasting of financial time series are Support Vector Machine(SVM),Support Vector Regression(SVR)and Back Propagation Neural Network(BPNN).In this article,we use neural networks based on three different learning algorithms,i.e.,Levenberg-Marquardt,Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared.Conclusion:All three algorithms provide an accuracy of 99.9%using tick data.The accuracy over 15-min dataset drops to 96.2%,97.0%and 98.9%for LM,SCG and Bayesian Regularization respectively which is significantly poor in comparison with that of results obtained using tick data.

MARKET PREDICTION FOR CHINA'S MACHINE-BUILDING INDUSTRY IN THE NINTH FIVE-YEAR PLAN PERIOD

In the mid- and late 1990s,China’s nationaleconomy will maintain sustained,rapidand healthy development in terms of itsoverall capacity.This has provided idealopportunities for the development of China’smachine-building industry.These include:

Research on Prediction of the Scale of OTC Drug Market in China Based on Quantitative Analysis

Objective To analyze the scale of domestic OTC drug market and its influencing factors,so as to predict its future market and provide a scientific basis for pharmaceutical enterprises to grasp the opportunities in the market.Methods The scale of OTC drug market from 1999 to 2018 in China and its influencing factors were analyzed by unit root test,Granger causality test and co-integration test.Results and Conclusion From the perspective of the global pharmaceutical market,OTC drug market has broad prospects and great development potential.Since the influence of GDP and the number of elderly populations on the scale of OTC drug market is positive,the predicted growth rate of OTC market in the next three years is 5.82%,5.86%and 5.90%,respectively.

Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss.The proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different sectors.The dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next quarter.Our model uses 3 main concepts for forecasting results.Thefirst one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning Factor.The value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters Algorithm.The second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback loop.The third concept is Recommendation System whichfilters and predict the rating based on the different factors.

A Self-Attention-Based Stock Prediction Method Using Long Short-Term Memory Network Architecture

The ability to analyze the trend of the stock market has always been paid high attention to.A large number of machine learning technologies have been used for stock analysis and prediction.The traditional time series prediction models,including RNN,LSTM and their deformed bodies,show the problems of gradient disappearance and low efficiency in long-span prediction.This paper proposes a long-term and short-term memory network architecture,which based on Encoder and Decoder Stacks and self-attention mechanism,replacing the feature extraction part of traditionalLSTMthrough self-attentionmechanism and provides interpretable insights into the dynamics of time.Through the results of simulation experiments,this paper shows the comparison of stock prediction effects through using RNN,Bi-LSTM and Encoder and Decoder-Attention-LSTM models.The experimental task shows that the prediction accuracy of this model is improved by an order of magnitude compared with the traditional LSTM-like model,and can achieve high accuracy when the epoch is small.

How to Share This“Cake”——Prediction on Chinese Passenger Car Market in 1996

In 1995, 325800 units cars were produced by Chinese car-makers, of which 312100 units were sold out. Concerning the market share, SVW Santanas enjoyed the top position, far beyond the other car models, and compared with the No. 2, Tianjin Charades, their market share was about 30% higher. Because of the

A prediction on each segmented commercial vehicle market in 2007

Heavy truck The market trend has recovered since May 2006 and it reported a year on year growth of 30.55 percent by the end of Dec. The result was partly due to the heavy fall in the second half of 2005.

IS TECHNICAL ANALYSIS INFORMATIVE IN UK STOCK MARKET? EVIDENCE FROM DECOMPOSITION-BASED VECTOR AUTOREGRESSIVE(DVAR) MODEL

The paper proposes a new approach -- The decomposition-based vector autoregressive （DVAR） model to scrutinize the predictability of the UK stock market. Empirical studies performed on the monthly British FTSE100 index over 1984-2012 confirm that the DVAR model does provide informative forecasts for both in-sample and out-of-sample forecasts. Trading strategies based on the DVAR forecasts can Significantly beat the simple buy-and-hold, which demonstrates the valuable information provided by technical analysis in the UK stock market.

Research-A blockchainofknowledge?

This perspective proposes that,by virtue of its sophisticated trust and consensus finding mechanisms,blockchain has the clear potential to substantially upgrade the processes and organization traditionally underpinning academic science and commercial technology development comprising funding,project delivery,generation of intellectual property,documentation and publication.For supporting this hypothesis,striking analogies between the concepts underlying blockchain technology with research are identified,and applied to the generation of verified knowledge in science and technology development.It is then elaborated how a blockchain-enabled token economy can efficiently and transparently incentivize and coordinate an integrative and community-inclusive participatory approach to fuel crowdsourcing of collective intelligence for contributing ideas,work,infrastructure,funding,data,validation,management,assessment,governance,arbitration and exploitation of projects.Quality,credibility and direction of projects are optimized by demanding collateral“skin-in-the-game”from contributors based on blockchain-enabled staking,reputation systems and prediction markets.This way research progress emerges as a chain of community generated and independently vetted blocks of scientific knowledge;these new blocks are concatenated with the state-of-the-art according to transparent consensus mechanisms.

Predicting Stock Price Movement with Multiple Data Sources and Machine Learning Models

Stock price trend prediction is a challenging issue in the financial field.To get improvements in predictive performance,both data and technique are essential.The purpose of this paper is to compare deep learning model(LSTM)with two ensemble models(RF and XGboost)using multiple data.Data is gathered from four stocks of financial sector in China A-share market,and the accuracy and F1-measure are used as performance measure.The data of the past three days is applied to classify the rise and fall trend of price on the next day.The models’performance are tested under different market styles(bull or bear market)and different market activities.The results indicate that under the same conditions,LSTM is the top algorithm followed by RF and XGBoost.For all models applied in this study,prediction performance in bull markets is much better than in bear markets,and the result in active period is better than inactive period by average.It is also found that adding data sources is not always effective in improving forecasting performance,and valuable data sources and proper processing may be more essential than providing a large quantity of data source.