Volume 1. Part 1 Forecasting earnings: earnings forecasting research - its implications for capital markets research, Lawrence D. Brown. Time series models: univariate time-series models of quarterly accounting earnings per share - a proposed model, Lawrence D. Brown and Michael S. Rozeff the information content of security prices, William Beaver, Richard Lambert and Dale Morse the information content of nonearnings accounting numbers as earnings predictors, Jan A. Ou. Judgement and behavior: security analyst superiority relative to univariate time-series models in forecasting quarterly earnings, Lawrence D. Brown, Robert L. Hagerman, Paul A. Griffin and Mark E. Zmijewski do security analysts overreact?, Werner F.M. de Bondt and Richard H Thaler biases in analyst forecasts - cognitive, strategic or second-best?, Gunter Loffler are financial analysts' forecasts of corporate profits rational?, Michael P. Keane and David E. Runkle. Part 2 Forecasting bankruptcy: financial ratios, discriminant analysis and the prediction of corporate bankruptcy, Edward I. Altman corporate distress diagnosis - comparisons using linear discriminant analysis and neural networks (the Italian experience), Edward I Altman, Giancarlo Marco and Franco Varetto predicting bank failures - a neural network approach, Kar Yan Tam and Melody Kiang introducting recursive partitioning for financial classification - the case of financial distress, Halina Frydman, Edward I Altman and Duen-Li Kao a comparative analysis of current credit risk models, Michel Crouhy, Dan Galai and Robert Mark. Part 3 Forecasting stock prices: stock market forecasting, Alfred Cowles an analysis of brokers' and analysts' unpublished forecasts of UK stock returns, Elroy Dimson and Paul Marsh the crash of '87 - was it expected? the evidence from options markets, David S. Bates. Statistical models: business doncitions and expected returns on stocks and bonds, Eugene F. Fama and Kenneth R. French forecasting stock returns - an examination of stock market trading in the presence of transaction costs, M. Hashem Pesaran and Allan Timmermann economic factors and the stock market - a new perspective, Min Qi and G.S. Maddala forecasting stock indices - a comparison of classification and level estimation models, Mark T. leung, Hazem Daouk and An-Sing Chen. Technical analysis: price movements in speculative markets, Sidney S. Alexander simple technical trading rules and the stochastic properties of stock returns, William Brock, Josef Lakonishok and Blake LeBaron, the Dow theory - William Peter Hamilton's track record reconsidered, Stephen J. Brown, William N. Goetzmann and Alok Kumar. (Part contents).

Stock market forecasting

Electrical energy consumption is affected by variations in temperature, seasons, humidity, weekdays and festive occasions. It affects electric load forecasting. This results in irregularity of production and distribution. Residential load forecasting can effectively help individual consumers to regulate their energy consumption and reduce cost. This in effect help utility to regulate the power consumed. This paper focuses on forecasting techniques for residential load. This paper compares the traditional load forecasting method with the neural network. The traditional forecasting models studied are ARIMA and ARIMAX. In the Neural Network, different models using feedforward backpropagation networks are studied. It is found that artificial neural networks give better results compared to traditional time series forecasting methods. The superiority of artificial neural network in enhancing load forecasting and the effect of transfer function used in the neural network is discussed in this paper.

Residential Load Time Series Forecasting using ANN and Classical Methods

In the Present years the requirement for performing secure exchanges online have been designated by the utilization of Virtual tokens called Digital forms of money. Financial backers can bring in cash by performing mining exercises of Digital forms of money like Ethereum and Binance coin or basically selling Ethereum and Binance coin at a benefit rate. Since market costs for digital forms of money depend on market interest, the cost of a specific digital money in contrast with the other Crypto changes generally, As each Crypto coin has its upsides and downsides and as the cost of the coin is differing generally as for the trade paces of coin and the fame of the Crypto coins in public. The work proposes to foresee the future costs of Cryptographic forms of money like Ethereum and Binance Coin utilizing AI draws near. The result of the proposed work is to gauge the costs of Ethereum and Binance Coin and ubiquity in the coming years

Ethereum and Binance Price Forecasting Using Machine Learning

In the Present years the requirement for performing secure exchanges online have been designated by the utilization of Virtual tokens called Digital forms of money. Financial backers can bring in cash by performing mining exercises of Digital forms of money like Ethereum and Binance coin or basically selling Ethereum and Binance coin at a benefit rate. Since market costs for digital forms of money depend on market interest, the cost of a specific digital money in contrast with the other Crypto changes generally, As each Crypto coin has its upsides and downsides and as the cost of the coin is differing generally as for the trade paces of coin and the fame of the Crypto coins in public. The work proposes to foresee the future costs of Cryptographic forms of money like Ethereum and Binance Coin utilizing AI draws near. The result of the proposed work is to gauge the costs of Ethereum and Binance Coin and ubiquity in the coming years 

Renewable energy sources, driven by the global concern for climate change and the urgent need to reduce greenhouse gas emissions, are gaining increasing importance. Among them, solar energy stands out as a crucial renewable source and accurate solar irradiance forecasting is vital for its effective utilisation. Deep learning (DL) techniques have emerged as state-of-the-art solar irradiance forecasting methods in past years. However, traditional models that are widely used have inferior accuracy in forecasting. To bridge this gap, this paper presents an improved state-of-the-art model that leverages the convolutional property to forecast the time series data. We got a reduction of 8.32% in MAE compared to the base model. The paper's findings highlight the potential of DL-based approaches to significantly improve the accuracy of solar irradiance forecasts. 

Solar Irradiance Forecasting using Improved Sample Convolution and Interactive learning

January-March 2016 533 JCPS Volume 9 Issue 1 Forecasting short-term stock prices using sentiment analysis and Artificial Neural Networks Binoy B Nair*, Kumar PN, Sidharth R Prasad, Lopa M Singh, Vijayalakshmi K, Sai Ganesh R, Reshma J Dept. of Electronics and Communications Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, India. Dept. of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, India *Corresponding author E-mail: b_binoy@cb.amrita.edu ABSTRACT Short-term fluctuations in stock prices are generally considered to be extremely difficult to predict, primarily due to their nonlinear nature. The authors believe that one of the reasons for such seemingly unpredictable fluctuations is the type of sentiment prevailing amongst traders at that point in time. An attempt has been made in this study to forecast the stock returns using the sentiments expressed on social media and Artificial Neural Networks. The proposed system is validated on stocks drawn from the Indian stock markets. Results indicate that the proposed technique can indeed be successfully used for short-term forecasting of stock prices.

Forecasting Short-Term Stock Prices using Sentiment Analysis and Artificial Neural Networks

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Forecasting Short-Term Stock Prices using Sentiment Analysis and Artificial Neural Networks