English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Stock prices are highly volatile and sudden changes in trends are often veryproblematic for traditional forecasting models to handle. The standard LongShort Term Memory (LSTM) networks are regarded as the state-of-the-art modelsfor such predictions. But, these models fail to handle sudden and drasticchanges in the price trend. Moreover, there are some inherent constraints withthe open, high, low and close (OHLC) prices of the stocks. Literature lacks thestudy on the inherent property of OHLC prices. We argue that predicting theOHLC prices for the next day is much more informative than predicting thetrends of the stocks as the trend is mostly calculated using these OHLC pricesonly. The problem mainly is focused on Buy-Today Sell-Tomorrow (BTST) trading.In this regard, AEs when pre-trained with the stock prices, may be beneficial.A novel framework is proposed where a pre-trained encoder is cascaded in frontof the multi-task predictor network. This hybrid network can leverage the powerof a combination of networks and can both handle the OHLC constraints as wellas capture any sudden drastic changes in the prices. It is seen that such anetwork is much more efficient at predicting stock prices. The experiments havebeen extended to recommend the most profitable and most overbought stocks onthe next day. The model has been tested for multiple Indian companies and it isfound that the recommendations from the proposed model have not resulted in asingle loss for a test period of 300 days.

Autoencoder based Hybrid Multi-Task Predictor Network for Daily Open-High-Low-Close Prices Prediction of Indian Stocks